Data driven decision making is an increasingly important part of the modern business landscape. Amazingly enough, 58% of business leaders who responded to one survey said that the majority of their decisions are still based on gut feelings or experience rather than data. While the human element will never be eliminated from the process of making decisions, there’s a strong argument for an organization focusing on developing a data driven attitude.
Data Driven Decision Making Frequently Fixes Biases
For most industries, making money is a question of discovering what hasn’t yet been exploited by other companies. Spotting and exploiting these sorts of inefficiencies allows firms to gain first-mover advantages.
The folks who run call centers at Xerox Services turned to big data to reassess how they pick job candidates for interviews. The initial proposed solution based on the data left some managers downright shocked. In some cases, the system was actually sending in people with no relevant prior experience. It also singled out individuals who were on four or more social networks to not be sent in. As the program moved forward, though, attrition rates for new hires dropped 20%.
How did this happen? Data driven decision making often moves companies past human biases. Human hiring managers frequently look for signals that feel relevant but aren’t. The machines cut out all the noise of human interaction, focusing on results rather than imputing biases.
The Data Analytics Arms Race
In some industries, building out data analytics capabilities is well on its way to being an arms race between companies. The NBA has been revolutionized by analytics, with the league utilizing technologies derived from missile-tracking systems to keep tabs on every footstep and dribble made in each game. A league that was once dominated by the slam dunk rapidly switched to 3-pt shooting, and the Golden State Warriors are widely considered the first champion built on hard data. Other teams have since been racing to catch up.
On Wall Street, companies that use programmatic trading and algorithms are considered dinosaurs stuck in the 1980s. Private equity has long since moved beyond learning from the past and is now focused on predictive data analytics. One high-frequency trading firm posted a profit in 1237 out of 1238 trading days. It’s easy to see why “data scientist” is the hottest job trend in finance.
Data Driven Marketing
Some sectors have found the concurrent rise of social media and big data to be the confluence of events they required to get out in front of the competition. For large corporations, this has allowed them to target niches that were often unreachable. If you’ve walked through the grocery store and read the packages, there’s a good chance you’ve seen data driven marketing in action. Brands like Betty Crocker and General Mills now frequently emphasize niche selling points such as “non-GMO” and “gluten-free.” These selling propositions were designed by sifting through social media data to find what concerns drove consumer decisions. The brands then adjusted their marketing to have appeal to both the general public and niche markets, allowing them to maximize their exposure without making massive investments in advertising. Instead, they changed a few things on their packages.
Cutting Costs
The difference between a profitable year and a bad one often boils down to nothing more than costs. Nearly 50% of Fortune 1000 firms say they’ve started data driven initiatives to cut expenses and seen a return on the investment.
In the fashion world, using big data to track trends has become a key part of the purchasing process. No one wants to be sitting on inventory because they made the wrong buy or bought at the wrong moment. Timing this out can be challenging, too, as most retailers depend on global supply chains to bring purchased inventory from overseas to target markets. By monitoring social media trends, for example, a fashion retailer can send real-time data to a buyer in Bangladesh informing them of what styles are trending and how strongly. That can be distilled to data that enables a buyer on the other side of the planet to determine everything from purchasing volume to shipping method.
Becoming a Data Driven Operation
It’s not enough to want your company become a data driven organization. You need to lay out a plan that gets you there. This includes:
- Fostering a culture that values data
- Putting standards in place
- Hiring professionals with big data skills
- Educating stakeholders about the advantage of driving decisions with data
- Building out the necessary infrastructure, particularly computer servers
- Adjusting hiring practices to incorporate big data skills
- Opening up the discussion to all parties from top to bottom
The move to a data-centric worldview also means getting tough about things. Companies often end up using severance packages to ship out folks who refuse to get on board with the changes. This requires a hard look at why certain people are employed and whether they can adjust to the new reality.
Ultimately, a data driven approach is about competitiveness. Other companies are already doing it and succeeding. The sooner your operation becomes one that values data, the sooner it can attract the right candidates for jobs and become more competitive.
