Highly Competitive - software industry insights

Cloud Expands Value of Purpose-Built Data Management Services

 

The ongoing value of purpose-built technology products stems from the savings in productivity and cost that come from delivering only the features and functions needed to get a certain job done. Purpose-built software solutions are an opportunity for cloud service providers to deliver well-differentiated services that are built on domain expertise for explicit problems related to specific industry scenarios. This approach often results in a strong competitive market presence for the cloud services provider.

As data management platforms and solutions continue to emerge as cloud services, purpose-built data management capabilities can be an important area both for the providers and consumers of cloud services. Data integration, in particular, has taken on immense importance due to the continuously proliferating data silos in the cloud and the need to integrate cloud data with on-premises systems. Purpose-built data integration is usually designed to meet a well-defined business need, with specific capabilities bundled in the solution, frequently delivered as an appliance.

Taking the purpose-built route for data integration services aligns with top advantages of cloud computing:

• Up and running quickly to handle integration tasks
• Enterprise-quality capabilities available to organizations of all sizes
• Always-on availability from any device or location
• The most current capabilities, without the need to purchase new licenses or new versions
• Pay-as-you-go, pay for only what is used

There is clear advantage for cloud services that support specific point-to-point integrations that can be performed easily and inexpensively, enabling less-technical business users to handle certain integrations between applications. A common integration need involves CRM and ERP systems, such as data flows between Salesforce.com and SAP. Services are built for focused use cases where business users do not have to make lots of decisions about how the integrations will be accomplished. Wizards are often employed to walk users through the process. However, business users must have good knowledge of the source and target datasets, and basic understanding of the data integration task, to work confidently and accurately through the purpose-built approach.

Cloud services for integration usually include data replication, migration, and synchronizations for customer data. Typically integrations involve certain cloud / SaaS applications as "pre-set" sources and targets, as well as on-premises systems. Data integration cloud services should also include data quality tools to involve business users in this important task.

Data integration solutions that can handle data located anywhere, as well as security and on-demand considerations, are important for connecting cloud services to on-premises systems. For many companies, the business technology that underpins those organizations is going through a transition from an infrastructure of all systems inside the firewall to more of a "hybrid" infrastructure comprised of part cloud-based services and part on-premises.

However, not all integration processes can be reduced to "simplicity". That's why data integration is still a tough nut to crack. In these situations, integrations will require the help of IT teams, consultants, and/or managed services using more sophisticated approaches. For organizations with broad needs, purpose-built data integration quickly becomes less useful as business needs change and the types of projects fluctuate. For such needs, a comprehensive and flexible data integration platform usually provides the best approach to handle current projects and to scale to address future integration challenges.

 

 

 

 

 

 

 

 

 

 

 

 

Image source: pomeroy.com

 

 

 

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

 

Data Savvy Business Roles for Better Analytics Outcomes

 

To put data and analytics to work usually requires multiple roles, not just one, to achieve ongoing value and to align with company functions. No one person can provide all the skills and expertise that are needed. A variety of viewpoints, knowledge and experience need to come into play to get the most from processing and analyzing data – and then to correlate analysis with other information sources and knowledge -- to derive the best overall insight and recommendations.

With the increased interest in big data and advanced analytics, there has been much discussion of the data scientist role. While the data scientist can provide significant value, not all companies will choose to add data scientists. Midmarket companies in particular may not want to invest in this usually expensive professional. But midmarket companies can turn to several existing positions to grow company expertise for data management and analytics. Companies can expand roles for business analysts and LOB users who are more deeply involved in data integration, data quality, analytics and the business processes that create and consume data. Several C-suite roles also need to become proactively data savvy.

Enhancing existing data-related business roles can be a means to better connect technology to business needs and desired outcomes, where many of these roles are strategic and straddle both business and technology activities. Expanded roles for business users are needed to include valuable domain expertise, business insight and real world judgment for analytics and decision-making processes.

Conversely, business users must expand their knowledge of data and analytics to better support their technical partners, so that the most effective activities take place to produce the most useful results. Midmarket companies should seriously invest in developing the skills of multiple roles to better understand data management and analytics – over time this investment will pay off quite well.

In companies where employees understand more about data and analytics, often less time is wasted looking for the right reports or debating whether the right data has been utilized. More time is spent on analyzing the results of analytics and on improving decision-making processes. More employees can be involved in spotting trends and potential problems, to more quickly and proactively manage issues and take advantage of market changes.

The more the consumers of analytics are involved with data management and analytics processes, the better the outcomes. For example, traditionally the strategic executive dashboard has been seen as the "killer app" for most CEOs, where the CEO is usually the 'passive' recipient of intelligence, rather than a participant in its creation. But the growing importance of data and analytics for most companies now calls for CEOs to have a more participatory relationship with the initiatives for generating intelligence and insight, including processes for continuous improvement and constant review of requirements.

