She described how she and her team were able to run very sophisticated analyses against global populations in days, not months. Her C-level executives were relying on their team to provide quantitative analysis for strategic business decisions, and many of these insights were needed in days to be relevant. Her secret? A pre-populated data lake, groomed over 18 months, with ready-to-use patient populations, commercial data, and publicly available information. Her team delivered insights their competitors didn’t have, and she played a critical role in mission-critical decisions.
As she spoke, she beamed.
It’s important to recognize the power of enabling talented people to do their jobs. I recall a data project at a large card company a decade ago. I asked who was sponsoring the program. “HR,” they replied. I was surprised. “We are losing talented marketing people every day because of the arcane, complex processes required to run a simple campaign. We need our systems to be agile, self-service, and sophisticated to keep the best talent.”
Many studies have shown the importance of talent on competitive advantage, and in the data economy, the talent is data scientists and analysts who can turn insights into profits. It’s also known that some of the best talent doesn’t work for money (alone)—they are professional problem solvers who want to do what hasn’t been done, get recognized by their peers, and make a real difference. They “beam” when they take on a challenging problem and solve it with their incredible talents. That’s their reward.
The fuel of this new economy is data, and if data is hard to find, difficult to use, and impossible to leverage, you will not attract or retain the best talent. This is especially hard in traditional businesses who have to compete with Amazon and Google, where traditional brands are a boat-anchor and they are not known for innovation and brilliance. What these companies do have is data and scale—enormous customer populations with detailed, historical behavioral data—a treasure trove for the talented analyst.
What often stands in the way of these companies are policies and standards that crush innovation and excellence. There needs to be a new approach—one that integrates important production controls with agility, self-service, and business-ready data.
It’s possible. The customer I profiled has measured the difference in productivity between the old data warehouse environment and her self-service data lake. The number (analysts always use numbers): 30-1. What used to take 3 months now takes, on average, 2 days, because her data is at her fingertips. She is always ready with self-service, and she can load, prepare, and analyze data without calling IT. She feels empowered, and her executives are getting her full potential.