Highly Competitive - software industry insights

The Resilience of Master Data Management

Final article of a six part series on the "New Age of Master Data Management"

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

For years there have been industry professionals and analysts who seemingly find pleasure in declaring some type of technology as "dead". This compulsion has been a constant in the software industry. Master data management (MDM) is no exception. Some have been convinced that MDM was DOA practically from the day it emerged as a distinct form of data management and data governance. Making such a declaration attempts to make the case that MDM no longer serves a purpose or that it never succeeded in its 'mission'.

The proof of what MDM can do to transform an organization is easy to find. Numerous case studies show how MDM successfully improved data management and data quality, took companies to greater use of data, and contributed to real value in support of goals and desired outcomes. Beyond case studies, the vendor landscape for MDM software solutions continues to grow and evolve. Both traditional and newer MDM software vendors have developed innovative solutions and approaches to master data management that offer improved alignment with business requirements and objectives, and faster paths to realizing value.

If MDM is so useless, why do we have all this activity over these many years? Why would new and innovative technologies continue to emerge continuously if organizations weren't finding that MDM helps them meet many data, process and business challenges?

 

If not MDM, Then What?

I'd hope by now that most organizations understand that they live and breathe data for just about every aspect of the business. And if these organizations want to at least ensure competent use of their data, they should also understand that data management is a must-have. But today companies have to do a great deal more with the avalanche of data that flows into, through and out of the organization. Data management moves several levels higher for meeting business needs when MDM and data governance initiatives are implemented. MDM practices and processes coupled with the right-fit technologies enable many business-critical objectives and contribute to competitive edge.

Those who denigrate MDM seem to imply that MDM serves no purpose. Without MDM, an organization will likely lapse into a sort of 'data anarchy' where there is no common standard for data and definitions, no confidence in the reliability and timeliness of data, no optimization of the systems and processes that run through every business function and activity. Such 'anarchy' can even infect the selection of technologies to handle data, due to the lack of an overarching framework based on corporate data strategies that would have been developed through MDM initiatives.

Organizations will always have to manage business data. But that's not enough. MDM introduces higher quality processes that are clearly aligned with business needs and objectives. Organizations benefit from master data that provides context for other sources of data to be able to achieve optimal results from comprehensive analytics, insights and improved decision-making. Master data processes can help identify missing data, inaccuracies and redundancies that will likely skew analytics results with potentially harmful consequences.

 

Unrealistic Expectations for MDM

If organizations have weak or no strategies around data usage for business, MDM initiatives and technologies can't fix what's already broken in organizations. MDM practices and processes combined with technologies do not deliver a 'magic wand' that will conquer all organizational ills. So-called MDM 'failures' have much more to do with difficulties and obstacles emanating from the politics and people aspects that are so critical to successful implementations of MDM and data governance initiatives.

For MDM to deliver value and benefits, management (often including the C-level) must have good comprehension of the important role that MDM plays across the organization. And management must deliver full backing of what it will take to successfully implement comprehensive MDM. There is now extensive guidance available through publications, blogs, consultants, and many other resources that show how to connect MDM to business value through means such as strategic business cases, and metrics that link MDM activities to desired business outcomes.

A well-implemented MDM initiative actually strengthens the strategic nature of organizations since such an initiative is clearly connected to business-drivers and to helping to achieve goals and objectives. The primary leaders of MDM-related activities should work to align managers across the organization as supporters of MDM. The benefits to each functional area should be clearly communicated, along with the strategic vision of how MDM contributes to specific business impact.

 

Why MDM Has Persisted

MDM provides a core platform of advanced data management and data governance capabilities that are essential for organizations building strategies around data and analytics. After all, master data comprises the consistent business-ready data that runs through many (if not all) processes, systems and applications. As data-focused organizations continue to evolve the use of data and analytics, MDM grows in significance and innovative substance. Master data has become the "rosetta stone" for highly disparate sources of data. When big data is connected to master data that provides context and relationships, organizations can now derive value and usability from big data.

MDM practices and technologies, and the development of reliable master data, are not the end objective, but are the means to enable organizations to realize strategic vision and achieve key objectives. MDM initiatives achieve success because they focus on specific business drivers and business-led initiatives, and are set up to change as organizations change. Such MDM success translates into organizations that continuously benefit from faster utilization of timely, reliable data.

MDM anchors many significant, even critical, functions:

• Real-time analytics / BI processes
• Big data analytics
• Automated processes for information and data processing applications
• AI and machine learning efforts
• Compliance and risk management 
• Improving customer experiences and interactions
• Cost reductions
• Ability to anticipate, analyze and adapt to market changes
• Agile responses to change for competitive edge

MDM has persisted because of what it tackles. MDM jumps into many of the messiest aspects of an organization: data silos, unconnected systems and processes, management turf wars, politics, and the organizational roadblocks to disparate people working together effectively for common purpose. What MDM takes on to achieve business-usable master data and better processes reflect many of the problems that organizations must solve to survive, adapt, compete, and achieve desired outcomes.

So it's about time for master data management to enjoy a 'New Age'. The creation and use of master data for business purposes has always been a good idea. The goal has been to make sense of all data that touches the organization: clean it up, add context, enrich it, and put it to work strategically as well as operationally.

 

Organizational Resilience in the Face of Business and Market Volatility

Many organizations continue to encounter increasingly volatile markets that have given rise to more complex business environments. Such volatility can become a hurdle for achieving financial success and often translates to significant risk. Organizations need more sophisticated analytics and decision-making processes to assess market conditions and to determine whether "risk" means opportunity, danger, or even both. Master data is an important component of essential processes for insight into market conditions, supporting functions like real-time continuous analytics and responsive decision-making.

While more senior executives do see the value of data analytics and MDM, many organizations are still making critical decisions based on spreadsheets. These organizations aren't able to respond to market volatility with comprehensive intelligence rooted in the right data and analytics. Since MDM is tightly bound to business drivers and functions, it's already positioned correctly as the right partner for real-time analytics that support critical business decisions.

Organizational agility and adaptability are defining facets of competitive edge, particularly in the face of market volatility. The agile business requires continuous processing of data from many disparate sources that must be transformed into consistent and trustworthy data with relevant context. Speed to decisions and to actions of change is improved and better grounded in the right information when MDM processes are involved.

MDM can provide pathways to help 'future-proof' organizations. Being future-proof means harmonizing technology, people, and processes to tackle the challenges ahead. When implemented effectively, MDM greatly improves methods used to address the challenges that organizations face every day. MDM practices, approaches and technologies continue to evolve to help solve complex business problems.

 

The New Age of MDM

Master data management and data governance have indeed entered the 'New Age of MDM'. The practice of MDM has become stronger through building on common cause with business, rather than a sole focus on technology. Technology vendors have made advancements with MDM software solutions by understanding the critical nature of MDM aligned with business needs, objectives, optimal processes, and involved business users.

MDM provides the greatest benefits when practitioners embrace the strategic aspects of an organization and help to enact them through processes, practices, policies, and organizational changes. Strategic use of MDM also gives rise to cross-functional and cross-organization integrations of people and systems. A successful MDM initiative comprises a holistic endeavor to provide strategic organizational and infrastructure change, as well as manage complex data processing. Part of MDM holism is the intertwining of business, people and technology in the most effective mix possible.

On the software solution side, MDM is the "poster child" of the critical need for business strategy to 'meet and mingle' with technology strategy. In a sense MDM technology is a conundrum. The technology should take a back seat to all of the non-technical activities, objectives and requirements for MDM implementations. But technology can also be a game-changing component of MDM initiatives.

Now, on to the 'Next Age of MDM'!

 

Image source: nicholaspetrie.com

 

About the author: Julie is an independent consultant and industry analyst for B2B software solutions, providing services to vendors to improve strategies for customers and target markets, products and solutions, and future direction. Julie has expertise in several solution spaces including: data integration and data quality; analytics and BI; business process, workflow and collaboration; digital marketing, WCM and social media; and the pivotal importance of user and customer experiences. Julie shares her takes on the software industry via her blog Highly Competitive and on Twitter: @juliebhunt For more information: Julie Hunt Consulting – Strategies for B2B Software Solutions: Working from the Customer Perspective

 

MDM – Speaking the “Language” of Business

 

Part 5 of a six part series on the "New Age of Master Data Management"

 

Data management continues to take on an ever-growing range of activities as the role of data has evolved and expanded for many organizations. Business agility and real-time insight are inherent requirements for many organizations. To meet such requirements organizations need right-time data on a continuous basis, where master data management initiatives are integral to processing business-ready data.

MDM software solutions are evolving as well, to better meet business needs. The new directions of MDM software obviously carry a heavy package of sophisticated technology. Analytics processes now utilize numerous disparate data sources that need context and relationship information that comes from trustworthy and relevant master data. Some MDM solutions are utilizing technologies like graph databases to take advantage of multi-dimensional capabilities to more expansively and agilely handle relationships across all data types. More analytics processes are now providing information and insight primarily for business users who aren't necessarily highly adept at hands-on technology or data management, which opens up new challenges and opportunities for MDM vendors.

The value of these solutions is heavily tied to business needs and objectives. And this value must be clearly and honestly communicated to a growing array of business roles -- in the language of those business users.

