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Top 10 Trends in Business Intelligence for 2010

March 5, 2010 - 2010 may bring some relief to the economic decline, and indications show that companies are starting to shift their focus from survival to revival.

Vickie Farrell, Manager, Market Strategy, Business Intelligence Solutions, HP Software & Solutions, explained that the list of trends was developed in part out of surveys HP conducted at popular business intelligence and data warehousing conferences throughout 2009. “The responses reveal that people are getting serious,” said Farrell. Much of that focus is on BI which, in spite of some characteristics of a mature market, remains a source of relative growth and innovation.

HP identified the following 10 BI trends and predictions:

1. Increased data and business intelligence program governance. Respondents in HP’s BI survey identified their top initiatives as data quality, advanced analytics, data governance and MDM, and reported their intent to invest within 12 months. This focus conveys a recognition that in order to make the most of BI and analytics, you need to get the data right. That means establishing a data governance discipline, achieving and maintaining a level of data quality that is appropriate for the applications and developing a corporate standard for data terms and usage.

2. Enterprise-wide data integration is a good investment. 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. Their efforts demonstrate the cost savings of a comprehensive data integration approach replacing multiple isolated redundant projects.

3. The promise of semantic technologies. Semantic technologies, including ontologies, taxonomies, classification, and content monitoring, filtering and analytics, applied to information management help organizations reconcile and normalize meaning across different sources of data and content.  Recent innovation allowing structured queries over unstructured data is providing greater precision, speed of delivery, and reduction of information overload when analyzing content, versus using enterprise search. According to the HP BI study, 30 percent of organizations have already implemented a standard taxonomy for defining business terms, and another 41 percent plan to implement within 12 months.

4. Expanding use of advanced analytics. Advanced analytics is the critical enabler in turning data into insight. The trend to make more use of the data warehouse for advanced analytics will accelerate as organizations strive to move from running BI point solutions to being a comprehensive analytics competitor.

5. Narrowing the gap between operational systems and the data warehouse. Increasingly, analytic results are being used directly in the workflow context to drive operational execution and dynamic process change. The data warehouse will expand from traditional BI query and report generation to intelligent decision management and ultimately, the convergence of operational and analytic applications, including support for automated decisions.

6. A new generation drives new priorities in data warehousing and BI. The EDW cannot be an isolated repository supporting standalone BI applications. BI systems have to make use of multiple event data, be able to find data that is needed, extract what is relevant, assimilate data from multiple sources, analyze it, and incorporate resulting information into applications as appropriate. It is important for analytic systems to be much more integrated into the organization’s operations and information infrastructure.

7. Growing impact and opportunity of complex event processing. According to the HP BI survey, 59 percent of respondents indicated that they use CEP or plan to do so in the next 12 months. Whereas traditional BI analyzes data stored in the data warehouse, CEP engines analyze streams, continuously computing findings as new data arrives, updating the situational awareness, and allowing consideration of many more variables and context dynamics in making a decision. CEP will have a growing impact on BI systems as they evolve to provide more operational analysis and more automated decision support.

8. Growing importance of integrating and analyzing unstructured/semi-structured data. Most organizations have content management systems to manage and search unstructured content, but have limited capabilities to use the information for decision-making. Frameworks like MapReduce have the potential to bridge the gap between analyzing structured and unstructured content, as well as enable advanced analytics.

9. Social computing and BI. BI can expand the insight it provides organizations if it encompasses the information from interactions that occur in social computing environments. Technologies such as social mining and social intelligence use sophisticated data mining and text analytics to understand the implicit meaning of this unstructured data, which is completely reliant on the context in which it occurs.

10. Growing interest in cloud computing for BI. As the sophistication of BI environments has increased, so has their complexity and cost of management. This is not unique to BI, and organizations have already begun to adopt alternative delivery models to reduce the cost and complexity of other IT solutions. These range from open source tools and embedded functionality to bundled tools to development and starter licenses. Most notably, research surveys by HP and TDWI indicate fast-growing interest in exploring the use of utility-based delivery models such as Software as a Service and cloud computing for BI. The promise of cloud computing is shared resources, standard technology and automated provisioning processes.

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