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

 

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

 

Developing Mobile Business Apps – Navigating Data and APIs

 

Most organizations are well aware that mobile devices and applications are changing how they do business with customers and partners, and how their employees accomplish their work. Mobile business apps are developed to provide useful services that are designed specifically for mobile devices, focusing only on the most relevant streamlined capabilities. Well-designed apps also reflect an in-depth understanding of how different people use mobile devices.

The effectiveness of business mobile apps is ultimately about connecting users to the information and data that they need anywhere, anytime. Business data is frequently stored on multiple platforms, inside and outside the firewall, with varying levels of security and portability. End-to-end task workflows may even take place on multiple platforms. Savvy organizations are catching on that the mobile revolution cannot be stopped. It's best to proactively work out how to govern the mobile tools of the workforce.

Data management and governance, as well as flexible data integration processes, are key components of acquiring and integrating cloud and mobile data with on-premises data repositories. Data in the cloud has been a significant challenge for a while, with each cloud service providing proprietary APIs as the main means for connecting to data. But data residing on mobile devices introduces a whole new world of challenges, including serious security concerns.

As the use of mobile devices and apps for business activities continues to proliferate and fragment data, the standing of data integration as a mission critical technology escalates. What has changed for data integration solutions is the demand for more ways to create and use integration processes. To meet such demand, data integration platforms not only need to be high performance, they must continue to innovate ways to connect to data where it lives and then perform any number of actions to take data where it is needed.

 

Speeding Mobile App Development: API Management Platforms

Agility, flexibility and rapid development are key attributes for the mobile app creation process. Developers of mobile apps have an increasing number of cloud services and platforms that provide reusable components, including numerous JSON and REST APIs that enable data access, as well as the means for navigating security and authentication policies.

Mobile apps both generate and use APIs. On the generation side, APIs that are not well designed or properly built come with a high cost over long-term usage. On the API usage side, in the past, mobile app developers had to embed numerous SDKs directly into the app, which would require constant care and management. API management and integration platforms have been created specifically for mobile app development to orchestrate and enable data integration needs, so that mobile developers don't have to keep re-inventing the wheel when it comes to API creation and usage. These cloud-based integration management platforms cover many of the services that are needed for well-performing mobile apps. Indeed agile API management has become essential for smoothly-running mobile apps.

 

Governance and Security

As with any data-related functions, working with mobile / cloud data integration and APIs requires comprehensive governance and management, to ensure streamlined usability in mobile apps (or cloud services) while adhering to the overall data governance practices for the organization. If an organization doesn't address API management as a critical component of managing mobile app development and usage, many problems can and will result. Security guidelines are also critical for mobile practices.

Ultimately all data usage must in some way align to centralized management to ensure integrated policies, practices and processes across the organization. While the mobile device may be the activity point, the data used and created is still part of a bigger picture. On the other hand, data management practices and policies have to be balanced with enabling the workforce to make good use of cloud and mobile technologies, to more effectively perform work on behalf of the organization.

 

Image source: thisismoney.co.uk

 

This post was brought to you by IBM for MSPs and opinions are my own. To read more on this topic, visit IBM's PivotPoint. Dedicated to providing valuable insight from industry thought leaders, PivotPoint offers expertise to help you develop, differentiate and scale your business.

 

About the author: Julie Hunt understands the overlap and convergence of many business processes and software solutions that once were thought of as "separate" – and how this impacts software Vendors and Buyers, as well as the strategies that enterprises implement for how technology supports the business and its customers. Julie shares her takes on the software industry via her blog Highly Competitive and on Twitter: @juliebhunt For more information: Julie Hunt Consulting

Data Integration Services Evolution

 

 

With the increased usage of data analytics to help solve business problems and improve strategies, data integration has come more to the forefront as a critical component of overall technologies for data-savvy organizations. New approaches and technologies for data integration are coming into play to better meet what organizations need, especially to support increased business user access to data management itself.

Over the past several years, data integration services have continued to grow and evolve, thanks largely to the cloud. Tapping into SaaS approaches, the early entrants were data integration as a service and purpose-built or pre-built data integrations.