 

Image source: http://www.barrysworld.biz/

 

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

 

What Big Data Brings to Decision-Making Strategies

 

 

                    

 

Potentially valuable and relevant "big data" is generated everywhere as wellsprings for new intelligence processes. But big data is only valuable if it can be used to answer business questions and offer new insights for business goals and future direction. While a lot of attention for big data has been focused on technologies that can process and analyze it, big data only matters if an organization knows what to do with it. When considering the value of big data from a strategic perspective, it's usually more important to focus on the ends, not the means, of what big data can provide.

Big data analytics can and should be part of executive-level efforts for building out overall corporate strategy, for uncovering new business opportunities and innovative direction, and for expanding decision-making processes. Organizations that are still trying to figure out what big data could do for them frequently fall into the trap of thinking that big data analytics are solely a technology initiative that belongs to IT. Often big data projects need strong and constant senior management involvement to succeed. The challenge for senior executives is to understand all of the implications of becoming a data-focused organization, particularly where big data plays an integral part, and then to put that understanding into action.

 

Getting into the habit of asking the right questions when working with analytics of any kind is an essential activity for strategic management:

What is the ultimate reward? – What is the organization trying to achieve with big data analytics? Opportunities may range from creating new lines of business or business models, to improving core operations.  

Do you understand your big data process? – Are you letting the data tell you something new, leading you to new areas of innovation? – Or are you focusing on finding answers to specific questions?  

Are you willing to embrace new business models or modes of competing, if all possibilities can be tested and analyzed? Big data analytics using commodity hardware opens the door to expanded ways of testing various propositions, especially 'offbeat' and cutting edge notions.  

Are you considering the impact of big data initiatives for the overall organization? What actions does the senior team need to take to enable the cross-functional teams and collaborations that will be needed to support big data initiatives? Frequently big data processes cut across organizational boundaries-- yet internal politics can throw up roadblocks.

Is the value achieved worth the cost? Consider big data initiatives in the context of other strategic priorities; then compare the costs of each, and identify which are most likely to contribute to performance improvements and business outcomes, with the most balanced cost.

 

Big data initiatives are the most successful when upper management enables and nurtures a data-savvy organization that can act quickly on the results of the analytics, when it makes sense to do so. A significant benefit from big data initiatives may be the transformation of how the organization operates internally, conducts business externally, and fulfills the needs and desires of customers.

 

Image source: http://makingdatameaningful.com/2013/02/14/forecasting-and-predictive-analytics/

 

 

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

Dazed & Confused About “Big Data”

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Not every business needs to initiate "big data" analytics, but every company needs to understand more about big data – even if the final decision is that big data doesn't yet have a role for the business objectives of a particular company. Big data sources can reveal vital information for every aspect of the business. But first, organizations have to gain a better understanding of what "big data" is and why it might matter for business insight.

Research firm Gartner has introduced 12 dimensions to clarify the many aspects of the confusing world of big data. Instead of "big data", Gartner opts for Extreme Information Management, to reflect the diversity of data formats that can be considered "big data". The various dimensions underscore the reality that "big data" isn't just one thing and isn't just 'lots of data'. Approaching big data through different dimensions can help companies understand whether or not big data holds any value for their business needs, and where their most valuable big data may reside.

 (To view original Extreme Information Graphic go here)

        

 

                

                        Image source: unboundid.com

 

I like the term Extreme Information Management, where Extreme maps to the massive and highly disparate data stores and data classes that defy and overwhelm conventional data management techniques; Information points to intelligence and insight; and Management reminds us that hard work must be done to achieve real results. Big data analytics are only useful if they benefit desired business outcomes, open doors to new possibilities, alert businesses to risks and opportunities, and so on.

Big data accumulates from many sources – a scan through the different categories of data helps an organization identify data sources that matter to the business:

• Multi-structured or unstructured data with highly variable formats and semantics – examples are social media content, log files, and e-mail. Multi-structured data comes with many difficulties – information can be mined, but for it to have meaning and value, sentiment and context must be derived as well and correlated with master data for customers, products, and so on.
• The fastest growing area of big data is machine-generated data – examples are medical devices, industry sensors, automated machinery and systems, and GPS locations. Current and newly minted enterprises are very interested in this kind of data.

• Big data can be big too – datasets that are scaling from hundreds of terabytes up to petabytes (and more). Often organizations need this data processed very quickly to achieve analytics in real time or right time situations.

More understanding of what can be accomplished from big data analytics is revealed by real world business cases:

• Evolution of Smart Cities: Cities need to ensure the sustainability of resources and services, while making city services responsive to more people. Big data analytics interface with interconnected devices, technologies and digital systems to improve operations and efficiencies.

• Continuous Operational Optimization: People use operational data for planning, improving and expanding business capabilities. Automated systems utilize machine-generated data to self-optimize based on continuous automated analytics.

• Fighting Cyber Threats: Sophisticated cyber criminals don't ever stop their attacks on organizations. Proactive security processes utilizing big data analytics must be put in place to continuously seek out active and potential threats.

 

Eventually each organization must decide if big data analytics are a worthwhile - even game-changing - investment. Much thought and exploration should go into such a significant decision.

