Can big data go wrong?
Big Data can be a vital component in creating strong, long-lasting, mutually beneficial relationships between businesses and their customers or customers-to-be. In the right hands, Big Data offers a cost-efficient means of optimizing return-on-investment and business growth. But the challenge with Big Data is that it is in fact big, with massive data stores and mixed data pools, hence the potential for unwanted results is ever-present.
The good news is that data management teams that are equipped with strong skill sets can minimize project failures, no matter the size of the data sets. According to a survey of Fortune 1000 executives by the Harvard Business Review, reliance on big data initiatives is on the rise. Nearly half — 48.4% — of the respondents reported measurable results from data analytics, with 80.7% characterizing their initiatives as “successful”[i].
There are a few famous examples of “big data gone wrong” that illustrate the potential for failure and how these busts could be avoided. High on the list, and a strong case for the importance of data governance, is a project undertaken by OfficeMax, an American office supplies retailer[ii].
In this Big Data Gone Wrong scenario, it was a case of having — and using — more data than was called for. OfficeMax sent a letter to an individual in Illinois, addressed to: Mike Seay, Daughter Killed in Car Crash. Sadly, Mr. Seay’s daughter was, in fact, killed in a car crash one year earlier, and to make matters even worse, he was on his way to attend a counseling group for grieving parents when he received and read this letter. This is a dark lesson about irrelevant data and how it’s checked and analyzed before being deployed.
And while the data was accurate, insufficient data governance — security and privacy — created a huge pitfall with adverse consequences.
How to avoid the common traps of big data gone wrong?
Avoiding potential perils begins with establishing big data as a central driver within the business organization. If data-driven decision-making is considered the foundation of business growth, it becomes a framework for success. As analytics are woven into a company’s business model, big data project management teams become an integral, high-powered engine that maximizes desired results.
Data accuracy, including paying attention to codes and their results, is another essential means of avoiding big data disasters. Precision programming and analytics can also prevent business headaches for organisations. A good example of that headache was felt by Amazon, who used a scripted programme running against a huge dictionary to generate phrases for t-shirt variations of “Keep Calm and ____”. A furor was created when they published the availability of these t-shirt options on their site, which included the likes of “Keep Calm and Rape A Lot” and “Keep Calm and Punch Her.” No one had checked the results.
Having a strong, well-trained data science team in place who can set process frameworks, establish concentric long-term goals, as well as maintain the highest standards from planning and execution to completion, is ultimately the best way to ensure big data success. And avoid the perils.
Is Your Data Analytics Team the Strongest It Can Be?
If you are looking to maximize the results of your big data projects, we can help equip your team with the expertise they need. We offer online training in leadership, technical skills, analytical thinking and communication skills — all to make your business data ready.
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