 

Data Integration -- as a Service

Data Integration-as-a-service (IaaS) became possible as more organizations made use of SaaS, cloud services and even B2B data exchanges. Business ecosystems began growing in size and complexity thanks to online connectivity, promoting ease-of-access for B2B relationships and interactions. As more data flowed in the cloud, the need and demand for integration picked up momentum. Originally IaaS functioned as an IT-oriented service, mostly in stand-alone instances. But IaaS instances were soon embedded in other cloud services and applications, as well as cloud infrastructure platforms.

The ability to quickly move data to where it is needed must evolve beyond cloud services that only provide basic APIs and web services that still require users to build connections. While leveraging APIs and web services for loosely-coupled integration processes, IaaS rapidly added more sophisticated capabilities such as virtualized data services, governance, REST-ful services, and agile interoperability at multiple levels.

Cloud-based IaaS has since progressed into Integration Platform as a Service (iPaaS). The data integration services evolution now involves comprehensive platforms that leverage cloud services for the creation and execution of integration processes or flows that connect to systems in the cloud and on premises -- within a single entity or across multiple organizations. Services include critical capabilities: data governance and management, metadata discovery, data quality, transformation rules, agile access to data sources – all delivered with automation and ease-of-deployment as important aspects of the overall platform.

 

Purpose-Built Data Integrations

As a more narrow approach to integration, purpose-built data integration is designed to meet a specific business need, with pre-defined capabilities bundled in the solution. Frequently purpose-built data integration comes as an appliance. Purpose-built data integration services in the cloud comprise an evolutionary step for making data integration services available to business users, to initiate integrations between specific sources or applications.

Cloud services for purpose-built integrations usually include data replication, migration, and synchronizations for customer data. Typically integrations involve specific SaaS applications like Salesforce.com and Netsuite as "pre-set" sources and targets.

These services work with focused use cases where business users do not have to make lots of decisions about how the integrations will be accomplished. Wizards are employed to walk business users through the process. However, at minimum, business users must have knowledge of the sources and targets, and basic understanding of the data integration task, to work confidently and accurately through the purpose-built approach.

 

Business Innovation from Data Integration Services

The evolution of data integration services must align dynamically to the business as it changes. Business users require rapid responsiveness to integration needs, through technology that is 'proactively' ready for many use cases that reflect expected and realistic business situations. Innovations for cloud-based data integration services contribute to optimal business performance and to the faster delivery of valuable data that frequently has a short shelf life of relevance.

Data integration services can tap into the rich value of the cloud and how it connects businesses through common services, with business processes providing contextual frameworks. Such data and process agility underpins must-haves for the competitive organization: always-on intelligence, new business opportunities, innovations in products and business models, robust collaborative processes for the ever-growing business ecosystem.

 

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This post was brought to you by IBM for MSPs and opinions are my own. To read more on this topic, visit IBM's PivotPoint. Dedicated to providing valuable insight from industry thought leaders, PivotPoint offers expertise to help you develop, differentiate and scale your business.

 

About the author: Julie Hunt understands the overlap and convergence of many business processes and software solutions that once were thought of as "separate" – and how this impacts software Vendors and Buyers, as well as the strategies that enterprises implement for how technology supports the business and its customers. Julie shares her takes on the software industry via her blog Highly Competitiveand on Twitter: @juliebhunt For more information: Julie Hunt Consulting

 

Riding the Mobile Data Highway

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Image source:  cisco.com

 

Vendors and services providers for mobility management solutions can overly fixate on the vast array of software platforms and services for "corralling" mobile devices and ensuring secure computing. While these are vital areas, ultimately to enable successful organizations it can be far more important to provide services to amplify the value of what employees are doing with mobile devices. Particularly as businesses have become more mobile, the never-ending ebb and flow of data has made comprehensive data management a mission critical element both for business agility and for guaranteeing trustworthy, useable data.

Mobility adds a distinct flavor to real-time data management, particularly since mobile is a top source of big data. But data by itself – no matter the source – carries no value. Even worse, poorly managed data can cause real harm to organizations. Real value accrues when data sources are streamlined and cleaned up, when the processes that consume and create data are more responsive to business needs, and when business users trust the data they are using for decisions and work requirements.

