Author name: sandrsn2@yahoo.com

There are several effective techniques for identifying BI problems in real time. Good data is one of the most important criterion for any BI solution. Good data builds trust with stakeholders and allows stakeholders to make data-driven decisions. Here are some of the things to do that you can do to ensure your organization identifies issues in real-time. Have well-defined problem(s) and objectives This will allow you to have clarity on your overall goal and what you are trying to accomplish. This will align all stakeholders (business, technology, and analytics) – so that everyone knows what the data is used for, and you have a joint effort to identify BI problems. Identify key metrics After defining the problem, the next step is to identify what success looks like and what metrics to calculate to measure success. Monitor Key Metrics One way of quickly identifying BI problems is to monitor key metrics, over time, as issues will be visible in the trend. For example, if you notice a trend with a key metric has changed when nothing in the business has changed, this could signal a data issue. Send automated alerts for outlying data and anomalies Sending automated alerts when data falls outside the normal is a quick way to catch data issues – especially when data is transferred or processed. This allows for a proactive approach in addressing issues. Have a fail-over technique? For any parts of the data journey, use a fail-over technique when possible. If systems and processes (such as ETL, ELT, data process, API calls, client-application processing, etc.) fail, there should be a standby system that takes over to continue the data processes. If a data process does not complete, it should automatically re-run so that there are no issues with the data. Have an updated & robust data dictionary Having a well-documented data dictionary and data processes further enables a collaborative effort in identifying BI problems The more people know and understand the data the better. Be diligent about data governance and data management Data governance and data management are important components of any data strategy and can help minimize data issues and improve data quality. Identify data owners and communication plan Data owners should be identified from the application data level to the analytics & BI tables. This should include responsibilities, SLAs, and a communication plan. For example, a change in the client application could create a BI problem; therefore, communication cross-functionally is paramount. Create Audit Reports Audit reports and data visualization audit reports should have overall counts, counts over time, missing data counts, and other audit metrics. These types of reports help to visually identify issues. Use Machine Learning to identify potential BI problems and send alerts Machine Learning (ML) can be used to identify anomalies in real time and alerts can be sent to the responsible data owner to check the data. AI agents can also be used to automate tasks. What I have shared above are a few ways to identify BI problems; however, there are other ways to consider. Overall, prioritizing data governance, data quality initiatives, communication, and working to proactively identify data issues reduce BI problems.