These days, there are a number of buzzwords being thrown around the marketing industry and the data management space. One of the biggest? Say it with me: Big Data.
NPR argued last December that ‘big data’ should’ve been the “word of the year,” in part due to the re-election of President Barack Obama. Obama’s campaign managers didn’t let the Republicans’ monetary advantage discourage them. Instead, they gathered information on their voters and compiled important analytics based on that information. By handling this mass of data in an organized and well thought out process, they were able to more effectively appeal to voters and ultimately win the re-election.
Marketers and corporations across the country were inspired by the campaign’s success, and have turned to big data to solve their problems as well. Anyone who catches the news on a regular basis, shops online, or owns a smartphone can see this evolution firsthand. However, it’s worth mentioning that this progression doesn’t necessarily mean “big understanding” or “big information.” Many companies are faltering in their efforts to harness big data and make real use of it. The pool of information is constantly changing, and as so many businesses rush to gather the data in real-time, it becomes even more challenging to keep pace and actively comprehend information as it becomes available.
And the challenges go beyond the initial harnessing of the data. As big data continues to grow, companies are running into issues of incorrect and duplicate data in their systems. This erroneous data is a result of poor processes that companies have in place, and oftentimes begins at the point of data input.
For a number of companies, data input is performed on a daily basis via their call centers. When incorrect data is recorded, it prevents sales representatives from getting leads in a timely manner, and hampers them further when they try to contact the correct individuals seeking assistance. The resulting slower response time then goes on to impact a company’s SLA and credibility to the population they serve.
There is no doubt that when processed correctly, big data can be integral to a company looking to improve their understanding of the customer’s needs and wants. But data quality is an important consideration during the transition, and one that must be confronted before big data can reveal all it has to offer.
To learn more about big data and how it relates to the data quality initiatives that may be taking place within your organization, watch Experian QAS’ webinar, “Ensuring Data Quality in your Big Data Initiative.”
Learn more about the author, Erin Haselkorn