Don’t Let Data Science Become a Scam

Companies have been sold on the alchemy of data science. They have been promised transformative results. They modeled their expectations after their favorite digital-born companies. They have piled a ton of money into hiring expensive data scientists and ML engineers. They invested heavily in software and hardware. They spend considerable time ideating. Yet despite all this effort and money, many of these companies are enjoying little to no meaningful benefit. This is primarily because they have spent all these resources on too much experimentation, projects with no clear business purpose, and activity that doesn’t align with organizational priorities.

When the music stops and the money dries up, the purse strings will tighten up and the resources that are funding this work will die. It’s then that data science will be accused of being a scam.

To turn data science from a scam to source of value, enterprises need to consider turning their data science programs from research endeavors into integral parts of their business and processes. At the same time, they need to consider laying down a true information architecture foundation. We frame this as the AI ladder: Data foundation, analytics, machine learning, AI/Cognitive:

To break the current pattern of investing in data science without realizing the returns, businesses can address key areas:

Our two previous VentureBeat articles cover the composition of a data science team and the skills we look for in a data scientist. To recap, great data science teams rely on four skillsets: Data Engineer, Machine Learning Engineer, Optimization Engineer, and Data Journalist. If you want to maximize the number of qualified applicants, try posting roles with those four titles and skill sets instead of seeking out generic “Data Scientists”.

Retaining talent requires attention on several fronts. First, the team needs to be connected to the value they’re driving: How is their project impacting the line of business and the enterprise? Second, they need to feel empowered and know you have their backs. Finally, when planning for your team, build in 20–25% of free time work on innovative, blue-sky projects, to jump into Kaggle-like competitions, and to learn new tools and skills. Carving out that much time might seem pricey in terms of productivity, but it provides an avenue for the team to build the skills that accelerate future use cases — and it’s far more efficient than hiring and training new talent.

Map out the decisions being made and align them to tangible value, specifically, cost avoidance, cost savings, or net new revenue. This is the most important step in this process and the first step in shifting data science from research to an integral part of your business. We’ve previously mapped out a process for doing this in Six Steps Ups, but briefly, it requires direct conversations with business owners (VPs or their delegates) about the decisions they’re making. Ask about the data they use to make those decisions, its integrity, whether there’s adequate data governance, and how likely the business is to use any of the models already developed.

You can drive decisions using a dashboard that’s integrated directly into processes and applications. However, beware of situations where data simply supports preconceived notions. Instead, look for chances to influence truly foundational decisions:

“Where should we position product for optimal availability at minimal cost?”

“What are our most likely opportunities for cross-sell/up-sell for specific customers?”

“Which are my top-performing teams? Bottom-performing teams?”

“How can I cut costs from my supply chain by optimizing x given y constraints?”

Value each decision. Making decisions more quickly and with greater efficacy avoids costs, saves costs, or creates additional revenue. Express this value using whatever methodologies and terms your CFO advocates.

Prioritize the decision portfolio.