The 4 Principles of a Successful Data Strategy

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:

  • It is associated with something of long-term value to the firm, (e.g., product, customer, financial information); and
  • It is used across multiple systems and business processes.

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:

  • Marketing creates interaction information as it reaches out to organizations and individuals through its marketing campaigns.
  • As sales leads emerge, a salesperson is assigned, partners are engaged, and sales opportunity information is maintained throughout the sales process.
  • When an agreement is reached, terms are shared with product fulfillment to deliver the product and maintain support.
  • Finance and sales validate commissions with the sales teams and partners.
  • Management uses an end-to-end view of these processes to evaluate the effectiveness of its pipeline and make ongoing improvements in and across the areas.

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.


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.