The following is adapted from Guaranteed Analytics by Jim Rushton.
Whoever you’re delivering analytics data to, it’s important that you understand who this person is, what information they need, and what the best means of delivery is. In this article, we’ll discuss the different kinds of people using your business’ data.
Once you’ve collected your data, you have to think about the spectrum of users that will be utilizing it. There is a clear stratification of user types, so any one-size-fits-all approach will be DOA. Each type consumes information differently, so it’s important that you consider not only the person’s role in the company, but also how you should communicate with them to ensure they’re getting the right information.
When considering how to deliver your information to a given person, think about the user’s technical capability, the sophistication of their analytics needs, their skills and familiarity with different tools, and the standardization and recurring nature of their information needs. Speaking broadly, the different user types generally fall into one of the following three segments.
First Segment: Knowledge Workers
Knowledge workers make up the majority — typically 80 to 85 percent — of your information users and represent the typical kind of information usage. They will have guided access to information with dashboards that will help them understand the agreed-upon metrics most relevant to their jobs. They might sort, filter, and rank things to gain insights, but their information needs are fairly standard and predictable and focused on the daily needs in their normal course of business.
Knowledge workers may not work directly with the granular data, but they’re the most important conduit for driving ROI because they represent 80 to 85 percent of the information-using population. They don’t need a great deal of technical competence or advanced tools — just basic computer knowledge and an understanding of web-based interfaces will do — but that in no way diminishes their value. The insights they produce with the information you deliver them directly lead to action, and therefore to your business’s ability to monetize results.
Delivering information to them in a guided environment may feel like you’re limiting their access to your data (but you are only pointing them away from unneeded,
distracting data), but by doing so you empower them to focus their efforts on the task at hand.
Second Segment: Super Users and Report Builders
This segment typically makes up about 10 to 15 percent of your users. While your knowledge workers simply use the information presented to them, super users and report builders will actually work with the more granular levels of data to build reports.
Here’s where the super user sits in the process. First, the IT folks source the atomic-level data, transform it into basic information, and store it. Then, the super users access that basic information and use it to answer sporadic, next-level or nonstandard questions. Similarly, the report builders will help roll that basic info into higher-level summaries for rapid consumption by the knowledge workers.
Think of the super user or report builder’s work as open ad-hoc; they build one-off presentations that others may find useful for a specific purpose. As such, they
have broader access to data that goes beyond the team’s agreed-upon metrics. This puts them in a somewhat tricky situation. Both your super users and report builders work close enough with your data that they could mis-match some information or even crash the system with the wrong query.
The potential benefits that the super user brings, however, far outweigh the potential drawbacks. Because of their unique position and training, your super users are likely going to come up with multiple answers from differing angles to pressing business questions and help you get to the root of understanding your challenges and opportunities.
Third Segment: Data Scientists
This segment is small and typically makes up only 2 to 3 percent of the users in your organization at most and are often limited to just a handful of people. Data scientists look at your data differently so that they can run important science projects. As data experts (they usually have a PhD or equivalent experience in statistics), your data scientist is able to create all kinds of crazy things that we mere mortals don’t understand.
As the name implies, your data scientists focus on performing “science projects.” Generally, the impetus for these projects comes from the data scientists themselves rather than from executive mandate (that’s the super user’s job). The data scientist, then, has great freedom to play with the data as they see fit as they search for unknown patterns and opportunities. As such, their research commonly requires data be assembled in unique, often messy, ways.
Using their own tools to grab never-before-seen data from anywhere they can, creating temporary tables as necessary, and building models to fill in missing data,
data scientists utilize whatever data they deem relevant to generate new insights. It’s important to note that, while both super users and data scientists are free to work with data in a variety of ways, they are generally not decision makers, or anyone else whose actions actually capture ROI. Their job is to identify opportunities for the project owner (or executive sponsor) to act on, but they do not take action themselves.
By keeping things simple, user-friendly, and, above all, actionable, you dramatically increase your likelihood for success and reduce the chances of your end users making an innocent — but nevertheless costly — error. If you can do this, then the road to converting information into insight becomes a lot easier to travel.
For more advice on end users in analytics, you can find Guaranteed Analytics on Amazon.
Jim Rushton began his career in analytics working with some of the biggest consulting companies in the world, including Accenture, Deloitte Consulting, and IBM Global Services. Jim then moved to an executive position with Verizon, where he oversaw the company’s customer and marketing information. Leveraging his experience across corporate America, he helped found Armeta Analytics, and in the past decade, his team has helped dozens of Fortune 1000 companies learn how to monetize their data.