Obviously business data is everywhere these days, as cloud services and mobile software offerings keep pace with worker preferences for tools. End-to-end task workflows may even take place on multiple platforms, off and on premises. The future of applications for mobile devices is ultimately about providing the workforce with the data they need, when they need it and where they need it. With a growing "mobile first" mindset for workers, organizations must enable as much functionality for their employees as possible while also ensuring data - and corporate - security.

One of the important changes for data management solutions is the demand for many different ways that integration processes can be created and used. Not only do data integration platforms need to be high performance, they must continue to innovate ways to connect to data where it lives, and then perform any number of actions to take data where it is needed. Added to integration and management imperatives is the ongoing battle to eliminate more data silos, to speed up access to the right data from mobile devices.

With 'speed to solution' as a crucial aspect of mobile data management, managed services can play an important role to provide what many organizations need, by applying expertise in mobility and cloud services to 'old school' data management processes and mindsets. True data mobility gives rise to data flows consistently and "effortlessly" (from the user perspective) between any points whether cloud or on premises.

Managed services have a natural fit with the management of the entire life cycle of mobile data and information. The objective is to keep up with changes regarding where data is created and stored, and where it needs to flow, to reach everyone in the organization who can benefit from such data. So mobile data management is as much about continuously creating and maintaining the right business processes as it is about data access and security.

Process-centric initiatives are often optimal approaches for the things that companies want to accomplish. Business processes make it possible to directly connect work to business outcomes that lend themselves to various metrics that provide clarity for business performance. It is through business processes that data can be mapped to business value.

Some of the difficulty for transitioning how enterprises derive value from business processes (and the data running through them) is the fact that most data-related initiatives have been driven by IT instead of by business-oriented roles. To enable better business outcomes, mobile data management initiatives need to bring business and IT teams together as symbiotic partners. Managed services providers can be the game-changing bridge to promote strong relationships between business and IT teams in order to best support a growing mobile workforce.

 

This post was brought to you by IBM for MSPs and opinions are my own. To read more on this topic, visit IBM's PivotPoint. Dedicated to providing valuable insight from industry thought leaders, PivotPoint offers expertise to help you develop, differentiate and scale your business.

 

About the author: Julie Hunt understands the overlap and convergence of many business processes and software solutions that once were thought of as "separate" – and how this impacts software Vendors and Buyers, as well as the strategies that enterprises implement for how technology supports the business and its customers. Julie shares her takes on the software industry via her blog Highly Competitiveand on Twitter: @juliebhunt For more information: Julie Hunt Consulting – Strategies for B2B Software Solutions: Working from the Customer Perspective

 

Finding Meaning in Big Data Through Metadata

 

 

With the growing interest in big data analytics combined with the shortage of affordable experts and skilled practitioners for deploying and managing big data initiatives, many organizations are looking to managed services as a way to fill the need. More MSPs continue to develop sophisticated services for big data processing, along with all other aspects of data management and analytics.

Big data processing can function well as cloud-based, end-to-end managed services that integrate bi-directionally with on premises data centers and systems. End-to-end services can include streaming data flows, data management activities like data governance, and data visualization and analytics. Big data analytics on cloud infrastructures require top notch management of security and compliance, areas that MSPs are currently improving anyway to proactively move into new technology areas in demand for many companies.

Remember: big data doesn't just refer to high volume or fast-moving data that is important to large corporations. More companies of all kinds need to extract value from a wide variety of data sources – often these sources are difficult and messy for traditional data processing. These sources are also part of the "big data" analytics story and often yield valuable insight. "Messy" data sources include: multi-structured or unstructured data with highly variable formats and semantics like social media content, log files, and e-mail; and machine-generated data from medical devices, industry sensors, automated machinery and systems, and GPS.

 

Metadata Management Services

An important must-have capability for big data processing services is comprehensive metadata management. Metadata provides information about a data item, such as product, that uniquely describes that item. Product metadata can include product ID, product category, supplier ID, size or dimensions, and so on. A field like product ID is also a means for linking to other data sources, for integration purposes. Through metadata descriptors, we can talk about data items in common terms, outside the actual item, and take advantage of metadata to integrate and better understand disparate data sources.