In the 'New Age of MDM', with greater business participation, should IT and data management team members utilize more of the language of less technical business roles when in discussions related to MDM purchases, implementations and usages?

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

The "Languages" of MDM

Often conversations regarding master data management have been among IT and data management professionals, including more tech-savvy business roles involved in data management. The "language" of MDM started off primarily related to the worlds of IT and data management teams – in other words "techy talk".

But it's likely that this original "language" of MDM is now insufficient for meeting current business objectives and requirements, with the increased (and necessary) involvement of business roles, and the importance of selling the value of MDM to business managers and non-IT decision-makers. A growing lineup of business roles are increasingly involved in data governance discussions regarding practices, policies, cultural changes, and processes, as well as how to develop cross-functional teams and remediate silos for systems and data. Discussions with business roles also focus on the many ways that MDM can underpin important business initiatives and desired outcomes for objectives.

Conversations about the purchase of MDM software solutions can involve less technical roles. High level managers and C-level roles may be part of the decision-making processes: deciding if master data management itself should be adopted; understanding how MDM supports business requirements and goals; choosing which MDM software technologies to utilize; determining metrics and assessing outcomes related to MDM initiatives. Obviously these conversations also involve IT and data management teams, who now must be prepared to communicate well with different business roles.

 

Communicating in Customer Business Language

Those of us who work with software vendors (no matter which "side") are clearly aware that marketing and sales efforts for software solutions can deviate linguistically from technical terminology when communicating with less technical business users. But with the recent evolutionary changes for MDM software offerings, there is an increasing need for software vendors and practitioners of MDM to speak the language of the business.

When marketing and selling their MDM software to business roles, some vendors are dropping the use of the term "master data management" in order to bypass the "negative baggage" of traditional MDM solutions. Software vendors with new approaches to MDM are utilizing 'new language' to better describe what these new approaches really do, and how they better align the value with Business. Beyond simply being marketing efforts, most changes in 'language' are fairly honest attempts to more clearly connect MDM practices and solutions to real business needs, desired outcomes and advantage – in terminology that conveys realistic meaning to business users and decision-makers.

Business roles involved in buying decisions may not know what MDM is, can get confused or lost in the terminology and technology, can see it as too complicated – all of which can interfere with the prospective customer's understanding of why MDM is important for making competitive use of data. It's legitimate for vendors to focus on the high level of what MDM contributes to improve data and business processes, and what the business benefits are for each organization. Focus should also cover how the MDM solution handles business needs relative to existing organizational infrastructure, along with business practices and processes – and ultimately business objectives and desired outcomes.

If MDM software vendors demonstrate clearly how the technologies connect to achieving business objectives and outcomes, it better enlightens prospective customers on the benefits and essential nature of master data management – both in terms of the MDM practice and the supporting technologies. And very importantly, business language is essential to uncover business problems to be solved and how data management fits into overall business strategies.

Obviously there is a danger of over-simplification. MDM implementations can take time, both for setting up the technologies, and to institute cultural changes, data governance practices and new cross-functional processes. These aspects of MDM practices and solutions must be honestly communicated. Fortunately we are seeing great advances by different vendors to more quickly get MDM implementations up-and-running, and to take new approaches to how MDM programs are run in order to more clearly show the benefits of MDM and to produce them in a timely manner.

 

How MDM Software Vendors Speak the Language of Business

Often the "buyers" of enterprise software consist of multiple roles in an organization. No longer is software purchasing solely the domain of IT or other technical teams. There is frequently a composite "buyer" that draws on both influencers and decision-makers. Obviously technical teams perform a very important role in the purchase of software, more often as influencers rather than the final decision-makers. Business roles involved in enterprise software purchases can include: mid-level managers and business unit managers whose teams will be impacted by the use of the software; VPs who approve purchases; CEOs and CFOs who influence or dictate purchase decisions; and even staff in accounts payable who enact purchase transactions.

There are numerous software vendors with various flavors of MDM solutions who are leaning further into the language of business to conduct marketing and sales activities, and discussions about implementations and usage. Here are a couple of examples:

Enabling Content Management and Digital Interactions

Data and analytics have become essential components of the marketing efforts of most organizations. As a natural evolution, certain marketing technologies have embedded master data management capabilities to provide a more seamless application for business users. Some vendors of such marketing technologies don't call out MDM per se as a component of the solution.

A company that started as a traditional PIM vendor evolved its software into a solution for managing complex product information and multichannel content, for multiple roles in the information supply chain. For the user, the MDM / PIM part is "invisible". Prospective customers see this solution as an application that supports multichannel interactions, sophisticated content management and digital communications / marketing. The vendor places a strong emphasis on business user access to solution capabilities which further encompasses the business user perspective and language.

The solution provides a means to help resolve the "information fragmentation" that plagues organizational efforts to address the complexities of content management and usage, including highly variable sources (user-generated content, outside experts, brand) and diverse distribution channels (corporate website, mobile, social). The solution supports efforts to reconcile brand consistency and customer experience requirements that are necessary for successful digital marketing.

These are all very much business problems that are being addressed by business teams: digital marketing, ecommerce, content management, product strategy, and so on. The vendor reveals the value of the solution in the language of both business buyers and users. In marketing the solution, the software vendor focus isn't on data / information per se, but on what business users need to do with information and content, and how solution capabilities help them manage these activities in support of digital business needs.

 

Breaking Free from Traditional MDM through Data-Driven Business Applications

A vendor has introduced a relatively new "master data management" platform that is designed to break away from the traditional aspects of MDM software solutions. Instead of basing MDM and other data-related capabilities on relational databases, which has been a common choice until recently, this vendor loads combined and curated data into a NoSQL graph database. This allows the vendor to provide a scalable multi-tenant platform-as-a-service to innovate the creation of 'data applications' that are frequently closely tied to business functions and initiatives.  

On the technical side, this approach supports oversized datasets and accommodates rapidly evolving schema, something most relational databases find difficult. Graph databases provide a greatly improved method to delineate relationships between people, data, and things.  

For business users, this data management solution provides the means to build data-driven business applications that align with the goals and functions of the business. Business users can construct applications that are industry-specific to perform exactly the processes that are needed.

Business users don't need to know that the platform is based on an MDM infrastructure. This vendor wants to give business users the equivalent of "consumer apps" that produce information and analytics results quickly and easily. The vendor frequently speaks "business language" to help business users grasp these concepts.

For business users to successfully take advantage of this platform, the software capabilities and processes will have to be couched in the language of the business. The platform will have to carefully manage business user access based on guidelines and activities that make sense to less-technical business roles. As with any data management software solution that strives to support usage by business roles, IT and data management teams must be partners with the business to ensure successful outcomes. Collaborative capabilities enable such partnerships. So it will be interesting to see how successfully this vendor empowers and protects business usage of its MDM-based platform.

 

Going Beyond the "Traditional Language" of MDM

In the 'New Age of MDM', we are seeing great diversity as to how vendors, practitioners and organizations talk about MDM. Changes in language are essential to better connect MDM practices and solutions to real business needs, desired outcomes and value – in terminology that conveys real meaning to business management and decision-makers.

We'll continue to see a broadening variety of solutions that build on MDM, while deviating from traditional MDM implementations, in order to offer more business-relevant capabilities. Realistically, even the term "master data management" can be confusing for business roles in many organizations that could benefit from implementing MDM practices and technologies. It can be advantageous and helpful when vendors use new but honest terminology to better demonstrate business value and help prospective customers understand the 'why' and 'how' of implementing master data management initiatives.

It seems that the more innovative the MDM-related software solution, the greater the need for new ways to work directly with business roles and to communicate in the language of business. One of the stumbling blocks for such innovation of software and language comes from analyst firms that cover MDM software solutions. Increasingly certain analyst firms have become trapped by their inexact definitions of what kind of solution constitutes an "MDM offering", when evaluating MDM vendors for benchmark reports. Many vendors with MDM-related offerings are trailblazing new directions for MDM, but are excluded from certain industry benchmarks because of the narrow definition held by these firms. Analyst firms following such a path are performing a disservice to buyers who are interested in the right approach to MDM for their organizations, or who are in need of an MDM-based offering that provides business applications tailored for immediate needs.

 

Image source: hastac.org

 

About the author: Julie is an independent consultant and industry analyst for B2B software solutions, providing services to vendors to improve strategies for customers and target markets, products and solutions, and future direction. Julie has expertise in several solution spaces including: data integration and data quality; analytics and BI; business process, workflow and collaboration; digital marketing, WCM and social media; and the pivotal importance of user and customer experiences. Julie shares her takes on the software industry via her blog Highly Competitive and on Twitter: @juliebhunt For more information: Julie Hunt Consulting – Strategies for B2B Software Solutions: Working from the Customer Perspective

 

Business People are Drivers of the New Age of MDM

 

Part 4 of a six part series on the "New Age of Master Data Management"

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Master data management and data governance have always been about business. However, there has been a history of MDM implementations putting too much emphasis on technology, and less time into over-arching business aspects. For successful outcomes and continuous improvement, the ongoing participation of various business roles is an essential element of MDM strategies and activities. It takes 'everybody' to ensure optimal data management processes that bolster important business activities.

