MBA Research

Trend #23: Analytics/Big Data

Business Trend

Analytics/Big data continue to garner attention and generate a lot of discussion with business leaders. As automation and digitalization continue their march through all fabrics of society, businesses increasingly have access to detailed information on consumers, employees, peers, and other stakeholders. The continued increase in computer processing power and decreasing cost of information storage make it feasible for businesses to analyze this information.

Workplace Implications

The biggest issue that businesses grapple with as it relates to data is not its capture or acquisition, but rather, its synthesis. Sifting through the noise to determine what’s important, interpret the data, and generate a response from the data are becoming more complex.  Businesses that have traditionally dealt with transactional data and other types of structured data are now challenged to capture and integrate unstructured, customer-generated data available through social media channels. However, the sheer volume of customer-generated data necessitates fine-tuning of the filter process to key in on what is most relevant for decision-making.

There are many uses of analytics within the workplace that are still being explored. Human resource functions are experimenting with different analytic solutions that could improve talent identification, acquisition, development, and management. Finance and Accounting functions are exploring how to use analytics to identify opportunities to improve profitability, spotlight potential fraud, highlight potential economic shifts, and signal industry changes that could be catastrophic. Sales and Marketing functions look to analytics to forecast sales, segment markets, understand consumer behavior, target customers, and identify new product opportunities. Operations uses analytics to predict demand shifts, forecast production needs, pinpoint capacity issues, and more.   

Classroom Implications 

Students need to develop a basic understanding of data mining, data analysis, and analytics. They should learn how to synthesize information to identify trends.

Students should be able to distinguish between correlation and causation. They should be able to ask questions of the information to determine how best to interpret and apply it.

Students need to understand the limitations of analytics and how analytics fits within an overall decision-making process. Teachers may want to explore analytics within different business contexts to illustrate the breadth of analytics available and the importance of having an objective(s) identified prior to examining analytics.

Teachers should stress that businesses need and are actively looking for employees with data-mining skills—not just finding the data, but interpreting the data to aid decision-making. Many businesses have been forced to hire internationally to find the needed skillsets.

Teachers can integrate data-mining into existing courses or create courses around data analytics. Teachers who are unfamiliar with data-mining should consider taking courses in it to be better prepared to help students acquire the needed skills.