Metadata management is one of the key areas of comprehensive information and data governance. Agile data governance tools are in demand and all sorts of data management vendors are chasing this business. As MSPs expand their presence in data management and analytics services, metadata management and data governance should be of great interest to them. Hopefully the availability of managed services in these areas will make comprehensive data management more affordable and available to organizations of all sizes.

 

What Metadata Does for Big Data Analytics

Metadata is not just about data integration and enterprise data warehouses; for other enterprise needs, metadata helps find data during data discovery, and points the way to interpret and use data correctly. Metadata can greatly streamline and enhance processes to collect, integrate, and analyze big data sources. Working with messy big data like multi-structured sources means that metadata is critical to understanding this data and to connect it to other data sources. Multi-structured data comes with many difficulties – information can be mined, but for it to have meaning and value, attributes like sentiment, purpose and context must be determined as well, and correlated with data sources for customers, products, and so on.

Traditional data sources, such as relational data, provide plenty of logical structure through more easily obtained metadata. However, big data often does not carry a lot of 'native' metadata, so metadata from external sources is essential to unlock meaning. Big data may have to go through certain analytics processes to construct the beginnings of metadata. Then this metadata is correlated with the metadata from other data sources to derive the most useful logical model. As big data processing evolves, new types of metadata may arise to meet the special circumstances of different kinds of big data.

 

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This post was brought to you by IBM for MSPs and opinions are my own. To read more on this topic, visit IBM's PivotPoint. Dedicated to providing valuable insight from industry thought leaders, PivotPoint offers expertise to help you develop, differentiate and scale your business.

 

About the author: Julie Hunt understands the overlap and convergence of many business processes and software solutions that once were thought of as "separate" – and how this impacts software Vendors and Buyers, as well as the strategies that enterprises implement for how technology supports the business and its customers. Julie shares her takes on the software industry via her blog Highly Competitiveand on Twitter: @juliebhunt For more information: Julie Hunt Consulting – Strategies for B2B Software Solutions: Working from the Customer Perspective

 

Smart Cities Technology -- Opportunities for Midsized Vendors

 

The term 'smart city' is becoming familiar around the world as urban areas realize that a great deal of benefit can come from connecting physical and digital worlds to better serve the needs of citizens and the administration of the city. A smart city doesn't just result from changes in infrastructure and technologies – it must embody new ways of thinking and planning.

And smart cities aren't just limited to large metropolitan areas. Many midsized cities need smart city technologies to better manage growth and provide quality of life and work for their citizens. Just like large metros, many midsized cities want to ensure the sustainability of resources and services, by using technologies and digital systems to improve operations and efficiencies, and to innovate how cities and citizens interact.

At the core of smart cities is the Internet of Things (IoT) – where many systems and networks interact with each other and often run without human intervention. The Internet of Things for the smart city can include not only municipal systems, but machine-generated data created by citizen-owned items such as home environment management devices, smart appliances and vehicle sensors.

With midsized cities pursuing smart city strategies, opportunities are quite promising for midsized technology companies to help these cities realize their plans. Cutting edge and forward-looking initiatives – using the latest technologies – are often thought to be the domain of large vendors working with large cities. But many midsized and smaller cities need and want to take advantage of similar technologies and capabilities to operate more effectively. Often smaller technology vendors are the better choice as a strategic partner for midsized smart city projects that are frequently implemented incrementally and at lesser scale.

The market for smart city technology solutions is still very young and in formative stages, which means there's plenty of room for midsized companies to enter this solution space. Midsized technology vendors who fully understand the needs, desires and challenges that midsized cities have for smart city initiatives will find success in this market. Part of meeting midsized needs includes in-depth understanding of what will work for the people in these cities. So the right solutions aren't just about technologies but must have a strong focus on the sociology of living in a smart city today and into the future.

 

Midsized Smart Cities Need Big Data Analytics

One important technology solution area for midsized smart cities: big data analytics for the volumes of data created every day by city systems. This data increases exponentially as more smart city technologies are implemented. Technologies for transportation, utilities, communications, and many other aspects of urban life are evolving faster than overall management strategies. All cities lag on fully gaining advantage and innovative benefit from this information.

