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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

 

How Big Data - and Critical Thinking - Lead to Business Value

                            Originally published on CMSWire

 

There is irony behind a discussion of the business value and metrics related to big data analytics, since there are a lot of people in the business world who still don't understand much about big data, including people who think they do have a handle on big data. Business analysts, IT, Marketing, Business Intelligence, Data Scientists, Upper Management – each of these roles can have a meaningful stake in big data analytics – and it's likely that each role has a different idea about the impact it can have.

Numerous misconceptions about big data – what it is, why it matters – make it difficult for organizations to know what to do with it and to understand the business value that can be acquired. Some organizations have been too quick to declare that big data is only hype or a big flop. Frankly, the same can be said for any business technology initiative - if the right approach and effort aren't undertaken, if participants don't understand why they're working with it, if beneficial strategies aren't in place.

Big data is not just one thing and there's not just one application for it. If big data analytics aren't producing good results, it's not the fault of the big data. Just like working with any data – you have to know what you want to do and why; you have to experiment and learn from the approaches that don't work; you have to adhere to continuous improvement, identify other needed data sources, and so on.

It's a very good idea for organizations to pursue a good understanding of big data and where it might produce value for current circumstances and for future direction. Initially an organization may decide that big data doesn't have a place in current strategies – but strategic thinking should also stretch into when big data analytics should come into play.

 

"Big Data" Isn't Always Big

A good many people think of extremely large datasets and high velocity data when "big data" is mentioned. But there are other kinds of big data sources that provide value to organizations, which means big data comes in disparate shapes and sizes.

Many big data sources are messy and complex, usually dubbed as multi-structured data (some erroneously say non-structured). These are the kinds of data that cannot be processed by traditional technologies and methods – data that doesn't fit neatly into a relational database. Multi-structured data and content comprise highly variable formats and semantics, such as log files, e-mail, social media content. Lots of big data isn't data but text or content that requires the additional dimensions of context and sentiment to attach meaning to the words mined from the original source.  

The largest chunk of big data originates with machine-generated data. This data is continuously produced by an ever-proliferating tsunami of physical objects in every industry that are embedded with sensors interconnected through every kind of network, and that often operate with little or no human touch. In other words, the "Internet of Things". Machine-generated data is definitely big, but also complex and messy.

 

Tools and Techniques – Hard and Soft

The exciting news for organizations is that newer ways to work with difficult data and content text mining continue to appear on the scene. These technologies vary from expensive and complex to lower cost cloud platforms, as well as the Hadoop ecosystem of technologies. The lesser expensive technologies allow for open-ended experimentation with data, content mining and analytics, especially to fine tune approaches and find new and sometimes unexpected sources of insight.

Usable, relevant and insightful analytics output frequently doesn't come from big data alone, which can be fragmented and vague. Big data needs further qualification through the context and relationships that come from core enterprise data, to make sense of what big data may be communicating. Other sources of data and information, including master data, extend the accuracy and value of big data and provide essential context to align big data with guideposts like customer identity, products, and locations or channels of interaction.

Pivotal tools for big data analytics are not always technologies. Critical thinking is still one of the most important tools for any analytics initiatives, and must be undertaken by many roles in an organization.

Critical thinking must be used to determine what business problem you want to solve or which questions you would like to answer. For those involved in big data analytics certain questions should hover in the background: Why are we doing this? What are we trying to achieve?

Further critical thinking is needed to correlate results with other intelligence sources, with the experience of domain experts – and with common sense. Analytics results have to be reconciled with decision making processes and action plans. Then teams must review, improve, change analytics processes – or move on. If the focus is put solely on data, technology and methodology won't magically conjure relevant and accurate insights.

 

Business Value: Subjective and Different for Every Organization

Since a lot of big data work is still in early stages, establishing value is also just beginning. However there are an increasing number of real world successes published by a wide variety of businesses, from large enterprises to midsized and smaller organizations.

Obviously there are hard and soft benefits that can be attained through big data analytics. Trying to calculate an actual dollar value is tricky but not impossible. It requires mapping data and business processes to desired business outcomes, and then measuring the impact or success of the outcomes. But there is also gray area involved in such calculations.

