1. Increased data and business intelligence 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 did not 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 crossfunctional 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 is not 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.
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. In the Semantic Web3, which is an extension of the current Web, information is given a well-defined meaning so that computers can relate information in a meaningful way and interpret and act on it appropriately. Humans can do that because they understand context and relationships and can infer implicit knowledge from explicit facts. Semantic technology helps computers to do that.
Computer-performed semantic data reconciliation is not only faster, but less error-prone, more consistent in the application of business rules, and more adaptive to change. Commercial application of this technology is becoming more widespread. Semantic technologies are being used today to:
- Automate product reclassification
- Enable accurate and consistent diagnosis and treatment across a hospital management community
- Perform dependency analysis for managing and reconfiguring software assets
Recent innovation allowing structured queries over unstructured data is providing greater precision, speed of delivery, and reduction of information overload when analyzing content, vs. using enterprise search. A good first step toward eventually leveraging semantic technology (and a valuable part of a data governance process) is the development of a taxonomy, or definition of business terms that is standard across an organization. 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 one within 12 months.
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.
To accommodate these conflicting pressures, technology providers have begun to push the advanced analytics computation closer to the data (similar to relational database management systems [DBMS] using stored procedures to execute procedural logic within the database). Analysts see in-database analytics as the heart of the predictive enterprise.
5. Narrowing the gap between operational systems and the data warehouse – It would be naive to think that providing broad access to information is enough to result in good decisions being made. Traditional data warehouses often do not provide up-to-the-minute data or results of analysis that is timely enough for making 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.
6. Data warehousing and business intelligence: A new generation drives new priorities –In the early years of data warehousing and business intelligence, conventional wisdom was to build data marts, one application or department at a time. Circa 2000, with leading organizations knee-deep in data marts and seeking a single version of the truth, the trend shifted toward consolidation and building a centralized enterprise data warehouse.
7. Growing impact and opportunity of Complex Event Processing – In last year’s trends list, we noted Complex Event Processing (CEP) as “coming of age”, and throughout the year, we have seen growing evidence of its increasing importance. In the HP BI survey, 59 percent of respondents indicated that they use CEP or plan to do so in the next 12 months. According to Gartner, “The market for commercial CEP products is expected to have a 31 percent CAGR from 2008 to 2013.
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. Text analytics is challenging, and difficult to scale to large volumes of data. It is further complicated when attempting to integrate the analysis of structured and unstructured data.
At the same time, it is becoming increasingly important to be able to glean information from content generated not only inside the enterprise, but outside as well, from new sources beyond the scope of the current BI environment. Twitter for example!
9. Social computing and the next frontier for business intelligence – An important influence in the continuing BI evolution is the impact of social computing on decision-making processes, methods of collaboration and interaction, and enhanced customer experience. BI can expand
the insight it provides organizations if it encompasses the information from interactions that occur in social computing environments.
The dynamic conversation channels available through blogs, online communities, Twitter, Facebook, LinkedIn, and a host of social computing venues engage customers, prospects, partners, influencers, and employees—touching virtually every key constituent in an organization’s value chain. Very importantly, these channels are reshaping how customers evaluate and choose products, how brands are perceived, how business processes evolve, and how people work together.
10. Growing interest in cloud computing for business intelligence – 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. They 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 (SaaS) and cloud computing for BI. Cloud computing alternatives vary, from solutions owned and operated by the customer to solutions that are owned and operated by a service provider, supporting multiple customers or tenants on the same infrastructure, and allowing customers to pay for additional capacity on demand.