Executive indecisionA sure fire way to drive your big data analytics team crazy is to tell them what needs to be done, when it's going to be done by, and how many people it's going to take to get it done. This sounds like a management best practice, and the responsibility for knowing for all these elements falls squarely on the shoulders of your team's manager; however, dictating all three as firm is not a best practice - it's a fait accompli. There are only two outcomes from this stance: either it happens or it doesn't. I'll let you guess what happens more often. The proper way to manage your big data analytics team starts with defining your philosophy around which dimension stands firm, which one will be adjusted, and which one will be allowed to adjust: this is what I call the post, lever, and balance method (PLBM) of management.
Of the three dimensions that make up the metaphysics of activity management - scope, time, and people (effort) - the most intuitive and commonly used dimension to post on is scope. This means that scope will hold firm and we need to make a decision on the two other dimensions. For example, let's say you must develop a breakthrough analytic service offering that puts you head and shoulders above anything else currently offered by your competitors. You've done both a competitive analysis and a discovery analysis on your internal data, and you're pretty sure none of your competitors can develop what you're envisioning. Furthermore, it will blow your competition out of the water. In this case, it makes sense to clearly define your vision, lock it into place (i.e. post on scope), and do whatever it takes to make it happen.
The next decision you must make is what remaining dimension (time or people) your lever will be (what you will adjust) and what dimension your balance will be (what you will allow to be adjusted). If you decide your lever will be time, then you should come up with a few scenarios with different end dates, based on how things develop. For instance, you could have a better case scenario that takes advantage of positive risk, a target or primary scenario, and a worst case scenario that adjusts for negative risk. Notice the use of comparatives instead of superlatives - it's not good to describe levers as ultimatums, because it looks like more posts instead of a true lever. Also, you'll need a very flexible way of adding and removing people (or effort) from the project, as every time you move the time lever and solve for how much effort you'll need, you'll have to make adjustments in your team composition.
In the alternate scenario - making people your lever and time your balance - you build scenarios that represent intentionally adding or removing people. Remember though, you must let it take as long as it's going to take - you are not controlling time at all with this philosophy.
Let's say you have a product in the marketplace that's doing extremely well and you'd like to offer your market an analytic service to complement it. Your competition has competing products, but they haven't really thought about analytic services, so your idea is pretty novel in the marketplace. That said, you don't want your competition to get any wise ideas before you have a chance to release something, so to maintain primo positioning, you must get something out in six months. This is a good time to post on time.
Again, the subsequent decision is whether to use scope as a lever or as a balance. In this case, using scope as a lever is very similar to the agile approaches that are popular these days. If this is your philosophy, I would adopt an agile management approach that's already been annealed like Scrum or LAMDA. With this approach, your offering's value will be broken up into small pieces of standalone value. You job as a leaders is to make sure the most important standalone functions are implemented first. Then, when the time runs out - stop! That's the definition of posting on time. If you've managed this properly, you will have developed something that's both valuable and ready to release into the market. Balancing on scope can work as well, but you must be even more diligent on chunking up the value and prioritizing.
This is more of a program-level view of your data science team. If you use scope as a lever, you can organize large objectives for the team, and sequence them in a way that makes sense for your overall goals. For instance, you can start them off by helping you with your strategic driving force: products-offered, markets-served, or maybe something else. When that's done, you can have them work on some specific discovery or qualitative analysis, like what's available in your operational data that might be valuable to your customers. Finally, have them work on some quantitative analysis that leverages the previous discovery, like the digital behaviors that identify your best customers.
If you use time as a lever, you can play around with different time periods to gauge what their productivity levels are during certain spans. This will also give you some control over when something should be available, but again, there are no guarantees on what will come out of any given period. So, you must make sure scope flexibility is not going to backfire on you. If scope flexibility is new to you, get an expert to start you off. This is a concept that can really blow up in your face if you're not careful.
Scope, time, and people are the interwoven dimensions that comprise any project, program, or strategic outcome. However, for all three to line up exactly as you expect them to, is a fool's paradise that will deplete your organization of energy, time, and morale. Of the three, decide which will be your post (unwavering, defend at all cost), your lever (manipulate at will), and your balance (whatever will be, will be). This is your management philosophy and it should be well understood by everyone and defended when challenged. Think about how you're running your team today. Do they understand where your priorities are, or have you given them a fait accompli? If they don't meet your fantasy projections, it's more your fault than theirs.
Big data is transitioning from one of the most hyped and anticipated tech trends of recent years into one of the biggest challenges that IT is now trying to wrestle and harness. We examine the technologies and best practices for taking advantage of big data and provide a look at organizations that are putting it to good use.
John Weathington is President and CEO of Excellent Management Systems, Inc., a management consultancy that helps executives turn chaotic information into profitable wisdom.