Originally published as a 4-part series on Hub Designs Magazine May 2011
Data governance can be seen as formalized policies, practices and processes set up to manage voluminous data assets across enterprises. Data governance also sits on an important growing convergence that encompasses multiple, and frequently separate, disciplines: data quality / data integration, master data management (MDM), business process management (BPM), business intelligence (BI) and analytics. The solutions surrounding the management of information, data and intelligence are emerging from artificial silos to acknowledge the greater overlap and interrelationships of these technologies and practices. Deliberate initiatives to tighten up the convergence of these solution spaces will not only improve the better overall functioning of all of these solutions but will help both IT and Business users see how it all works together. A greatly beneficial result should be the elimination of duplicate demands on business users and IT for implementing and managing these systems.
Data governance is not – and should never have been – about the data.
High-quality and trustworthy data sitting in some repository somewhere does not in fact increase revenue, reduce risk, improve operational efficiencies, or strategically differentiate any organization from its competitors. It’s only when this trusted data can be delivered and consumed within the most critical business processes and decisions that run your business that these business outcomes can become reality.
So what is data governance all about? It’s all about business process, of course.
Let’s add this note to Karel’s assessment: business process is about optimizing the Business.
The current state of the increasing interrelationships between Data Governance, MDM, BPM, BI / Analytics (as well as DQ, DI) is also the story of the teaming up of Business users and IT as collaborative partners in doing the work that help businesses meet strategic goals, bring value to company customers, add to competitiveness. Both sides of the story must start with the business problems and the jobs to be done, plus the desired outcomes and benefits. Then decisions must be made regarding the role to be played by the software solutions to be implemented. Starting with the business also encourages the exploration and creation of the specific business cases that reflect needs and requirements. With such business cases defined, monitoring outcomes of data governance, BPM, BI, etc., should have clear basis.
While business processes are a major focus for strategies around data governance, BI and BPM, data still retains a significant role as a strategic asset that enterprises can use to differentiate themselves. These two entities (process and data) must be accorded respect when developing interrelated approaches that incorporate all of these solutions. Just as these solutions are best in synch with one another, so must the enterprise itself work with these solutions across departments and diverse teams. For business and intelligence processes to yield useful, timely and accurate results, the underlying data must be trustworthy.
Knocking down the Silos
Data, information and content permeate most enterprises where business users must become much more directly involved in creating the overall solution for handling these vital resources. Business users usually hold the key to the context for data and processes, as well as future usage considerations. The integrated components of MDM / data governance, BI / analytics, and BPM need to deliver real results to business users but are also highly dependent on participation of business users to reflect reality and provide on-target solutions. True collaboration between Business and IT teams starts with requirements, results, procedures, and then leads to collaborative work on joint projects. Such collaboration fosters reuse, reduces duplication of effort and processes, and builds a network of Business and IT staff that is aligned with how systems support key business objectives, initiatives and future direction.
Commonality also crosses the business drivers for data governance, MDM, BI, and BPM where key benefit areas consist of growing revenue, cost efficiency, different levels of agility, and compliance. Efforts should provide improvements for:
- Faster, more accurate decision-making - real-time intelligence
- Identifying and engendering new markets, new products, new customers
- Enhancing and supporting customer relationships / customer service
- Operational procedures
- Customer cross-selling / up-selling
- Ease of compliance, audits, risk management
Rajan Chandras, Data Gaps Plague Process Initiatives:
For one, the genesis of these types of initiatives is often very different. BPM is undertaken when organizations find themselves beset with process shortcomings, and with the primary purpose of better understanding, improving and integrating business processes. Data governance initiatives, on the other hand, are usually justified by shortcomings in data quality, consistency and integrity.
Then, data governance is often closely intertwined with master data management, and thus organized alongside important "master" business data entities -- customer, product, organization and such.
That said, Rob Karel, Forrester analyst and lead author of the report, suggests that IT must learn how to better educate and evangelize data issues in a language and a context that matters to the business -- a responsibility that Karel puts squarely on IT, "because IT often has a more cross-enterprise view than siloed business units and functions."
For data governance and process governance efforts to be successful, they both (business and IT) must frame their priorities and business value in the context of which business processes they are aiming to improve, transform and optimize, says Karel.
From Forrester study, September 2010
Intelligence, People and Process
MDM is a reflection of how an enterprise uses data, information – and content - 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 develop access to the information that is so necessary to effectively and efficiently achieve business goals. It’s extremely important to analyze, manage and provide access to all forms of information – structured and semi-structured data, as well as “unstructured” content.
For BI and analytics solutions to provide real intelligence, they must be based on accurate and timely data sources. BI outputs are based on data aggregations, until recently, from structured data in data warehouses, with delivery in report or dashboard form to enterprise users. Analytics add the dimension of mathematics and formulas, with variants addressing forecasting and prediction.
