In-Depth

BI Experts: Why Enterprise Data Strategies Must Support Business Goals

Success with enterprise business goals demands strategies for enterprise data.

• By Philip Russom, Ph.D.
• 01/24/2012

[Editor's Note: At the upcoming TDWI BI Executive Summit (February 13-15, 2012 in Las Vegas), award-winning users and other industry experts will share their tips for executing data strategies that advance both BI and the entire enterprise. Visit the summit's Web site.

Data has long been managed in isolated technology and departmental silos without much alignment to business goals. This has to stop if organizations are to achieve business goals that reach across silos, such as enterprise-scale business intelligence, 360-degree customer views, operational excellence, risk reduction, and compliance.

Organizations pursuing these goals need one or more data strategies that reach across the organizational boundaries within an enterprise. They also need localized strategies for departmental initiatives and point projects in data management that align with enterprise strategies.

Here are some of the components of an enterprise data strategy:

IT-to-business alignment: Most organizations have openly stated business goals. Yet, the goals don't easily translate into general IT requirements, much less specific data requirements. For example, a business goal such as "reducing customer churn" may require more sophisticated integration of customer data across departments. Increasing business efficiencies may need new data quality solutions. As organizations become more data driven, business management and IT management must strengthen IT-to-business alignment by collaborating more rigorously to define and prioritize data requirements with greater granularity than ever before.

Data management solutions linked directly to business goals: Stronger IT-to-business alignment is a foundation for future-facing enterprise data strategies. Although some of your data strategies may seem technical (such as cross-system standards for metadata, master data, and data quality), improvements to data and its management should link directly to one or more enterprisewide goals (typically those that depend on shared data, such as 360-degree views and business intelligence).

Collaboration of enterprise data and its strategies: Achieving the new level of IT-to-business alignment (as a foundation for an enterprise data strategy) demands greater collaboration than most organizations are used to. For this reason, many organizations have beefed up organizational structures that are inherently collaborative for data, such as data governance boards, data stewardship programs, steering committees, and data management competency centers. In particular, TDWI has seen the discipline of data architecture grow aggressive in recent years, usually as a "big picture" method for coordinating multiple data strategies.

Data as an enterprise asset: Many business strategies involve a concerted effort across multiple departments and business units, including governance issues, operational efficiencies, and anything involving customers (e.g., service, retention, and account growth). Success with these efforts is thwarted when a line of business hoards its data and refuses to share. Hence, a first step for many enterprise data strategies is to change the ownership of data such that it becomes an enterprise asset instead of a departmental one.

Coordination among data management teams: Part of the concerted effort of an enterprise data strategy involves aligning multiple data management teams, including those for database administration, data warehousing, data quality, master data management, and so on. These teams need to collaborate and agree upon common standards for defining and modeling key business entities (such as customers, products, and financials) and how data about these can be improved and shared across IT systems. Standards for data and application development should align with stated business goals for data, not just the local, technical exigencies of the IT systems involved.

New data strategies for new business practices: Businesses need to react sooner to events (such as customer churn or sales opportunities), so they need fresher data, sometimes delivered in real time via data services. To manage big data and leverage it via analytics or the development of information-based products, enterprises are investing in new storage systems and databases, sometimes based on appliances or clouds.

Conclusion

Enterprise data strategies have their challenges. Since they reach across organizational boundaries, the strategies may be stymied by turf issues such that they won't get to first base without proper leadership from a highly placed executive and/or a governance board. However, enterprise data strategies are worth the effort because they enable better business decisions, focus data management work where enterprise goals need it most, contribute to business performance and execution, and address new requirements in real-time data management, analytics, and compliance.