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This article discusses the key differentiators between traditional and operational business intelligence solutions (BI), and what are the benefits and challenges of implementing and deploying these solutions.
Traditional business intelligence software has been around at least 20 years, focused mainly on supporting executive decision makers and a subset of strategic analysts and power users knowledgeable in the use of business intelligence software. As a consequence, the reach of traditional business intelligence solutions has remained limited due to lack of support for decision makers such as line-of-business managers and frontline employees and sometimes even for external stakeholders such as suppliers or customers.
Traditional BI has focused simply on information delivery in the form of dashboards or reports, where the solution itself has been a noncritical component of the broader architecture. It has been common for such BI solutions to introduce significant latency in information availability, exhibit lack of scalability and availability to support daily operational decision-making processes, and provide minimal support for other components of the decision-making process, such as hypothesis development, forecasting, what-if analysis, performance modeling, scenario planning, decision workflow support, and collaboration. However, decision making is usually a collaborative process that involves evaluation of alternatives based on feedback from and input of multiple people.
More recently, the focus has shifted toward operational business intelligence, where the business intelligence solution is deployed in parallel with specific operational solutions and processes, thus enabling "right-time" decision support to a broader base of users at all levels of the organization. The resulting tools guide users through a decision-making workflow by performing some analytic functions "behind the scenes" while leaving enough flexibility for users to perform their own personalized analytic activities.
There is an important conceptual difference between traditional and operational business intelligence. Traditional business intelligence was designed to provide an overarching view of the enterprise for fewer decision makers based on aggregation of historical data, while operational business intelligence is about supporting repeatable decisions made on a daily basis by many people. The resulting operational business intelligence solution infuses intelligence into all of an organization's business processes by providing the right information to the right people at the right time. On a practical level, to achieve this goal, operational BI solutions introduce certain new requirements in both the user interface and the IT infrastructure.
Although the number of decision makers increases in an operational business intelligence environment, these individuals are likely not to be experts in data analysis or the use of specific BI software. As a result, the interface of an operational business intelligence solution must either closely match the existing operational solution of the user or be exposed through such a solution. Alternatively, the operational business intelligence solution must exhibit the features of common online information sites that combine intuitive interfaces with links to reports and alerts, as well as promote a collaborative environment. This can be accomplished through the use of interactive, graphical interfaces as well as support for workflow (or guided analytics) and collaboration (e.g., annotations, embedded email, and extended data accessibility).
On the IT infrastructure side, operational business intelligence solutions must be able to handle more people potentially launching queries and putting higher workload demands on the system, where availability and scalability requirements are approaching those of operational systems. If business intelligence is going to be truly operational, then it can't be down or otherwise unavailable. Such solutions must also utilize business activity monitoring or real-time data integration that supports access to not only transactional but also subtransactional information while complementing an existing data warehouse with its historical data.
Traditional business intelligence applications aren't appropriate for this task because they have too much built-in latency. Too much time passes from the collection of data from operational systems, to the building or updating of the data warehouse, to the analysis of the data, to when the final results are made available to end users. By the time the data is ready, it's either irrelevant or just too outdated to be used for decision making.
A best practice should be to combine the relevant features and functions of traditional and operational business intelligence depending on the specific decision-making needs of individuals or groups of individuals in an organization. While many companies have already deployed traditional business intelligence solutions to a subset of their employees, the clear trend today is to expand the availability of decision support solutions to a broad user base of internal and external operational decision makers by adding business intelligence functions to existing operational and business processes within a collaborative infrastructure.