The Credit Union Executive’s Guide to Data and Analytics

According to PwC, nearly 4 out of 5 CEOs believe that remote collaboration will last after the pandemic. And Gartner predicts that through 2025, 80% of organizations trying to scale digital business will fail because they don’t take a modern approach to data and analytics governance.

If you missed out on all the trends for 2019 published in December, or even if you caught some of them, this one merits your time. We’ll be going into 2019 and beyond, since the winners will have an eye on the long view for the source of competitive advantage that is analytics. It is a fascinating, explosive time for enterprise analytics. It is from the position of analytics leadership that the mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics.

Businesses succeed when they can extract more contextual, relevant, and predictive insights from ERP data in real time. ERP analytics can deliver more powerful data-driven insights if deployed in a cloud-based platform. The scalability of cloud-based data repositories—such as data lakes and data warehouses—can allow more users to concurrently query, explore, correlate, analyze, and visualize more ERP data more rapidly than would be possible on many on-premises analytics platforms.

Business users have a tremendous appetite for data. The “single version of the truth” was a rallying cry to deliver business data in data warehouses for years. Users were able to digest and analyze large volumes of corporate data. They reviewed trends, identified anomalies, and supported decision-making because they had the detailed data to support action. As the business/data environment matured, the need for more diverse detail and increased delivery speed only grew. The data lake became a successful mechanism for delivering data from diverse systems in a timely manner.