Ques. No. 3: What is our data ecosystem? – For most businesses, data is an active asset that is captured, created, enhanced, and used in many business processes and applications. To manage this dynamic environment, the flows of data across systems and processes need to be organized in a coherent way.
We use a business architecture (not a technology architecture) to define core data capabilities that business and IT must create together. These capabilities organize technology platforms and business processes based on their function in the ecosystem: capturing and creating data, cleansing and organizing it, mining business insights from it, and using those insights to drive intelligent actions in the business.
By capturing data that measure the outcomes of our actions, we create a closed loop that allows companies to use their data to test, learn, and improve their processes.
The diagram below depicts three broad classes of core capabilities: data, insight, and action:
Data capabilities are responsible for creating and managing usable, high quality enterprise information assets. These include all standard data management capabilities such as data sourcing and integration, quality and metadata management, data modeling and data governance.
Insight capabilities include tools, data, and processes for management reporting and advanced analytics.
Action capabilities provision data and business intelligence to applications, business processes and business partners, and capture responses to interactions.
This capabilities model can categorize thousands of applications and data repositories into 12 logical buckets which will guide their simplification and evolution toward a common strategic blueprint.
Ques. No. 4: How do we govern data? – Ultimately, the implementation of a data strategy is not a project, it is an ongoing function of the company that must be governed. Because data is so ubiquitous, the governance structure must be federated, with a central governing body addressing the most important, common data, and most of the data managed locally in the lines of business.
We have found several elements of this model critical to successful governance.
First, the stewardship community is business heavy, with executive business data owners supported by business data stewards who report to them. IT custodians ensure that the systems incorporate and monitor the requirements of the business.
Second, companies should incorporate data governance as a part of other standard governance procedures as much as possible, including architectural review boards, audit and risk review processes, system development methodology, and security processes. Over time, a distinct governance body for data may disappear as it is fully embedded in other business governance activities.
Third, it is important to launch data governance with a small facilitation team and some data governance related infrastructure, such as data quality, metadata, and lineage tools to provide visibility and measures to the data governance board.
The alignment of the four principles for successful data strategy is the foundation for establishing a manageable, meaningful change in the way that companies deal with data. Note that technology is not the key to success — it is merely a supporting element in the development of core capabilities.
For many firms, the first attempt at a coherent data strategy is a daunting effort, with stakeholders learning each other’s language for the first time. But over time, the common understanding of how data is vital to the business establishes an effective dialogue so that truly strategic initiatives can be launched that make every business process more informed and intelligent.
Paul Barth is the managing partner and founder of NewVantage Partners. Paul brings decades of experience as a consultant to the nation’s largest companies. He is a recognized thought-leader and practitioner in leveraging information as a strategic asset and in emerging approaches and best practices in data management. Previously, Paul was founder and CTO of Tessera Enterprise Systems. He holds a PhD in computer science from MIT, and a MS from Yale University. Paul was formerly VP of Technology at Epsilon Data Management (an American Express company), and held senior technology positions at Thinking Machines and Schlumberger.