Four Principals For Building A Better Analytics Team
One of the most important elements of any effective analytics program is its guiding set of principles. I’m not talking about a mission statement or core values, although those should both play a guiding role in your analytics program, as well. What I mean by data principles is a solid framework that defines how your team is willing to gather data, how it can be used, which data needs to be treated differently than other information, and other types of issues that provide “bumpers” for your overall data protocol.
It’s no secret that an overwhelming majority of data projects fail. Data principles may help you avoid that fate. They are by no means stiff rules that leave little wiggle room for growth or creativity. These are just meant to be guideposts as you think about creating your team and collecting data. Even if you’re well into data collection in your company, now is the perfect time to press pause on your data programs to see what’s working and what isn’t. Let these four principles act as your guide.
Now more than ever, companies understand the need to be agile and pivot quickly. You can’t pivot quickly — or meaningfully — without data. For instance, companies that once sold primarily in brick-and-mortar stores may have paid little attention to the online habits of their customers. Now, they have little option not to. Being able to change the type of information you collect is part of growing your business in a changing world.
There are, however, certain elements of your data principles framework that should always remain the same. Some countries, for instance, even have data principles to guide their analytic work. These types of principles provide a moral foundation for data use throughout the country. In the United Kingdom, for instance, data principles include understanding that data sets are assets that must be managed throughout their lifecycle; that data re-use is important and will be created with common terms that encourage that concept; that data will be governed with clear rules regarding sensitivity; and that data will be used with openness and transparency. They’re simple rules but they offer a clear pathway to the use of data in essentially any context.
Having said all of that, success with data also comes down to tools. And while I won’t spend a lot of time on that specific topic today. I want to make sure not to gloss over it. There is a reason why there has been an explosion of analytics tools and why IT architecture is being built around accommodating these tools. Companies like Microsoft, Amazon, Oracle, SAP, SAS, IBM and Salesforce, to name a few have seen explosive growth by enabling customers to do more with data. This has led to a rise in tools that can meet complex data environments, regulatory constraints, personalization needs and vertical industry requirements—because data is fueling growth in just about every part of every industry.
Regardless of your industry, there are a few types of data principles you might want to consider in ensuring meaningful impact with your data strategy. The following are my top four.
1) Data must be diverse. First and foremost, you will want to make a commitment to create diverse data sets that allow for well-rounded, complete profiles of your customers. Without diverse data, you’re gaining a glimpse into just one small part of your customers’ lives.