Organizations need to make data available to more people, which may mean expanding master data management participation across more of the organization. With additional business personnel involved, new ways to use master data will emerge that will benefit more initiatives, strategies, and decision-making at multiple levels. Data consumers may also exist beyond the organization: partners, business ecosystems, third-party developers, and managed services providers.

Data quality is the cornerstone of producing reliable data. Poor data quality prevents organizations from realizing full value from data and can cause serious problems that derail important business initiatives. Business domain knowledge is a critical part of ensuring data credibility. And, of course, it's business people who own the understanding of how data is used, for which processes, and what future uses may be in the offing.

For all of these activities, business roles may need access to the appropriate capabilities in MDM software solutions -- but will come with different levels of technical proficiency.

 

Data Governance Leads the Way for Business User Inclusion

Like many well-defined MDM initiatives, data governance approaches that focus on business cases often better connect data management processes to necessary uses by different business roles. Data governance teams that are comprised of business and technical personnel can take advantage of an array of real world experience to improve processes and practices for tracking data ownership and governance accountability.

A collaborative framework (supported by software technology) is essential for successful data governance efforts. Such a framework can support both direct and indirect collaboration between business and technical roles, ensuring continuous work on defining and managing policies and practices, expanding and clarifying business rules, and tracking activities by all users to maintain compliance and accountability for information lifecycles.

Data management software that offers data profiling capabilities extends an important tool to business users across the organization. Business users can be included in data quality / data relevance processes through continual use of data profiling, to better examine the accuracy and value of data at any point. This is particularly important since a great deal of data comes with a short shelf life of relevance for business operations and initiatives.

Data governance is also a critical component of self-service approaches to data analytics, data integration and MDM. Self-service technologies are growing particularly to provide business users with access to more of the data that is essential for many activities in an organization. There is real risk associated with such self-service data platforms that must be addressed through centralized IT management and governance, which need to be in place to prevent piecemeal and poorly managed efforts scattered throughout the organization. Cloud-based self-service solutions are well-positioned for governance that maps into organizational data governance practices and policies.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Business User Access and Pervasive Master Data Management

Master data management appears to be on the threshold of becoming a pervasive platform. 'Platform' refers to both a key go-to initiative for data-smart organizations and to the integrated technologies that underpin master data management. Making MDM pervasive comes from a technology solution base that equips organizations to handle any data-driven scenario and support any business case, where business and technical roles take on innumerable data-related tasks. It's a mindset of business and technology teams working together to use data to improve business results and competitive edge. This is aided by increased access, as appropriate, for less technical business users and for line-of-business developers who don't have deep MDM expertise.

More MDM software vendors are recognizing that different user roles, especially business roles, comprise a major sustaining driver for the future of master data management. When vendors create role-based UIs and capabilities focused on less-technical users, the work they do also benefits the UIs and capabilities for the more technically adept. Organizations benefit greatly from better software design and the push to expand what MDM technologies can accomplish for diverse MDM activities, and for the people involved in completing them.

Of course, it takes very sophisticated technologies behind the scenes to deliver "simple" UIs that give everyday business users access to MDM and data governance capabilities. A lot of work must be done especially to build in safeguards to keep less technical business users out of trouble. To ensure that everyday business users do the work correctly and avoid serious missteps, each category of MDM activity has to be specifically defined, with straightforward guidance at every step.

If an MDM solution doesn't provide pre-built objects, IT teams should take on the development of appropriate (and reusable) workflows, and other objects to enable business user participation, while building in safeguards. IT teams also have the responsibility to develop guidelines for business users, and then monitor and assist these users as needed.

Well-structured MDM initiatives can actually move certain tasks, like exploratory analysis, from technical teams to business roles. Business domain experts can take on responsibility for managing and validating business rules and data hierarchies that operate optimally for business needs. The right business roles can also create, validate and enhance approval workflows.

 

Drivers of New Age MDM

Tremendous potential exists for the increased participation of business people in MDM and data governance activities. Opportunities are opening up for business users to utilize powerful MDM software capabilities to explore, manipulate and access data without constant IT involvement. There is obvious advantage to organizations that fully support and empower business users to work more directly in many aspects of data management. When technical and business teams cooperate to engender improved collaboration, many MDM activities can be performed quickly and effectively, while ensuring that data governance practices and policies are upheld.

Organizations would be wise to develop management plans to assure that business users are empowered but also protected when working with MDM platforms. Cooperative action is also needed from technical and business teams to ensure that business user "protection" doesn't inhibit necessary access and use of capabilities.

Executives in many organizations understand the value of trustworthy and relevant data for improved operations, productivity, competitiveness, and overall decision-making. But these executives still struggle with creating the strategies and organizational transformations that put usable data in the hands of many more business users. With data as a clear strategic must-have, organizations are obligated to do more to build out a comprehensive plan for business user participation and then actively work with teams to execute it.

 

Image source: Native American Weaving

 

About the author: Julie is an independent consultant and industry analyst for B2B software solutions, providing services to vendors to improve strategies for customers and target markets, products and solutions, and future direction. Julie has expertise in several solution spaces including: data integration and data quality; analytics and BI; business process, workflow and collaboration; digital marketing, WCM and social media; and the pivotal importance of user and customer experiences. Julie shares her takes on the software industry via her blog Highly Competitive and on Twitter: @juliebhunt For more information: Julie Hunt Consulting – Strategies for B2B Software Solutions: Working from the Customer Perspective

How Does Technology Enable Effective MDM?

 

Part 3 of a six part series on the "New Age of Master Data Management"

 

No Room for "Tech for Tech's Sake"

Many organizations have routinely made technology purchases based solely on the predilections of IT groups. This approach to technology decisions has often proven to be a limited path in terms of providing the technology infrastructure imperative for business needs in today's world, one that has become faster and more complex for many organizations. More organizations are now working to better connect business requirements and strategies to the selection of technologies. After all, most of these technologies should tie directly to the core aspirations and strategies of the company and provide beneficial means for improvement, even changing how the organization does business. That said, the selection of technologies still requires serious input from technical teams, particularly for integration and interoperability of systems, depending on the architectural strategy of the organization.

Frequently technology decisions must include many different stakeholders working together. This is especially the case for selecting the technologies for MDM initiatives that are meant to fully support numerous business objectives and functions. MDM is the "poster child" of the critical need for business strategy to meet and mingle with technology strategy. In a sense MDM technology is a conundrum. The technology should take a back seat to all of the non-technical activities, objectives and requirements for MDM implementations. But technology can also be a game-changing component of MDM initiatives.

The start of any MDM discussion should be core questions like "What data and analytics problems are you trying to solve?" -- where "you" encompasses diverse stakeholders in the organization. One MDM imperative is to eliminate silos in the organization. Not just to eliminate silos for data repositories, systems and processes, but silos of people, objectives and functions.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

How Technologies Improve MDM Effectiveness

Sometimes an examination of the relationship of technology to business needs becomes a "chicken and the egg" question: which came first? While technology definitely should be in the service of business requirements, innovative technologies can change how businesses operate. The reality is that many MDM activities couldn't be accomplished without the right software technology. My previous post on new approaches should communicate clear implications that technologies are very important to the successful execution of "new age" MDM initiatives.

Vendors are transforming MDM offerings, old and new, to better serve organizations and their business objectives. We are seeing more vendors incorporating business realities into their technology platforms. Newer iterations of MDM solutions have expanded access for roles in business and technical groups. Several MDM offerings concentrate on ways to speed up implementations, and provide templates for quickly "test driving" projects, to demonstrate in shorter time frames that MDM does work and can make a difference.

Just like everything else "data", real time access to continuously updated master data is increasingly important. The variety of data needed for master data processing continues to grow. And more organizations need multidomain and multivector data to derive the data connections and context necessary for relevant insight for complex business conditions. Most MDM software solutions have sophisticated capabilities to collect, process, create connections and relationships, and provide context for increasingly difficult data sources.

To do a better job of deriving different aspects of context, MDM solutions now include graph repositories, visualizations, and analytics capabilities. Business cases are often the starting point for contextual explorations of data. Different technologies generate data views in response to business needs and objectives. An initiative like improving the customer experience will cross all data repositories and then travel beyond organizational data to include customer behavioral and social information from sources outside the firewall. "New Age" MDM solutions must be able to handle all of this.

Businesses are finding that various MDM capabilities are effective for connecting real business impact to MDM activities, better aligning MDM metrics to business outcomes and goals. Further alignment comes through data governance processes that track the compliance of master data to governance policies, again with an over-arching connection to the outcomes and goals that are achieved (in the context of the MDM contribution). A more effective – and real – assessment can be conducted to show the value of MDM and data governance where it matters.

 

Meeting Business Needs: MDM Platform Overhaul

A well-known data management and analytics vendor spent the past few years revamping and simplifying its MDM software platform. Basically the vendor looked at what MDM can accomplish from the perspective of business use cases and how to increase the business value of MDM. The framework provided by the platform starts with the desired end state (usually business-related) to ensure that the MDM project will deliver what is needed to support business requirements. The framework also enables the participation of business roles to bring in their extensive knowledge to accelerate the development of MDM processes.

