Business intelligence is at a crossroads, and moving forward requires an innovative approach that combines traditional and new ways of thinking, analysts agreed at the Gartner Business Intelligence summit in Las Vegas in March. 

Conference chair and Gartner analyst Kurt Schlegel kicked off his keynote presentation by identifying one of the biggest dilemmas in the business intelligence (BI) market—the battle between a traditional, centralized approach to business intelligence and a decentralized, new-age approach that “embraces citizen developers, analysts, scientists, and data integrators,” Schlegel said.

Historically, shadow business intelligence, which involves a more in-house, self-service approach to BI, has been looked down upon by data scientists, but during his keynote, Schlegel urged organizations to “stop treating shadow BI like the enemy.” 

But because shadow BI, citizen BI, and other decentralized BI environments cannot support organizations’ needs on their own, the key, Schlegel explained, is to adopt bimodal business intelligence, a strategy that brings together decentralized and centralized solutions. Forty-five percent of organizations already have bimodal capabilities, and that number will only continue to grow, Gartner predicts.

Another classic dilemma that has become exacerbated by the proliferation of Big Data and other changes in BI is the fact that data scientists often have to decide between making decisions that ensure low-risk certainty or taking the plunge into high-performance risk, according to Gartner analyst Lisa Kart. An audience poll revealed that when faced with this choice, most people want to gather more information about their options and collect relevant data for a period of time to reduce uncertainty. Ultimately, though, Kart said the organizations that are most successful in Big Data and analytics take an experimental approach.

To drive the message home, she compared Blockbuster and Netflix. When Netflix introduced the ship-to-home model, Blockbuster delayed a major overhaul and instead spent a great deal of time analyzing local data from its branches. While Blockbuster stalled, Netflix disrupted its own business model with in-home streaming, and Blockbuster was forced out of business. “Change is always risky, but not changing is riskier,” Kart said, calling for boldness in BI decision making.

The third and final dilemma that analysts highlighted centered on company culture and the idea of “hoarding data.” Gartner analyst Frank Buytendijk argued that protecting and “hiding” data behind corporate politics should become a thing of the past. Letting go of data is a sign of leadership in the modern BI environment, Buytendijk said, and he encouraged attendees to “lift false constraints to synthesize new opportunities.”

Because these three primary dilemmas have left many organizations with difficult decisions to make, the BI market experienced slow growth in 2014. Analyst Dan Sommer revealed that while BI market growth was roughly 17 percent in 2011, the past few years have been a “dark time” for BI. In 2014, growth was a mere 4 percent, he said.

The market is in a transitional phase, he pointed out, especially when it comes to cloud analytics solutions and predictive and prescriptive solutions. For the moment, there is “no clear leader in cloud analytics,” he said, though several companies are worth keeping an eye on. Salesforce.com’s introduction of Wave Analytics, for example, could be a disruptive force in the market, but the solution is not there yet, Sommer said. The vendor’s huge ecosystem makes it compelling, however. 

As for predictive and prescriptive analytics, Sommer likened the market to space exploration. So far, ventures into this area have been largely unsuccessful, but these solutions will be the “next frontier,” he said. 

As Big Data continues to “explode” and organizations continue to collect all sorts of insight on consumers and businesses, that collection process must eventually turn into “connection,” Sommer concluded. 

Despite the growing challenges, connection must be the goal for every organization that wants to propel its BI ecosystem forward. The shift to algorithm centricity will be key for developing these crucial connections, and the transition from business intelligence to algorithm intelligence is inevitable, Sommer said. “That’s where all the money is going.”  —Maria Minsker