As companies grapple with the tsunami of data coming from connected devices, mobile, and the Web, there is the potential for a big data bottleneck to block business innovation.
That’s the assertion of Brian McCarthy, managing director of information and analytics strategy at Accenture Analytics, in a new Harvard Business Review blog post. He suggests that organizations take three steps to avoid the analysis paralysis that can result from embracing data-driven decision-making.
First, despite the warp-speed that data may appear to be flowing through the corporate network, organizations should start slowing with analytics, focusing on the things that really matter, he notes.
“Once the shortlist of focus areas is determined, organizations can then more effectively chase their desired outcomes by doubling down their analytics efforts in data automation and embedding insights in decision processes to help achieve their wanted results, quicker,” he notes. “This should also be done in tandem with continuing to drive analytics adoption in the business for an even bigger benefit.”
An energy equipment manufacturer, for example, took this tact to delve into the amount of time production equipment sat idle. Using data discovery, the company was able to show more than $70 million in savings within 10 weeks from a subset of the equipment locations.
Next, McCarthy advises firm to be open to exploring new technologies to improve decision-making with data analysis.
“Machine learning, or the growing set of data discovery and analysis tools used to uncover hidden insights in the data, is a sophisticated technology that can do just this,” he notes. Its data exploration capabilities and simplicity are also becoming necessities to ensuring competitiveness in the connected world.”
Machine learning can aid a company to learn from the past behavior of customers and predict future behavior and segment customer data.
Finally, McCarthy suggests that companies interpret and act quickly based on the insights uncovered with analytics or risk losing the competitive advantage such analysis can offer.
He points out that a large bank used data discovery to understand a dip in customer satisfaction. The analysis took weeks, instead of months, with the bank discovering that its most affluent customers were the most digitally savvy and also dissatisfied with their digital interactions with the bank.
“Organizations shouldn’t run from this new digital reality, but learn to embrace it by adopting and adapting their analytics strategies to remain competitive,” McCarthy concludes. “By applying the power of data and analytics techniques such as machine learning, a firm can make smarter, faster decisions for their business and its customers, and actively disrupt their industry.”