Various teams will then work back from the end state to build all that is needed to attain it. The platform helps these teams create the models that achieve a particular end result, and then automatically generates the processes that will carry out the appropriate MDM tasks, as extensions of the models. Reusability is well-supported both for the creation of services and the utilization of pre-built components, and through an iterative development framework. The purpose of this new approach is to shorten and automate as much of each implementation or project as possible, while creating powerful MDM components.

With this new platform, departments within larger organizations can also initiate their own MDM efforts. The MDM solution provides capabilities to prevent master data silos and to map department-level master data into higher organizational levels. Any work done has to map into existing practices and processes for data governance, data quality, and other centralized data management functions, which obviously provide a fail-safe.

 

How Cloud is Changing MDM

Cloud has had a tremendous impact on every software category. Many technology solutions are now exclusively developed for the cloud. Adoption of cloud-based MDM or MDM-as-a-Service is on the rise, opening up new dimensions for how organizations take advantage of MDM and data governance. Access for different user roles improves with cloud-based MDM services.

MDM often has to provide solutions for complex business problems. These days the technologies of cloud services are well able to support sophisticated software solutions that are capability-rich. The key word is sophisticated, meaning smoothly-performing software made possible by thoughtful design that enables the right experiences for diverse users.

Cloud platforms for data and application integration have proliferated greatly, many of which are cloud natives. Vendors for cloud-based integration solutions have shown clearly how to address sophisticated requirements for security; connectivity; movement and management of data between cloud and on-premises as well as cloud to cloud; and well-managed services. These vendors also offer a range of pricing models that should be helpful to vendors developing cloud-based MDM.

Cloud-based MDM solutions can also open the door to MDM usage by mid-sized organizations. This usage can come from more affordable offerings that streamline MDM activities. Or such usage can come from managed services providers (MSPs) who can bring both MDM expertise and pre-built services tailored to the needs and budgets of mid-sized organizations. Cloud-based MDM solutions enable MSPs to quickly develop services that can be reused by different organizations.

 

Different Stakeholders Working Together

MDM and data governance activities usually involve diverse stakeholders from both the business and technology camps in an organization. While many of these activities are non-technical, capabilities in MDM solutions can help manage, store and coordinate such activities. Well-constructed role-based access to software solutions can protect and guide less technical users in the tasks they need to complete for MDM initiatives.

When organizations recognize the importance of all stakeholders, they can then do a better job of selecting MDM technologies that will meet the needs of all end users, not just the highly technical ones.

If a company is buying software technology to improve performance and competitiveness, then the company had better focus on key contributors to success: empowered end users who deserve to have high quality reliable tools to carry out the work of the company, without wasting a lot of time each day on software that is difficult to use and doesn't meet requirements.

Several MDM solutions support different kinds of collaboration between stakeholders, including interactive work. In a way, data governance has been a strong trail-blazer for technology solutions that better serve different roles and the interaction that needs to take place between different users. For example, intelligent workflow structures are strong technology tools for empowering and enabling improved collaboration and communication.

One of the major challenges to overcome is the difficulty of orchestrating many different teams to work as one. On the technology side, harmonizing and automating MDM activities require strong orchestration capabilities, considering all of the moving pieces that can be in play. The same applies to the non-technical aspects of MDM initiatives, with the added "difficulty" of orchestrating diverse humans.

 

MDM: Always a Holistic Venture

The increasingly complex strategic and tactical needs of organizations have put more focus on the value and benefits of master data management. MDM is a core function for trusted data and more efficient business processes, which are must-haves for most organizations. A successful MDM initiative comprises a holistic endeavor to provide strategic organizational and infrastructure change, as well as manage complex data processing. Part of MDM holism is the intertwining of business, people and technology in the most effective mix possible.

There is a wide range of MDM technologies available to organizations. This variability of MDM and data governance offerings should again drive home the critical point that MDM initiatives must start with thorough strategy design and development of the non-technology aspects of what MDM should accomplish for the organization. The strategic framework then provides the key elements that should guide organizations to select the best-fit MDM technologies.

MDM technologies are evolving to better support modern MDM requirements -- but "traditional" MDM software solutions still abound. These are increasingly unwieldy platforms that aren't able to handle many of the new approaches to MDM. On the other hand, several vendors that have offered traditional MDM solutions in the past now have revamped their offerings to better support new approaches. And, of course, there are new vendors with new MDM solutions that are often cloud native technologies and offer new ways to address MDM and data governance.

Stand-alone solutions for PIM (Product Information Management) and CDI (Customer Data Integration) are still part of the mix, especially for organizations not considering a comprehensive MDM initiative. PIM in particular is offered by several vendors, with specialized products for industries like retail and eCommerce. PIM solutions can be sharply business-oriented, providing broader native access to business users.

Organizations must be prepared to develop a well-structured MDM vision that addresses not only how business is done today, but what organizations want to accomplish further down the road. Such a vision will better inform which technologies are the best investments to enable effective MDM.

 

Image source: feng-shui

 

About the author: Julie is an independent consultant and industry analyst for B2B software solutions, providing services to vendors to improve strategies for customers and target markets, products and solutions, and future direction. Julie has expertise in several solution spaces including: data integration and data quality; analytics and BI; business process, workflow and collaboration; digital marketing, WCM and social media; and the pivotal importance of user and customer experiences. Julie shares her takes on the software industry via her blog Highly Competitive and on Twitter: @juliebhunt For more information: Julie Hunt Consulting – Strategies for B2B Software Solutions: Working from the Customer Perspective

 

MDM for Business Value: New Approaches

 

Part 2 of a six-part series on the "New Age of Master Data Management"

 

Master data management is entering an 'evolutionary' era where MDM implementations better mirror how businesses use data for numerous purposes, and where people, practices and processes come to the forefront as essential elements. MDM functioned as yet another solution space that was harmed by disproportionate attention to its underlying technologies. But MDM has always been far more about business objectives and needs, as well as the problems to be solved to achieve them. Along with a more overt "de-emphasis" on technologies, we are now seeing more vendors enabling the critical integration of technologies, people, practices and processes with near term and long term business objectives, to achieve the successful utilization of MDM programs.

A major change to MDM approaches calls for combining technology and non-technology paradigms, to reflect the new reality that the business world isn't just "relational". Relationship (network / graph) data continues to grow in importance – but traditional row-and-column databases are inadequate to properly handle such data. MDM approaches and technologies must now process complex connections, hierarchies and relationships between multiple entities / domains. Organizations want to go beyond traditional transactional data to capture data that describes behavioral aspects that can amplify context.

 

 

 

 

Out with the Old

Over the years, organizations often took wrong turns with MDM by relying on problematic approaches like "big bang" or "bottom up". Either way has usually resulted in big problems and poor outcomes. And both of these approaches have had a history of focusing too much on IT driving the MDM initiative, while failing to incorporate the voices of essential business stakeholders. These traditional approaches usually take too much time, carry too much risk, and end up being too expensive -- often not even achieving essential objectives.

Successful MDM projects require strategic vision that connects to current and future business needs and objectives. But too often organizations have decided that 'strategic vision' means tackling a large scope of work ("big bang"), instead of fleshing out the right strategies before starting any work. Organizations that attempt to execute an entire MDM strategy at once encounter significant obstacles including serious cost overruns, project management turmoil, and chaotic use of IT resources.

The "bottom up" approach relies on IT and / or lower level managers to drive the MDM effort, while disregarding vital upper management and business stakeholders. Often disconnected teams work in silos, but are expected to somehow arrive at the same "desired end state". This introduces the near-impossible task of trying to orchestrate non-collaborative work.

 

New Approaches Abound

Obviously new approaches for MDM are made better, or even possible, because of newer technologies. But MDM technology by itself will fail to achieve what businesses need. Of primary importance is the work done to establish the right vision and conceptual framework for what master data can accomplish for the business. Organizations need to develop hierarchies of strategies, plan processes and practices, make decisions for design and modeling – among the many preparative tasks that usually ensure more effective utilization of MDM. Then organizations can build out from there, ultimately considering which technologies should be implemented.

Offerings from new MDM vendors and fresh approaches by established vendors are responding to what organizations need and want from master data: centralized reliable data that reflects how organizations work and the kinds of data they need, increasingly in real time contexts. MDM is moving towards faster implementations, broader capabilities in a single extensible platform, and the ability to work with any kind of data.

 

Organizations are encouraged to explore new MDM approaches and software solutions that are designed to overcome the traditional challenges that have plagued MDM initiatives and that will better fit on-going business objectives, needs and functions:

 

The Cloud and MDM-as-a-Service

Obviously, SaaS and cloud services have distinctly influenced how customers buy and access software, and how most software solutions are now deployed. Master data management is no exception. As with other software solutions moving to the cloud, MDM can become more user-oriented and fully focused on the real purpose of MDM, without a lot of the technology "baggage" that comes with on premises solutions. Better partnerships between IT and business roles can be forged to optimize MDM processes and how they contribute to real business needs and value.

