3 Shifts Happening in Business Analytics

In a world of raw information, the ability to generate actionable insights quickly sets organizations apart.

Accordingly, new technologies and tools surface regularly, promising next-generation analytics solutions. Per Statista, the big data market is forecast to be worth $49 billion in 2019. By 2027, that number is expected to climb to $103 billion!

What are the major shifts happening in business analytics? Let’s discuss three of them below.

Augmented Analytics

Per Gartner, more than 40 percent of data science tasks will be automated by 2020. They further predict that the number of citizen data scientists will grow five times faster than professional data scientists. These stats illustrate the rise of augmented or self-service analytics.

Photo: Chinnarach / Freepik

Augmented analytics uses elements of machine learning (ML) and natural-language processing (NLP) to process end-user text or voice queries and deliver accurate insights in seconds. By running several algorithms on human-cleansed data sets, augmented analytics uncovers insights that humans might overlook. This is sorely needed with all we have on our plates. Self-service capabilities act as a sidekick to handle the array of tasks our workday brings, removing frustrating roadblocks in the process.

Through a simple search, employees in any type of role can find answers to their questions or receive relevant queries to explore. Making the data discovery cycle shorter allows the data team to focus on strategic projects instead of repetitive tasks. It also provides employees with more autonomy in their workflows, which could lead to more engagement and less turnover long-term. Above all, overall company productivity is the real winner by eliminating time-consuming parts of the process.

Commercially Driven AI Features

Artificial intelligence still conjures discussion of future dystopian horrors, but it’s presently used in all sorts of application. Open-source platforms were responsible for much of the early developments, but now commercial software offers optimistic use cases in virtually every industry.

This shift to commercial products is significant because of the scaling capabilities these tools bring. The specific solutions look as different as the industries they’re built for, but in general, they allow companies to see further into the future.

As, Doug Bordonaro, CTO of machine learning analytics company ThoughtSpot writes, “when it comes to churning through large amounts of data, nobody can beat a machine. In fact, recent advances in neural networks are pushing this technology to a new level, giving business people actual answers instead of models they can use to predict those answers.”

Multi-Cloud Framework

Traditional on-premise warehousing had a good run, but it’s no match for the fast-paced nature of today’s digital economy. The amount of data and volume of sources generating it are ever-growing. If that sounds overwhelming, you’re not alone. Plenty of business leaders get intimidated at the thought of how to handle their accumulation of information.

Multi-cloud frameworks are a welcome change from traditional on-premise warehousing techniques, which perpetuated data silos and slowed data interactions. Multi-cloud solutions give organizations a more cost-effective data storage solution, reduce the possibility of data silos forming within one vast data lake, and safeguard against downtime and data loss.

The exciting thing about business analytics is that it’s continually evolving. For companies to keep pace with the state of the industry, they need to leverage augmented analytics through commercial AI-powered tools and take advantage of a flexible multi-cloud storage format.

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