The data-driven organization, as it comes about, will most likely to be led or guided by Batman, not Superman. That’s because few people really have super-human powers, but do have access to the tools and technologies to help make things happen — just as Batman did. At the same time, technology is but one leg of the stool — driving change to become a digital, data-driven enterprise requires a healthy mix of technology, tenaciousness, and training.
Data science illuminates new worlds for decision-makers.
Those are some of the takeaways from a recent panel discussion I had the opportunity to join, hosted by The Cube’s Dave Vallante and John Walls. Our panel focused on “Data Science for All” as a theme, featuring Jennifer Shin of 8 Path Solutions, New York University, and University of California-Berkeley; Dion Hinchcliffe of Constellation Research; Bob Hayes of Business Over Broadway; and Joe Caserta of Caserta Concepts. The discussion was held in conjunction with IBM’s recent Cloud & Cognitive Summit in New York.
The Batman analogy came up early in the discussion, triggered by Hayes’ Batman shirt. “Batman is a superhero but he doesn’t have any supernatural powers,” Hayes explained. “He can’t fly on his own, he can’t be invisible on his own — but he has this utility belt, with, for example, this batarang. As data professionals, we have all these tools that now that vendors are making.”
Of course, just as Batman is a human aided by technology, the data-driven organization has some very human matters to address before moving forward. “I always think there’s opportunity to be smart. If you can be smarter, you know it’s always better,” said Shin. “It gives you advantages in the workplace, it gets you an advantage in academia. The question is, can you actually do the work? The work’s really hard. You have to learn all these different disciplines, you have to be able to technically understand data. Then you have to understand it conceptually. You have to be able to model with it,you have to be able to explain it. There’s a lot of aspects that you’re not going to pick up overnight.”
Ultimately, the ability to introduce data-driven thinking and transformation into organizations goes beyond simply throwing the latest technology at it. Consider the analogy of a scientist versus a data scientist, Caserta illustrated. A scientist requires tools, “such as microscopes and a laboratory and a clean room. And these tools have evolved over years and years. And since we’re in New York we could walk within a 10-block radius and buy any of those tools. It doesn’t make us a scientist because we use those tools.”
Likewise, having the latest data science technology “doesn’t make you a better data scientist, it just makes the data more accessible,” he continued. “You know we can go buy a microscope, we can go buy Hadoop, we can buy any kind of tool in a data ecosystem, but it doesn’t really make you a scientist.”
Trained data scientists, not individuals in day-to-day business jobs, are the ones who will deliver data-driven insights and capabilities, Caserta added, noting the “the people running the day-to-day jobs in corporate America are going to be the recipients of data science.”
At the same time, Hinchcliffe predicts that many data science capabilities may be packaged up and made available on a widespread basis – delivered as “Data Science as a Service.” He notes that “just like you had to be a computer science at one point to develop programs for a computer, now we can get the programs. You don’t need to be a computer scientist to get a lot of value out of our IT systems. The same thing’s going to happen with data science. There’s far more demand for data science than there ever could be produced by having an ivory tower filled with data scientists. We need them, don’t get me wrong, but we need to productize it and make it available in packages such that it can be consumed. This is a constant of Data Science as a Service, which is becoming a thing now. It’s, ‘I need this, I need this capability at scale. I need it fast and I need it cheap.’ The commoditization of data science is going to happen.”
Introducing data science requires an understanding beyond the tools and technologies – while insights may increasingly be commoditized and automated through advances in artificial intelligence, its important for people to have an understanding of where the data is coming from and its context. “by making data more accessible, you allow people who could have been great in science to have an opportunity to be great data scientists,” said Shin.
The challenge, then, is increasing executives’ and employees’ understanding and capabilities in data science. Hayes pointed to a recent study that estimates that only 17 percent of employees have the ability to use data in their jobs. “Think about that — hese people don’t have the ability to understand or use data intelligently to improve their work performance. That says a lot about the state we’re in today.”
Shin hopes within the next decade there will be progress — “hopefully more people will understand how to use these tools. They’ll have a better understanding of working with data and what that means. Just being able to use these tools and be able to understand what data is saying, and actually what it’s not saying.”
A basic knowledge of data science — including statistics and math — will be essential at all levels of the business, Shin continued. “I’ve seen a lot of companies implement the same sort of process from 10, 20 years ago, just on Hadoop instead of SQL. It’s very inefficient, and the only difference is that you can build more tables wrong, than they could before.”
Joe McKendrick – Forbes.com