 

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

 

 

 

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

 

 

The Journey to Insight and Action

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There's no doubt that most companies are seeing massive increases in data assets, both internal and external. Beyond social content, enterprises encounter data explosions from business activities such as M&A, outsourcing initiatives, and highly diversified partner and supply chain programs. Healthcare, utility and telecom industries generate enormous stores of machine-generated data related to customer activities as well as to operational performance and 'product' pricing. GPS data continues to yield more and more customer and market insights for certain industries. And it's all quite overwhelming for many companies.

Midsized organizations are aware of the growing importance of extracting more value from data already in enterprise repositories, as well as continuously looking to external data sources. But to extract value from data, it's not about the data. The key focus should be on connecting data and analytics processes to what matters to the business, whether it's operational improvement, overall high performance, enhanced customer experiences, or innovative product development.

To help make the connection to desired outcomes, midsized organizations must understand the vital relationship between data and business processes, and must define the business cases and problems to be solved that have strategic and business-critical purposes. Value will be achieved from data when organizations identify processes and problems that impact revenue, business agility, competitiveness, and overall positive performance.

Strong data management initiatives are essential to ensure reliable, unified and timely data for key systems and processes. Such data must conform to how information is used by the business. Technologies play a crucial role in data management, but organizations must resist the temptation to think of data management as just a technical or IT initiative. Data management delivers best when it is directly tied to business opportunities, goals and objectives.

New processes must now come into play to understand how to:

• Validate data sources to ensure trustworthy and relevant information for analytics
• Verify and correlate analytics results with other intelligence sources to secure the right path for decision-making
• Use analytics results for deriving insights and reach decisions more quickly
• Take action on decisions in an agile and timely fashion while managing risk

 

The journey to continuously improved decisions and actions doesn't really start with data – it starts with what the organization wants to accomplish with its data assets. But the right data, business processes and analytics results can propel companies to better achieving goals and sustaining competitiveness. To make the journey to agility and high performance requires strong data management initiatives that combine analytics processes with cross-functional collaboration and practices that inject intelligence into corporate DNA for all purposes.

 

                      

 

 

 

 

 

 

 

 

 

  

 

 

 

 

 

 

Source: Accenture, 2012

 

 

Image source: Real Food Rules

 

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

Data Governance: "Lean Is Not a Destination, It’s a Journey"

 Lessons in Quality, Reliability, and Risk

 

 Originally published on Hub Designs Magazine May 2012

 

"Lean" and "Agile" concepts have infiltrated aspects of many different kinds of businesses, from managing manufacturing processes to developing software. While there are several principles that define Lean processes, one of the most important ones is to provide more value more quickly to customers (both internal and external). There now seems to be interest in applying Lean and Agile to data governance, including very specific applications for data integration, MDM and data quality.

One of the difficulties for discussions of Lean/Agile concepts is that different constituencies in different industries and professional roles can have disparate definitions for Lean and Agile. Also important to remember: Lean and Agile have resulted in varying results, some good and some not-so-good. The following list covers what many who advocate for Lean consider the core principles (but many variations - and debates - exist):

The five-step thought process for guiding the implementation of lean techniques is easy to remember, but not always easy to achieve:

1.     Specify value from the standpoint of the end customer by product family.

2.     Identify all the steps in the value stream for each product family, eliminating whenever possible those steps that do not create value.

3.     Make the value-creating steps occur in tight sequence so the product will flow smoothly toward the customer.

4.     As flow is introduced, let customers pull value from the next upstream activity.

5.     As value is specified, value streams are identified, wasted steps are removed, and flow and pull are introduced, begin the process again and continue it until a state of perfection is reached in which perfect value is created with no waste.

The fifth principle is the well-known concept of continuous improvement, which underscores initiatives for data governance and management, as well as data quality. If an enterprise is working on establishing an over-arching data strategy along with sensible processes and practices to enact the strategy, the enterprise needs concepts that will lead to very good results. Lean and Agile are tempting roads to travel, but will they take the enterprise to an optimal "destination"?

 

Lean Data Governance

The mantra of Lean management principles is often: Lean Is Not a Destination, It's a Journey. The reality for data management and governance initiatives is that the work is never done simply because data-related processes must evolve as the business and its markets change and contort, and as data strategies mature and point the way for providing more value to business needs.

We know that data governance is a convergence of many areas that directly impact the relevance and usability of data in an organization: besides data-related practices and solutions, business process management and risk management are also part of the mix. Even more important are considerations for people and politics (or 'culture'). Lean data governance must reflect all of these areas, or undesirable results can easily occur. Risk management, as a means to minimize risk levels for operational and other processes, has synergy with Lean principles through concepts such as eliminating "waste", bringing more value to customers (internal and external), and engendering continuous improvement.

 

Lean Data Integration

Recently interest in lean data integration has increased as organizations seek faster means for integration processes, while still effective and comprehensive. Forrester lists "lean integration" as one of the Seven Top Integration Trends For 2011 To 2012. Paralleling the principles of Lean for manufacturing and adding Agile software development approaches, Forrester's take on lean integration includes:

• Delivering quickly. Agile development techniques like Scrum (an iterative, incremental software development methodology) are a key component of Lean Integration, and this approach helps developers deliver results faster than using traditional techniques.
• Making continuous improvements. Integration tools support continuous improvement with features that enable easy modification, manipulation, and configuration of a wide range of application metadata artifacts, such as rules, policies, mappings, and processes. Speeding such change makes it easier and faster for developers to respond to the rapidly changing requirements typical of Agile processes.

