Regular readers of our blog will be familiar with our frequent suggestion that you should deploy data review teams and use subject matter experts to come up with clever solutions to data-based problems. “That’s all well and good,” you might say, “for companies that have dedicated staff for working only on data problems or massive amounts of information that need to be sifted by subject matter experts. What about companies that are smaller, have a less intense need for dedicated staff, or who simply don’t want to spend the money?” Fair enough, that’s probably a good representation of how most businesses feel about managing the data in their possession.
We should clarify things a bit, then. While it may seem like there’s a need to have specialists and dedicated staff, that’s not what we recommend at all. Certainly, if you have the ability to bring in data scientists and data visualization experts, and there’s a need for it, great: go for it. But most businesses are not in that position and shouldn’t need to feel like they have an obligation to lay out the kind of resources necessary to build that kind of a team. Instead, what they need to do is engage in a prioritization and re-categorization thought exercise.
“Great,” you may think, “we’ve moved from unrealistic expectations of staff and resources to unhelpful jargon!” That would be fair enough, too. So let’s break down what we mean a little bit.
Prioritization, in the data context, is really about identifying what use cases and what data sources are the ones most important for a given task, product, or endeavor. We’ve mentioned many times before the risks of the “collect now and figure out a use for it later” approach to data intake and data deployment. One reason is that, if you start without a clear idea of the purposes for which the data collected, there’s no guarantee that you’ll figure one out later. The flipside of that is if you do have the general concept of how you want to use the data you’re pulling in it’s much easier to prioritize those use cases, start work on the potential products you want to create, and identify the people who can help you do both.
Let’s take an example. Say that you’re an e-commerce business selling widgets. One of the data sets that you collect on a voluntary basis from consumers is their general view of your business, your product, and how you stack up against competitors. This is the sort of standard customer satisfaction data that we’re all familiar with. If, from the outset, your view was “this data will not only allow me to understand how consumers feel about my product, but also who are the most engaged customers are,” you’ve already presupposed quite a few of the potential use cases for the dataset. As a result, you can direct the analysis of the data to the people who are already on your team and who will understand the nature of the uses that you’re thinking about. Here, the natural use case is customer out reach, brand loyalty development, new promotions, and the kind of customer relationship development work that builds superstar customers. Contrast this clear idea of data uses directed to the people who will best know how to put it to work with the sort of “let’s-take-it-all” approach that leads to data lakes, stagnant customer information, and wasted opportunity.
Rethinking the Team
Recategorization is all about recognizing how to pair the use cases that you’ve laid out before you collect the data with the personnel and systems where they’ll be most easily deployed. The goal is creating action items with meaningful business applications. Pick your uses, spot the right person, and put the data to use. The importance of all of this, of course, is that even if you don’t see it, you likely already have subject matter expert‘s on your team. These are the people who understand your business best, but they may be in need of data to fuel their activities, and your goal is to pair them together. Customer engagement data? The marketing team. Analytics on product delivery times and comparative sales tax and product costs? The sales team and your CFO can use it to craft more efficient and more profitable strategies. In other words, you already have the team, you just need to put them to work.
The ideal time to do this pairing is during a quarterly review of your data practices and/or a data inventory. One of the interesting side benefits of these undertakings is that you’re better able to identify which members of your team feel a sense of mastery of datasets and how they’re used. We’ve found that it’s often a good idea to take breaks from the inventory process itself to discuss with individual team members how they use the data they hold, what other information they would like to have, and what they’d do if they have unlimited information. It’s a great way to give team members a sense of ownership in the data review and productization process, and it also gives you insight into which team members are already something like subject matter experts themselves.
The central point of the prioritize-and-recategorize process is reframing how you think about your data, your personnel, and the right way to match them with one another. Sometimes the relationships will be easy to spot, and others will only emerge organically in the process of discussing your data and how you plan to use it. We suggest a flexible approach, and we strongly believe in the idea of data champions: if someone in your company is enthusiastic and energized about data or how to use it in a given context, it almost always turns out to be worthwhile to give them to room to run with their eagerness. Give them space to advocate for their uses and — even if you don’t end up adopting their suggestion — you’ve created a new member of the team who thinks about the business from a data-driven perspective. That’s the kind of process that builds data teams internally and utilizes the skills and talent you already have, without the need for hiring new experts or buying new tools, and is just another way that you can and should put your data to work.