Big data analytics technologies are continuously evolving to better collect, integrate, process, and analyze this highly disparate information to both improve the systems of the smart city and to apply results to other needs and opportunities. Midsized technology companies have new ways to deliver cost-effective services and solutions through cloud platforms, lower priced data analytics tools, and mobile applications.

As a solution example, midsized technology vendors could provide services for midsized cities for the continuous data analytics needed to govern and improve urban transportation:

• Real-time operational responsiveness
• Personalized interactions with citizens using transportation systems
• Creating new services and changes to policy more quickly and accurately
• Measuring the effectiveness of current decisions and actions

Such services and solutions should be tailored for midsized cities to keep costs down and provide exactly what these cities need for faster, more effective implementations. Understanding future direction will help midsized technology vendors build in the right scalability, usability and interoperability capabilities to keep smart cities continuously smart in real time.

 

 

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This post was brought to you by IBM for Midsize Business and opinions are my own. To read more on this topic, visit  IBM's Midsize Insider. Dedicated to providing businesses with expertise, solutions and tools that are specific to small and midsized companies, the Midsize Business program provides businesses with the materials and knowledge they need to become engines of a smarter planet.

 

About the author: Julie Hunt understands the overlap and convergence of many business processes and software solutions that once were thought of as "separate" – and how this impacts software Vendors and Buyers, as well as the strategies that enterprises implement for how technology supports the business and its customers. Julie shares her takes on the software industry via her blog Highly Competitiveand on Twitter: @juliebhunt For more information: Julie Hunt Consulting – Strategies for B2B Software Solutions: Working from the Customer Perspective

Fighting Cyber Crime – A Critical Priority for Every Business

 

No organization is immune from cyber attacks. So it's fitting to address cyber security challenges for midsized companies since October is National Cyber Security Awareness Month. The main message: your midsized business is a prime target for cyber attacks.

Speaking at a recent government cyber security briefing at the Armed Forces Communications Electronics Association he (Bill Wright) cited Symantec's 2014 Internet Security Threat Report, noting that targeted attacks on mid-sized firms (those with 251 to 2,500 employees) and small firms (those with 250 employees and fewer) rose by 61% in 2013 from 2012 levels.     (Source: Information Week)

 

Often midsized organizations help cyber criminals succeed in their attacks through lack of preparation and company-wide awareness. Many companies think cyber criminals only target large enterprises. This is a dangerous assumption to make. Fortunately, cyber security technology solutions are becoming more affordable for midsized organizations to empower them to fight back.

The approach companies take to cyber security is crucial for success. Organizations can no longer view digital security as a function of "wall building" and defensive maneuvers. Attacks by sophisticated cyber criminals call for proactive security processes, where enterprises continuously hunt for current and potential threats. Big data analytics have an important part to play for proactive protection initiatives that continuously get smarter for anticipating future attacks. The availability of less expensive technology for big data processing infrastructures is making a positive impact on using advanced analytics in support of cyber security.

 

Cyber security threats often show up as patterns that usually deviate from the expected behavior for most authorized users or for activity on particular devices and technology platforms. To identify and fight such attacks, security teams need technology that can find and analyze deviant trends. This is a natural fit for big data mining and analytics -- predictive analytics in particular.

Data that has been extracted from big data sources can be enriched with other kinds of data and information to reveal patterns and trends related to cyber attacks. Analytics now take on a forensics mission while searching through data for patterns of irregular, suspicious or unexpected activity. Organizations can now take the necessary actions to block such cyber threats and make improvements to prevent future attempts.

Big data analytics provide faster access to continuously updated information from diverse sources – speed is essential to head off many cyber attacks. These days cyber criminals move more quickly to foster attacks and digital thefts, to try to subvert current security systems and measures. Analytics and monitoring approaches have to be constantly altered and fine-tuned to anticipate such ever-changing tactics.