Big data analytics are becoming a significant piece of the business performance puzzle for many organizations. Accenture Research took a look at the impact of analytics (including big data analytics) on high performance companies and found that:

• High performers are five times more likely to aggressively use information and analytics to improve decision making and business performance than lower performers
• Companies that invest heavily in advanced analytical capabilities outperform the S&P 500 on average by 64%
• Companies that invest heavily in developing analytical skills and adopting an analytical mindset recover quicker from economic downturns

 

Selecting Big Data Metrics

As with any strategic initiative, the right metrics are the ones that measure actions and events that relate to key business context and goals. But with the volatility of business and markets, such metrics will need constant assessment for relevance and effectiveness. It's likely that metrics will change continuously, hopefully to reflect actions taken in response to analytics results, evaluations of decision-making processes, the emergence of new markets, and company innovations.

To determine the right metrics, one approach is to start with what you want to achieve (goals, outcomes, effects) and then "reverse engineer" the process of attaining the end results. Identify the parameters of key steps or milestones that can be measured and that map back to big data analytics results. This approach also introduces a discipline of connecting data and analytics to events that matter to the business.

Former Forrester analyst and current IBMer James Kobielus offers these guidelines for measuring the benefits of big data analytics, in this case related to better customer intelligence and improved customer relationships:

Volume-based value: The more comprehensive your 360-degree view of customers and the more historical data you have on them, the more insight you can extract from it all and, all things considered, the better decisions you can make in the process of acquiring, retaining, growing and managing those customer relationships.

Velocity-based value: The more customer data you can ingest rapidly into your big-data platform and the more questions that a user can pose more rapidly against that data (via queries, reports, dashboards, etc.) within a given time period prior, the more likely you are to make the right decision at the right time to achieve your customer relationship management objectives.

Variety-based value: The more varied customer data you have – from the CRM system, social media, call-center logs, etc. – the more nuanced portrait you have on customer profiles, desires and so on, hence the better-informed decisions you can make in engaging with them.

Veracity-based value: The more consolidated, conformed, cleansed, consistent current the data you have on customers, the more likely you are to make the right decisions based on the most accurate data.

 

What Comes Next?

It's still very early in the big data story. Still lots to learn, lots of change coming to how analytics are done. Even for organizations with early starts and lots of resources for big data analytics, a great deal of change will come. And around the world, many organizations have barely begun to tap into big data, if at all.

The tools of big data also must evolve. Yes, a lot of the work of big data analytics is highly technical and mathematical, requiring sophisticated tools, algorithms and the right experts. But more tools need to be developed for the involvement of business roles in analytics processes and for assessing the validity of the results and the right direction for applications.

 

Image source: moniquestoner.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 Competitiveand on Twitter: @juliebhunt For more information: Julie Hunt Consulting – Strategies for B2B Software Solutions: Working from the Customer Perspective

 

 

Where’s My Magic 8 Ball? -- Customer-focused Analytics

                                                                                                         Cross-posted on CMSWire

 

Your customers know that they have handed you a lot of their personal information through a variety of interactions and channels. And your customers fully expect that your company has done its job and processed all that information to know what they want, how to give them excellent customer service and streamline doing business with you.

Customers don't really care that you need to work through heaps of data to continuously update your customer intelligence. Customers only want you to shake the Magic 8 ball and have the right answer pop up. If the wrong answer materializes, customers know you aren't taking advantage of their information. Customers quickly conclude that you haven't bothered to get to know them, meaning you really don't care about them.

But you should care -- and how you interact with your customers should show it.

 

 

Source: thestrong.org

 

From Big Data Analytics to Relevant Customer Intelligence  

Surprise! -- big data analytics is not a Magic 8 ball that you can just shake for the answers. Indeed, it would take a lot of different "big data" Magic 8 balls – testing, experimentation, creating different analytical and data models – to reach insight from the analytics.

Big data comes in many shapes and sizes; all kinds of big data sources can have value for marketing and sales. Besides social media and eCommerce data, sources such as machine-generated data (sensors, GPS and location, automated operational and digital networks) point to interesting possibilities. But it takes time and resources to explore and amplify those possibilities.

Certainly many companies have the resources to set up dedicated marketing science functions to include big data analytics in decision-making and innovation processes. But for many companies it may be just as likely that marketing can't do this alone, due to budget and staffing limitations. Many companies may be better off centralizing all of their advanced analytics functions. Centralizing analytics requires a strategic approach where data scientists and analysts collaborate extensively with the experts in different business functions – this is what is needed anyway to improve the accuracy and usefulness of analytics. Centralizing advanced analytics efforts will also spread intelligence benefits across the enterprise.

