When running a strategy that involves big data, it's important to align your strategic objective with your leadership, management, and overall approach to data science.
There are too many chefs in the science labHerding cats is not as hard as it sounds - if you have the right shepherd. Sometimes, managing a data science team feels like herding cats. For some reason, people tend to underestimate the creative capacity that some data scientists have, which translates to a management challenge if you're not prepared. There's another side to the coin.
Sometimes it seems like you're giving your data science team all the direction in the world, but they're going nowhere fast (and burning your cash while they're at it). This is all indicative of a common problem I see on analytic efforts. When running a strategy or initiative that involves big data, it's important to align your strategic objective with your leadership, management, and approach to data science.
Leaders and managers and data scientists, oh my!
Misalignment between leadership, management, and approach to data science is a common mistake that's easily fixed, if you understand some fundamentals of why things get messed up. Let's start with leadership versus management. This whole topic is rife with confusion - even within management community! So let's clear things up.
In simple terms: leadership deals with change; whereas, management deals with complexity. What leaders do and what managers do are two seemingly dichotomous functions that drives people crazy. Leaders are visionaries who keep their eye on the horizon; whereas, managers are planners who keep their eye on the bottom line. Leaders motivate people with their influence; whereas, managers direct and control people with their authority. Good leaders establish an emotional connection with their followers, keep an open mind, listen more than talk, and reduce boundaries to explore new territory. Good managers maintain emotional distance, have an expert mind, talk more than listen, and create boundaries to contain scope. As you can see, it's dangerous to assign the right people to the wrong job.
Next, let's look at strategic objectives and your approach to data science. When you develop a product or service to offer to your market, you must decide whether your new offering will be competitive, distinctive, or breakthrough. Competitive and distinctive offerings tend to be transitional: you know where you want to go; you just need a good route to get there. These efforts are best suited for quantitative data science.
Breakthrough offerings tend to be transformational: you know you need to change, but you're not yet clear on what the solution looks like. These efforts are best suited for qualitative data science. Like leaders and managers, qualitative and quantitative data scientists have very different cultures (I bet you didn't know that). Qualitative data scientists value exploration and are okay with uncertainty and relatively blunt, crude measurement devices. Quantitative data scientists value structure and numerical precision, and often criticize qualitative approaches.
Aligning the stars
Team structure and development should then follow from your strategic objective. If you're shooting for a transitional offering (i.e., competitive or distinctive), then your data science team should favor management.Let's say you're way behind the curve on digital marketing so you need to create a relationship offering (a service that you don't charge for) that's on par with your key competitors to prevent churn (lost customers) from the Generation Y market. Since you already know what your competitors are doing, you have a clear idea of where you're going with this offering, and you have a tight but achievable timeline. To achieve this objective, build a team of quantitative data scientists that favors management. You still need leadership to keep people motivated; however, the emphasis should be on planning, organizing, directing, and controlling. Your quantitative data scientists will appreciate the structure and clear objectives.
If however, you're shooting to achieve a breakthrough, transformational offering, then the team structure must be radically different.
Let's say you're already the market leader; however, competitors are closing the gap and you need a breakthrough innovation to sustain your market leadership. You're witnessing the mega-trends - like social journalism and the emergence of India as an economic power - shift like tectonic plates, and you need to ride the ensuing tidal waves, but you're unsure of where they will take you. Putting a management team on this effort would be a disaster - you need strong leadership. Assemble a team of qualitative data scientists who are more comfortable with the upcoming uncertainty, and install strong leadership. You will need some management to keep things under control; however, the emphasis should be on team development, change leadership, and flexibility to navigate unknown and often turbulent waters.
Strategic alignment problems are common, costly, and easy to avoid once you understand a few underlying principles. You never want to be devoid of analytic leadership or management; however, the emphasis of one over the other depends on what you're trying to achieve.
To make a strategic transition to a competitive or distinctive offering; a management-centric, quantitative approach that emphasizes planning, control, and precision would serve you best. However, to make a strategic transformation to a breakthrough offering, a leadership-centric, qualitative approach that emphasizes exploration and change leadership is your best bet.
Now that you know how to line everything up properly, take some time to examine your data science team structure and see if it fits your strategic objective. If it currently feels like herding cats, maybe you just need a cat-herder.