Forrester expects many organizations to move aggressively to implement Lean Integration to speed up application delivery, especially those that value rapid delivery because it drives increased revenue and/or customer satisfaction (see Figure 4).

 

 

    

    Source: Forrester, Seven Top Integration Trends For 2011 To 2012

 

 

The Quality Lesson from Toyota Lean -- for Data Governance

For years, Toyota epitomized the promise of Lean manufacturing – after all, Toyota invented Lean Manufacturing. But Toyota painted itself into a corner when quality was compromised and dangerous part failures continued to plague the company for several years:

 

3.8 million cars recalled due to floor mats, of all things, getting caught up in the accelerator.  What makes it so inexcusable is that it is not the first time. 

"Toyota recalled 55,000 Camry and Lexus ES 350 models in 2007 because of complaints of unintended acceleration caused by the mats sticking under the accelerator pedal. The NHTSA said consumers continued to report instances of uncontrolled acceleration in Toyota models after that recall."

So much for quality, and so much for continuous improvement and learning.

 

There is still debate whether Toyota Lean itself failed or if Toyota management failed to act wisely and failed to sustain Lean – likely all of the above contributed to very serious lapses in quality and value-generation at Toyota. Some want to argue that the problems trace back to design errors. But this situation is more complicated simply because of the history of technology enterprises in Japan and at Toyota: Business became Culture. Perhaps the most harmful action by Toyota was to lose sight of the principle of delivering value to the customer, a key component in Lean concepts, and a central business cultural construct.

So what do we learn from this Toyota story that matters for data governance? Quality cannot be sacrificed for efficiency when it comes to data in the enterprise; if the outcome of governance and management solutions is not reliable highly usable data, the solution efforts are useless, and potentially harmful. Lean also parallels the notions of "good enough" data quality and of context setting the rules to decide which data elements must be reliable – and both of these approaches have problematical aspects.

The Lean Iceberg Model was developed to help show the way to a more sustainable use of Lean principles. The idea is to understand the more overt artifacts of useful processes and continuous change -- as well as the more subtle, but very significant, "hidden" artifacts that anchor, enable and sustain Lean processes. And the artifacts beneath the "water" are those of people, practices and politics / culture that are so important to organizational change. The parallels to data governance concepts are interesting and useful.

 

 

© 2008 P. Hines, P. Found, G. Griffiths, & R. Harrison

    Source: "Staying Lean; Thriving, Not Just Surviving" – Peter Hines, Pauline Found, Gary Griffiths, Richard Harrison

 

Like Lean, Data Governance has to be more than technology and processes. Data governance must provide continuous improvement, and potentially faster data improvement results for the enterprise, through the never-ending interleaving of people, practices, processes and technology (and politics…). Healthy and constant collaboration across the enterprise to enable and empower vital data-related processes is a key element for beneficial outcomes and change. Hopefully the growth of cross-team collaboration will also engender faster and better decision-making related to enterprise data strategies.

 

Lean Has Some 'Gotchas'

Going back to the principles of Lean listed at the beginning of this article, the fifth principle presents a potential misstep or flaw that has caused problems for corporations trying to implement Lean and Agile processes, no matter the purpose:

 As value is specified, value streams are identified, wasted steps are removed, and flow and pull are introduced, begin the process again and continue it until a state of perfection is reached in which perfect value is created with no waste.

This fifth principle trips over a highly subjective, vague term: Perfection. Perfection is a chimera, never-attainable, and diverts attention from the realities of evolution, volatility, and change that impact most businesses. The realities of business environments, present and potential, must be recognized as part of the ongoing data governance journey. Organizations should strive for continuous improvement, but perfection is an impossible and useless goal. Competitive enterprises never sit still.

 

 

Related Links:

Business Priorities Point the Way for MDM

Connecting Data Governance to Business Outcomes That Matter

Ungoverned Spaces: Innovating Information Flow

'Big Data', Intelligence and Multi-faceted Innovation

Data Management: Reaching into the Cloud

Information, Intelligence and Process: Combining Forces to Better Answer Business Needs

The Ever-Expanding Importance of Analytics–True Intelligence for Business

Goals and Metrics – Measuring Customer Engagement on the Web

Internal Disruption–No Longer Business As Usual

 

About the author: Julie Hunt is the Editor of Hub Designs Magazine. She is an accomplished software industry analyst, providing strategic market and competitive insights. Her 25+ years as a software professional range from the very technical side to customer-centric work in solutions consulting, sales and marketing. Julie shares her takes on the software industry via her blog Highly Competitive and on Twitter: @juliebhunt For more information: Julie Hunt Consulting – Strategic Product & Market Intelligence Services

 

 

Business Priorities Point the Way for MDM

 

Originally published on Hub Designs Magazine March 2012

  

Like data integration processes, MDM solutions can be seen at times as 'plumbing', but in both cases the work to be done has strategic, business-critical purpose. Master Data is meant to be at the heart of most business transactions, applications and decisions that occur in an enterprise. In today's volatile business world, it is important to focus MDM on what matters most for the goals of the business to ensure reliable, unified and timely data for key systems and processes. Such data must conform to how information is used by the business. So while it is tempting for enterprises to think of MDM as a technical or IT initiative, MDM is far more about business purpose and business problems to be solved.  