There are still some obstacles to fully delivering MDM-as-a-Service to business users. But, as with other data management solutions that have moved to the cloud, a great deal of progress has been made. Self-service models contribute to the expansion of different kinds of access as appropriate for different kinds of users. For example, a number of cloud integration platforms provide seemingly simple graphical interfaces that can accelerate usage development and ensure better runtime management. Even technology-adept data management experts want self-service access to MDM solutions. Point-and-click graphical interfaces accelerate all aspects of creating and maintaining MDM processes and applications.

 

Business-centric Platforms to Solve Business Problems Faster

New and established MDM vendors are working to revamp the MDM design approach. One business-centric approach is based on starting with the desired end state, and all that is required to define it. Then teams work backwards from the desired end state in a reverse-engineering process to figure out how to attain it. The MDM solution platform helps the organization create the models that are needed for a particular end result, and then automatically generates the processes that will carry out the appropriate MDM tasks as extensions of the models that were created. Most "end results" are sharply focused on business needs and value, particularly since extensive business knowledge is required to accelerate development of the resulting MDM processes.

Often this approach brings about a shorter, automated and rule-driven lifecycle that means more iterative development and much shorter implementation times. Business and IT teams can work in parallel, while utilizing the MDM platform to orchestrate and synchronize their efforts. A further extension of such business-oriented platforms leads to simplifying the development of industry-specific business applications that take advantage of master data to solve business problems.

 

Multi-Dimensional Data – Complex Relationships and Hierarchies

Contextual MDM solutions handle new approaches for multi-dimensional and complex hierarchical data, including social and commercial graphs that underlie business use cases such as customer-responsive interactions across digital channels. Such solutions allow unlimited combinations of categories, context and definitions. Multi-dimensional often means multi-view, reflecting the need to view domains or entities (such as customers) in different ways, in order to derive better insights from data. Today, entities extend beyond the traditional customer and product pairing to include items such as asset, location, supplier, finance, and personnel data.

To better serve business needs, contextual master data management can provide a consistent and multidimensional profile across data and business silos. To meet this imperative, the emphasis falls on the orchestration of master data and processes, to customize a trusted, relevant master view aligned with data consumption needs. The growing interest in multi-dimensional models of master data reflects the huge growth in data elements that are needed by most organizations, as well as increased dimensionality and deeper levels of data.

Contextual MDM utilizes graph databases for additional flexibility in managing multiple domains as "one", instead of creating separate domain models. This approach takes MDM implementations from systems to views. Graph databases can help organizations use master data in more analytic and insightful ways, as well as take of advantage of real-time graph analytics.

 

Rise of Analytical MDM

With the increase in high performance computing, particularly in support of advanced analytics, analytical MDM now occupies an important position for many organizations. Vendors are integrating MDM capabilities with analytics platforms, for more immediate insight benefits. Underlying technologies include capabilities for machine learning, processing big data, graph databases, and visualization tools. Applications are extensive: fraud detection (real-time and predictive); real-time customer intelligence and product recommendations; reducing costs for many aspects of healthcare.

Unlike traditional approaches for setting up data warehouses for business intelligence (BI) purposes, analytical MDM can perform all MDM functions to ensure data quality, useful data enrichment and the addition of context through consolidated "views" of customer, product and other data domains. Today's MDM hubs often accommodate up to tens of thousands of data elements for multi-domain approaches. Numerous MDM architectures can manage multi-dimensional data with complex hierarchies. Such data provides the wide array of metadata and attributes that are necessary for real-time business processes, particularly for initiatives focused on customer experience and systems of interaction. Analytical MDM approaches (and technologies) are seen as stronger solutions for these sophisticated business needs.

 

Knowledge-Based MDM – and Taxonomy

An MDM initiative faces a tremendous challenge to align disparate data sources and then perform accurate data quality processes to derive reliable and usable data. Part of the challenge is verifying the validity of data quality results.

Traditional table-driven approaches for data quality needs can be arduous and time-consuming. Organizations have adopted knowledge-based approaches that consolidate information from all records that are accessed in conjunction with an MDM system in order to provide context in terms of customer and other domain-related data. Such knowledge bases often include millions of entries that provide correct and incorrect spellings, relevant patterns, and other data quality attributes. Proponents of knowledge-based MDM claim rapid processing of entire data sets for data quality purposes.

Knowledge graphs bring agility to visualizing master data models and linkages. Conversely, changes can flow into master data and data models through the knowledge graph. Knowledge graphs are built for known needs; which means they can accurately align with business needs. Salient entities and relationships, concepts and content can be incorporated into knowledge graphs.

A frequent partner for knowledge graphs is taxonomy which brings in semantic architecture. Until recently, taxonomy has been used more frequently for enterprise content and information management, and enterprise search. Taxonomy helps MDM data quality processes better handle ambiguity through inherent capabilities for understanding context, resulting in precise meaning. Since many of the underlying processes for MDM and for enterprise taxonomy development are similar, it can be effective to run these efforts concurrently or use one to jumpstart the other.

 

Re-Thinking MDM with Integrated Approaches

One of the most important changes to master data management approaches has grown out of the fact that much of MDM and data governance revolve around business use cases and business users. Everything done with MDM is first and foremost about the business. Ironically - and painfully - the increased involvement of different business roles constitutes a 'new' approach for many organizations.

Approaches that can help the "people issues" of MDM programs include:

• Embedded collaboration practices and technologies – integrating business and technical users to achieve intelligent and optimal usage of MDM
• Business user access and visibility into appropriate MDM processes, including continuous reviews of data and use of tools like data profiling
• Make it easier and faster for everyone to do their share of the work
• Comprehensive and responsive orchestration of many different teams and processes, to work as one

While new approaches have been discussed as individual items in this article, for real world implementations, multiple aspects of different approaches usually come into play for a particular organization, and need to be well integrated for effectiveness. Once again, such integration reflects the complex world of business.

In the past few years, new vendors have entered the MDM space with solutions that have a better focus on what businesses need. New and old vendors are also dismantling the old ways of how MDM has been implemented to more realistically support the diverse array of data sources that businesses need to utilize, particularly for faster, more agile analytics processes. The next article of this series will examine the evolution of MDM technologies.

 

Image source: landarchs.com

 

About the author: Julie is an independent consultant and industry analyst for B2B software solutions, providing services to vendors to improve strategies for customers and target markets, products and solutions, and future direction. Julie has expertise in several solution spaces including: data integration and data quality; analytics and BI; business process, workflow and collaboration; digital marketing, WCM and social media; and the pivotal importance of user and customer experiences. Julie shares her takes on the software industry via her blog Highly Competitive and on Twitter: @juliebhunt For more information: Julie Hunt Consulting – Strategies for B2B Software Solutions: Working from the Customer Perspective

 

Master Data Management Rebooted

 

Part 1 of a multi-part series on the "New Age of Master Data Management"

 

The 'New Age' of Master Data Management

It's about time that master data management (MDM) enjoyed a 'new age'. The creation and use of master data for business purposes has always been a good idea. The goal has been to make sense of all data that touches the organization: clean it up, add context, enrich it, and put it to work strategically as well as operationally.

Unfortunately, MDM has been plagued with certain problems from the very start. This stems mostly from the fact that MDM lands hard on some of the messiest aspects of an organization: data silos, unconnected systems and processes, management turf wars, and organizational roadblocks to disparate people working effectively together for common purpose.

 

Why Does Master Data Matter?

As the importance, value and sheer volume of data assets grow, so does the critical need for trustworthy, up-to-date information that is ready-to-go for any purpose. MDM processes consolidate data from numerous sources in meaningful ways. Master data also provides context and reveals data relationships for sources like big data that often exist as fragments without useful referential connections.

Master data has a large and growing role in areas that matter to organizations: strong brand presence, multichannel customer interactions, right-fit content and information, and highly variable buying journeys.

Analytics, particularly of the real-time variety, produce more realistic and trustworthy results when utilizing master data. Master data is often a key part of predictive analytics in support of: supply chain processes; personalization for improved customer experiences; logistics; operational optimization; and safety and preventive procedures.

A core element of MDM is data quality. Improved data quality impacts endless aspects of the business for reasons that should be obvious. But all too often, business management fails to comprehend how much poor data quality impacts profitability, productivity and even perception of the brand. Real world incidents that have been repeated at far too many organizations include:

• Inconsistent data of poor quality has led to extensive quantities of items in warehouses to "disappear" from systems records, never to be sold or consumed by the business. This can amount to millions of dollars of losses.
• Contradictory and confusing product lists and catalogs that deter purchases by customers and make sales staff look stupid – again with no purchases resulting.
• Costly pricing errors that persist undetected over time can have breath-taking negative impact on the bottom line.

 

Roadblocks, Detours, Dead Ends

Over time traditional approaches for MDM implementations have faltered for several reasons, many of which echo the failures of traditional ERP installations:

• Implementations take too long and cost too much.
• Project scope is often much too large.
• Business and IT teams are poorly coordinated for achieving the desired end state.
• Results fail to meet important business needs and / or show value in an obvious and timely fashion.
• Traditional approaches lag in keeping up with new technologies.
• Solutions have delayed including new data sources like the many varieties of big data.

A continuing obstacle is the lack of involvement of upper management with such important activities. If organizations want to tap into more data that is trustworthy and business-ready, it requires buy-in from upper management on down. Such buy-in is shown through clear commitments, upper management champions, and real data strategies – basically understanding the high value of data and what it takes to achieve it.

