Big Data For Business Takes A Page Out Of Google Search’s Book

Google revolutionized the way we access data. Technology can search through more than 1 billion websites to pinpoint the exact information we’re looking for, all within a fraction of a second.

Now, imagine trying to find a certain website or piece of information without a search engine like Google. Ajeet Singh, CEO and co-founder of data analytics firm ThoughtSpot, says that can be what it feels like for a company that has implemented sophisticated data and business intelligence technology — usability can be a massive hurdle to overcome, severely limiting that technology’s effectiveness.

“Most solutions in the market either require extensive technical training to utilize or cannot handle the large data volumes common in most businesses today — or a combination of both,” Singh told PYMNTS in a recent interview.

But the enterprise today will be hard-pressed to grow and strategize without analytics capabilities that can offer actionable insights into everything from spend patterns to customer experience to supply chain efficiency.

“In today’s digital economy, enterprises in every industry need to equip their team, from the C-suite to the frontlines, with the ability to analyze data and inform their decision-making,” said Singh, noting that analysis released in March from International Data Corporation (IDC) pegged the Big Data and business analytics market to reach a $150.8 billion valuation this year — and surpass $210 billion by 2020.

At the time of the report’s publication, Dan Vesset, group vice president of IDC’s Analytics and Information Management unit, said the data suggests that “Big Data and business analytics solutions have finally hit mainstream.”

Though sophisticated analytics solutions are growing in popularity within the enterprise, challenges remain plentiful.

In February, Gartner research found the vast majority of businesses — 91 percent of those surveyed — have yet to reach a “transformational” level of data and analytics maturity; that is, data and analytics is central to business strategy.

“Today’s enterprises need to ensure a culture of data literacy throughout the organization,” said Singh. “In order to obtain value from AI [(artificial intelligence)]and advanced analytics, employees need to know what data is available, how it can be applied and how it should be applied to address their specific goals.”

ThoughtSpot, which recently announced a $145 million Series D funding round, aims to address one of the largest hurdles of data analytics technologies: usability. According to Singh, the company wants professionals to gain insight from data analytics tools in the same way they use Google. Search capabilities enable them to find answers to their questions, while artificial intelligence powers insights to solutions professionals may not have known existed based on patterns in search history, data anomalies and other trends.

“With all the information companies have collected, relevant insights are often available to employees but get lost in the data deluge,” Singh said. “There are simply too many potential questions to find the exact right one that leads to the insight you need.”

He pointed to one client of ThoughtSpot, an insurance company, that deployed the analytics solution to assess the performance of various categories of its loaning portfolio. Artificial intelligence enabled the company to uncover patterns they didn’t know to look for, including analysis that found loans for two particular types of cars had higher default rates.

“While this customer may have known to ask about profitability by loan category, they wouldn’t have thought to ask about the specific default rates for specific car brands within one loan category,” noted Singh.

Uncovering insights like these can enable businesses to make a more meaningful impact on their bottom lines. Singh explained that savings and revenue generation can come from multiple angles, including cutting down on time lost due to multiple staff leveraging multiple systems to generate a single report.

Addressing the issues of usability and functionality for data analytics solutions may be a key hurdle, but it’s not the only one. Singh said that even as adoption increases, the enterprise isn’t past its doubts.

“With the proliferation of AI and sophisticated analytics solutions comes the fear that such emerging technologies will steal jobs, making many analytics experts apprehensive about large-scale adoption,” he said. “The challenge then becomes about educating employees so a culture of trust in AI emerges, one in which employees understand the ways AI can make them more effective and productive.”

According to Singh, barriers to adoption will lead to the emergence of “internal data evangelism.”

“To bridge this gap, companies will invest in data evangelist roles,” he continued, “specifically focused on working across the business to create this data-literate culture, educate users about available solutions and help different teams change traditional workflows to take advantage of new data capabilities.”

He also predicts the technologies themselves will become more powerful.

“In the near-term, I expect to see the analytics offerings continue to get smarter, in the sense that these systems will be able to do more sophisticated analyses and leverage larger and more complex datasets,” he said, adding that integrating AI and machine learning will propel this evolution too.

Even as these tools become more capable of benefiting businesses, Singh said the pre-adoption hurdles still must be addressed.

“For these massive shifts in analytics to have a real impact on business today,” he said, “they must be adopted by business people throughout the enterprise.”