 

To solve the problem of ensuring high quality 'cleansed' and unified master data, interest in MDM practices and solutions is increasing. But in many ways MDM implementations are still immature and the solution space is still evolving. William McKnight notes that often MDM solutions are focused on "simple" issues, rather than on larger, more strategic value, but that he is "starting to see some pioneer MDM implementations explore the full range of benefits". Among the benefits are data governance practices that elevate data to strategic positioning in the enterprise in support of: operational and productivity improvements, reliability of BI and analytics results, timely customer insights, and ultimately, better decision-making.

 

          
        

  

Understand Business Processes to Understand Data Priorities

First it is imperative 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 solution 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.

Data quality plays such an important part in the MDM solution. Analysis must be done to determine overall quality, where the weak links are, and the impacts on business processes and operations from inadequate data quality. But data quality is a subjective measurement – part of the process and data assessment will be the setting of requirements for acceptable data quality for individual data elements to provide the level of quality needed by the selected business processes. Some thought should also be given to future needs, whenever possible. The potential "trap" is that data that is "good enough" for one business process may be inadequate for another, particularly when considering future direction.

Data derives its real value from its usability in significant business processes, including those that drive and benefit from analytics, intelligence and decision-making processes. And value attaches to data when it positively impacts business outcomes. Unified, quality data attained through MDM solutions must also be integrated into business processes, which is not an easy task. Such integration is not only at the technology and systems levels, but involves the synchronization of people, practices and 'super' processes across the enterprise to reflect how work is accomplished.

 

          

 

 

Extreme Information Management aka 'Big Data'

All data management solutions are now challenged by the proliferation of data, content and information in sites that are not owned by the enterprise, such as cloud and mobile services, social sites and partner repositories. It has become clear that such data can be valuable to enterprises, particularly for insights into customers, future trends and markets, and directions for innovation. MDM solutions are also challenged to now pull in and harmonize some very unwieldy data with existing master data repositories.

In particular, integrating customer master data with customer-related content in social media will likely lead MDM solutions into new territory. The difficulty of content from social media is capturing the data 'clues' to be passed to analytics for accurately determining sentiment and context. So-called 'big data' needs to be aligned with reliable enterprise master data to properly correlate social content in decision-making processes. How the MDM solution maintains an overall master customer 'profile' will include continuous integration processes for targeted social media sites.

Enterprises continue to experience massive increases in data assets. Beyond social content, enterprises encounter data explosions from business activities such as constant M&A, outsourcing initiatives, and highly diversified partner and supply chain channels. Industries such as Telecom have enormous stores of machine-generated data related to customer activities as well as operational performance and 'product' pricing. GPS data relates more and more to customer and market insights for certain industries.

Achieving consistent master data with such disparate, sometimes unwieldy and complex, data sources will continue to be difficult, but will likely lead MDM practices and solutions to new levels of value to the enterprise and vital business process. Again the focus will have to be on the key business processes that need these new data sources integrated with existing master data.

 

Related Links

Connecting Data Governance to Business Outcomes That Matter

Ungoverned Spaces: Innovating Information Flow

'Big Data', Intelligence and Multi-faceted Innovation

Data Management: Reaching into the Cloud

Information, Intelligence and Process: Combining Forces to Better Answer Business Needs

The Ever-Expanding Importance of Analytics–True Intelligence for Business

Goals and Metrics – Measuring Customer Engagement on the Web

The incongruous worlds of "data integration" – Making data accessible for any-size companies

Custom Code or Data Integration software tools: Strategic Use of Technology for Company Competitiveness

Levels of Social – and Integration – Cut Across Enterprises

 

About the author: Julie Hunt is a software industry solution strategist and analyst, providing market and competitive insights. Her 25+ years as a software professional range from the very technical side to customer-centric work in solutions consulting, sales and marketing. Julie shares her takes on the software industry via her blog Highly Competitive and on Twitter: @juliebhunt For more information: Julie Hunt Consulting – Strategic Product & Market Intelligence Services

 

 

Symbiosis: Customer Experience and Business Process

Companies continue to struggle with prioritizing the Customer over their business-as-usual, ingrained product and self-focused strategies.  Upper management in many industries seem to be dragging their feet when challenged to quickly and energetically change business practices to better serve customers. These leaders seem to look at the “Customer” as if it were an abstract representation of a value on a spreadsheet -- instead of probably the single most important factor for most businesses.

Now from the customer perspective, which is what upper management should have as top priority, interactions with most companies are becoming more and more a multi-channel journey of multiple touch points that are used to communicate, gain information, compare vendor offerings, seek assistance, make purchases, register complaints, and so on. If companies meet the needs of customers, then those experience journeys will repeat over customer-preferred channels. Multi-channel strategies for customer engagement are imperative because customers are more and more the ones who choose how they will interact with companies and on which channels.

