The Next Phase of Big Data: Simplification

Big data’s defining characteristic is that it’s “big.” Collectively, we’re generating more than 2.5 quintillion bytes of new data every day, and companies are growing exceedingly efficient at collecting and using that data to drive more profitability and form stronger customer relationships. One can’t help but wonder if the future of big data is even bigger, with more data generated and more intensive algorithms driving even more business decision making.

But in reality, the next phase of big data is likely a scale down—a simplification of what we already have.

How will this evolutionary phase manifest, and how will companies transform to adapt to it?

More Approachable Software

Your big data strategy is only going to be as effective as the software you use to drive it. Overcomplicating any data-oriented platform can cause problems, such as scalability issues, duplicate and inaccurate data, and the learning curve for your employees to access the software. We’re already starting to see the emergence of more intuitive, straightforward platforms, which even people unskilled in data management can use to store, organize, or retrieve information.

Gathering the Right Data

We’ll also see companies getting smarter about what types and quantities of data they procure. For example, it may not make sense to spend $100,000 on 100,000 points of customer data when you can spend $10,000 on the 10,000 points that make the most sense for your business. Companies will likely restrict their vision based on the most predictive variables or based on only one specific audience. It’s also likely that companies will focus more on data integrity and reliability than sheer quantity.

Forming Coresee and money, while getting a conclusion that’s 90 percent reliable.

Reducing Insights

Speaking of insights and conclusions, part of this refining process will necessitate businesses focusing more onts

Complex algorithms could be useful in helping companies form coresets, or small subsets of data that are representative of much larger sets. No matter what, this approach will lose some of the integrity of the full data set; by only looking at a fraction of the data available to you, you can’t possibly generate as accurate a conclusion. The tradeoff is that companies will be able to gain those insights faster, and with far fewer resources; you might be able to spend only 25 percent of the tim a handful of highly actionable, reliable assumptions, rather than an ongoing list of fascinating takeaways or to-dos. It’s far more efficient to focus on one insight that has the power to improve your profitability significantly than a dozen insights that will only marginally improve your performance. Companies that realize this will be able to dedicate far fewer resources to the field of data management while still getting the same (or better results).

Relying on Visuals for Communication

Data visualization is a growing trend, and that momentum isn’t likely to decline anytime soon. Visuals make everything simpler; complex relationships between data points can be seen at a glance, reporting is reduced to a handful of pages, and the esoteric mathematics and statistics behind variable relationships disappear when you’re communicating with someone inexperienced. That said, visualizations aren’t a perfect solution either—they can lead to oversimplification and cognitive biases kicking in—but they will make things simpler and more streamlined.

The Data Analyst in All of Us

Finally, simpler processes and more approachability will make it easier for people in all roles to assume responsibilities of collecting and managing data. Rather than hiring a specific data scientist, or a team of data experts, companies can make data analysis a part of every role within the company, streamlining its integration in the entire organization.

Right now, the world of big data is a giant chunk of marble, and we’re only now starting to chisel out the sculpture underneath it. Companies have mastered the art of collecting and trying to interpret vast amounts of data, but if the field is going to keep growing, it demands refinement. Fortunately, these and other avenues of refinement should help to make the field simpler and more approachable for everyone—and ultimately, that will lead to even more success.

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