 

The Reality of MDM: People and Politics

What many people get wrong about MDM is thinking that it's all about the technology. In reality, it's much more about people, processes, and (unfortunately) politics. The people and politics of the organization can throw up significant obstacles to successful MDM programs. This is more likely to happen in organizations where upper level management has failed to provide its leadership and involvement in MDM implementations.

Silos permeate organizations from top to bottom, and not just in terms of software systems or digital assets. Collaboration between people is often limited. Internal politics can bring on secrecy or "turf wars" when it comes to the ownership of data and business processes. Resistance to change, lack of trust, desires to "hoard" data and information all come into play. Orchestration and collaboration of business and technical teams have always been critical to MDM success, but can be derailed by internal silos and politics.

Of course, technology provides an important means to achieve MDM implementations. But many vital activities aren't technical. For MDM and data governance to be successful, distinct changes must take place in organizations to eliminate the many silos that prevent the generation of reliable and up-to-date master data.

Not surprisingly, when things go wrong, organizations want to blame MDM technologies for the failures, instead of facing up to what really ails the organization. However, traditional MDM solutions have had significant limitations and often don't do much to help with the "people problem".

 

Wanted: Improved Approaches

A classic approach to MDM, the "bottom up" method, has not worked well simply because unsynchronized teams are trudging separate long roads to achieve the "desired end state" and often don't ever get there. It's easy to imagine the many ways that this method can be derailed. Instead, businesses benefit most from MDM approaches that work the way the business and its employees need to function to achieve real goals and objectives.

One of the most important changes to MDM approaches has organically grown out of the fact that much of MDM and data governance revolves around business use cases and business users. So it's gratifying to see more overtly business-defined approaches that not only delineate the approach but are enabling changes to the technologies that support them. These business-defined approaches reflect the complex, sophisticated needs of many organizations:

• Working from the desired end state and / or from the requirements of complex business problems to create master data management processes.
• Choosing contextual and analytic MDM solutions over traditional MDM tools that can handle new approaches for multi-dimensional and complex hierarchical data, including social and commercial graphs that underlie business use cases such as customer-responsive interactions across digital channels.
• Utilizing new MDM platforms that simplify the development of industry-focused business applications that take advantage of master data to solve business problems.
• Transforming model-driven approaches to be more flexible and agile in order to work with existing business processes as the starting point (instead of creating new processes), to deliver business value more quickly. IT and business teams can then transition to optimizing various processes for additional value.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

In a real world example, a pharmaceuticals company has utilized a new MDM solution to uncover significant information that improves the success of promoting drugs to individual healthcare organizations. Such information includes:

• Which insurance companies or payers accepted by the healthcare organization actually cover the drugs
• And for those payers, which insurance plans provide coverage
• What contracts for the pharmaceuticals company are already in place
• What other sales teams in the same company are already promoting to the healthcare organization
• Other important influence and decision-making factors that derive from complex hierarchies and networks of relationships

Running through such an analysis based on master data before approaching a healthcare organization helps the sales team determine if any business opportunities exist, and reveals better ways to approach contacts in the organization.

 

Master Data Management Rebooted

In the next five posts, I'll present and analyze what is changing for master data management approaches and solutions, including new technologies, and the evolutionary involvement of business roles in MDM and data governance.

One of the final aspects that I'll explore is this: is it time to quit calling it "master data management" and devise a name that better fits MDM Rebooted?

 

Image Source: hbr.org

 

About the author: Julie is an independent consultant and industry analyst for B2B software solutions, providing services to vendors to improve strategies for customers and target markets, products and solutions, and future direction. Julie has expertise in several solution spaces including: data integration and data quality; analytics and BI; business process, workflow and collaboration; digital marketing, WCM and social media; and the pivotal importance of user and customer experiences. Julie shares her takes on the software industry via her blog Highly Competitive and on Twitter: @juliebhunt For more information: Julie Hunt Consulting – Strategies for B2B Software Solutions: Working from the Customer Perspective

 

Cloud Integration: One Platform, Diverse Users

 

Final article of a six-part series on the essential interwoven nature of Business and Modern Data Integration

 

When we're talking about diverse categories of users for data management software solutions such as data and application integration, it's essential to understand the perspective of each group of users and their needs and abilities, in order to develop the most suitable user interface and the right capabilities for each category.

The future of cloud integration platforms may belong to vendors that step up to truly understand diverse users and then deliver what they require to be successful.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Today cloud-based integration platforms are opening access to different categories of users including less-technical business users. Business users have begun to assume more hands-on use of integration capabilities to run pre-built integrations, profile datasets, perform data prep functions, and even create basic integrations through wizards and pre-configured components. This is a very important means to an equally important end: taking advantage of the availability of more data to help these users do their jobs better, to benefit the organization.

Vendors for cloud integration platforms are also beginning to recognize and support diverse technical users. There can be quite a bit of difference between a technically-adept line-of-business developer who lacks deep experience for creating integration processes, and a developer who is a data integration expert and likely tackling complex integration scenarios. Each category of developer requires the right user interface and capabilities to create, run and manage integrations, in order to accomplish work requirements and support business objectives.

I recently talked with a vendor that is focused on providing a single cloud integration platform for diverse user categories. The vendor is building out a framework of ten different categories of technical and business roles with a clear view to how the platform would be used by each category. This exploration of how different users make use of integration capabilities should help this vendor streamline and perfect what it takes for different users to work well not only on their own but with other user categories when needed. This requires the platform to guide each user category to the appropriate capabilities, including a role-responsive or adaptive UI.

Another cloud integration vendor is developing a vision for multiple categories of users with schematics for how they may continuously interact during several aspects of typical integration phases. For each category of user, the appropriate platform capabilities are pinpointed along the integration phase continuum. The vendor wants to expand the solution platform to shepherd and enable this symbiotic approach to business and IT working together for more effective integration processes.

When we're talking about different categories of users for data management functions, it's essential to understand the perspective of each group of users and their needs and abilities, in order to develop the most suitable user interface and the right capabilities for each category. In that sense, it takes an outside-in perspective, working from what different users need from data management functions like data integration, and how they are comfortable working. This is a very different approach for most integration vendors – the focus has long been solely on the technology (which obviously is still important). However usability from the user perspective has been a critical topic for software vendors for quite a while.

 

The Technologies Behind "Simple" User Interfaces

A cloud integration platform that is designed to support a variety of user access options clearly requires the development of sophisticated technologies that essentially make the complex seem simple to the user. Different categories of users often require variable versions of the same user interface that are streamlined for ease of use, and that provide access to role-appropriate capabilities.

As such, the integration solution can be distinctly shaped by the variety of user roles that are provided access to the platform.

Often the work done to enable less-technical users can also benefit UIs and capabilities for more technically-adept users, providing faster paths to creating integrations. More technical users can take advantage of pre-built integrations, shared services and components that require little or no coding, just as less technical users can. For those who need it, most cloud integration platforms include the ability to customize and extend pre-built integrations through coding and other methods.

Obviously to keep less technical users on the straight-and-narrow, monitoring and management by centralized IT teams behind the scenes are essential elements. Setting up the right balance of management and freedom for business users will take some hard work, but will pay off as certain integration activities can be handed off to business users. This is another area of business-IT collaboration that requires IT to enable business users to work with integration processes, not to prohibit them.

Cloud integration platforms provide other technologies to guide all categories of users to more quickly and confidently build, test and run integration processes. Such technologies include role-aware, interactive "tooltip" constructs that pop up in the UI in response to an action or choice. The influence of mobile apps and digital games can be seen in the better "tip systems".

UI-based guidance techniques include "crowd-sourced" recommendations that are derived from the analysis of all integrations in multi-tenant platforms. Analysis processes look at how similar integrations have handled certain steps or are constructed overall, elucidated by the context of what the user is trying to accomplish. Equally helpful, interactive assistance tools focus on quickly resolving errors by showing the most likely solutions in order of relevance.

The UI has become an essential element for usability in cloud integration platforms and obviously has inspired a great deal of innovation to benefit all users.

 

The User Experience of Data

In a sense, diverse business roles and different kinds of developers have both been "underserved" by integration solutions. All too often non-IT users are lumped into the single blob of "business users" which is pretty useless for understanding much about who those business users are or how they want to – or even should – use integration solutions. And developers are usually seen primarily as data integration experts, rather than acknowledging the diversity of developer categories that need to create integrations regardless of the extent of their knowledge about integrations.

Diversity of users for cloud integration solutions isn't going away. More roles in organizations want and need to work hands on with data management functions to help generate the datasets that they need for their jobs and their business objectives. So the vendors that step up and deliver what different users require will take cloud integration platforms into the future.