On the flip side, customers don’t see their interactions in terms of “channels” -- just like customers don’t care how or why a company’s business processes fail to provide an overall positive experience with the company. Customers see that they did or did not have a successful interaction and that they did or did not achieve what they wanted. The outcomes of these interactions influence how customers regard companies and whether they will continue doing business with them.

Once a company has made a commitment to becoming customer-focused for its business strategies and operations, there is work to be done:  integration of channels, integration of business processes, and integration of data from customer interactions all become imperative. Integration of channels leads to understanding of touch points in individual channels as well as cross-channel. Aligning business process integration with the customer leads to creating single customer experiences across the enterprise and across communication channels. Integration of interaction data with operational data provides a more complete view of the customer in real time. Individual department and program goals across the enterprise must be also integrated and related to customer experience excellence.

Business processes must become a dynamic, intelligent and highly integrated interface for company interactions internally and externally. Most internal systems in companies should incorporate factors like customer experience and engagement, rather than functioning solely from a product, service or departmental perspective. Processes play a key role in bringing these factors together to achieve customer focus and relevance. Integration internal to the organization means cross-functional / cross-department teams to envision and implement customer strategies, translated into business processes that holistically connect the enterprise.

 

Symbiosis, Integration

Like symbiotic relationships in nature, business-customer symbiosis is founded on the belief that customer goals and business goals can be aligned in ways advantageous to both parties. The relationship that a company has with its customers affects almost every area of the business and every point of interaction with the customer, internally and externally. That’s why effective business processes are so important to enable top-notch customer experiences.

For mutual benefit, the focus for all business processes shifts to customers to embrace what they want from companies, how they view companies and how they want to work with them. Agile business processes connect all parts of the enterprise to the individual points and stages of the customer experience journey. Business processes also encompass other enterprise practices and strategies. Business-customer symbiosis can ensure the integration of both perspectives to the benefit of both parties. Business process ties directly to desired business outcomes – which now must align with customer desired outcomes.

 

Data Fuels Process and the Customer Experience

Happily, companies have a lot of data sources to help them gain insights into their current and potential customers through analytics, real conversations, social media content, customer service interactions.

Data, information and intelligence are the fuel for effective business processes that support and promote successful customer experiences and interactions. By considering business processes and business information in parallel to customer-focused strategies and initiatives, business outcomes are likely to improve.

Customer interactions produce an escalating amount of data related to internal systems such as CRM and call center management, and related to external sites such as corporate websites and social venues. Significant issues impact the ability of enterprises to correlate all the data into the so-called “single view” of the customer:

• Social media data that is primarily content and crucially dependent on context for meaning
• Company-controlled data sources that do not easily reveal customer process or outcomes
• Complexity of integrations needed to bring all this disparate data together
• Pulling data together to reflect the customer perspective rather than the internal systems architecture

Data directly related to customer-focused processes is likely to get closer to providing the elusive customer “single view” where an integrated process spans multiple interactions, touch points, transactions, and systems. The right data fueling processes is an important part of designing innovative business strategies that truly focus on the customer. Deriving timely customer insights benefit the strategic and operational aspects of an enterprise. However, a good many customer interactions or touch points are increasingly shorter-lived due to the proliferation of digital channels and their use by customers. The opportunity for enterprises to meet customer desires and needs is becoming shorter for each interaction and increasingly difficult to capture, analyze and understand.

 

Business Process Management Meets Customer

 Business process management (BPM) can no longer be solely focused on achieving operational efficiency and optimizing the business. Business processes must become more responsive to a focus on the customer, and become well-integrated across multiple systems to do so. BPM is crucial to customer-oriented systems as well as operational systems, and actually provides an essential piece to integrating these systems in order to better serve customer needs. Business processes are key to an agile holistic business strategy that helps customers achieve success with the company’s products and services. An interesting development in BPM is adaptive case management (ACM) that may be leading the way to greater convergence with customer systems such as CRM and call center applications.

An important aspect of BPM is continuous improvement, something that must happen for enterprises to respond to ever-changing markets and customer desires. To bring about customer-focused continuous change, enterprises need to articulate vision for strategies and initiatives from the customer perspective; and then tie process improvement to the customer experience. Working with customer data and analyses, enterprises may benefit from analyzing the ‘reverse engineering’ of outcomes expected by different customer segments, and then use insights to define how the enterprise should operate across process, organization, and technology domains to deliver optimal customer experiences and success.

Enterprises that allow separate solution and process silos for each channel to persist will greatly impede responsiveness to customers and will add to customer frustration by failing to provide one experience across channels. These enterprises probably did not start with a customer experience strategy and likely developed and/or acquired solutions specific only to individual channels without heeding the need for seamless integration of processes.

 

Business Process and Customer Experience Management Technology

Responsive and accurate support for customer interactions through people, processes and practices is a cornerstone of the management that enterprises can apply to enabling positive customer experiences. Enterprises must recognize that ‘management’ doesn’t mean controlling and dictating to the customer what the ‘experience’ will be. It does mean managing how well the enterprise provides a single experience across channels, how well it fulfills the needs and desires of customers based on customer terms, and even how well it delivers high quality products and services.