 

Centralized Customer-Focused Analytics

The starting point for customer-focused big data analytics shouldn't be Marketing strategy, programs or campaigns. It has to start as an initiative for the entire enterprise to learn more about current and potential customers and what they want and need. First the organization should find out if it is even creating products and services that customers want, if it understands how customers want to interact with the company and make purchases. Key activities like customer segmentation isn't just for marketing – it is important for corporate customer strategies, innovation, product groups, and sales.

Centralizing analytics can eliminate redundant effort to glean intelligence from big data and other sources. Adjunct analytics functions can be set up for individual departments like Marketing that fine-tune specific analytics and carry out special objectives that support departmental programs. Marketing and sales find the best success when all functions across the enterprise work in harmony. Centralized customer-focused analytics also contribute greatly to engendering consistent customer experiences across channels by sharing the right insight with all customer-related functions.

Another reason to centralize big data analytics is that big data alone is not enough. Big data is often fragmented and difficult to connect to specific customers or customer segments. Context, corroboration and relevance have to be introduced to link big data analytics to what the enterprise needs for customer strategies. So many other data sources are needed, including master data which can provide what is missing from big data sources by making the connection to mission critical data across the enterprise. Master data management also connects analytics to the business processes that should be consuming intelligence and integrated data views.

Often marketers (and other departments) rely too much on their own silos of data and don't include a wide variety of data sources. Centralizing advanced analytics should dynamically work to eliminate data silos in the enterprise and bring different perspective into the formulation of analytical models and a wider breadth of benefit to the enterprise.

 

Big Data Analytics Mistakes Don't Just Belong to Marketing

A bit too many articles are popping up on "what marketers are doing wrong for big data analytics". Big data and advanced analytics are still very new – you could point to every business function in the enterprise for taking wrong steps with various analytics. A lot of hard work has to happen before significant and reliable results emerge – particularly those that lead to expedient decisions and actions. But that doesn't mean it isn't worth doing. Marketing can gain a great deal from advanced analytics.

Focusing on the customer and the customer's perspective will help steer marketing and other business functions along fruitful paths. Outside-in perspectives keep activities connected to what matters most and should keep functions from detouring into internal-facing concerns that offer no value to customers. Advanced analytics based on customer big data won't deliver value if the insights are diverted into "business as usual": building marketing messages that try to persuade customers to buy products, instead of working from the customer perspective for buying journey preferences. Marketing might successfully use analytics to identify the right customers for the company's products -- but fail by taking the wrong course of action when communicating with customers.

 

Most of the Time Your Gut Isn't Enough But…

Marketers are still relying heavily on intuition and past experience, and still lag on including well-crafted integrated advanced analytics in their decision-making processes. In most cases your gut alone doesn't make a very good Magic 8 ball:

Our latest survey of 358 marketing professionals confirms – and expands on – these findings. Only 23 percent claim to be highly effective at uncovering new insights to generate additional business value. Only 25 percent claim to be highly effective at identifying and capturing new markets. And only 32 percent claim to be highly effective at engaging with individual customers.  

82 percent of Traditional Marketers still rely largely on hunches and experience

  

But big data analytics alone won't get the job done either. Combining advanced analytics with marketing experience and creativity is where marketing can shine. Making use of advanced analytics is not meant to replace creative vision and innovative ideas that come from the human element. Just as big data alone isn't enough to connect to context and relevance, the same goes for marketing endeavors. Marketing functions need advanced analytics to keep the customer in full focus as the most important piece for context and relevance.

The "gut factor" for decisions still matters. 'Gut+Data' adds intelligence behind brainstorms and fresh ideas. This combination connects the gut to what's going on in markets and how customers are behaving.

The 'Pure Gut' road is a riskier path, but sometimes trumps 'Gut+Data', if the idea is disruptive and, of course, succeeds. In many cases, creativity is made better with insight from analytics for validation and amplification of the creative spark, as well as indicators of how to apply creative thinking.                                                             

 

 

 

 

Source: consciouslifenews.com

 

 

Related Links

How Master Data Management Improves Your Understanding of the Customer  

Business Priorities Point the Way for MDM

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

Goals and Metrics – Measuring Customer Engagement on the Web

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

Connecting Data Governance to Business Outcomes That Matter

Symbiosis: Customer Experience and Business Process

Perception: The Customer Journey and Business Success

 

 

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 – Strategies for B2B Software Solutions: Working from the Customer Perspective

 

How Master Data Management Improves Your Understanding of the Customer

 

    Cross-posted on CMSWire                    

  

Like other enterprise functions, marketing cannot exist as a silo, especially when it comes to the technologies that are needed to address how well companies engage their customers and help them purchase their products. Sophisticated, integrated technologies have to come into play to provide the continuous data, information and content that most marketing initiatives must have to be successful.

