dcsimg

The 4 Principles of a Successful Data Strategy

By Paul Barth

(Back to article)

Many Fortune 500 companies are recognizing enterprise data as a strategic business asset. Leading companies are using troves of operational data to optimize their processes, create intelligent products, and delight their customers. Also, increased demands for regulatory transparency are forcing companies to capture and maintain an audit trail of the information they use in their business decisions.

 

Despite this, large companies struggle to access, manage, and leverage the information that they create in their day-to-day processes. The rapid growth in the number of IT systems has resulted in a complex and fragmented landscape, where potentially valuable data lays trapped in fragmented inconsistent silos of applications, databases, and organizations.

 

IT's not your fault

Experience has shown that this is not a technology problem, it is a business problem. Creating an effective data environment requires change and coordination across the board, with business and IT joined at the hip. To ensure success, they must create a practical data strategy that guides process changes as well as ongoing investments in their data assets.

In our work with Fortune 100 companies over the last 10 years, we have identified four principles behind a successful data strategy. These principles align and focus the strategy, breaking initiatives into manageable projects with a measurable business benefit. Principles are presented as questions that business and IT must answer. Those answers, in turn, help shape the framework and priorities to drive implementation of the strategy:

Ques. No. 1 - How does data generate business value? - Improving the quality or accessibility of enterprise data is not an end in itself it is merely an enabler for creating business value. The data strategy must be driven by an understanding of how information can enable or improve a business process. For example, increasing cross-channel sales (a business value) requires data about your current customers and the products they own (the data); or reducing the cost of manual reconciliation for financial reporting (the business value) requires standardizing and consolidating redundant and inconsistent data across business applications (the data).

The table below lists five categories of business value delivered by data improvements, along with examples from our experience with our clients.

 five categories of business value

 

The data strategy does not need to identify all possible business benefits, but it should define several that are material to the business and measurable. Establishing some early, visible benefits is important to launching the data strategy and giving it momentum.

Ques. No. 2: What are our critical data assets? - Not all data in the business is critical. In fact, most data is specific to an application, business function, or transaction. Data that is critical typically has two characteristics:

The diagram below shows an example of critical data assets:

 

The chevrons along the top depict a high-level process flow through marketing, sales, fulfillment, and finance for a top 10 technology company that goes something like this:

This process analysis reveals several critical data assets and associated attributes. For example, customer organization and individual information is used by every one of the process steps. If this information is siloed and inconsistent, customers will get inconsistent messages and service. Process owners will have difficulty measuring their effectiveness. Analyses will not reconcile. And implementing new controls or improvements will require changes within each process step.

Conversely, improvements to these critical data assets will likely yield business benefits in all five of the categories listed above.

 

ROI

In our experience, identifying and improving critical data assets in large companies can yield tens of millions of dollars in benefit, and justify millions of dollars of investment in implementing a data strategy.

However, we believe it is just as important to keep the set of critical data assets as small as possible. Note that there are very few attributes listed above; the most critical data asset for these subject areas is a common identifier. Maintaining the unique identity of customers, products, interactions, and contracts is what links information across the enterprise. Once that is tackled, attributes can be added to the enterprise record incrementally over time.

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.

 

Conclusion

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.