Often overlooked, awareness is an important weapon against cyber threats. When midsized businesses accept that these threats are ever-present and educate their staff on how to secure their data and equipment, quite a bit can be achieved. But with the growing sophistication of cyber criminals of all kinds, midsized organizations must also arm themselves with smart technologies like big data analytics to keep ahead of the continuous attacks that threaten their businesses.

 

Image source: uwf.edu

 

This post was brought to you by IBM for Midsize Business and opinions are my own. To read more on this topic, visit  IBM's Midsize Insider. Dedicated to providing businesses with expertise, solutions and tools that are specific to small and midsized companies, the Midsize Business program provides businesses with the materials and knowledge they need to become engines of a smarter planet.

 

 

About the author: Julie Hunt understands the overlap and convergence of many business processes and software solutions that once were thought of as "separate" – and how this impacts software Vendors and Buyers, as well as the strategies that enterprises implement for how technology supports the business and its customers. Julie shares her takes on the software industry via her blog Highly Competitiveand on Twitter: @juliebhunt For more information: Julie Hunt Consulting – Strategies for B2B Software Solutions: Working from the Customer Perspective

 

Mobile Data and Security Challenges

     

Mobile platforms are flooding into enterprises, whether it's through BYOD "consumer" apps like Dropbox, or from internal development teams creating apps for on-the-go employees. Just like SaaS and Cloud solutions, we now have more data in silos. And the same goes for security issues, with the added kicker that an entire device full of data and other compromising information can be easily lost or stolen. Quite frankly, the very people who want mobile platforms available for work also present the biggest security risks to enterprise data assets.

The knee-jerk reaction of many enterprises has been to try to block or limit the use of mobile devices and applications. This is not an option – the floodgates are open. In fact mobility is critical in enabling a distributed work model for a global workforce. It's clear that not only do many employees want to work this way -- for many corporate roles, mobile empowerment is essential.

There is a deluge of mobile data being created by mobile apps, that is stored on devices or in the cloud. Many times this data must be integrated with on-premises systems, which introduces new proprietary APIs and challenges for integration processes. Employees, including upper management, also want all kinds of data from internal systems available on a mobile interface, including complex data visualizations. Data is definitely at risk as mobility proliferates:

• Loss or theft of smartphones, tablets, and laptops compromises corporate data and access to enterprise systems
• Many people mix professional and personal usage on a single mobile device, such as a single consolidated "Inbox" for all email accounts -- this opens the door to sending proprietary corporate information to and from non-corporate accounts, and even to the wrong recipients

• Mobility can lull people into forgetting whose data and information they are handling. Without physical corporate walls to remind them, they can inadvertently expose data and credentials

• Malware on mobile devices is on the rise with obvious implications for data security

 

Proactive Policies - Fluid Security

In anticipation of potential mobile security issues, enterprises need to lay out plans to manage the associated risk. First, upper management should develop a realistic strategy for mobility. Then data security and management strategies must be created that map into the corporate mobile strategy, and also align with corporate data governance practices. Finally the enterprise must put policies and practices into place, with straightforward education for employees about how mobility will work.

As with on-premises and cloud data sources, enterprises must proactively develop thorough data management and governance plans to secure the data sources themselves, as well as guidance for the bi-directional movement of data and integration processes.

A mobility security plan must be flexible to allow high volumes of interaction, but extensive enough to moderate risk and meet compliance standards. Enterprises can turn to advanced security tools to provide coverage for the internal and external sides of the mobile data equation. Mobile technologies are rapidly evolving along with employee usage – enterprises must keep pace by adopting ever-changing approaches to sophisticated protection and risk management.

 

 

 

 

 

Image source: IdroidWeb

 

 

This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet. I've been compensated to contribute to this program, but the opinions expressed in this post are my own and don't necessarily represent IBM's positions, strategies or opinions.

 

About the author: Julie Hunt understands the overlap and convergence of many business processes and software solutions that once were thought of as "separate" – and how this impacts software Vendors and Buyers, as well as the strategies that enterprises implement for how technology supports the business and its customers. Julie shares her takes on the software industry via her blog Highly Competitiveand on Twitter: @juliebhunt For more information: Julie Hunt Consulting – Strategies for B2B Software Solutions: Working from the Customer Perspective