Marketing activities should be designed to align with buyer journeys, but customer buying journeys are all different which makes the effective management of information and content even more complex. Multichannel customer experiences are often fragmented and hard to assemble into a "singular" experience. It's not about providing the "same" customer experience across all channels – it's about making sure that the same reliable and timely information is available to every channel to handle any customer experience with agility, relevance and accuracy. The driving force here is Context - what does the customer want to do? Do your enterprise systems know what to deliver to meet customer desires? Do you have the best content or information available? 

 

 

  Source: Agility Multichannel   

  

 

Finding Context: Master Data Management

Marketing organizations are constantly challenged to synthesize a comprehensive understanding of each customer or customer category. Achieving a full-bodied view of customers requires the right technologies for collecting, validating, and analyzing a lot of information about customer interactions, including responsiveness to marketing and sales efforts, buying behavior, and purchase outcomes. One technology solution that is becoming increasingly important to many organizations for ensuring reliable data and information is master data management.

Until recently, master data management (MDM) had been regarded as one of those techie functions, deeply internal to enterprise infrastructure. As it turns out, MDM is very much a business asset that has a growing role in many areas that should matter to enterprises: strong brand presence, multichannel customer interactions, right-fit content and information, and highly variable buying journeys.

Behind the scenes, MDM works to eliminate the data and process silos that threaten the ability of enterprises to do a better job with customer interactions. The most up-to-date and useful data now comes from many sources, some internal to the enterprise and others from sites all over the digital landscape. But it's not just the data that needs management. MDM handles identity resolution for all of that disparate data to connect it to the right customers, products, and channels.

Data quality and data governance processes and practices are important parts of MDM. To succeed with accurate analytics and customer initiatives, centralized, high quality data is a must-have. Hard work has to be done to make sure data is relevant, particularly if it originates from multiple sources of varying reliability. Many data-driven processes need to take on adaptive capabilities to handle those highly variable customer journeys and multichannel interactions.

Why do business functions like marketing and sales have to get into the world of data management?

Sales and marketing teams are the business experts who know how data needs to be used and in which business processes. They also know the importance of high quality data – and the pain of analytics that produce bad recommendations when the data is unreliable. Increasingly, MDM solutions are opening up access to business users to involve these usage experts in the management of master data.

For customer experiences and buying journeys, context is where master data shines. It takes both the experience of business users and analytics processes based on accurate data to understand context for customer interactions.

 

"Information and Content Central" for the Multichannel Customer Experience

Successful outcomes for multichannel buyer journeys depend on the ability to engender customer experiences that are consistent, but not necessarily the "same", across channels. To provide the right content and information to the right channel for a specific customer interaction requires the right technologies. Master data management is integral to building and maintaining a reliable centralized repository of ready-to-go information and content that can be segmented by products, customer categories, channels, media, and so on. A "360 view" of each customer or customer category is another aspect of MDM that is obviously important for improving customer experiences.

Marketing efforts often include rich content, for digital and offline purposes. Repurposing content and the availability of ready-to-go content are distinct needs as well. As with any content marketing effort, strategies must be in place to connect content to the corporate focus on customers, marketing and sales, and business outcomes. Multichannel requirements add to the importance of a strategic approach to improving customer experiences.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

  

 

Personalization Improves with Master Data

More attention is going into personalized communications with customers that work well for a particular channel of engagement. But personalization can only be effective with accurate, up-to-date information on customers, products, channels, locations, and other relevant data domains.

Stephen Powers and Ron Rogowski of Forrester discuss the role of contextualization for creating on-the-money personalized customer experiences that will bring about successful outcomes for digital commerce, where contextualization provides "tailored, adaptive, and sometimes predictable digital experiences":

While this sounds like a no-brainer, companies still fall short of meeting this goal because they guess at what their customers want and need, don't share customer data across silos, and don't take advantage of real-time cues. In fact, only 41% of customer experience professionals from our Marketing & Strategy Research Panel say that they consistently use customer research when considering customer experience design projects.

Channel appropriate personalization is a key ingredient for nurturing positive customer experiences. The content itself and the delivery mode must be the right stuff for each channel. "Responsive design" must partner with master data. Master data can make a big difference for better understanding context: the elimination of data silos; clarity around the relationships of customers to products and channels; access to customer activities on social sites; and ensuring the reliability of data for accurate personalization.

