Let maturity and evolution determine where the data science team reports

Recently, Mary Shacklett, a colleague and contributor to the TechRepublic Big Data Analytics Blog, produced a concise and accurate treatment of a long-standing debate in the IT industry on centralization vs. decentralization. Notwithstanding my professional disagreement with the philosophy that drove her thesis, Mary's right on target with the classic challenges that still haunt the leader who's trying to structurally align their data science team with the goals of the organization. In brief, here's what she surfaces:

• IT doesn't fully understand what the business needs
• The business doesn't fully grasp the depth of technology required to succeed
• Dotted line reporting relationships don't work
• Joint reporting relationships don't work
• There's no universal right way to structure the organization

All true.

Data science teams only add more complexity to an already perplexing problem. Should they report to IT, as Mary suggests? Should they report into the lines of business, as I would probably suggest. Or, should they be on their own: perhaps assign a Chief Data Science Officer that reports to the CEO or President (as if we need another CxO to talk about)?

The right answer depends on whether or not it works for your organization (how's that for a typical consultant's response). More specifically, the size and maturity of your organization has a lot to do with how your data science team should be structured within your organization.

Growing Pains

When you understand how organizations grow, you'll be better equipped to make a good decision on where your data science team should go. One of my favorite articles on organizational growth was published in the Harvard Business Review by Larry E. Greiner, a professor of management and organization at USC's Marshall School of Business. Greiner asserts that there are five stages of organizational evolution and maturity:

• Creativity
• Direction
• Delegation
• Coordination, and
• Collaboration

Bridging each stage of evolution are revolutions, or crises: leadership, autonomy, control, and red tape, respectively. For instance, nascent companies are driven by creativity until they face a crisis of leadership; if they successfully navigate this crisis, they enter an evolutionary phase driven by direction.

This is very important to understand as a leader, because many who struggle with the misalignment of their data science team(s) are either trying to model what they did in the past, or emulate a paragon that's beyond their current level of maturity. When I was consulting for PayPal, many of their decisions were influenced by what Visa was doing. That's a bad idea! Visa's been around for 37 years and has about 65% market share. PayPal’s been around for about 14 years and has - a lot less than 65% market share.

In this case, PayPal decided to separate their analytics team from their IT team; however, they were facing a crisis of control. They had over-delegated and now top management needed to rein things in. This is the point at which they should have started bringing IT and analytics closer together, so that top management could get better cohesion with the lines of business and more direct visibility. But the bigger problem I see is when companies grow too big.

Too Big to Flail

So decentralizing at the wrong time is a bad move, but so is centralizing at the wrong time. This is the problem I saw when I consulted for Visa. Visa wasn't really dealing with a crisis; however, they were approaching one - the crisis of red tape. The privilege of experiencing a red tape crisis is only reserved for the larger organizations - and it's a tough one to cross. In my experience, it's the toughest, because the organization has already been successful with a centralized model, which has had the opportunity to take deep roots and blossom nicely - until the weight of bureaucracy starts pulling it down.

The only way to survive a red tape crisis is to decentralize. Your data science team has probably lost touch with what the business really needs, so your initial emphasis should be to bring that back. Put data science teams in each of the major lines of business, while maintaining a center of excellence that supports them. What's important at this stage is that you foster an environment where objectives are clear, but procedures are flexible. Creativity, innovation, and self-organization must be reintroduced without losing the best practices that got you where you are today. If you wait until the crisis hits then it will be a painful transition - so don't! You can usually see this coming; start making the transition before the burden of bureaucracy overwhelms the organization.

Conclusion

To centralize, or decentralize, that is the question. The answer has troubled leaders for decades and you can never be assured of the right structure until you see it work in your organization. That said, you can improve your chances by understanding how organizations grow and successfully navigate growth crises. To get through a crisis of control, you should centralize. To get through a crisis of red tape, you should decentralize. In all cases, leadership matters. If you're struggling with where to place your data science team, think about what your organization is dealing with today. Then, make an educated decision, and move on. It's time to enjoy your next phase of evolution.