As we’ve frequently discussed here at the APEX of Innovation, companies are increasingly tapping into data to achieve their strategic objectives. Whether monetizing insights to become more profitable, leveraging data to become more risk tolerant, using enterprise information to become more sustainable, or deploying AI to accelerate innovation, analytics is essential to survival in our ever-changing world.
With that in mind, a recent InformationWeek article by McKnight Consulting Group president William McKnight offers some critical steps for upleveling enterprise analytics and attaining long-lasting ROI:
1. Get All Data Under Management
If enterprise data is mismanaged, its value becomes limited to only very particular situations. To achieve ROI, McKnight stresses that companies must house their data in a leverageable, modern platform. Such a framework ensures that the data is built mindful of:
- The data warehouse(s)
- The data lakes(s)
- The operational hub(s)
- The master data management hub(s)
It’s also critical that the data management platform consider the data’s profile and usage. Post-operational analytics data should be columnar analytics databases that address availability, performance, and scalability, among other concerns. According to McKnight, this data should also be captured at the most granular level, at a data quality standard, and enabled for self-service.
2. Big Data Tooling for Big Data
Unstructured big data requires a different class of tooling than that associated with relational data warehouses. Cloud storage is generally the best platform for big data, with this information contained in data lakes. While data scientists are currently the primary users of data lakes, McKnight believes this will change soon to include the analyst community.
3. Manage the Change to an Analytics Culture
As this previous APEX of Innovation article underscores, self-service analytics is essential to the overall success of any analytics program. However, as McKnight notes, not all users will embrace the change inherent in transforming to a self-service culture. He stresses that data leaders must anticipate this resistance and provide late adopters with examples of peers successfully utilizing analytics to help get them over the hump.
You can read more from McKnight on these and other considerations for upleveling your analytics game via the original InformationWeek piece.