Every year, digital data growth hits new records. IDC predicts the world’s collected data will surge to 175 zettabytes by 2025.

Enterprises are collecting more data than ever before, attempting to derive business advantages from both proprietary and outside data sources. But uncovering the benefits of the massive volumes of data they’re generating comes with both challenges and opportunities. 

Some of the challenges are obviously related to data management. We’ve seen a growth in tools and technologies that build data pipelines and data lakes. At the same time, cloud and on-premise data management grow ever more complex in the face of multicloud strategies. The choices are more varied, more complicated and more difficult.

But there are plenty of opportunities as well, and as we embark into a new decade, looking at current and future trends can illuminate the biggest potential areas of value for businesses. 

Here are six data trends for the next decade — the biggest opportunities and challenges for managing, digesting and taking advantage of your data:

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1. Real-time analytics visualization continues to advance.

The velocity of information flow will make real-time visualization a staple of digital businesses. In addition to timeliness, form factor and modes of communication will become important.

We saw the impact of real-time analytics visualization with the Johns Hopkins University dashboard that maps the spread of coronavirus across the world. The real-time data gives officials an immediate sense of where hot spots are developing and where to direct resources to help flatten the curve and try to get ahead of the virus. Visualizing the data on a map in real time accelerates that entire decision-making process.

2. Analytics continue their shift from ‘what’ to ‘how.’

Data analytics has also taken steps beyond better ways to visualize the “what.” It now has the capability to tell us “how” a particular scenario might play out and suggest courses of action. 

While the “what” helps in spotting patterns, the “how” aids decision-making. The new generation of prescriptive analysis tools will attempt to answer how to fix a certain situation or achieve a desired business outcome, providing even more value from your data.

The applications are endless. Marketers will be able to optimize their mix of products, health care providers can prepare for spikes in demand, and transportation companies can eliminate unnecessary travel and increase delivery speeds while reducing costs.

3. Business insights will become more timely and meaningful.

This is driven in large part by two things: relevant data and distributed location processing. 

As more businesses can sift through vast stores of data for the most relevant subset to a particular area of business, they’ll be better equipped to arrive at stronger conclusions. 

With distributed location processing, edge analytics will deliver more timely and meaningful business insights.

4. Regulation will heat up.

GDPR and CCPA provide just a glimpse of what’s to come: an environment where businesses must define how they collect, handle and use consumer data. 

Enterprises have to get used to expanding regulatory scrutiny in the new world of privacy concerns around data. Maine is next to clamp down, with its new privacy law set to take effect July 1, 2020. Other states will inevitably follow. It’s better to be proactive now than catching up later.

5. Data-as-a-service will mature.

Many have long advocated for DaaS as a way to internally reuse collected data for external monetization. The trend will mature into functional services driven by processed data. Enterprises will seek to integrate such data services to stand up complex real-time analytical systems to create business differentiation.

6. Automation will become nonnegotiable.

With the magnitude and velocity of data, automation at every stage of data handling will be imperative. Equally important will be automation in governance and compliance due to the variety and veracity in both data and regulation.

Businesses will only generate more data as time goes on. How they manage and leverage that data will determine in large part whether they can take full advantage of all the opportunities that data presents. 

How can you harness these trends in your organization?

If you’ve been pursuing any of these trends, you already have a head start. If you haven’t, it’s not too late. Here are three ways to get started: 

1. Prioritize data. You might need to make the case to leadership, earn their buy-in and have them throw their weight behind any new initiative. You might need to break down silos in your organization to discover the full riches of data you have on hand. There are many different scenarios, but one way or another, make sure collecting, processing and analyzing data is a priority for your organization. 

2. Set goals and define success. As with any other aspect of your business, you should have clear goals you’re aiming to achieve with your analytics program. Maybe it’s increasing the efficiency of a process or anticipating customer needs in your product development. Maybe it’s automating time-consuming tasks to free up resources. It’s hard to make progress in your data program without knowing what you’re trying to accomplish. 

3. Find the right tools for the job. Every organization is starting in a different place with different goals, different budgets and different needs. So while it’s impossible to recommend any specific tools or starting points for home-grown systems, some broad guidelines apply. Make sure any tool aligns with your goals, budget, needs and level of in-house data experience. Start small, looking to automate some aspect of your business or to answer a pressing question — and get some early wins before expanding your data analytics program over time. 

Your data has a lot to offer, from supporting business decisions to suggesting them — and from uncovering new product needs to data becoming a product itself. All of this is made possible by the advances in tools and platforms we’ll continue to see in the next decade of data innovation.

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