Why Analytics Will Be At Least 4X More Important in 2018

At the recent Gartner Data and Analytics event in Frankfurt, analyst Rita Sallam predicted that “analytics will have an even bigger impact on society in the next twenty years than the internet did in the last twenty.”

The incredible growth in machine learning and artificial intelligence, in particular, is set to remake every facet of today’s society and redefine every business process.

But even without taking those sweeping changes into account, I believe that analytics will at least four times more important than ever in the coming year.

Here’s why:

1. Faster innovation cycles

The core purpose of analytics has always been to support executive decision-making — according to Gartner, analytics remains the most important technology priority for companies around the world, as it has been for most of the last decade.

But decisions are more important than ever. In today’s fast-changing markets, the future belongs to agile companies that can adapt quickly. Having the right insights at the right moment, and being able to act on them –- in other words, having effective analytics — is increasingly the difference between survival and extinction.

2. From a process-driven world to a data-driven world

In the world of analytics, we’re used to processes creating data that we can use for analysis. But now data is being used to create processes for digital transformation. In these new digital processes, the different steps are constantly changing based on real-time information and algorithms.

For example, think about customers buying products. In the old days, this was a fairly linear process: I might see an ad, say, then go to a store, and make a purchase. But now we live in a much more complex, omni-channel world. The most advanced companies are optimizing the end-to-end customer journey using real-time analytics at every point of interaction – steering customers to the best outcomes: “Is the customer profitable? Should we offer them a discount? What other products might they be interested in?”, etc.

The result is that every customer is essentially following a unique, personalized process, powered by data and analytics. And these processes change automatically as the data changes, making them more agile and flexible, and more suitable for today’s fast-moving markets.

And it’s not just the customer experience that’s changing — all modern business processes, including human resources, logistics, finance, and manufacturing, are now constantly being adjusted and optimized, on the fly, based on real-time data. Analytics moves from being separate from “operations” to being an integral part of it.

3. Analytics is part of the products and services that you sell

Companies are moving from selling products to creating experiences, and data is an essential part of that process. Whether it’s an estimate of how long your Uber will take to arrive, or the quality of a book recommendation from Amazon, or the ability to analyze your B2B invoices, data is increasingly a direct part of your customer experiences, and what you use to differentiate from your competitors.

This means that your product managers need to be able to experiment with data in new ways, testing and iterating these data-based experiences for your customers. This relies your existing analytics platform, but it’s about using data for top-line growth rather than bottom-line efficiency, and requires more creative, flexible self-service than in the past.

4. Direct data monetization

It’s now easier than ever to monetize information in new ways, turning information more directly into income.

You have been gathering vast amounts of information to run your business better – but others may be able to benefit from that information, too. By aggregating, augmenting, and anonymizing information, you can sell it in new ways to new customers – creating new business models based directly on your data. Data from retailers can reveal deep insights about brand choices. Data from telephone companies can estimate footfall in front of retail stores. Providers of contingent labor can tell you the average salary differences between data scientists in Palo Alto and Bangalore.

What you should do next

Digital transformation means data is more important than ever — here’s how to get more from your existing analytics investments:

• It’s time to double down on analytics best practices — especially data quality — using these new opportunities to create the necessary business cases.
• You should reach out to the innovation teams in your organization that are rethinking the end-to-end customer experience (and the internal processes needed to support that), and make sure you’re aligned on the new role of data.
• Take the opportunity to do an inventory of your data assets, and identify a leader who can help investigate the new monetization opportunities.