Just like any user experience challenge, the gateways to integration solutions - user interfaces – have to work well for each category of user. So vendors have to understand a lot more about the different kinds of users who want access to integration solutions and about the user experience they should have, to ensure the success of all users. For cloud integration platform vendors, providing outstanding user experiences – for diverse users – is an invaluable differentiator

 

Image source: http://synergy.kuniv.edu/

 

About the author: Julie is an independent consultant and industry analyst for B2B software solutions, providing services to vendors to improve strategies for customers and target markets, products and solutions, and future direction. Julie has expertise in several solution spaces including: data integration and data quality; analytics and BI; business process, workflow and collaboration; digital marketing, WCM and social media; and the pivotal importance of user and customer experiences. Julie shares her takes on the software industry via her blog Highly Competitive and on Twitter: @juliebhunt For more information: Julie Hunt Consulting – Strategies for B2B Software Solutions: Working from the Customer Perspective

 

Modern Master Data Management – Responsive to Business Needs

 

Part 5 of a multi-part series on the essential interwoven nature of Business and Modern Data Integration

 

As a ubiquitous partner for Modern Data Integration, Master Data Management continues to amplify its focus on transforming data into the crucial game-changing business asset that it should be. MDM has been evolving from older approaches that take too long to make an impact on the organization, to an agile platform that responds to what organizations need and want from master data: reliable data that reflects how organizations need to work to achieve successful outcomes for themselves and their customers.

 

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Master Data Management and Modern Data Integration

Master data is very much a valuable business asset that has a growing role in many areas that should matter to organizations: strong brand presence, multichannel customer interactions, right-fit content and information, and highly variable buying journeys. To more deftly keep pace with business needs, master data management has been evolving to better mirror how businesses use data for numerous purposes. In this sense, MDM plays a significant part in the evolution of modern data integration. The recognition of the now more overt relationship between business and data management is a defining aspect of modern data integration and MDM alike. While it is tempting for organizations to think of MDM as a technical or IT function, MDM is far more about business objectives and the problems to be solved to achieve them.

Behind the scenes, MDM works to eliminate the data and process silos that threaten the ability of organizations to quickly and accurately put data to work in a myriad of processes and initiatives. The most up-to-date and useful data now comes from many sources, some internal to the organization and others from sites all over the digital landscape. Master data is critical to providing context to sources like big data, to connect big data fragments to customers, transactions and other organizational entities.

Data quality and data governance processes and practices are important parts of MDM. To succeed with accurate analytics and customer programs, centralized high quality data is a must-have. Hard work has to be done to make sure data is relevant, particularly if it originates from multiple sources of varying reliability. Many data-driven processes need to take on adaptive qualities to handle the increasing dynamic nature of business activities today. But this only works well if data is continuously trustworthy and timely.

 

The "New Age" of Master Data Management

Offerings from the newer master data management solution vendors, and new approaches by established MDM vendors, are responding to what organizations need and want from master data: reliable data that reflects how organizations need to work to achieve successful outcomes for themselves and their customers. Many MDM solutions are moving towards: faster and more streamlined implementations; broader capabilities in a single extensible platform; and the ability to work with any kind of data. Organizations need multi-dimensional knowledge, context and insight related to relevant entities, such as customers and products. Today, master data entities extend beyond this classic pair to include domains such as asset, location, supplier, finance, and personnel data.

The world in which businesses operate isn't just "relational". Relationship data grows in importance. MDM solutions must handle complex connections, hierarchies and relationships between multiple entities or domains. And companies must go beyond traditional transactional data to capture data describing behavioral aspects that amplify context. These newer kinds of master data may not be best stored in relational data repositories; as a result, graph databases are gaining importance for MDM effectiveness.

Cloud-based solutions are becoming the norm for modern data integration, especially with the growth in rich options for secure data flows from anywhere to anywhere. MDM solution vendors are following suit for cloud offerings, though at a slower pace. Organizations should already be extending data governance processes and practices to include any SaaS / cloud generated data, and most organizations pull in data from cloud sources for master data processes. So the cloud transition should be a "natural" move for MDM solutions themselves.

 

Data Governance Future Direction

Data governance is the pathway for orchestrating practices, processes, policies, and the involvement of many business roles, to assure that master data is available as a trusted and vital asset for the organization. A new direction for data governance is to create policies and practices that reflect the continuously changing needs of dynamic or adaptive organizations that want to be able to respond quickly to business opportunities and challenges. This means that data governance activities have to develop faster to be effective. At least that's the ideal goal.

Unfortunately internal politics in organizations continue to weigh down data governance efforts. Direct involvement of corporate leadership is essential to ensure that cross-enterprise collaboration and cooperation are kept as a top priority to empower data governance efforts so necessary for optimal use of data assets. Tasking upper management to recognize that data and the solutions that manage it are strategic functions for organizational success is a game-changing move.

 

Data Priorities Run on Business Processes

An imperative of master data management is to understand how the business operates: how the business uses key data and processes, how processes interact with data, and which business processes matter to strategic focal points. From an understanding of business processes, the best approach or approaches can be determined for implementing a master data 'plan of attack' that not only creates trustworthy, consistent master data but is responsive to changes in the business and to new data that continues to be collected / generated by business processes. The convergence of data and application integration and the increasing importance of orchestration hubs that manage various data-related processes, pave the way for master data management to have a natural partnership with modern data integration.

Organizations strive to determine how to extract value from data assets. Data and data-oriented solutions by themselves don't usually map directly to business outcomes, and as such, usually can't be measured to determine the value contributed to desired outcomes and the achievement of key corporate goals. It's through business processes that data can be mapped to business value. The key focus should be the business processes that impact revenue, business agility, competitiveness, and overall positive performance, connecting work to desired outcomes that lend themselves to various metrics. However -- both reliable data and effective data governance are essential enablers of gaining such value from business processes.

 

MDM: Never Just About the Technology

The increasing focus on master data in relation to business processes extends usage across the organization to more functions and business roles. For MDM and data governance, technology has long taken a back seat to strategy, planning and the creation of practices and processes – all of which are people-intensive activities. The involvement of business roles is critical for data governance to work, and to validate the MDM approach and keep it current to business needs.

As a reflection of the long-time participation of business roles, MDM technology solutions continue to provide more access points for a variety of business roles. This aligns with efforts by modern data integration solution vendors to recognize the importance of integration activities to be performed by both technical and less technical roles. Modern data integration solutions could benefit from taking a closer look at the trail already blazed by master data management and data governance initiatives, in terms of involving more business roles in many aspects of strategies, practices and processes.

 

Image source: geek.com

 

About the author: Julie is an independent consultant and industry analyst for B2B software solutions, providing services to vendors to improve strategies for customers and target markets, products and solutions, and future direction. Julie has expertise in several solution spaces including: data integration and data quality; analytics and BI; business process, workflow and collaboration; digital marketing, WCM and social media; and the pivotal importance of user and customer experiences. Julie shares her takes on the software industry via her blog Highly Competitive and on Twitter: @juliebhunt For more information: Julie Hunt Consulting – Strategies for B2B Software Solutions: Working from the Customer Perspective

 

 

Data for Everyone? Self-service Data Integration

Part 4 of a multi-part series on the essential interwoven nature of Business and Modern Data Integration

 

                                                   

 

The notion of self-service data integration has been around for a while, somewhat hand-in-hand with self-service BI – both primarily coming from the perspective of power business users like data and business analysts. But a door also opened for less-technical LOB business users to participate in data integration activities. SaaS and cloud services introduced self-service data integration where SaaS providers or third-party vendors supply wizard-like UIs for basic pre-defined integrations or data synchs between specific applications. This version of self-service DI gained impetus from the Salesforce ecosystem of data integration services, many of which targeted "everyday" business users.

Data Integration as a Service seemed to be a new force for the democratization of data. However, when looking at current modern data integration solutions, it appears that tech-savvy business users are the main focus of self-service DI capabilities. So what has happened to data integration for everyday business users?

 

 

 

 

 

 

 

 

 

 

 

 

 

Data for Everyone?

The democratization of data has a place in Modern Data Integration, as both conceptual and actual future directions for solutions. Such democratization is meant in the context of sharing data more widely throughout the organization, connecting many more people to the data they need (not just upper level executives). Organizations thrive and sustain success with better and faster availability of the right data at the right time.

Democratization of data includes self-service data integration and other data services, such as data preparation, that can give business users more hands-on access to data, frequently without the direct intervention of IT. But it would seem that there is quite a ways to go to actually connect different kinds of business roles to the appropriate self-service data integration tools. There has been way more talk than actual availability of such solutions.

Of course, it will take very sophisticated technology behind the scenes to keep it simple in the UIs that give everyday business users access to data integration capabilities. A lot of work must be done, especially to build in the safeguards to keep less technical business users out of trouble. To ensure that everyday business users do the work correctly and avoid serious missteps, self-service data integration has to be narrowly defined, with guidance at every step.

Cloud data integration and data prep solutions support dynamic data catalogs or libraries that provide "certified" data sets that can work well for self-service DI. The resulting data sets are available to the appropriate user roles, including the less technical. Governance of which roles access what data furthers the democratization of data in a risk-controlled environment. It's important that business and IT teams continuously validate the contents of such data libraries, as many data sources come with a short shelf life of relevance.

 

 

 

 

 

 

 

 

Data Governance for Everyone

Self-service data integration is clearly moving forward, and it must go hand-in-hand with self-service governance. There is real risk associated with self-service DI that must be addressed through centralized IT management and governance, which need to be in place to prevent piecemeal and poorly managed efforts scattered throughout an organization. Cloud-based self-service data integration solutions are well-positioned for governance that maps into overall organizational data governance practices and policies.

