January 17, 2017 by Dr. Jian Huang

Deep learning was prominent in the venture capital world of 2016 and rightfully so. This wave of excitement about AI and computing grew strong, because of a new-found comfort on letting unprecedented rich data guide progress. Interestingly, the term “deep learning” draws another contrast, that is, previous generations of machine learning lacked support of real data—in other words they were “shallow”.

Similarly, data analytics is transforming business decision making from “shallow” to “deep”. This leap from shallow to deep is embodied by a change in mindset—what used to be “data-supported” has become “data-enabled”. Pfizer is one of the pioneers in this, their vision is to change data-supported pharmaceuticals to data-enabled pharmaceuticals. Unlike before when you had an idea first and then looked for data to support that idea, now you let good rich data show you the full spectrum of opportunities, you then decide which to prioritize and pursue. Such a change in mindset brings more confidence and focus to all business strategies.

“Unlike before when you had an idea first and then looked for data to support that idea, now you let good rich data show you the full spectrum of opportunities, you then decide which to prioritize and pursue.”

This business-world transformation is tough to scale up, however, because the horsepower in the business-world is people, not specialized GPUs as in deep learning. Unfortunately, the skill set required of a complete data-enabled business team is in short supply.

There are many articles, including ours, that talk about the ongoing struggle to recruit data scientists. The consensus is that you have to do more with your current team. Why? First, there is a dearth of available talent that matches your strategic needs and academia has been way too slow in addressing the gap in the talent pipeline. Second, like championship teams, data-enabled business teams need great chemistry between lots of different specialties. The fundamental glue has to be the company’s very own business know-how and contexts. Again, academia is stuck in the ivory tower, it really can’t teach the necessary business context and acumen. Thankfully, you hold all of that information. The power to teach and transform is in your hands.

You will need to be the one who builds a lean agile data-enabled team—by equipping them with the best tools, by providing them the coaching on best practices, and by instilling team chemistry. After that, empower them to make prompt, confident, prioritized decisions using clear evidence based on good data.

A data-enabled business team is part science, part art (and a lot of shared vision, trust, and grit). Here at Survature, we want to join your team as your data partner and help enable your team with opinion data collection and analytics—specifically coaching your team on finding the opinions that matter. Like many tech companies who find and attack crucial gaps in the market, we learned that helping our customers build a full fledged data-enabled business team is how we can best help complete their transformation from shallow to deep, to know what to prioritize and why.

“We learned that helping our customers build a full fledged data-enabled business team is how we can best help complete their transformation from shallow to deep.”

For example, a business unit leader in a big chemical company is concerned with supply chain management. Even though relatively new to the process of using Survature as part of his business operation, he was quickly able to cut through all of the noise in the opinion feedback and make the key discovery that his BU’s biggest priority to improve is not more and newer IoT tracking, it’s instead to communicate with his supply chain partners using channels that fit the partners’ unique preferred work style. That discovery led to subsequent changes in their business processes and significantly improved quarterly business results in the past year. Needless to say, their VPs and C-suites were impressed by that BU’s mindset and ability to innovate and make breakthroughs.

To improve safety culture, an HR leader of a manufacturing facility used Survature and learned from employee feedback that, “safety is not everyone’s priority”. In the eyes of the workforce, it’s crucial for top management’s to set the tone for workplace safety, and for front-line management to execute. Mid management doesn’t really have much of a role from the employee’s perspective, which allows them to focus on other priorities.

The marketing director of a leading wine retailer had an unproven “gut feel” about the key drivers for their top customers (and hence a different assessment of the situation from other directors in her organization). Using Survature, it was confirmed. Product selection, availability, tasting, etc. are important but they are not the first priority of their most loyal in-store customers. Instead it is their “relationship with the staff”. Guess what, now all of the staff understands that knowing their customers by first name is a key starting point towards fostering loyalty from their best customers.

In every example, the business leaders saw the need for making data-enabled decisions. Survature was along the way to help as the technical platform of choice and as their data partner who coaches and assists.

“But to debunk the myth—data analytics, designed to suit your needs, can have a very short learning curve.”

The process of making a team data-enabled may come across as adding one more thing onto a long list of duties. You may not have the time or experience. But to debunk the myth—data analytics, designed to suit your needs, can have a very short learning curve. Survature is one such platform. Our user support is your data partner that can show your team the secret sauce—the turbocharger to better engage your employees, partners and stakeholders—to better manage your product or service in the face of competition, and to create greater value for your audience, customers or clients.

Jian Huang is the Chief Executive Officer at Survature providing the vision for reinventing the way the world experiences surveys. He is a professor of computer science at the University of Tennessee (UT) researching data analysis, visualization, and human-computer interaction. His research has received funding from the National Science Foundation, the US Department of Energy, the US Department of Interior, Intel, NASA, and UT-Battelle. Jian received his PhD from the Ohio State University.