BPM can be instrumental for designing and maintaining effective and agile processes that are a sophisticated means to respond well to customer needs, while navigating many complex variables moving from customer to customer. Well-designed and customer-oriented processes make a big difference when integrating touch points for a ‘single view’ of customer interactions.

In the 2011 Forrester Wave for Web Content Management for Online Customer Experience, Stephen Powers defines customer experience management (CXM or CEM) software solutions as:

A set of solutions which enable the management and delivery of dynamic, targeted, consistent content, offers, products, and service interactions across digitally enabled consumer touchpoints. Previously, organizations, processes, and technologies to serve the online channels were silo’d, resulting in overhead, inconsistent experiences, and inability to measure results. However, as online initiatives have evolved and matured, so have product offerings to support those initiatives.

Three categories of solutions comprise online CXM: those based on process, delivery, and measurement:

Process-based solutions enable business users to create experiences. Process-based solutions consist of tools that business users (as opposed to IT users) use to create and manage structured and unstructured content for customer experiences. This category includes WCM, digital asset management (DAM), eCommerce, customer relationship management (CRM), marketing campaign management, marketing resource management, desktop publishing tools (such as Microsoft Word), and interactive development environments (IDEs).

Delivery solutions bring interactive experiences to customers. Vendors often tightly couple both WCM and eCommerce solutions with native delivery tiers, enabling businesses to create content and design and deliver experiences in a single package. But other technologies enable experience delivery, including rules- or algorithm-based content targeting solutions like search, personalization, and recommendations engines; marketing automation solutions such as campaign management and offer management; and customer service interaction management solutions that support customer data and history, customer self-service, knowledge management, and customer feedback management.

Customer intelligence solutions enable businesses to gauge the success of experiences. A/B and multivariate testing enable marketers and business users to test out variations of experiences on certain demographics before rolling them out to a broader audience. Web analytics tools give insight into how consumers consume content and interact with experiences. Social analytics provide insight into how consumers engage with companies by monitoring social interactions. 

 

Source: BP Trends

 

 

Process Alignment: Voice of the Customer Meets Voice of the Business 

Enterprises are accustomed to setting success metrics through KPIs related only to internal business process performance and internally-defined business outcomes. Unfortunately these metrics are frequently out-of-synch with customer perceptions of how well the enterprise met their expectations. Enterprise and customer metrics can be mismatched in other aspects of the customer experience:

 For instance the business may define the process cycle time – the length of time the process takes to run – as measured from when the customer places the order to when the products are dispatched. The customer, on the other hand, may well perceive the time taken to be from when they first went on to the website to look for a product, to the point at which the product started working. Clearly there can be a substantial difference between the two measures. 

Ideally we want to ensure that our measures of process effectiveness (the Voice of the Process) are the same as the customer’s measures (the Voice of the Customer), albeit the customer’s measures are informal measures. 

 

Source: BP Trends

 

Juxtaposing customer and enterprise views of process, experience and success through customer journey diagrams will reveal the disconnects between enterprise perceptions and what customers are likely experiencing. Enterprises are still working on finding more ways to capture the actual customer experience since true direct insight is not easily attained. Insight is pieced together with data from many sources including customer surveys, content from social sites, repeat business with the same customer, customer transactions, and interactions with customer service.

 

Symbiosis at the Heart of the Enterprise

Enterprises continue to be challenged to deliver positive customer experiences that are mutually beneficial and crafted from the customer perspective. Instead of designing business processes integrated with a customer experience strategy, many companies just tack on aspects of monitoring customer interactions to existing processes. For many companies, the main focus is still on their own needs, their own problems to be solved by the business process. And frequently when enterprises say they are customer-focused, they really mean they are trying to engineer upsells and cross-sells for their own benefit, without necessarily delivering real value to the customer.

As I continue to emphasize in much that I write, technology alone is never sufficient: enterprises need integrated and agile processes that also recognize the importance of people and practices to achieve goals and positive business outcomes. To become a truly customer-focused organization that enables customer experience excellence, the entire enterprise must be part of the overall experience:

• Starting with Upper management: choose Customer as The strategic focus
• Enable and empower customer-focused people in every part of the enterprise
• Design processes that are flexible enough to change with dynamic markets and customer needs
• Draw on innovative information systems to fuel business processes and provide continuous customer insights for fostering customer experience excellence

Keeping the focus on customer outcomes will help align people, processes, practices, and technology to the continuous changes needed to facilitate optimal customer experiences over time. Neither customer experience excellence nor well-integrated business processes should be after- thoughts for business execution – they are the symbiotic heart of success.

