Analytics and Reporting are Two Different Practices – You Need Both – Start with Reporting

Analytics and Reporting are two different practices for sure, and you need both to maximize continuous improvement within your organization.  The first step is to create a base of information that can be shared widely to drive understanding.  That is reporting.  Then, you layer in analytics, to drive a better understanding of new data, and drive visibility to new variables and KPIs that should be reported on going forward.  To state it differently, Reporting tells you what is happening and analytics seeks to tell you why something is happening. Best practice reporting concerns aggregating and summarizing data from a fixed time period or batch, on a set of metrics with the delivery tailored to the audience.  Analytics, on the other hand, is designed to answer specific business questions and give recommendations as to why something has changed in a report, or improvements that can be made to enhance the variables in a report.

This article by Philip Russom, TDWI Research Director for Data Management explains it well.

The key take-a-ways are:

• Despite a fair amount of overlap, analytics and reporting should be seen as complementary, which means you most likely need both and neither will replace the other.
• Reporting is like a “high volume business,” whereas analytics is like a “high value business.” For example, with so-called enterprise business intelligence, thousands of concurrent report consumers access tens of thousands of reports that are refreshed nightly. By comparison, a small team of data analysts can transform an organization with a few high-value insights, such as new customer segments, visibility into costs, correlations between supplies and product quality, fraud detection, risk calculations, and so on. For completely different reasons, you need both reporting and analytics to serve the full range of user constituencies and provide many different levels of information and insight.
• Advanced analytics enables the discovery of new facts you didn’t know, based on the exploration and analysis of data that’s probably new to you. New data sources generally tell you new things, which is one reason organizations are analyzing big data more than ever before. Unlike the pristine data that reports operate on, advanced analytics works best with detailed source data in its original (even messy) form, using discovery oriented technologies, such as mining, statistics, predictive algorithms, and natural language processing. Sure, DWs can be expanded to support some forms of big data and advanced analytics. But the extreme volumes and diversity of big data are driving more and more users to locate big data on a platform besides a DW, such as Hadoop, DW appliances, or columnar databases.Providing separate data platforms for reporting and analytics is a win-win data strategy. It frees up capacity on the DW, so it can continue growing and supporting enterprise reporting plus related practices. And it gives advanced analytics a data platform that’s more conducive to exploration and discovery than the average DW is.