But remember: data governance is more business than tech: business people are essential for implementing successful data governance. Once again, the crucial partnership between business and IT is a core piece of data governance to make sure that it will work well and make sense both for business needs and objectives, and IT management of self-service DI activities.

 

Data Prep Solutions – Blazing a Trail for Self-Service DI for 'Everyone'?

Data preparation vendors have very effectively shared studies that highlight the costly nature of getting data ready for use by organizations: identifying the right sources; cleansing and validating data; testing for context and relevance; creating the appropriate integrations. Business and technology teams alike are better understanding why data integration and data preparation matter, and why they must be done well. These are not trivial undertakings, and require sophisticated technologies to produce the usable data sets greatly needed for many business activities.

An extensive array of data preparation technologies have emerged to help connect more business people to the data they require for analytics and BI, seeking to greatly reduce onerous preparation time to generate that data. Data integration and data prep do have intertwined destinies, since the goal of either should be reliable, business-ready data. Data prep draws on data quality processes that are have been very much part of data integration for quite some time, as has data lineage.

It looks like data prep solutions are creating the next level of tools that more quickly achieve reliable data, by utilizing applications of newer technologies like machine learning. Data prep offerings incorporate focused machine learning / algorithmic capabilities to automate more of data testing and quality as well as approaches to data integration.

Obviously a core aspect of data preparation is self-service through SaaS / cloud platforms. The vendors behind these self-service offerings are clearly interested in extending the variety of roles, both business and technical, that can take advantage of data prep platforms. Right now the primary users are very technical DI experts and tech-savvy business analysts. But various vendors are working to include less-technical LOB business roles, as well as LOB developers. And these vendors do seem to understand that different user roles require different UIs, capabilities, guidance and governance, and access to data.

 

Self-Service Options and Modern Data Integration

We are seeing significant changes in integration processes, where data flows from and to anywhere: on-premises, cloud, partner systems, third-party enrichment, and so on. With Modern Data Integration, application and data integration are converging to provide everything organizations need in a comprehensive platform. Ultimately for business needs it's still all about the data.

And it's about more people in the organization gaining faster access to data to help them do their jobs better, with improved effectiveness and innovation for the business. Through cloud-based platforms, self-service options can be available to all kinds of roles in an organization.

Self-service data integration is an important goal or Modern Data Integration. But self-service data integration - and preparation - still has a long road to travel before data can be accessed by "everyone".

 

Image source: linkedin.com

 

About the author: Julie is an independent consultant and industry analyst for B2B software solutions, providing services to vendors to improve strategies for customers and target markets, products and solutions, and future direction. Julie has expertise in several solution spaces including: data integration and data quality; analytics and BI; business process, workflow and collaboration; digital marketing, WCM and social media; and the pivotal importance of user and customer experiences. Julie shares her takes on the software industry via her blog Highly Competitive and on Twitter: @juliebhunt For more information: Julie Hunt Consulting – Strategies for B2B Software Solutions: Working from the Customer Perspective

The Much-Needed Business Facet for Modern Data Integration

 

Part 3 of a multi-part series on the essential interwoven nature of Business and Modern Data Integration

 

Data integration has always faced complex technical issues. Obviously the technically savvy will always be needed for many aspects of data integration, and require solutions that can handle complex problems. But this approach isn't enough for what businesses need and want today from Modern Data Integration. Making room for business ubiquity – business user participation and input – continues to be a challenge.

SaaS and cloud services are important technologies for modern data integration. SaaS in particular has brought into play ease-of-use and focused workflows to help non-technical users move through pre-built integrations. More vendor solutions are improving user experiences and solution efficiency, for both technical and less-technical users. There is increasing development of streamlined UIs, powered by sophisticated technologies behind the scenes, thus speeding data integration tasks.

There seem to be more discussions addressing business data needs, connecting data integration more directly to desired business outcomes via business processes. Including business users in more areas of data management has seen growth both in "lip service" and actual implementations. For the majority of vendors, a business user remains a tech-savvy power user. So we still have miles to go before business ubiquity in data integration includes more types of business roles.

 

 

The Much-Needed Business Facet of Modern Data Integration

Business ubiquity is not limited to direct participation by business users, but includes the overarching notion of Business informing all activities around data integration functions. This means empowering all sorts of business roles to work in partnership with IT, to inform, validate, and provide context to data integration processes. Business roles can help determine if data integration functions will deliver the right data, and will know where problems are likely to occur.

The Modern Data Integration technology platform should provide access points for business users, as well as templates for practices and processes that help organizations engender useful, dynamic and continuous collaborations between business and IT – and the growing involvement of business roles at many levels. Collaborative approaches are usually iterative and involve processes that are both human-based and technology-based. Modern data integration solutions should include all appropriate collaborative aspects.

 

Modern Data Preparation

We're seeing growing attention on tools that more quickly connect business users to the data they need, without much intervention from IT. Data integration is not an easy-peasy undertaking for anyone. But we can break out components that are more amenable to business user access and participation, such as data preparation. There is real value in moving these tasks from IT to business users who can connect data to business processes, usage realities, professional knowledge, and pertinent requirements.

Self-service data preparation solutions include the integration of disparate data sources. These solutions address all of the functions that make the data business-usable and reliable: profiling or exploring, cleansing (which should follow organizational data quality guidelines), enrichment, and so on.

Business users working with data preparation solutions can achieve greater agility to respond to new data sources, new business initiatives, narrow windows of opportunity, impending competitive threats, and unexpected market changes – all of the realities of fast-changing and highly digital business worlds.

 

Business Use of Data Profiling

Data profiling started off as a technology and methodology for IT use. But data profiling can be an important tool for business users to gain full value from data assets. When given the right tools and practices for data profiling, business users should quickly identify inconsistencies and problems for data, before it is used for reporting and intelligence purposes. It's also a sensible way for business users to understand more about the data they utilize in applications, processes and analytics.

From such "data intelligence", business users can have a greater understanding different data sources to be able to ask the right questions for BI and analytics projects. They'll also know if they have the right data and all of the data that they need to answer their questions.

 

Business Data Libraries for Ready-to-go Data

Data libraries are another aspect of improving business-IT collaboration. Modern data integration solutions are doing a better job of attaining the holy grail of business-ready data: directories or libraries of available data views, particularly the data that results from a variety of integrations. This data could be accessed by IT or business users, with the safety net of role-based constraints built in. Such business data libraries greatly aid reusability, as well as allow business users to utilize the data sets that they need, but may not know how to generate.

 

Modern Data Governance for Data and Application Integration

Today's myriad of data sources, data integration tools and approaches, means that data governance is mission critical to ensuring trustworthy and business-usable data. As data and application integration processes overlap more and more in modern data integration, data governance now must oversee all integration approaches, as a centralized function.

Because of the new world of modern data integration, there is more pressure on data governance functions to accept new realities involving business users and self-serve data solutions. Organizations must establish guidelines and processes to directly manage when it's beneficial for business users to perform data prep or integration – or when then work is best done by technical teams due to complex technical requirements.

 

Modern Data Quality

For modern data integration evolution to work well for business user participation, data quality is paramount. More and more organizations understand that data can be a valuable asset, that intelligence can be derived from a variety of data sources, and that analytics of many kinds can greatly benefit decision making, future direction, competitiveness, and innovation. But many organizations aren't well-prepared to tackle the work that must be done to ensure that data is reliable, timely and relevant.

One reason data management and data quality are so important is that a lot of data that can be useful to the enterprise has a very short shelf life. So business processes and activities must be able to tap into data as soon as possible. But that data is only useful if it has high quality. All of the amazing new technologies of modern data integration solutions will mean nothing if the data is unreliable, and therefore unusable.

 

Business Ubiquity – Organizational Symbiosis

Business users increasingly have the potential to utilize powerful capabilities to explore, manipulate and merge new data sources without IT support. There is obvious advantage to organizations to fully support and empower business users to work more directly in many aspects of data management.

Modern data integration solutions should not only support business ubiquity, but can also benefit from it. Vendors can achieve this by making sure that these solutions:

• Provide natural access points for business users backed by built-in guidance to make sure these users don't misstep
• Document and support collaborative processes between IT and business roles in ways that improve data integrations
• Participate in building a comprehensive plan for business user participation and help execute it
• Trace and monitor metrics that are established to connect data integration processes to business outcomes and impact
• "Know" that technology is only part of what is needed to create, implement and reap value from data integration processes
• Support agile change involving both IT and business roles

 

Image source: theyellowsparrow.com

 

About the author: Julie is an independent consultant and industry analyst for B2B software solutions, providing services to vendors to improve strategies for customers and target markets, products and solutions, and future direction. Julie has expertise in several solution spaces including: data integration and data quality; analytics and BI; business process, workflow and collaboration; digital marketing, WCM and social media; and the pivotal importance of user and customer experiences. Julie shares her takes on the software industry via her blog Highly Competitive and on Twitter: @juliebhunt For more information: Julie Hunt Consulting – Strategies for B2B Software Solutions: Working from the Customer Perspective