In a world of voluminous – and growing – data, we need more than just analytics.

There’s a lot of staggering statistics about data that we cannot ignore as we approach 2020. It is estimated that 1.7 megabytes of data will be created every second for every person on earth. IDC also predicts that we would accumulate over 40 trillion gigabytes of data by the end of 2020.

At this point, analytics alone will no longer be enough for businesses to make sense of the data they generate. We need synthesis AND analysis. We need to connect all that distributed data to an analytic supply chain and use catalogs as the connective tissue for data and analytics. This way, we would be able to prepare and generate immediate, trusted, analytics-ready data to draw actionable insights that can significantly grow the business.

Here are four ways this can happen:

1. DataOps for analytics

We are seeing the emergence of DataOps as a powerful discipline that accelerates the entire ingestion-to-insight analytics value chain. It is neither a product nor a platform; it is a methodology that encompasses the adoption of modern technologies, the processes that bring the data from its raw to ready state, and the teams that work with data.

It focuses on continuous delivery and does this by leveraging on-demand IT resources, and by 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.

2. “Shazaming” data

We all know Shazam, the famous service with which you can identify the song that’s being played. We’ve seen how this concept has been expanded and used across various industries; you can shop simply by analyzing a photo or identifying plants or animals with a simple click of a button. In 2020, we’ll see more use-cases for “shazaming” data in the enterprise space.

This means we could potentially point to a data-source and get telemetry such as where the data comes from, who is using it, what the data quality is, and how much of the data has changed today. Algorithms will help analytic systems fingerprint the data, find anomalies and insights, and suggest new data that should be analyzed with it. This will make data and analytics leaner and enable us to consume the right data at the right time.

3. Computer/Human interactions

We will also see breakthroughs in our interaction with data – going beyond search, dashboards and visualization. Increasingly we’ll be able to interact sensorially through movements and expressions, and even with the mind. This offers a powerful potential, because we would be able to combine the best of humans (intuition, business knowledge) and machine technology to draw out new and smarter insights.

Facebook’s recent buy of CTRL Labs (a mindreading wristband) and Elon Musk’s Neuralink project, are early signals of what’s to come. In 2020, some of these breakthrough innovations will begin to change the experience of how we interact with data.

4. Data literacy as a service

Connecting synthesis and analysis to form an inclusive system will help drive data usage, but no data and analytic technology, or process, in the world can function if people aren’t on board. And dropping tools on users and hoping for the best is no longer enough.

A critical component for overcoming the industry standard of 35% analytics adoption rate is to help people become confident in reading, working with, analyzing and communicating with data. Companies will expect data literacy to scale and would want to partner with vendors on this journey. This is achieved through a combined software, education and support partnership as a service – with outcomes in mind. The goal could be to drive adoption to 100%, to help combine DataOps with self-service analytics, or to make data part of every decision.

For this to be effective, one needs to self-diagnose where the organization is and where it wants to get to, and then symbiotically work out how those outcomes can be achieved.

These trends form tiles for laying a data mosaic in this complex and busy world. Our approach to analytics needs to grow with the amount of data we create. Leading with data will usher us into the next phase of success in this digital age.