 

Navigating Customer Relationships

In many enterprises, data-related processes need to align with the real world intricacies that companies face, especially in terms of complex relationships and hierarchies with respect to customers and their extended networks. Social networks are prolific creators of data related to potential and current customers. And social data is now an important source for master data management. Including social data should help the enterprise see the customer's network of relationships as well as reveal non-obvious relationships and spheres of influence – these views frequently are not possible with traditional systems such as CRM. MDM helps to analyze customer relationships in social networks for correlation to master data domains.

Another navigational aspect of customer transactions and interactions is location. Location or spatial analytics help reveal relationships, patterns and trends that can prove to be quite valuable. Through analytics for customer locations, it's possible to visualize new understanding of relationships between the data for products, marketing and sales channels, and revenue, as well as for the customer. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

   

Source: Pitney Bowes Spectrum MDM Platform  

  

Master Data Keeps the Brand in Context

Efforts to support multichannel customer experiences and buying journeys should lead to business outcomes like happy customers, sales growth, and ultimately a strong brand. These results are not possible for enterprises that never go beyond data silos that perpetuate redundant, contradictory and out-of-date information for customers, products and pricing. Many organizations continue to try to operate with separate systems where neither data nor processes are well integrated. The result: fragmented cross-channel visibility and incomplete views of customers that damage the efforts of the enterprise to engender positive multichannel experiences.

MDM initiatives are not trivial exercises. But once in place, enterprises can reap large rewards on a continual basis. Master data feeds into analytics and decision processes that translate information into actions that nurture better experiences for customers.

Brand is the reflection of overall customer experiences over time. Master data provides the means to better connect all functions in the enterprise to accurate customer intelligence, the current context of customer needs and activities, and the relationships of customers to products and channels. These are the building blocks of improving customer interactions on any channel.

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About the author: Julie Hunt understands and sees the overlap and convergence of many business processes and software solutions that once were thought of as "separate" – and how this impacts both 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 – Strategies for B2B Software Solutions: Working from the Customer Perspective

 

The Adaptive Enterprise: New Directions for MDM?

Originally published in Hub Designs Magazine, November 2012

 

As market forces continue to fluctuate, business leaders are searching for the best ways to work with uncertainty and still deliver improved performance. As a result, organizations are transforming into adaptive enterprises.

Performance for the adaptive enterprise means agile, dynamic, and responsive to continuous change. But this kind of performance requires enterprise data that is reliable, usable and timely, so that analytics and decision-making processes are quickly on target with relevant recommendations.

Information management has an important new challenge for provisioning right-time data to emerging adaptive enterprises. MDM is a reflection of how an enterprise uses data and information for business purposes. The creation and management of master data touches more than the information itself. By creating data repositories and processes that reflect business functions, enterprises should now have access to the information that is so vital to effectively and agilely adapting to constant change.

 

 

Agile Processes, Agile Data

In 2011, Gartner surveyed 300 user organizations: 49 percent said process improvements are the key business priority driving interest in MDM. Gartner's Andrew White commented:

"MDM treats information as assets that indirectly contribute to enterprise performance by improving process execution via data consistency," White wrote in his presentation slides. "BPM is a management discipline that treats processes as assets that directly contribute to enterprise performance."

MDM has a clear role in improving business processes; both MDM and optimized processes contribute to better performance management. The increasing focus on master data in relation to business processes extends usage across the enterprise to more business roles and functions. For many enterprises, data governance policies are the means for the orchestration of master data management, practices, processes, and people, so that compliance with data governance policies assures that master data is available as a trusted and vital asset for the enterprise. A new direction and challenge for data governance will be creating policies that reflect the continuously changing needs of the adaptive enterprise.

 

Dynamic Business Processes: Adaptive Case Management

Adaptive case management is an interesting example of adaptive and responsive processes that depend on reliable data. Forrester Research defines adaptive case management (ACM) this way (Forrester calls it dynamic case management):

A highly structured but also collaborative, dynamic and information-intensive process that is driven by outside events and requires incremental and progressive responses from the business domain handling the case. Examples of a case folder include a patient record, a lawsuit, an insurance claim, or a contract, and a case folder would include all of the documents, data, collaboration artifacts, policies, rules analytics, and other information needed to process and manage the case.

Immediate access to the right information is essential to ACM due to the unpredictable nature of case management knowledge work. 'Unpredictable' is also a driving force of the adaptive enterprise, where there is increasing need for organizations to adapt to the volatile nature of business as it happens. ACM may evolve beyond its 'roots' in business process management to become a much-needed internal platform of process orchestration, practices and technology to more relevantly and quickly surface the most-needed information, content, and data, wherever they exist in the enterprise -- all in the context of situational business processes related to a particular employee's responsibilities.

