Enterprises that deploy analytics to obtain deep insights boost the odds for success, but ones that stumble may find their organization reeling or even failing.
As organizations wade deeper into the digital economy, there’s a growing realization that data is the fuel that propels the enterprise forward. Yet, coping with massive amounts of structured and unstructured data—along with the growing velocity of data—is a daunting challenge for organizations of all sizes.
Increasingly, those that deploy analytics to obtain deep insights boost the odds for success, while those that stumble may find their organization reeling or even failing. “It’s a critical time for many organizations,” states Goutham Belliappa, principal for insights and data at consulting firm Capgemini.
Over the last few years, mapping out a strategy and embarking on the journey has evolved from important to mission-critical. The ability to put big data to work and make lightening fast and insightful decisions can prove transformative.
Today, 63 percent of organizations rely on data for day-to-day operations, 60 percent use it to better understand customers, and 59 percent rely on data to measure business objectives, according to industry association CompTIA. Moreover, business leaders are looking for deeper and broader insights and perspectives.
“Organizations are looking to expand big data initiatives and incorporate advanced analytics capabilities.” says Scott Schlesinger, principal in the IT Advisory group at consulting firm EY. Nevertheless, “more and more of today’s data-driven organizations are drowning in data, yet starving for insights. Many still do not fully understand what it takes to turn existing and new data sources into innovative business-based insights.”
Breaking through the big data barrier requires the right tools—everything from platforms like Apache Hadoop to powerful analytics software—as well as an understanding of the framework required to transform raw data into insight and, ultimately, value.
Taking the Path to Value
A critical starting point for organizations looking to increase the value of data is to think beyond simply combining and recombining huge data sets in the quest for answers. “Volume and variety are fairly easy to deal with because it’s fairly simple, using today’s technology, to dump a lot of raw data into an analytics program,” explains Capgemini’s Belliappa.
“But creating value and moving the organization forward are far more difficult because many business leaders aren’t even clear about the business problem they’re trying to solve or what they want to do with all the data.” Equally vexing: Some executives think up challenges that aren’t feasible or possible.
One organization that has learned how to use big data to unlock value is Oberweis Dairy. The century-old company operates a dairy, 42 company-owned stores and a grocery store distribution business in three Midwest U.S. states: Illinois, Michigan and Missouri.
The business relies on several SAS Institute analytics tools, including DataFlux, Studio and ETS/Time Series, to take marketing to a new level. For example, Oberweis now plugs in National Oceanic and Atmospheric Administration (NOAA) data about weather and dew points and then compares it to point-of-sale (POS) data to better understand which marketing campaigns are successful and which are less effective.
“The effect of weather on sales is significant, so we’re looking at results independent of weather to understand whether marketing messages succeed,” says Bruch Bedford, vice president of consumer insight and marketing analytics. “If we don’t account for weather conditions, we receive skewed results that are artificially good or bad.”
The system uses daily sales reports that include prior day’s sales, month-to-date results and year-to-date totals. Altogether, the company relies on 18 different data elements.
“The capability is especially valuable when we launch a new ice cream product,” Bedford adds. “We are able to adjust our marketing focus very quickly and home in on what works.”
But Oberweis doesn’t stop there. It also uses analytics to better understand customer acquisition, retention and attribution models. This includes which channels and approaches work best for different sets of customers.
“For years, we had engaged in telemarketing, and it worked well,” Bedford explains. “But with the advent of ‘Do Not Disturb’ and changes in society, that withered.” At the same time, existing direct- mail strategies had not worked particularly well, and door-to-door sales efforts had become expensive and resource-intensive.
As a result, business leaders identified key factors and variables, and then began A/B testing different approaches. The end result? The company obtained results—sometimes ones that were counterintuitive—that led to far more effective direct-mail initiatives.
Bedford says the company plans to expand the use of big data and analytics into operations and other areas over the next couple of years. This includes a far more sophisticated approach to inventory management.
“We are developing analytics models that allow us to understand the business in ways that weren’t possible in the past,” he reports. “We are able to cut through the complexity of the business and gain a competitive advantage.”
The challenges surrounding big data and analytics aren’t getting any easier to resolve. As sources for raw data grow—including through the Internet of things (IoT)—organizations must develop a viable strategy and put the right tools and technologies in place.
EY’s Schlesinger points out that hardware and infrastructure are an essential foundation, including open-source components and solutions such as Hadoop and Spark that support data management and sharing. But there’s also a need to understand data sources in a deeper and broader way. This includes legacy systems, public sources and new data generators, which might include beacons, sensors and crowdsourced data platforms that rely on smartphones.
Ultimately, Schlesinger says, it’s important to treat data as a strategic program and establish a long-term road map. “It’s about identifying the right data, discovering and acquiring it, storing it so that it’s accessible when it needs to be used, and integrating and organizing it for maximum value,” he explains.
While data scientists who can write algorithms are invaluable for framing the strategic direction of a program and building functionality, savvy data analysts are also crucial for success.
“You really need people who understand how to use data sets and find value-added insights,” Schlesinger advises. In addition, he says that it’s essential to view data in a holistic way and adopt a lifecycle approach tied into Master Data Management (MDM) and data governance.
Capgemini’s Belliappa says that a growing level of business disruption translates into a need to analyze data in entirely new and sometimes unfamiliar ways. Over the coming years, almost every industry will face new threats that could revolutionize business and industry.
“Big data and analytics are fundamental to building a culture that embraces innovation and identifies opportunities,” he says. “Putting the concept to work means building a modern data architecture, but also having the skills and knowledge to take advantage of the disruption and transform it into a competitive advantage.”
About the Author
Samuel Greengard writes about business and technology for Baseline, CIO Insight and other publications.
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