Deriving consistent value from analytics is no simple thing. It’s common to focus on the technical challenges, and with good reason: Getting a robust, reliable
deployment of analytics on your sites and apps is certainly a lot of work in and of itself.
Over the years, the vast majority of the organizations I’ve worked with have had a vision of analytics as a self-serve resource that takes root once an implementation is complete and yields fruit forever after. I prefer to be a bit more pragmatic — rather than chasing a vision of “self-serve,” I try to help organizations get to a place where analytics creates consistent value, regardless of exactly how that happens.
In the real world, self-serve doesn’t make sense for every organization — so how can you decide how best to move analytics forward in your own environment?
Once you’ve gotten tools selected, customized to your needs and successfully implemented, you’ll want analytics to scale. And for that to happen, your decision-makers need to consistently use analytics to make important decisions.
Of course, there are a variety of conditions under which that is more likely to happen. For instance, your stakeholders have to trust that data is accurate. They have to understand what the data means. They have to feel comfortable that they can ask the specific questions that matter to them and get relevant, interpretable answers.
So, how realistic is that? If you’re reading this and thinking, “That doesn’t sound like my organization,” what should you do? More foundationally, what can you do?
Know your organization
Before making big investments in analytics, it’s important to understand the context of your organization so that you can map out a path forward that not only makes sense on paper but is likely to work in your unique situation.
If digital data doesn’t come as a “native language” for many of your decision-makers, consider setting up a “Center of Excellence” for digital analytics. This is a small team of subject matter experts who can work on putting the necessary building blocks in place. For example, this team will ensure you have basic data quality, a trustworthy and durable implementation and a base level of measurement that provides data that’s relevant to stakeholders.
From there, consider some training options. You know your organization best; if you think decision-makers will want to dive directly into Google Analytics or Adobe Analytics once they get some basic training, fantastic. It’s probably still a good idea to build out some reporting templates to help keep things standardized, but from there you can basically let people go to town. Build out (or buy) some training modules, make them available to your stakeholders, and bask in the glow of empowering your teams to use analytics for themselves.
On the other hand, if you sense that no amount of training, cajoling or incentives will get people to actually get their hands dirty in the analytics tool themselves, you’ll want to consider other options. A common mistake is to invest heavily in analytics training that ultimately goes unused. If that’s a worry, dashboards could make a great alternative.
Dashboards are great because they’re always on, and they can surface the data required by decision-makers without forcing them to wade through complex interfaces. All of a sudden, you’re not worrying about teaching people the difference between segments and filters. Fantastic!
Of course, there’s a trade-off. Dashboards typically won’t provide the answers to more complex questions — and if they do, they’re probably no less complex than a full-fledged analytics platform.
If dashboards don’t work (because they’re too expensive, too unreliable, too complicated, or for any other reason), you might consider creating an internal consulting team to service analytics requests or, in a staff augmentation model, bringing in an outside consultancy to function as your analytics team.
Of course, consultants generally don’t come cheap (we have mortgages too!), but they’ll provide a consistent level of expertise, follow standardized processes and share their knowledge with your teams. Plus, you can cut them loose pretty much whenever you want.
Ultimately, the question of whether analytics can realistically be “self-serve” comes down to your organization’s culture. Don’t make the mistake of assuming that analytics should be self-serve. In some places, a little training and support provide all the motivation people need to dive into the data and embrace analytics as one of their responsibilities. In other organizations, no amount of training and support will get people to take on analytics.
So, rather than forcing a solution on your organization, take stock of your team and its culture. Ask them about how they’d like to be able to take advantage of analytics. Once you’ve got an idea about how viable “self-serve” really is, you can chart a path forward that makes the most sense in your organization’s unique circumstances.
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