The coming economic recovery will be an impetus for some to consolidate, update software and upgrade hardware. But we predict that many will seize the opportunity to invest in a new generation of intelligent decision management systems that are designed to exploit the new computing environment and leverage innovative technologies that have been emerging for several years but are now being proven in commercial use.
In 2010, organizations will push their BI systems to improve decision-making and support business transformation. This will increase the need to better manage data and information assets. Data quality, governance and MDM will be critical. The market will strive to expand the use of BI and apply analytics to many more applications in a number of ways. Although we have categorized our view as ten trends in order to explore each one, they are very much interrelated.
1. Increased data and BI program governance
BI is becoming more pervasive as organizations move to a culture of more fact-based decision making, and BI expands to include operational decisions. In addition, regulatory requirements dictate the need for transparency, consistency, and reduced errors. Finally, there is an increasing need for inter-organizational collaboration requiring cross-departmental data integration. These demands put pressure on organizations to manage their BI initiatives more strategically.
2. Enterprise-wide data integration a good investment
For many years, data warehouse and BI environments were built one application, one report, one data mart at a time. Project budgets didn’t allow for a holistic approach to data integration and it usually wasn’t necessary. But recently, leading organizations have begun to employ a coordinated, enterprise-wide approach to data integration, enabling cross-functional analysis and enterprise-wide performance management, and improving applications such as customer and risk management.
3. The promise of semantic technologies
One of the most daunting data integration challenges is establishing an enterprise solution to metadata management. Many existing systems rely on programmers to manually code data transformations for each application, on data stewards to arbitrate conflicting data definitions and uses, and on content experts to classify incoming data. There isn’t enough time to manually reconcile the differences among data from different sources to make operational decisions. Or enough people. New approaches are needed, and semantic technologies hold part of the solution.
4. Expanding use of advanced analytics
Advanced analytics is the critical enabler in turning data into insight. The pressure to use data, not only to make real-time decisions, but also to predict relevant business events is increasing dramatically. A common approach is to extract data from an enterprise data warehouse (EDW) into analytic data marts for advanced analysis. But adding another layer to the data architecture increases complexity and potentially reduces the speed of decisions.
5. Narrowing the gap between operational systems and the data warehouse
It would be naïve to think that providing broad access to information is enough to expect good decisions to be made as a result. Traditional data warehouses often do not provide up-to-the-minute data or results of analysis that is timely enough for operational decisions. Information is provided as reports, in a format for use by humans, not applications or processes. Where more active data warehousing has been attempted, it has been labor-intensive and the results are brittle and resistant to change.