Why Analytics Alone is No Longer Enough

Why Analytics Alone is No Longer Enough

“Likes” in social media polarize us, where algorithms favor inflammatory content, evoke stronger reactions and keep us hooked longer. We’ve seen fragmentation when it comes to local laws, regulations and privacy. In the private sector, business schools, strategy heads and activist investors preach to divest anything that’s not a core competency but in a fragmented world, with digital giants lurking around the corner, do we need to think different? For regulations, business models, and data – which increasingly is the same thing – we can turn a fragmenting landscape into an opportunity. But analysis isn’t enough. We need synthesis AND analysis   to connect distributed data to the analytic supply chain – with catalogues as the connective tissue. The tech is there today but it also needs to be followed by the right processes and people. Synthesis and analysis is critical to make use of pervasive data and facilitate the evolution towards what we call “laying the data mosaic.” Below is a curated subset of the top 10 Trends we see being most important in the coming year, a full version of which we will publish in January 2020.

Big Data is a relative term, and a moving target. One way to define Big Data is if it’s beyond what you can achieve with your current technology. If you need to replace, or significantly invest in extra infrastructure to handle data amounts, then you have a big data challenge. With infinitely scalable cloud storage, that shortcoming is gone. It’s easier now than ever to do in-database indexing and analytics, and we have tools to make sure data can be moved to the right place. The mysticism of data is gone – consolidation and the rapid demise of Hadoop distributors in 2019 is a signal of this shift. 

The next focus area will be very distributed, or “wide data.” Data formats are becoming more varied and fragmented, and as a result different types of databases suitable for different flavors of data have more than doubled – from 162 in 2013, to 342 in 2019.* Combinations of wide data “eat big data for breakfast” and those companies that can achieve synthesis of these fragmented and varied data sources will gain an advantage.

Self-service analytics has been on the agenda for a long time, and has brought answers closer to the business users, enabled by “modern BI” technology. That same agility hasn’t happened on the data management side – until now. “DataOps” has come onto the scene as an automated, process-oriented methodology aimed at improving the quality and reducing the cycle time of data management for analytics. It focuses on continuous delivery and does this by leveraging on-demand IT resources and automating test and deployment of data. Technology like real-time data integration, change data capture (CDC) and streaming data pipelines are the enablers. Through DataOps, 80% of core data can be delivered in a systematic way to business users, with self-service data preparation as a standalone area needed in fewer situations. With DataOps on the operational side, and analytic self-service on the business user side, fluidity across the whole information value chain is achieved, connecting synthesis with analysis.

Demand for data catalogues is soaring as organizations continue to struggle with finding, inventorying and synthesizing vastly distributed and diverse data assets. In 2020, we’ll see more AI infused metadata catalogues that will help shift this gargantuan task from manual and passive to active, adaptive and changing. This will be the connective tissue and governance for the agility that DataOps and self-service analytics provides.

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