Three Trends That Will Drive Enterprise Analytics in 2020

February 5, 2020 Vawn Himmelsbach

 

Big data keeps getting bigger. Consider that the smartphone in your pocket has more than 100,000 times the processing power of Apollo 11’s guidance system that landed a man on the moon in 1969.

Data isn’t just growing — it’s growing exponentially. IDC predicts we’ll be coping with 175 zettabytes of data by 2025 (one zettabyte equals one trillion gigabytes). And the world’s collective data, what IDC dubs the “datasphere,” will reside as much in the cloud as in data centres.

While businesses are dealing with the staggering growth of the datasphere, they’re also dealing with increasing data complexity, not to mention privacy and security issues, a complex regulatory environment and a lack of skilled talent to make sense of all that data.

But analytics is evolving alongside the datasphere, and even becoming more mainstream. Almost every application across industries requires analytics, from the supply chain to finance, manufacturing, health care and customer service. Even a smart thermometer produces reams of data for analysis.

And with so much data available, companies are clamoring to monetize it. By 2021, two-thirds of analytics processes “will no longer simply discover what happened and why; instead, they will also prescribe what should be done,” says David Menninger, SVP and research director of data and analytics research with Ventana Research.

Looking ahead at this year’s analytics trends, it all boils down to digital transformation. Companies are swimming in oceans of data and looking for ways to make better use of it — to predict supply and demand, to provide better customer service and to get a competitive edge. Here are three trends that will address these data challenges:

 

1) Augmented Analytics

Analysts, statisticians and data scientists are hired to sift through this data, spot trends and make predictions. But, according to Gartner’s Top 10 Data and Analytics Trends, “relying on business users to find patterns, and on data scientists to build models manually, may result in bias and incorrect conclusions.”

That’s where augmented analytics comes in, which augments manual processes with automation, finding insights to optimize decision-making in near real-time. For example, Gartner says banks traditionally targeted older customers for wealth management services, but augmented analytics demonstrated that younger clients, aged 20 to 35, were more likely to make that transition.

Gartner expects augmented analytics to be a “dominant driver of new purchases of analytics and business intelligence” that will address these types of data challenges in the coming three to five years.

 

2) Augmented Data Management

This trend goes hand-in-hand with augmented analytics. Not only are manual processes time-consuming and prone to bias or incorrect conclusions, but there’s also a shortage of skilled talent in the tech world. In fact, data analysts and scientists top the list of in-demand roles in the World Economic Foundation’s Future of Jobs report.

But through the use of automation and machine learning, data can start to take care of itself — from data quality to data integration. A sophisticated analytics platform provides a solution for organizations that can’t find skilled talent or don’t have new hires in their budget.

This also leads to a related trend: the democratization of data. Traditionally, data scientists were the gatekeepers of the data. Now, analytics platforms are breaking down barriers so business and technical users alike can engage with their data; an adaptable user experience empowers a variety of users, regardless of skill level, broadening the reach and impact of analytics beyond ‘experts’. And that will become even easier when combined with trend No. 3.

 

3) Natural Language Processing

NLP, or natural language processing, is making analytics even more accessible — and conversational — to a range of business users. Much like we might ask Siri or Alexa to look up information or provide directions, NLP will allow business users to ask questions about their data in natural language. But through 2021, conversational experiences involving analytics will primarily be text-based rather than voice-based, according to Ventana Research.

It’s taken some time to get here. NLP has had to contend with everything from sarcasm and slang to context and sentiment. Computers need to ‘learn’ those nuances for this to work, but progress has indeed been made.

And it has set a foundation for conversational analytics, where business users can make voice queries. Gartner predicts that, by 2021, NLP and conversational analytics will boost analytics adoption to more than 50 percent of employees, “including new classes of users, particularly front-office workers.”

 

The Road Ahead

Analytics is evolving, but there’s still a ways to go. “Most business leaders still rely on experience, ‘gut feeling’ or opinions when making decisions — only 48% of decisions are made based on quantitative information and analysis, a stat that has changed little in the past few years,” says Forrester VP and research director Gene Leganza in Forrester’s Predictions 2020.

That means organizations must launch a “data literacy lifeline” to ensure survival. And there’s great benefit to doing so, according to Leganza: “Companies that are advanced at being insights-driven outcompete the majority of less insights-mature firms by a wide margin.”

 

About the Author

Vawn Himmelsbach

Vawn Himmelsbach is a writer and editor specializing in enterprise IT, writing for national newspapers and technology trade magazines on everything from AI to zero-day threats. She also spent three years working abroad as an Asian correspondent, covering all things tech.

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