Taking lessons from KM to influence business intelligence pervasiveness

BI market evolution

As shown in the chart on page 21, KMWorld, Vol 18, #4, although business analytics solutions are not new, only now are they entering the mainstream. The market, which seems to be moving in 15-year cycles, continues to evolve by incorporating new components into business analytics solutions. What started out as standalone batch reporting and statistics tools have matured into broad suites of components that address data integration, data warehousing, query and reporting, advanced analytic and other related decision support components.

Long-term trends suggest that the business analytics market is in the early stages of a new solution adoption cycle (which began in 2005) that will extend the reach of various decision support and decision automation solutions to a much broader set of new users. Those users—whether they are executives or customer-facing employees, line-of-business managers or suppliers—will span all levels of an organization and will be involved in a spectrum of strategic and operational decision making. Some of those business analytics activities will be based on straightforward information access through reports, dashboards or alerts to various devices, or they may be enabled through search functionality within business analytics solutions. Other business analytics activities will include advanced analytic techniques for descriptive and predictive analysis of data. What is clear is that without increased use of technology, such as internal business intelligence and analytics, IT resources will unlikely be able to keep pace with the increasing demands of a growing user base.

Going beyond technology

A solution that impacts an employee’s de facto standard operating procedure will face challenges to adoption beyond simply deploying relevant technology. Experience with knowledge management has taught us that decision processes and organizational behavior must also be evaluated and refined to meet objectives for improved decision making. Based on best practices of leading organizations, the following are some of the key recommendations that should be considered when seeking to deploy successful analytics solutions:

• Create an enterprisewide BI strategy, but deploy solutions iteratively. A common characteristic of BI system design among leading organizations is the extensive use of rapid prototyping and the AGILE method
  of software development. That seems to be the only effective method to match IT development plans with frequently changing user requirements.
• Initiate a requirements-gathering process that is not predicated on asking users, "What data do you need?" When IT groups deploy BI solutions without direct business user input, they find that technology deployments remain idle or substantially underutilized. Asking users for their BI system requirements usually results in a question from users about what data are available, a wish list of all possible information or simply a request for electronic versions of previously available paper reports. Leading organizations evaluate user decision-making processes, not simply data requirements. In other words, they ask, "What decisions do you make?"
• Allocate sufficient time to bringing various internal parties into agreement about the meaning and value of data, metrics and KPIs to resolve data governance, MDM and data quality issues. Part of the problem is that in most sizable organizations, the division of labor has resulted not only in data silos but also in process silos, with no single person or group responsible for end-to-end processes and associated data.
• The prominence of governance refers specifically to the existence and the importance of a data governance group and associated data governance policies to BI system design or enhancement initiatives. Organizations that have more experience with BI assign more importance to governance. Also, those organizations that rank themselves as more competitive within their industry tend to place greater importance on governance. The prominence of governance is defined by the development of agreement on the meaning of data elements and the subsequent need to train users on what the data represents. Those are key to the diffusion of BI solutions. Without
  governance, there may not be consensus regarding what the data means, thus guaranteeing BI a non-central role in decision making.

Expose as much of BI content metadata or information about the data, metrics and KPIs as possible directly in reports and dashboards. Assuming that organizationwide definitions of such BI content exist, exposing the BI content metadata will help eliminate misunderstandings about the information made available through a BI solution.

Be aware of the positive impact that improving the degree of training can have on the pervasiveness of BI. Training on the use of data and on the BI tools is independently important and additively important. Training about the meaning of the data is often overlooked by IT when considering its role in the training process in favor of training users on how to use BI tools.

Organizations investing in BI and performance management have many opportunities to take advantage of the growing body of evidence suggesting a direct link between these solutions and organizational performance. Those opportunities must make effective use of both IT products and services as well as business process reorganization and organizational behavior changes necessary to shift toward more fact-based decision-making processes. Knowledge management practices have long extolled that broad need, and we can now understand its application and relevance when seeking to make business intelligence more pervasive throughout the organization.