Self-service BI doesn’t just happen. Organizations must ensure data quality and watch how analysts work. Here you can find a selection of 5 Doâ€™s for deploying BI tools in your company and enabling a self-service culture.
Enterprises face a number ofÂ challenges around adopting BI toolsÂ for users, ensuring the infrastructure is in place and overcoming cultural challenges in the organization. The following doâ€™s will help ensure a smooth implementation of self-service BI tools.
Self-service analyticsÂ requires a culture that not only makes data accessible to all employees but also enables employees without data management experience to work hand in hand with data experts. Pairing employees with and without data management experience allows employees less steeped in analytics to bring fresh eyes and ideas to the table that could help the organization break out of traditional development approaches.
CreateÂ curated data setsÂ with good validation and veracity, so the organization can consider it a system of truth. The organization should also define a business validation process that can fail data that doesn’t meet quality constraints. A good strategy is to find a way to fail this data early in the data pipeline lifecycle, so errors can be corrected at the source.
According to aÂ Harvard Business ReviewÂ study byÂ Tadhg Nagle, Thomas C. Redman and David Sammon, 47% of newly created data records have at least one critical error. Data quality shouldn’t be its own silo. It should be pervasive, working almost like a virus checker to ensure proper quality across the organization. And it should be done before it reaches analytics.
Additionally, lines of business need to be responsible for their own data quality, as they know their data best. The line of business should help curate data and collaborate with IT to contribute to overall data quality.
Implementing self-service BI tools requiresÂ balancing different cultural perspectivesÂ around data sharing. On the one side, there’s the view that controlling data provides power, which can lead to a reluctance to share information or invite partners into the process. At the other extreme are the people who believe data should be democratized and seek to enable access. This has a lot of advantages over all, but clear governance around data access needs to be established.
Analysts typically are very loyal to their tools and are reluctant to switch unless there is a compelling reason and clear benefits to them, as well as to the larger organization. Analysts will use data more readily and effectively if it can be piped into existing analytics tools.
If it is necessary to replace existing tools, then change management processes and in-depth training with ongoing support becomesÂ critical to success.
It is amazing how often we see powerful analytics tools sitting on PCs lying unused because the decision-makers and implementers did not really understand how analysts work.
Begin by implementing a self-service BI tool for each department, rather than a main centralized reporting tool. Using the self-service BI tool as theÂ main reporting toolÂ can only be achieved after designing the requests of the data elements for each department in the organization.
Implementing it department by department, rather than as a centralized tool, initially will allow you to implement the tool efficiently.
Adapted/Retrieved from https://searchbusinessanalytics.techtarget.com/tip/10-dos-and-donts-for-deploying-self-service-BI-tools Â by Â George Lawton