What immediately comes to mind when you think of “self-service”? My mind immediately takes me to these two scenarios:
There are many other scenarios that imply that the customer can take care of the end-to-end request on their own, without requiring human assistance. Across all examples, there are varying degrees of support that’s available if you need it.
Let’s now transfer “self-service” to the data world, where it’s most commonly referred to in association with business intelligence. In self-service business intelligence at an organization, the “customers” are usually the business users, who consume data in order to derive insights for decision-making. These business users — think Sales, Marketing, Operations, Finance teams — tend to not be technical in their roles, and their responsibilities don’t require them to know SQL, python, or any kind of software engineering.
The “human assistance” tends to be the technically-skilled data teams, who are generally responsible for owning the company’s data, centralizing data in the warehouse, ensuring high governance and cleanliness, and transforming the data for analytics. The end goal is to serve up the analytics for the “customers” that need it.
While the data teams are generally deemed responsible for “all things data,” the disconnect is that they’re not the end users who need to consume the data for mission-critical decision-making at a company level.
The profiles between the data owners (data team) and the data consumers (business team) are highly different, with the major barrier being the SQL skillset. While there are data-savvy business users out there, there’s only so far they can go with data modeling and transforming without learning SQL or code.
So, to close the gap and encourage efficient collaboration between these two teams, for the purpose of quickly going from raw data to insights, what can be done?
From an organizational standpoint, the difficulty is that there’s not really a “middle ground” on the spectrum of who puts in more effort to meet the other.
The second scenario is far more common, for obvious reasons, and is what happens in most companies. As a result, data teams are painfully swamped. They’re expected to act like a support team (quite literally, the “human assistance”), fielding ticketed requests all day from business teams. Oftentimes, they’re expected to drop everything they’re doing to work on last-minute business requests, which prevents them from progressing on the more strategic or long-term work they have on their plate, to improve data quality, or to drive data process efficiencies. What’s worse, these requests pile up, resulting in a bottleneck and a longer wait for business team members. The consequence: this impacts the speed at which they can leverage the right data for their decisions.
Since this disconnect is so tough from an organizational perspective, we have higher hopes for technology to step in and enable a world where business users can self-serve themselves analytics, whenever and however they need it.
Usher in the era of the self-service business intelligence platforms. Metabase, Domo, Looker, Whaly, Tableau, Qlik, and many more — these platforms are a godsend in finally freeing up some of the data teams’ time and bridging the gap.
Before blindly implementing a self-service BI platform, however, it’s important to define what “self-service” means and what it should look like, taking certain questions, cautions, and nuances into consideration. For example:
Without further ado, here’s my take on self-service in BI.
Self-service should be about collaboration and open communication between both teams, like in a team sport. In a team sport, there are specific positions and objectives that different players are responsible for. Each player has their own space to do what they do best, and shine in their own right. With this in mind, don’t expect business teams to “become analysts,” but expect them to become experts at fetching and interpreting actionable insights from data.
For this to work, the self-service component of the BI platform must be easy enough for business users to use. Which takes us to the next section.
We’ve gone through how roles should be divided, like players in a team sport, and boundaries should be clear. This applies to the work environments within the BI platform as well. There should be clear lines around where the data team works, and where the business teams work, within the same platform. Of course, these two areas (or tools) must be highly effective at communicating with each other, leading back to the collaboration point. Collaboration is of high importance in both the organization aspect and the technology aspect.
Not only does the separation of having two tools/interfaces enable each team to do their jobs better, it also ensures high governance so that data isn’t at risk of breaking or becoming unreliable.
All in all, if we had to tie it back to one of those real-life, every day self-service examples, it would be a little closer to the grocery store example — but not because you’re “just buying a few things.” Regardless of the complexity and depth of the questions they have, business teams should always be able to leverage “self-checkout” (self-service) to save time, be autonomous, and avoid the bottleneck that “waiting in line” for the data teams could have. When it comes to making business decisions, there’s no time to spare.
This is what makes self-service BI so important, and something every company should invest into. You’ll be glad you did, because it comes with some pretty great benefits like boosting data adoption and data familiarity beyond the data teams, setting the foundation for implementing a data-driven culture at your company. Plus, you’ll be empowering data teams to do what they do best, as opposed to treating them like a support team whose main job is to react to business requests 24/7.
To learn more about Whaly's self-service BI platform and how it can help you, get in touch!