 

 

Related Links:

In Pursuit of Customer Experience Excellence

Information, Intelligence and Process:  Combining Forces to Better Answer Business Needs

The Ever-Expanding Importance of Analytics–True Intelligence for Business

Goals and Metrics – Measuring Customer Engagement on the Web

Achieving the Social Business, Inside and Out

Findability Inside The Firewall – Still Trying To Find The Information We Need

 

About the author: Julie Hunt is an accomplished software industry analyst, providing strategic market and competitive insights. Her 25+ years as a software professional range from the very technical side to customer-centric work in solutions consulting, sales and marketing.  Julie shares her takes on the software industry via her blog Highly Competitive and on Twitter: @juliebhunt   For more information: Julie Hunt Consulting – Strategic Product & Market Intelligence Services

Data Management: Reaching into the Cloud

Originally published on Hub Designs Magazine August 2011

 

In a form of “shadow IT”, Line-of-Business groups have been turning to cloud-based services to quickly set up technology solutions that support their business needs and objectives. IT teams are already carrying heavy workloads with ever-shrinking staffing levels, and frequently don’t have the resources to immediately respond to time-sensitive LOB needs. However without IT involvement, these LOB groups do not usually have the expertise to understand the implications of creating data in the cloud and its relationship not only to data in other SaaS / cloud offerings, but to on-premises systems as well. These business users need a partnership with IT to ensure that comprehensive data management processes are put into practice.

It’s clear that enterprises will continue to increase usage of cloud and SaaS offerings to find new ways to operate more competitively and efficiently. Unfortunately the rapid creation and adoption of cloud and SaaS services have led to increased data silos in the cloud. The same challenges that enterprises face for on-premises data management obviously apply to data repositories in the cloud: data quality and reliability, integration, governance, security and compliance, MDM, as well as improved data accessibility. There is now a bigger picture for data management, where all of these domains need governance as an interoperating whole.

If enterprises fail to address a rigorous data management strategy for cloud computing initiatives, deterioration in data quality and reliability of cloud data are likely to kick in quickly. Data that cannot be trusted nor aligned with enterprise datasets will destroy the value and cost savings that enterprises want from cloud services. One of the most important requirements of business data generated through cloud services is security for that data. Not all data created in the cloud requires security, but most of it does. Comprehensive data management policies are needed in the cloud as well as for on-premises data repositories to ensure secure data processes.

The concerns of both business users and IT regarding data in the cloud have been articulated in many surveys including one recently completed by IDC where issues that relate directly to data governance and MDM initiatives covered security, data integration, operational costs and compliance – all pretty much to be expected when considering management strategies for cloud data. The IDC study goes on to discuss an important point that again requires IT and LOB groups working together: Business Relevance.

The next two challenges – the perceived difficulty of cloud services integration, and the limited ability to customize – are both related to the important issue of business relevance.  While customers certainly enjoy the economic and operational benefits of the off-the-shelf, standardized nature of many cloud services, this survey shows they nonetheless want greater ability to “fit” cloud services more tightly into the context of their specific business.

MDM and data governance provide clear value when they reflect the actual activities and processes of the enterprise. This approach also makes it easier for corporate decision-makers to understand the role MDM and data governance play to support strategic goals and even how they might map (at the high level) to key business processes. Malcolm Chisholm and Fabio Corzo flesh out these ideas in The True Role of MDM: The importance of master data is not just data values or quality, but the real-life concepts they represent… We are looking for fundamental business transformation that will take the business into new ways of perceiving, understanding and developing customers, employees, suppliers, assets and securities. We are looking for business transformation that moves in line with the business goals. And though executives understand that data is at the heart of proper and well-executed business transformation, experience tells us that more often than not, the business transformation is limited to improved data quality side effects that fail to realize the true value of an MDM program. The above concepts become even more imperative when establishing management of data-driven business processes between the cloud and on-premises systems. There will need to be an evolution of data management and governance approaches to deal with new challenges that come from data in the cloud and from an integrated approach to managing data across disparate data domains, many of which are not owned by the enterprise. Cloud services have been very effective in converting (or commoditizing) IT practices through well-formed repeatable processes. To accommodate proper data management for cloud and on-premises solutions, IT and business users will need to work together to build out data management solutions through practices and technologies that support the creation, use and storage of data “anywhere anytime”. Both IT and Business are important to setting the right strategies, including the crafting of a data management architecture that will kick in before an enterprise’s cloud data becomes too distributed for thorough governance. While more and more data will be owned by non-enterprise entities in the cloud, the ownership of comprehensive data management processes and practices continues to fall squarely on the enterprise through business users and IT working to ensure reliable data availability.     Related Links The incongruous worlds of “data integration” – Making data accessible for any-size companies Custom Code or Data Integration software tools: Strategic Use of Technology for Company Competitiveness Levels of Social – and Integration – Cut Across Enterprises  Information, Intelligence and Process:  Combining Forces to Better Answer Business Needs The Ever-Expanding Importance of Analytics–True Intelligence for Business Goals and Metrics – Measuring Customer Engagement on the Web     About the author: Julie Hunt is a software industry analyst, providing strategic market and competitive insights for vendors and buyers. Her 25+ years as a software professional range from the very technical side to customer-centric work in solutions consulting, sales and marketing.  Julie shares her takes on the software industry via her blog Highly Competitive and on Twitter: @juliebhunt   For more information: Julie Hunt Consulting – Strategic Product & Market Intelligence Services