Frequent mandates for ACM include managing risks and compliance, containing costs, applying a systems approach to non-conforming processes, and the ability to respond well to the unexpected. MDM parallels these mandates with a number of its value areas for enterprises:

• Compliance and risk management
• Revenue growth
• Customer interactions
• Cost and performance efficiency
• Business agility

Both MDM and ACM can contribute positively to customer-focused activities, along the lines of real-time analytics, decision-making, dynamic process, and timely responsiveness.

 

Performance, Outcomes, Metrics

Obviously, ineffective business processes and data bottlenecks are obstacles to the adaptive enterprise that needs to nimbly change in response to events and new insight, and to rapidly make decisions to make the most of new opportunities and challenges. Achieving performance improvements starts with identifying how data-fueled processes can be optimized, and how MDM and BPM can work together to do so. Well-functioning business processes, whether explicitly defined or adaptive (as in case management), are an essential contributor to agile business performance.

A classic example of setting an improvement objective aligned with MDM is the reconciliation of customer identity across systems of record and systems of engagement, since both domains must be integrated to support and capture customer interactions. Many business functions - and strategies - greatly benefit from a unified view of the customer. The organization can quickly and accurately interact with customers. Now metrics can be applied to measure outcomes and performance relative to customers, based on the organization's KPI preferences, and tied to corporate and line-of-business objectives.

Processes and performance improvement initiatives can be decomposed into master data elements that can be sorted and aggregated properly for metrics to quantify value. Focus is put on the data assets that are the most essential for overall business improvement. MDM makes such a focus repeatable across the enterprise to integrate business processes and metrics around the same master data elements

 

The Successful Adaptive Enterprise

Success factors for enterprises almost always include effective leaders, the right strategies, and high quality products and services that meet customer needs. But more and more demands are pushing enterprises to constantly monitor and respond to changes in markets, customers and competitors. These changes will then alter other success factors: strategies now have a shorter shelf life. Enterprises are challenged to:

• Transform to an organization comfortable with continuous adaptive behavior while relying on business processes and data systems to increase flexibility at tactical and strategic levels
• Make better use of data, processes, analytics to improve decisions, to predict future trends and markets, and to proactively manage change
• Develop responsive risk management and compliance processes that are ready for the uncertainties of business, both good and bad

Data analytics are seeing wider adoption as important tools throughout the enterprise, reflecting the convergence of BI, BPM and MDM. Analytics are also being used to evaluate enterprise data to improve both information management and business processes, more frequently in real time. Analytics-fueled data management capabilities bring improvements and evolution to data integration and data quality approaches. More enterprises are looking for analytics embedded in a wide variety of systems, including operational systems that now make use of real-time analytics and decision-making technology.

MDM initiatives have the right capabilities and strategic purpose to underpin business agility and the adaptive enterprise. Master data can be extended to provide improved use of information systems and repositories throughout the enterprise. MDM can stretch its data muscles to take on new business requirements and objectives where data and process are key components, and now venture into new categories that need MDM to better serve the enterprise. To achieve agile business actions takes highly disciplined approaches to MDM and data governance; and it takes flexibility bred into information management DNA to be able to quickly but reliably handle change.

 

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

Internal Disruption–No Longer Business As Usual

"Lean Is Not a Destination, It's a Journey": Lessons in Quality, Reliability, and Risk for MDM

 

 

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 both 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 – 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

 

 

Ungoverned Spaces: Innovating Information Flow

 Agile Governance to Ensure that Reliable Information is Available Anywhere Anytime

 

                  Originally published in CMSWire November 28, 2011

 

A good many companies now have Information functions in the Cloud, for a variety of reasons. There is the purposeful selection of cloud services for the usage and management of data, content, records and documents – in other words, Information – that supplements or replaces systems inside the firewall. There is also the proliferation of data and content – more Information – created in many cloud-based sites not under the control of companies. But companies want and need all of this Information -- and also need energetic ways to govern all of this Information as it flows in and out of the spaces used by enterprises.

Going with the notion that for most enterprises knowledge is power, information-led business innovation and competitiveness is becoming a recognized path for potential success. In parallel, services in the cloud are talked about more and more as enablers of innovation, as an agile, dynamic and cost-effective environment for new business direction and experimentation.

