How are enterprises adapting to the rapid pace of technological change? Corinium Digital’s recent survey of 100 senior data professionals working for North American companies has some surprising, and some not-so-surprising, answers.
The survey, covered in a report titled “The Innovation Game: How Data is Driving Digital Transformation,” contained a number of insights into the outlook held by many large enterprises, as 94 percent of respondents worked for a company with at least $1 billion in annual revenue. Many of the survey’s questions focused on how these enterprises plan to use and analyze their ever-growing troves of data. In addition to the survey’s detailed questions, it also presented a number of quotable responses from those surveyed, which gives us a rare glimpse into the minds of the people behind the numbers of a data set.
Why are organizations eager for analytics?
The vast majority of survey respondents — 93 percent — said their organizations will be investing more than $1 million in new analytics technology and initiatives in this year and in 2019. The rationale for doing so varies from company to company. 43 percent of respondents view analytics as a way to create cost savings and efficiencies, while 34 percent said they pursue analytics to comply with regulatory requirements.
This indicates that many enterprises still utilize analytics reactively rather than proactively. In fact, only 23 percent of respondents said that generating new revenue was their primary analytics focus. Those respondents might be the only ones using analytics to pursue new opportunities rather than to risk substandard outcomes in their existing operations. Even fewer respondents reported measuring analytics success, in terms of ROI, on a profit basis. Only 5 percent said profit generation was their key measure of success, while 51 percent reported using efficiency gains or cost savings as their top success metric.
Virtuity chief data and analytics officer Dipti Patel-Misra, a featured speaker at Corinium’s 2018 Chief Data Scientist conference, was one respondent who seemed to be ready for her organization to take advantage of the income-increasing potential of analytics. She told Corinium, “In our organization we used to think of data as a cost center, but now it is front and center in all discussions leading to diversification of revenue.”
The challenges of keeping compliant with data storage
The reactive nature of many organizations’ use of analytics is further highlighted by responses to questions on data storage and IT capabilities. 62 percent of surveyed enterprises utilize a hybrid infrastructure that combines on-premises hardware with cloud storage solutions. This continued reliance on in-house hardware could explain why only 31 percent of respondents were reportedly happy with their IT team’s ability to provide analytics support. The other 69 percent gave half-hearted praise with some reservations or simply noted their team’s inability to address the organization’s analytical needs.
Some of this difficulty may very well be tied to the regulatory compliance requirements many businesses face. This is one reason why many enterprises must adopt hybrid infrastructures, which can be difficult for many IT professionals to properly maintain. They must address both the configurations of complex on-premises systems and remotely-delivered cloud services that may not be expressly built for compatibility with those on-premises systems.
Oracle CEO Mark Hurd has noted the absurdity of regulatory restrictions on data storage before, most notably at his company’s Modern Finance Experience conference this year: “If there was a wall, and the Oracle data center was on one side, and this bank was on the other side… and we shared the wall, [the data has to stay] right on that other side of the wall.” While Hurd claims that Oracle has developed cloud-based workarounds to this infrastructure issue, a regulated company like a bank is nonetheless required to store certain sensitive data in on-premises hardware. This is likely to keep the rate of hybrid infrastructure high and dissatisfaction with organizational IT teams simmering into the future.
The rise of AI (and ML)
One emerging aspect of modern data analytics that survey participants seemed keen to utilize is the combination of artificial intelligence (AI) and machine learning (ML), two technologies that are often used in conjunction with each other to derive better insights from larger data sets.
Some 73 percent of survey respondents said that their organizations used and invested in AI, and 53 percent of respondents said that AI and ML are integral to their organization’s decision-making process. This number is certain to rise going forward, as only 29 percent of the survey’s respondents said that their AI and ML technologies have “transformed business processes,” while 45 percent of respondents admitted that their organizations had pursued only some as-yet-incomplete AI and ML development and testing efforts.
Narendra Mulani, the chief analytics officer of Accenture Applied Intelligence, told Corinium, “Understanding and embracing the convergence of analytics and artificial intelligence is critical to creating new ‘data native’ enterprise cultures. You can hardly overstate the impact AI will have on mobilizing and augmenting the value in data, in 2018 and beyond. AI will be the single greatest change driver and will have a lasting effect on how business is conducted.”
Survey respondents are almost as gung-ho about investing organizational resources into AI development and implementation — 90 percent of them said that they’d be investing at least $1 million into AI by the end of 2019. The most popular AI and ML technologies for organizational investment were strategic planning systems (81 percent), artificial creativity systems (63 percent), diagnostic systems (62 percent), and computer vision, image processing, or virtual reality systems (61 percent).
Organizations in many industries seem excited about the prospect of AI- and ML-driven data analytics technologies, but many of them also seem to be pursuing these solutions to avoid problems rather than to identify opportunities. Where is your organization in its use of AI and ML? Are you on the cutting edge, or are you just getting started?