Talk of big data is all the rage in technology circles these days, and though plenty of people don’t have a clue what it is, practically everyone is involved in its collection.
Anyone who shops online or uses a credit card to make purchases has likely become a subject for data analysis by the vendor who offers the merchandise. Amazon, for instance, has revolutionized online marketing through the collection and analysis of huge amounts of data about what people are buying. Its application of big data analytics is what enables the company to forecast inventory needs and deliver customized suggestions on what to buy next when a shopper logs onto the Amazon website.
From a small business owner’s standpoint, it would be easy to assume that big data solutions are out of reach, and to a degree the assumption would be correct. But that doesn’t mean small and medium-size companies can’t reap the rewards of using data analytics.
“Sometimes, all it takes is looking at the data you already collect in a different way, and it can mean a big difference in your return on investment,” says Joni Shreve, who directs a master’s degree program in analytics at LSU’s E. J. Ourso College of Business.
Dealing with the use of information technology and advanced statistical methods for problem-solving, LSU’s program teaches students how to use analytics tools to improve business decisions.
Shreve says many companies already collect data that could be used to improve operations and boost profits. But few business owners have the technical skills needed to extract critical information, or business intelligence, from the data they have in hand, which is where analytics experts come in.
Data analytics, sometimes called predictive analytics, is a process of using statistics and models to identify patterns in activities, such as consumer buying habits, and analyzing the patterns to better understand and possibly forecast future behavior.
The process uses computer power to apply complex algorithms to the collected data and create a picture of what the data reveal.
Local analytics specialist Rusty Frioux says the process tends to be most helpful when it is targeted to solve a well-defined business problem or question.
“If you start with a very specific problem, such as We know we’re losing customers, and we want to figure out why,’ you can build data points that will help you understand that,” he says.
The owner of Baton Rouge business intelligence firm DataClear, Frioux delved into data analytics after about 15 years of doing business consulting and building software applications, including accounting systems, for government and business clients.
When LSU launched its analytics degree program a few years ago, Frioux became one of its first graduates. “I thought this emerging practice area could become a good consulting business,” he says.
Since then he has worked with small and midsize professional services firms and clients ranging from the insurance industry to municipal government. For the most part, the clients are interested in improving their bottom line.
“Their first data projects tend to ask questions like Are all of our clients or business lines equally profitable, and if not, why?'” he explains.
Once a client has homed in on a particular question or problem, the next step is to identify all the data available that might contribute to a solution. Frioux points out that most businesses use accounting, payroll and customer relationship management software that usually includes some basic analytical tools.
“Most likely you can get some level of intelligence from the apps you’re already using,” he says.
And sometimes, this data-gathering phase by itself can be eye-opening. Frioux says that finding data and “cleaning it up”—or integrating diverse statistical information into a uniform mode that allows for good historical comparison—can be the most time-consuming part of business intelligence work. But he says the results can provide insights into the business, even before applying an algorithm and testing various business hypotheses.
Frioux says one of the most crucial aspects of predictive analytics is pairing up the data analyst with the person in the client company who best understands the workings and information needs of the business. “You have to have a quantitative understanding and match it with a qualitative understanding of the problem,” he says.
When the right team comes together to dig for the appropriate data, the analytics process has a much higher likelihood of revealing information that a business can use to increase productivity, reduce costs, enhance profits or meet some other specific business goal.
Frioux says many of the area’s largest companies have invested in internal business intelligence teams and are running predictive analytics projects in-house.
“I think the next step is to bring some of these newer tools and techniques to midsize industrial and manufacturing clients,” he says, noting that in addition to improving financial performance, analytics could be used in smaller firms to enhance industrial safety and operational efficiency.
Frioux predicts that it won’t be long before data analytics becomes an integral part of managing businesses of all kinds, as younger owners and managers, who grew up in the Google era, take charge.
“For people who are used to having full information available when making decisions in their personal life, the idea that they would have to make major business decisions with incomplete information doesn’t sit well,” he says. That factor alone will produce a rising demand for business intelligence.
Meanwhile, LSU’s Shreve says that nearly all of the 50 or so students who have so far completed the university’s analytics program landed jobs soon after graduation. She says many have gone to work in the health care and insurance industries, with others hired into banking, accounting, telecommunications, energy and marketing firms.
Not surprisingly, she says employers who have hired LSU analytics grads include Amazon.
