The business realm is known for its bias toward what gets the job done — systems, technologies, resources. In this regard, big data has been the most revolutionary player in recent years. If you can quantify the large volume of information from internal and external sources, you can predict or solve things.
Big data got the job done for Netflix with its recommendations based on people’s viewing behaviors. It got the job done for Target with its mailers based on women’s shopping habits. When put to good use, it can help companies make informed decisions.
But sometimes, quantitative data will not be enough to solve organizational problems. Experts like Cathy O’Neill, a mathematician and data scientist, encourage users to not put their faith on it blindly. Before applying it, understand how it can affect your company, industry, and society in general. Here are 4 ways to approach big data that will help shed some light:
Algorithmic audit: data integrity check.
Ensuring the accuracy and consistency of data from recording to retrieval is crucial in computing. However, organizations must look beyond the physical and logical aspects. In her TED Talk, O’Neill spoke about the necessity of a data integrity check in the context of fairness. Data scientists need to interrogate the data. They should account for human biases, according to her.
For example, it is not enough to use big data in the hiring process to narrow down the talent pool. There is a need to question the culture that sorts out and separates the winners and the losers. O’Neill cited the Fox News case in which women would be less likely to make it if the hiring process focused on the historical trends of successful journalists alone.
Quantitative data as performance indicators: an incomplete picture.
Thanks to automation, it has become easier for companies to measure the performance of employees. But there is a downside to this. Think about Amazon’s productivity tracker. The e-commerce giant is notorious for making such an important decision as laying off workers based on hours logged.To put it simply, this method is seen as inhumane.
It also goes to show that big data does not always give businesses the whole picture. In Amazon’s case, one can argue that the company should consider other factors in determining employee value. These include the kind of support they are receiving from immediate supervisors.
Communication also plays a role. This Forbes article reported that 92 percent of highly-engaged employees had someone in the company talk to them about their progress.
Big data, algorithms, and human input: an ideal dynamic.
Today, businesses are either collecting or producing loads and loads of information. Then they are fed to automated software that provides stakeholders with the analytics. This reliance on tools and technologies can make humans complacent. In another TED Talk, tech ethnographer Tricia Wang reminded her audience that the way they use big data is still their responsibility.
The ideal scenario is to have organizations make wiser and more informed decisions. However, Wang added that there is a need to focus on thick data that comprises “precious, unquantifiable insights from actual people”.
Real-world insights: cybersecurity applications.
On a more positive note, algorithms are pretty useful in detecting suspicious activities and potential security risks. Big data is an appealing target for cyber attackers whose methods have become sophisticated. And what is a better way to protect big data than investigating and analyzing the information breaches that occurred on platforms? Looking into the techs that organizations are actually using, such as VPN Services, social sign-ons, and biometrics, will provide insights into the movement of the attacks.
Final words.
There is so much more to understand when it comes to big data. But as organizations become more familiar with approaches like automation, deep learning, and artificial intelligence, the human side of the equation should not be cast aside.
As Wang said, “investing in big data is easy, but using it hard.” In the end, everybody hopes their participation in forging a new way of doing things will be worth it. But the determining factor is what the business world do: follow the data blindly or lead it to new heights.