If you believe the current glut of prime time ads, every device is incredibly intelligent, every app can telepathically determine your every need before you even think of it, and IBM Watson has figured out everything mankind might ever wonder about. But as Mark Twain might have said, “Rumors of the intelligence of modern technology have been greatly exaggerated.”
Business intelligence (BI) professionals, then, need not worry that their field is being gobbled up or that they are destined for the unemployment line — at least for now. But where is the field heading? Here are some predictions from the experts.
Let’s get the bad news out of the way early. Self-service BI is not going away. In fact, it is only going to become more pronounced as a trend.
“The market is continuing to shift towards more business users and analysts trying, purchasing and using self-service BI and analytics tools to address their agile business needs,” said Tapan Patel, principal product marketing manager at SAS. “As a result, deployments will grow in the number of users, more content will be created, and more data sources tapped.”
The good news is that this opens the door to ample opportunity. Patel said the need for BI governance criteria will gain more importance to avoid chaos, redundancies and inconsistencies. Those with BI skills can find plenty of demand in that area.
“Organizations [that] can collaboratively balance the need for agility and governance will be successful and gain more adoption from their BI and analytics projects,” said Patel.
With attention multiplying around how to extend BI and analytics capabilities to a broader number and range of users, BI professionals will find demand for their talents growing in several directions, said Patel:
- Easy-to-use and easy-to-understand user interfaces featuring common workstreams for data preparation, reporting, discovery and analytics
- The ability to smartly visualize clusters, outliers, correlations and segments without building queries or models
- The ability to augment data explorations with search and natural-language query
- The interpretation of results for the user with analytically-driven visualizations and natural-language generation.
More Cloud BI
SAS has seen a jump in the number of inquiries related to cloud-based (private, public/SaaS, or hybrid) BI, visual data discovery and analytics applications. Get ready, then, for more cloud BI rollouts. If your application data (e.g. social media, HR, marketing, online advertising) resides in cloud, then BI or analytic applications will have a higher chance of residing in the cloud.
Organizations understand, after all, that the cloud can provide flexibility if they are starting a new project or modernizing existing projects. They want on-demand scalability, faster time-to-market and reduced dependency on IT for their BI needs. But the cloud is not yet ready for every organization because of challenges with security, privacy, compliance, hybrid data integration and IT architecture.
“Even though most current or planned deployments are on-premises, more organizations will consider evaluating cloud-based deployments,” said Patel.
Technological tools are cool. With self-service BI tools being deployed more and more, many line of business managers have had a chance to play with them for some time. But results have been spotty at best. Rado Kotorov, Chief Innovation Officer and vice president of product marketing at Information Builders, noted an increased focus on using BI and analytics technologies to achieve specific business outcomes.
“It is no longer about new and cool technologies alone, but what business benefits are possible when the right technology is combined with the right organizational behaviors, processes and cultures,” said Kotorov.
The reality is that more than 50 percent of organizations are now beginning to embrace digitization as part of their new business and operational models. This elevates BI and analytics to a strategic level in the enterprise, but it also puts the burden on practitioners to monetize data and analytics. As a result, the thinking will change from analyze to monetize.
“Analysis must be converted into applications that support operational decision-making — profits and costs are made at the operations level,” said Kotorov.
This shift from analysis to monetization requires a new approach. One that is gaining ground is the development of applications that are “data products.” These applications help to democratize BI by hiding the raw data and analytics sources from the user, making these fact-based insights easier to obtain since all he or she needs is to get the answers to inform decision-making.
“We’re going to see a lot more engagement with data on an operational level, since these insights are no longer reserved for BI analysts or power users,” said Kotorov.
Think of the billions of statements sent to consumers in PDF format, leaving users unable to get more value out of the data as they cannot analyze it in static PDFs. The capability exists, so users will be asking for enhanced documents that represent an interactive, analyzable and sharable source of information. This empowers users to analyze the data in each statement in a self-service fashion — right there in the document without having to export or learn new tools.
Dan Sommer, senior director at Qlik, believes freemium has become the new normal, so 2017 will be the year users have easier access to their analytics. More and more data visualization tools are available at low cost, or even for free, so some form of analytics will become accessible across the workforce.
“With more people beginning their analytics journey, data literacy rates will naturally increase — more people will know what they’re looking at and what it means for their organization,” said Sommer. “That will go towards providing more people with the tools and training to increase data literacy. Just as reading and writing skills needed to move beyond scholars 100 years ago, data literacy will become one of the most important business skills for any member of staff.”
Smarter Data Management
Most of the attention goes onto putting more intelligence closer to the user, building BI into more apps and distributing intelligence in general. But behind that, a whole lot of organization is required to ensure the right information is collected, cleansed and managed. That’s where intelligent data management comes in. Enterprises will need to deploy intelligent data management technologies as it becomes increasingly clear that traditional data management solutions can’t handle their growing need for a platform that can manage the interaction between their various data lakes, transparently protect all their data and ensure compliance with data governance regulations.
“Only intelligent data management can automate access to, transfer between and the synching of data between dozens of applications, databases and various other enterprise data lakes — automation that is needed if enterprises do not want their IT administration costs to skyrocket and their business processes to slow to a crawl,” said Don Foster, senior director of solutions marketing and technical alliances, Commvault. “2017 will be the year when enterprises will be forced to finally wake up and realize that intelligent data management is not a luxury — it is a necessity.”
In 2016, the conversation dramatically shifted from digital IT to digital business. Companies increasingly targeted digital business innovation with new applications, more and better use of data, and adoption of new technology infrastructures. McKinsey Global Institute noted that digitally advanced companies in sectors such as finance, IT and media achieved average profit margins up to three times greater than their less digitally-mature industry peers. Every industry, then, is vulnerable to digital disruption.
“To meet the digital business imperative in 2017, IT and line of business leaders must act together to accelerate continuous integration/continuous deployment of new applications, effectively harness the massive and volatile amounts of available data, and gain the agility and speed of software-defined infrastructures,” said Gur Steif, president of workload automation at BMC. “Creating this reality will require a new adaptive approach to automation, one that easily bridges from today’s IT model to the ‘hyper-convergence’ of applications, data and infrastructure going forward.”
Mike Ehrenberg, Microsoft Technical Fellow and CTO for business applications platforms and intelligence at Microsoft Dynamics, sees many trends taking place but is most excited about what the ability to embed optimization and artificial intelligence in the business process software can do to empower users to make better decisions faster. Consider manufacturers better able to plan production; retailers able to make the best choices about where to fulfill orders from for the best customer experience, profitability and sustainability; and equipment and machinery everywhere reporting its own health to enable more efficient predictive maintenance, he said.
“The key developments will be around seamless embedding of BI in application solutions, real-time BI with tremendous data scale and without latency, and the ability to give customers both the agility of power user self-service BI connected to the right tools for tracking provenance and approval to manage the promotion of BI assets to become department or organization wide official artifacts,” said Ehrenberg.
Drew Robb is a freelance writer specializing in technology and engineering. Currently living in Florida, he is originally from Scotland, where he received a degree in geology and geography from the University of Strathclyde. He is the author of Server Disk Management in a Windows Environment (CRC Press).