As we get deeper into winter, temperatures may be dropping, but data science in the enterprise will remain red hot as more organizations look to harness the power of data. Data science gives organizations the insights needed to improve business outcomes across industries. With “data scientist” topping the list of best jobs in the U.S. and the number of data science positions skyrocketing, every company needs to get onboard the data train or risk getting run over by the competition. In particular, there are a few industries that are turning up the heat on data science in the coming year: aviation, cybersecurity, law enforcement, retail, and human resources. An eclectic list for sure, but all areas that are ripe for data science.
By 2026, annual airline data generation will reach 98 billion gigabytes. This volume of data presents the opportunity to make airplanes connected devices: From fuel levels to flight routes, this could solve some of the aviation industry’s top problems. They could avoid cost-draining, and potentially dangerous, concerns such as air congestion or changing flight courses based on weather patterns or overbooking. Data scientists have even gone as far to say that airplane food waste could be mitigated using passenger data. Oliver Wyman speculates that the entire flight ecosystem will be revolutionized by data science—from the airports, to the airlines, to the passengers. The biggest roadblocks that aviation is facing are monetary—many airlines are hesitant to invest upfront in the technology, but I anticipate more will come around when the savings potential becomes clear.
I recently read an article with a shocking headline—cyberattacks could cost us more than hurricanes. Estimates show cyber threats have cost $121 billion; comparatively, Hurricane Katrina cost $108 billion in damages. While cybersecurity used to be a reactive industry, as the stakes grow higher, many organizations are looking for more proactive solutions. This is where we’re seeing data science emerge, making data-driven connections that allow IT teams to spot threats before they occur. If data scientists focus on the abnormalities in a company’s available data and make the move toward automated solutions, they’ll be able to build powerful cybersecurity solutions.
Data science has the potential to reshape law enforcement in a number of ways. One example that’s been making headlines recently is the use of machine learning to combat human trafficking. The State Department estimates 27–45.8 million people globally are trapped in a form of modern-day slavery. Organizations are starting to use data science to identify at-risk areas, ID victims, and unearth trafficking networks.
YouTube also announced recently that it’s stepping up its machine learning tools to curb the distribution of terrorist videos. This comes on the heels of YouTube’s partnership with Facebook, Twitter, and Microsoft that is designed to fight online terrorism. The partnership is built off of a Shared Industry Hash Database that pools data across all four companies, giving data scientists access to a wealth of information to fuel powerful machine learning tools.
Business intelligence tools have been long-standing resources for retailers, but data science is taking that to a new level. Now, more than ever before, customers are interacting with retailers across multiple touchpoints. From smartphones to rewards programs, companies have the data needed to build solid data science tools. There are retailers using machine learning to choose the best neighborhood for their brick-and-mortar locations, to send targeted push notifications to customers, to combat online retail fraud, and to make optimized purchasing decisions.
Retail Dive has released research findings that 45% of retailers plan to start using artificial intelligence technology over the next 3 years. Its study also found that the biggest roadblocks retail marketers are struggling with are tailored recommendations, segmentation, and personalization. However, the year ahead promises a change in tune for retailers, with stores such as Amazon Go and Warby Parker leading the way.
Over the past year, U.S. companies have spent $2 billion on HR technology, and global investments have skyrocketed. The need for data science in HR comes from a number of different places—automated interview scheduling, scanning resumes and employee reviews for keywords, improving payroll systems; the list goes on.
Additionally, there are some larger concerns for which HR is turning to data science, including the elimination of implicit bias in the hiring process. It’s something that can be impossible for organizations to measure: Is every candidate receiving a fair interview? Infusing artificial intelligence into this process can alleviate that concern by automatically selecting the most qualified candidates for HR’s review.
Eye on the Horizon
There is no industry, department, or job title that won’t be touched by data science in the coming years. Businesses are changing as they become empowered by their own data and the AI-backed tools that are available to them. And in the months ahead, we can look forward to working with these five industries as they crank up the data science heat.