With so much information created anywhere-anytime, information governance has a vital purpose: ensuring the flow of reliable information to the right people at the right time. This can also mean reducing former barriers and changing processes, to make more of this information and data are available to more people in the enterprise, sometimes including partners and customers. What should be emphasized is governance that aligns with how information is used in the enterprise, so that governance also connects to business value and corporate strategic goals.

So when we’re talking about innovating information governance, we’re no longer in a vacuum or in a silo, off somewhere in that “information department” – we’re talking about the entire enterprise of integrated business processes and management infrastructure, running on-premises, in the Cloud, and in between. Everyone in the enterprise has to be on board, from top to bottom, across teams, internally and externally, working in concert to enact effective information governance.

  

Priming Information Flow – Anywhere Anytime

There is no doubt that most companies must extend their information strategies to capitalize on highly relevant information that is continuously created in the cloud.  Strong information governance policies and practices must realistically enable the flow of information to continuously bring information to the enterprise -- to improve decision-making, to better understand current and desired customers, improve competitiveness, and support all significant goals and strategies in the enterprise. Information Governance can be a vital part of the ‘framework’ for business agility, responsiveness to competitive pressures, support for new product and services directions, and innovation shepherding.

Enterprises need to break free of old perceptions and implementations of information governance where silos and disconnectedness dominate, and where governance has been viewed as difficult, esoteric, and somebody else’s problem. Companies generally have not taken a holistic view of business information governance, its impacts across end-to-end processes or business units, or the technologies to support it. Instead enterprises have treated information as islands within specific system silos, and have taken the same approach to its governance. Information Governance overwhelms many – many get lost in the details.

If information has become essential to corporate success, then Information Governance must become part of a company’s overall business strategy.  An upper management team, comprised of cross-departmental members, has to set the high level ‘culture’ to enable agile Information Governance:  a corporate ‘infrastructure’ of cross-team collaboration, an enterprise-wide commitment to levels of excellence when connecting with and serving customers, recognition that content from social sites matters, the dynamic means to process all potentially useful data and content no matter the source, and the ability to provide on-demand data and analytics ready for any device.

Individual responsibilities for different components of Information Governance can be segmented to include most groups in the enterprise and to enhance accountability. Services-oriented Information Governance may be an optimal approach with partnerships across teams to enable governance functions and processes. Legal and financial teams could take key responsibility for risk management and compliance, as well as retention policies and practices. IT teams can take on key responsibilities for security, storage, resource provisioning, and user access. It is also now the responsibility of enterprise users of information to understand, partake in and help enforce policies and practices for security, information quality, delegated access, sharing, and so on – these responsibilities are no longer “somebody else’s problem”.

 

    Creating Information Advantage in a Cloudy World

 

Detours For Information Anywhere Anytime

Not surprisingly, persistent issues for Information Governance and Management are mostly the same, whether the repositories are in the cloud, on-premises, or in a hybrid flow based on the two:

 

• No clear holistic strategy for governance of distributed information sources
• Prolific growth of information sources and systems
• Need for extensive integration between information sources, often existing as silos
• Synchronizations to eliminate redundancies and to update with most current information
• Unreliable quality of information, content, data
• Timeliness of information
• Fragmentation that will block information flow and devalue assets

The creation of content and data in cloud repositories has added complexity to ownership and the chain of custody. Enterprises that strongly manage complex chains of information ownership and usage, both internally and externally, will help corporate goals and achieve advantage from all information assets.

Companies must take on the responsibility of monitoring vendors of cloud services to ensure that the vendor is meeting SLA provisions for security, privacy, backup and so on. This monitoring falls both to business users and IT groups. But companies must also develop comprehensive Information Governance plans to supplement what cloud service vendors provide. Companies are liable for proper information governance regardless of where the repositories sit. Business users as well as IT teams must understand the implications of “anywhere anytime” information flows.

 

            IBM: Enabling Multifaceted Innovation

 

Cloud Inspires Governance Innovation

Innovation often relates to finding new ideas and / or applying existing ideas in new ways to bring about beneficial change that affects many aspects of what an enterprise does. Innovation is not limited to the products and services in an enterprise, but also can be applied to business model and strategy, operations, employee and partner enablement.

Again, enterprises need to break out of seeing governance only in terms of risk containment and security (which are important) to also see how it can be a means to innovation. Governance is valuable for setting up policies and practices to guide information usage but it can also provide scope for experimentation and value creation through the orchestrated flow of information to and from many sources. Governance is evolving to handle the challenges of data in the cloud – extending information governance to cloud sources can also help innovate how governance is done. This is a new challenge for teams across the enterprise to work differently and creatively when governing vital information that must constantly flow, while ensuring that the enterprise meets reliability, compliance and risk requirements. Making accountability a distributed function across the enterprise extends the value of information governance and takes it out of its usual silo.

