If you’ve had your doubts about the benefits of business intelligence, a new study might go a long way toward dispelling them. According to a recent study from Nucleus Research, titled “The Stages of an Analytic Enterprise,” enterprises attain an average ROI of 188 percent in the initial automation phase and an average of 1,209 percent in the later predictive phase. This means that in contrast to ERP software deployments, which yield an initially strong ROI that diminishes over time, businesses who employ business intelligence software gain greater ROIs as they continue to broaden and deepen their use of analytics.

The report, based upon 58 case studies of companies in diverse industries leveraging analytics tools used over five years, covered the gamut of small, midsize and large deployments of analytics software, and included companies using products from business intelligence giants, as well as companies using products from second-tier vendors and startups.

The more companies broaden and deepen their use of analytics, such as BI, product management and predictive analytics, the greater ROI they see. This trend in analytics ROI stands in sharp contrast to that of most enterprise software, which typically shows an initial strong ROI that diminishes over time.

Four Stages of Business Analytics

There are four stages of analytics deployment:

  • Automated Analytics. Enterprises at this stage use analytics primarily to automate report building. These companies achieve benefits that include increased productivity for data analyzers and reduced workloads for IT departments. Data management capabilities at this stage typically include the construction of data warehouses and data cubes.
  • Tactical Analytics. Organizations at this stage have multiple analytics deployments and have begun using analytics to improve decision-making, rather than just increase productivity.Tactical users expand their data management capabilities to include data migration, data integration and better data quality control. Companies at this stage achieve an average ROI of 389 percent. Drivers of higher returns include the addition of new end-user groups and the addition of extra capabilities to existing deployments.
  • Strategic Analytics. Enterprises that use analytics strategically deploy technologies across most of their organization and use analytics to align daily operations with the goals of senior management. Strategic analytics organizations typically use advanced data governance tools and practices. Generally, they also use metadata to ensure data is interpreted uniformly across their organizations. Organizations at this level achieve higher returns on their investments in analytics — averaging 968 percent — because they use analytics pervasively. Typically, these organizations embed analytics capabilities into non-analytic processes and deploy enhancements such as competency centers and governance.
  • Predictive Analytics. Predictive analytics deployments achieve higher returns by tapping into what is commonly referred to as “Big Data,” data sources that are large, contain a broad variety of data sets and change rapidly. Such deployments reach beyond the traditional limits of internal enterprise data to the Web, customers, vendors and partners. With an average ROI of 1,209 percent, organizations at this level achieve higher returns via projects such as Web-based customer sentiment tracking and demand forecasting. Another driver of higher returns is the use of non-proprietary data sources.

The report identified a special type of organization, the Analytic Enterprise, an organization that improves its competitiveness and operating results by continuously broadening and improving its use of analytics.

As organizations become more analytic, they go through a significant evolution. Employees’ work practices change as they increasingly embrace analytics as a way to make better decisions and incorporate more data into their analyses. Decision-making improves as analytics is embedded into more processes and enables employees to base their conclusions on data rather than intuition.