We’ve been on site at a new client in South Africa since the Gartner MDM Summit. Here’s a great series of new articles by Julie Hunt, an accomplished software industry analyst.
Combining Forces to Better Answer Business Needs
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 solutions will not only improve the better overall functioning of all of these solutions, but they’ll 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.
Forrester’s Rob Karel had this to say on aligning Business Process and Data Governance initiatives:
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 relationships between Data Governance, MDM, BPM, BI / Analytics (as well as DQ and 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 customers, and 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 those business cases defined, monitoring outcomes of data governance, BPM, BI, etc., should have a 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 when 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.
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 technical side to customer-centric work in solutions consulting, sales and marketing. Julie shares her take on the software industry via her blog Highly Competitive and on Twitter: @juliebhunt. For more information: Julie Hunt Consulting – Strategic Product & Market Intelligence Services.