Neil Raden responds to the ebizQ question: How does master data management change BI?:
…to describe how organizations collect data as part of a process, but manage to make it infinitely more valuable by using it for other purposes.
What does this have to do with BI and MDM? In our research, we have found that most knowledge workers shun BI for two reasons (not performance or ease-of-use): relevance and understanding. MDM adds nothing to address these concerns because its representational framework, a relational schema, is inadequate. It's a representation of a model. An ontology is a model. Until MDM rejects the relational model as its underlying schema, it will be unable to add the rich meaning, relationships and even reasoning that an ontology can do.
So, the point is, if you're going to go to the massive effort and expense of an MDM solution, take some advice from 21 years ago…Make the data useful for people, not just governors and black belts.
One newer area of interest for BI / analytics solutions is the inclusion of collaborative activities to add contextual and qualitative layers to the output of BI processes. To achieve authentic intelligence, contextual / qualitative layers can provide a strong basis to test, fine tune and filter the artifacts of analytics. Analytics can benefit greatly from human filters that bring experience, knowledge, creative thinking. Context has a big role here: context for sources, context for outcomes, context for usage with other data points, to achieve the optimal Intelligence for “making better business decisions”.
The possibilities for new applications of analytics increase with collaboration. Inviting in many-to-many interactions also opens up processes to new ideas from participants. Gartner found that social venues and collaboration help to track and capture outcomes of the decisions made based on BI / analytics:
Gartner's user surveys show that improved decision making is the key driver of BI purchases. However, most BI deployments emphasize information delivery and analysis to support fact-based decision making, but fail to link BI content with the decision itself, the decision outcome, or with the related collaboration and other decision inputs. This makes it impossible to capture decision-making best practices. Solutions are emerging that tie BI with social software and collaborative tools for higher-quality, more transparent decisions that will increase the value derived from BI applications.
With convergence, employees in the enterprise should operate more effectively, where improved data governance/MDM lead to better BI, where collaborative processes also enhance and validate BI, where a collaboration setting for BPM works to deliver more of the information that the enterprise needs. A possible approach to marrying collaboration, data and intelligence to business processes, and, more importantly, to the way people work, can be seen in Tibco’s re-work of tibbr. Dennis Howlett provides this description of tibbr:
It intelligently marries people, process and context, delivering information the way people want to consume
Tibco connects tibbr to business processes and event-triggering that are then exposed in tibbr for taking action. While tibbr is built more for real-time information streams than archives of content and information, tibbr and Tibco have created a platform with a lot of potential for improving overall information findability that adheres to context and worker roles. It connects to process, people/workers, collaboration venues, data and information streams, centralizing all event streams into one dashboard. Information can be organized by subject or topic, rather than by people. tibbr enables users to create, contribute to, and subscribe to the real-time event streams that matter most to them.
Convergence Clarifies Benefits and Value
In a recent Gartner report covering predictions for data integration and MDM:
Through 2015, 66% of organizations that initiate an MDM program will struggle to demonstrate the business value of it.
It’s probably not a stretch to extend this “struggle for value” to BPM and BI / analytics. But through the convergence of MDM with BI / analytics and BPM, an analyst like Sandy Kemsley sees increased business value, in this Loraine Lawson interview:
Lawson: It's become more important with MDM to identify pain points and a real business case. Yet MDM is still pretty IT-centered. Does connecting it to BPM allow it to be more business-focused as a project?
Kemsley: Absolutely – connecting it to BPM, and also connecting it to business intelligence and analytics, because that's one of the things that many business users are interacting directly with analytics, whether it's just simple reporting or dashboards or a more complex, drill-down. You can get better analytics if you have consistent data models and those consistent data models are going to be helped by MDM, that's one of the key business benefits. When you want to have a report that gathers data from a number of different systems, it's not going to be some huge, big, long project because somebody has to figure out what data maps onto what other data. It's going to be a much more straightforward thing because while the data may not all be in one place, at least it will be in a consistent format so that you can easily bring it together.
Individually these solutions can achieve some value and success for an enterprise, but at the cost of much duplicated effort and resources. In convergence, with initiatives for business improvement and better decision-making, an integration of BI/analytics, BPM, data governance and MDM has definite synergistic outcomes that can greatly increase business value and reduce wasted effort. Integrating business insights into process execution will significantly improve the quality of decisions and lead to continuous process optimization. And process events kicking off BI and analytics for real-time decision-making increases beneficial outcomes.
All of these solutions should be about solving frequently complex business problems and supporting the work to be done in the enterprise. Each solution brings a significant piece of the “big picture”: better decisions for the business with reliable data feeding both intelligence and process. Ultimately the biggest payoff is how this convergence feeds directly into business agility and continuous change to constantly fine tune sustainable competitiveness.
About the author: Julie Hunt is an accomplished software industry analyst, providing strategic market and competitive insights. Her 20+ 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