Lots of clients ask me to assess their current situation and recommend steps to help them become a more mature business intelligence provider for their internal consumers. One of the first steps is to help identify what makes an organization mature. In other words, if we are working to get better, how do we compare our efforts to organizations that have more maturity and what in the world does that even mean? In 2007, the SAS institute did a study and their findings are very consistent with my personal experience. What they found is that few organizations are really using information as an effective asset for decision making. In addition, many don’t treat information as an asset. Few have fully implemented practices to provide quality data, integrated data or even data standards that support effective consolidation. So if organizations that don’t do these things are considered immature, then it is natural that organizations who treat information as an asset to be handled as any valuable part of the decision making process would be considered mature.
Organizations that focus on data quality, standardization and consolidation as a start are laying the foundation for a mature BI platform. Mature data (clean, standardized, centralized, etc.) is the basis for mature reporting, analytics, etc. Maturity is usually found in organizations that are larger in terms of revenue or employee count or are publicly traded or regulated. It certainly makes sense that the more data, people, processes, etc., the greater the need for a controlled and managed data delivery environment. Size is one factor, but not the only one. Many organizations have embraced a performance management culture, one that relies on information for decision making. In those organizations, maturity of the data and process for delivering it is considered competitive advantage and worthy of investment. It can be a differentiator and a way to outperform competitors when done correctly.
Here is how I would break it down:
Stage 1: Make information accessible.
If it is accessible, then it must be standardized, centralized and clean. If the data can’t be found, it won’t be used. If it can’t be trusted it won’t be used. To complete this stage requires process and governance, security management and many other difficult tasks. Many organizations lack the political will to complete this step, because it is challenging and difficult. It is not glamorous, and the impact of the effort is not obvious until later stages of maturity.
Stage 2: Model information for delivery.
When the data has been centralized, the task remains to apply business logic to the data to produce decision quality information. Lots of rules, institutional knowledge, etc. has to be codified and exemplified into data models that can be used for all types of delivery tools. By connecting the logic and the data, delivery becomes mechanical and the integrity of the data from source to delivery is more effectively maintained. Models also permit the use of advanced analytical tools and technologies that can broaden the ability of the platform, and therefore its reach into the decision processes of the organization.
Stage 3: Deepen the maturity of the platform.
The data is deliverable, manageable and proven. The final stage is to employ processes like master data management and automated data quality checks into the platform. The scope and reach of the platform at this stage demands attention to managing the non-transactional data or nouns of the organization because at this point there may be many sources and consumers of that information increasing the needs to manage master data across the entire organization. As more data and more sources begin to build in to the platform, the need for automated data quality checking becomes acute. It becomes a task where data must be regression tested just as any high quality component of any product would be.
Take a short self-test. Imagine an issue or request that would address multiple areas of your organization. Can you quickly pull the information together and trust the result? What if there was a unique opportunity; can you put information together to assess the risk and reward of taking advantage of that opportunity? If your answer is hedged in any way, don’t feel like it is unique to your organization. In the SAS survey, on 18% of respondents felt their organizations had a centralized, trusted information source. I wish I could point you to a technology that can solve all your problems, but there is more to a mature business intelligence platform than technology. Organizations need a strategic approach to the design, implementation, management and support of their business intelligence platform.