Timely, reliable information in the right hands is more essential as business cycles shorten and become more competitive, and as customer perspective and buy-in become more important. Information created in the cloud along with cloud services comprise a new platform for quickly sharing data and content that matters to the enterprise. “Working the problem” of information governance from the focus of ensuring the open flow of reliable information to be used by many teams in the enterprise anywhere-anytime is a positive move into innovation that will make a difference.

  

Related Links:

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

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

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

Shallow Thinking Ensures Bad Business Decisions

Connecting Data Governance to Business Outcomes That Matter

Data Management: Reaching into the Cloud

 

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    

 

 

Connecting Data Governance to Business Outcomes That Matter

Originally published on Hub Designs Magazine September 2011

 

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. On the other hand, data and data-oriented solutions, by themselves, don’t usually map directly to business outcomes, and as such, usually can’t be measured by value contributed to desired outcomes and achievement of key corporate goals. But -- business processes and data are inextricably bound together. Processes create, modify and consume data – one might even say processes live and breathe data. It is through business processes that data can be mapped to business value.

Companies generally have not taken a holistic view of business information, technologies to improve it, or its impacts across end-to-end processes or businesses, instead treating data as information islands within specific system silos.

Kalido, The State of Data Governance Maturity 2011   

 

Notions like “Big Data” and “Information as an Asset” have stimulated thinking in many enterprises about extracting more value from the data already in enterprise repositories to benefit the business and its goals. But to extract that value from data, it’s not about the data nor is it about data governance. The key focus should be the business processes that impact revenue, business agility, competitiveness, and overall positive performance. However:  both reliable data and effective data governance are key enablers of gaining value from business processes.

Data governance is an important strategy both to maintain data in its most usable condition and to ensure consistency in how processes work effectively across the enterprise, by sustaining the quality, accessibility and relevance of data used by processes. Instead of many silo’d processes working on silo’d data, data governance contributes greatly to connecting essential pieces to synch significant business processes to the bigger enterprise strategic picture.

Martin Atherton on data governance as an enabler of business value:   

For many companies, it is common to focus on the minimum standards required to achieve compliance, instead of seeking to incrementally expand the effort and reap the benefits of being compliant more broadly across the business. In a previous research note, we used the phrase ‘value driven compliance’ to highlight the difference between approaching regulatory compliance as a chore versus seeing it as a business enabler.

 

Some of the difficulty of 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 more business-oriented roles. To enable better business outcomes, data governance needs to bring business roles and IT teams together, as well as incorporating the support of various levels of management and strategic decision-makers. Too many times, data governance has not been implemented strategically. Instead it has been marooned by ad hoc decisions and “policies” tied more to daily operational situations. Without involving data governance in key corporate strategies, enterprises are not in good position to handle internal data issues, let alone address the ever-growing proliferation data from many new sources inside and outside the enterprise.

Ross Dawson expands on the notion of “governance as an enabler”: 

Governance should focus as much on enabling innovation and taking useful risks as about managing and mitigating risk...What needs to happen is that governance, risk, and compliance become part of the process of creating business value...More generally, I think it is important to note that IT governance and corporate governance have long been treated as separate. As I pointed out in my keynote this morning, technology – including in data management – is increasingly central to corporate strategy.

 

With good commitment from management, business units and IT teams alike, data governance initiatives are a very good way to manage data more effectively for greatly improved use in business processes. Data governance should provide the means to coordinate efficient and non-redundant business processes, along with the necessary data assets, across the enterprise, between lines of business. From data governance coordination, standards for reliable data and optimized business processes can be implemented, maintained and changed as needed. Data governance is also an enabler of acquiring important insights into both data and business processes.

 In my interactions with Forrester clients, I get the sense that data governance is receiving the most senior-management-level attention today than I've seen throughout my 18+ year data management career.

Rob Karel, Forrester

 

Tangible Value

Data alone carries no value. Unusable and poorly managed data carries no value. Real cost savings obviously result when data sources are streamlined and cleaned up, when processes are more responsive to business needs. Great value can result when business intelligence and other processes are based on the right data that can be trusted to lead to decisions that are more accurate and timely. The tangible benefits and value of data governance and data are further revealed through their connections to business processes and the desired outcomes from those processes that connect to corporate strategies and goals.

  

Related Links

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   

 

 

 

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