There is an elephant in the room. And it’s called BIG DATA.
As big data increasingly drives strategic decision-making and competitive advantage, it is transforming the business landscape before our very eyes; its ability to help businesses better understand their consumers, supply chains, sales and employees is being touted everywhere. The Wall Street Journal recently dubbed big data as one of three grand technological transformations, alongside smart manufacturing and the wireless revolution, that could rival the contribution of electricity and the telephone at the beginning of the 20th century.
It all sounds good so far, so why the elephant?
“The issue is the actual data,” explains Allison Jones-Farmer, Associate Professor of Statistics and Business Analytics at Auburn University’s College of Business. “Everyone assumes that just because it’s data, that it is clean, correct and that it’s being measured and analyzed effectively. That actually couldn’t be further from the truth. The fact is that we just don’t know.”
Companies, large and small, now view big data as an indispensable tool for their success. They envision enhanced capabilities that allow them to increase their margins and to squeeze every last ounce of profitability from their clients, manufacturing processes and transportation channels.
“The challenge in big data lies in the same characteristics that make it such a compelling phenomenon for business,” explains Jones-Farmer. “Its size, growth rate and variability offers tremendous opportunities to know more than we ever thought imaginable. At the same time, it also introduces a level of complexity that we have yet to fully master.”
Just because it’s data doesn’t mean that it all comes packaged straight-forward like digits found on a financial statement. Increasingly, more and more of it comes unstructured, such as data originating from social networking sites like Facebook and Twitter.
All of this data comes in different formats, and this has to be reconstructed and stored correctly for mining and analytics purposes. The extra level of complexity adds room for human error.
Consequently, we cannot establish with certainty whether the data we are mining, storing and analyzing is accurate, timely, consistent and complete, and, until those capabilities are developed, we are making certain assumptions about big data that may not be true.
A data error continues down this same process unaltered, affecting the quality of the final data product as well as analytics interpretation, which another opportunity for error in itself.
We have clearly entered the information age, and the importance of data quality cannot be overrated. Let’s take the necessary steps to ensure we are prepared to benefit from it all.
A few are being taken right here, at the Auburn University College of Business. Come back in a couple of weeks for a look at how big data is affecting education and the job market.
Dr. Jones-Farmer is an Associate Professor and member of Auburn University’s Department of Aviation & Supply Chain Management. Professor Jones-Farmer’s research interests include Statistical Process Control, Control Charts, and Multivariate Methods. Auburn University’s College of Business is a preeminent public institution located in America’s beautiful Southeast, consistently named a top 100 business school by Forbes and a leader in executive education by European CEO.