The Right Mix: Improving Decisions with Data Analytics

The scene might be a familiar one: executives around a conference room table debating opportunities and threats to the company, like declining sales, an oversaturated market, and fast-moving technological changes. “We couldn’t keep the product on the shelves a while back; now it’s hardly moving” . . . “We’re spending a lot of money on marketing and product mix and it’s not helping” . . . “The organic cereal cartel is killing us.”

That last revelation, while unfamiliar to most, was a problem for Brian Coe, marketing director at a muesli cereal brand. They needed something new to counter the declining sales that had long plagued the cereal industry. With a complex mix of ingredients, muesli demands spot-on decision making for everything from sourcing, to production scheduling, to pricing. That put all the more pressure on Brian to gather product and market information that could make a difference.  

The data streams that increasingly fuel business decisions are rich, but also incomplete, observes Shirley Shmerling, a faculty member in Isenberg’s Operations & Information Management program. How does a business decision maker—often on the fly—maximize that wealth of information efficiently yet wisely? “By making the most of business analytics,” she insists. “It gives us the tools to evaluate alternatives with greater speed and precision.”

Blending Data and Experience

The emergence of data analytics has created new career opportunities in business for specialists with high-level skills in mathematics and data analysis. It has also made a firm understanding of Big Data essential for decision makers across all disciplines.

Without question, companies that embed analytics in their operations make better decisions and perform better than their competitors, as measured by productivity and profitability, says Shmerling.

At the same time, she cautions, “Human intuition and experience can and should come into play when companies are exploring data patterns, evaluating what the data reveal, and making decisions characterized by variety and complexity in the data.”

Getting the Most from Big Data

Even in companies where business analytics are widespread, too much data can cloud decision making.  This “analysis paralysis” can be crippling, which is why Shmerling advises her students not to let “perfect” be the enemy of “good.” The process should be iterative and ongoing, she points out: Gather data, analyze it, act, analyze, gather additional data, and repeat the cycle.

Being nimble with data, Shmerling emphasizes, is essential in a highly competitive corporate environment. To that end, she also teaches a seminar that employs simulations to help students understand how theoretical knowledge can be tested under fire.

Assets, Limitations, Issues

Analytics and Big Data become significant assets when you deploy them judiciously and recognize their limitations, emphasizes Shmerling. In courses like her own, Isenberg students gain first-hand facility with those tools, which they leverage as they become proficient in various stages of decision making:

• Descriptive analysis, the most common stage, provides decisions based on records and phenomena that have already occurred
• Predictive analysis, a more advanced stage, maps out future scenarios
• Prescriptive analysis, the most refined stage, generates data-driven courses of action based on past performance and likely future scenarios

Back in the surprisingly complicated world of cereal, data revealed that both healthy eating and special dietary needs were increasingly key drivers for consumers. Coe drilled down into the data and compared it against his own deep knowledge of the industry as well as past performance and future scenarios. That allowed him to identify strategies for differentiating his products and campaigns, which in turn increased their market demand. The result was a new gluten-free product line that gave the brand new life on the shelf.