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Last week we introduced the 4 tenets of highly effective data teams - a framework built from hundreds of conversations with data leaders over the last 6 months. To learn more about the motivation and process behind these tenets I encourage you to check out last week’s article here, because today we’re diving deep on the tenet that’s generated the most excitement so far - operational clarity.
It’s hard to imagine that only a few years ago the big issue with data was getting enough of it. There was an insatiable hunger across the business for more and more information on how to gain the slightest competitive edge.
Today, the picture is very different.
“I’m constantly bombarded with information - a Slack alert about this week’s numbers, a direct report tells me a conversion rate from their last experiment, I have dozens of reports bookmarked that I need to sift through, and even more spreadsheets I get sent from who knows where. It’s a constant battle just to parse through all that.” - Anonymous business leader
The cost of this kind of chaos is multi-pronged, and familiar:
This kind of chaos can be hard for a data team to see on a regular basis, but is impossible not to ignore once you’ve seen it:
“We were always so frustrated when we got asked for similar things over and over. At some point we realized that in order to get a holistic view of the business, leaders had to make sense of data spread across 6 or 7 different reports. It was a lightbulb moment for us. We realised what's really needed is a clear sense of what is going on, and alignment on the biggest problems.” - Anonymous data leader
This is the idea behind the tenet of operational clarity. The most celebrated teams we spoke to oriented themselves around ruthlessly adding more clarity to the rest of the business. They make sure the work they do - each project and their larger presence - continually turns noise and complexity in to clear and transparent signals for the rest of the organization.
An organization with high operational clarity feels simple and connected. Simple in that even the most complex processes, markets or environments is easily understood. And connected in that every employees understands the operating model of the business, and how their work directly contributes to bigger picture and what the priorities are.
One data leader explained this concept well:
“The more aligned your organization is - and I don’t mean aligned around vision necessarily or strategy, I mean aligned around a common language, a common understanding, and common idea of what is and what isn’t - the more successful an organization is…I think the data team’s responsibility is to ensure those connections are around something objective, like data, as opposed to something subjective, like team goals or team culture.” - Phillip van Blerk
This shift towards operational clarity can take many shapes, but to give you a flavor, we’ve outlined 3 examples below:
One team we spoke to adheres to the concept of perfect reporting - that the minimum number of reports should exist such that each person has all the information to do their jobs.
This means both a strict process for what new reports get built but also an audit process that ensures there’s no confusion from stakeholders about which reports should be used when. In this audit they look for:
An audit process like this one was not uncommon - it is perhaps the most straightforward way to make sure the reports you have today are as useful as possible.
“You don’t just give a dashboard and forget about it. You own it, you maintain and update it and deprecate when it needs to be. And use it in your decision making process when working on any new potential report or tool.” - Anonymous data leader
This process is focused on removing noise from the system, while making sure the business has what they need to succeed.
Metric trees are an excellent example of adding operational clarity. However, as this example demonstrates, effective metric trees are about more than a new lay-out.
In this company, the Head of Data was asked by the CEO to help design their new North Star metrics. He sat down with the top leaders one day a week, gradually building out the key high-level metrics for the organization. These conversations revealed key differences in understanding of common terms or metrics that they were able to address through this exercise. The resulting tree showed these key metrics as well as a downstream list of drivers and influences to those metrics that they used to identify top priorities for the quarter.
This tree went a long way in clarifying the operating model of the business, common terms, and priorities - all key to operational clarity.
“People are all using the same terms now - they’re talking about establishing users, visitation, and thinking in terms of funnels. For the first time we’re all speaking the same language.” - Anonymous data leader
This metric tree is still growing today as they dive deeper into the tree and metrics evolve over time, proving its value across the business.
A common theme in operational clarity is owning more and more of the company metrics. For one company this meant the data team was responsible for holding quarterly OKR reviews with each department.
“They are adding clarity on what is a good measure, how it can be measured as a driver for business, can make enough impact and is this Key Result can be further cascaded by other teams.” - Anonymous data leader
These sessions put the data team at the heart of deciding how these teams will measure themselves, which clarifies what their priorities should be.
A smaller, but no less impactful, example of adding operational clarity is by those teams that emphasize effective communication. This means making sure their outputs are easily understood, they build visuals that are easy to consume, they use language and vernacular that’s meaningful to the business.
These kinds of initiatives were all the rage in the 00’s but are no less important now. Effectively communicating findings is one of the simplest and effective things data teams can do to ensure they’re adding clarity not confusion.
In these conversations, we’ve also noticed a few big lessons and patterns that are worth noting if you and your team are to start focusing on delivering operational clarity.
You would be forgiven for reading over the last few sections and thinking ‘This isn’t my job.’ Data teams aren’t responsible for setting goals or making sure the business leaders do their job.
You would be right, but the reality is the best-performing data teams we’ve worked with make it their job.
Alan Cruickshank of tails.com explained it best:
“You have this moment where you sit down and think ‘Why am I doing this? Shouldn’t our leaders have been able to work this out? Why is it my job to tell them how their business works.’ And that voice will always be there. But an unavoidable part of our role is understanding other people’s challenges, and helping them do that better.” - Alan
Looking at this from another perspective, I would argue operational clarity is exactly the opportunity many data teams have been looking for. So many leaders we speak to say something like “We can do so much more.” Adding operational clarity is something undeniably valuable that data folks are uniquely positioned to do well.
“As a data team, you have more visibility on the data you have and the metrics the company (ie. other teams) is already looking at. You have the knowledge to recommend what will create the most value without compromising the existing operational clarity.” - Anonymous data leader
So the first step is to recognize and take ownership of operational clarity.
One of the biggest takeaways for me was that there is no “3-step process to achieving operational clarity.” We came across so many different tactics deployed by teams at different stages and in different environments it became clear that the mindset far outweighed the specific actions.
Don’t limit your team’s creativity by following the trends, or blindly following what others have done. Try asking yourself and your team what could you do to start adding more operational clarity today, in the project you’re working on, in your next meeting, etc.
A significant requirement for operational clarity is empathy. To simplify the complex world our business partners are living in we have to understand that. That means we cannot approach operational clarity with the same kind of ‘give me two weeks and I’ll come back with a finished report’ approach.
In each of the examples above, the data team was working in partnership with the business. So a simple rule of thumb is if you’re doing this but still feeling like a service team, you might need to readjust your approach.
A common question we get asked is ‘How do we get approval for this kind of thing?’ The simplest answer for that is: that you probably don’t need approval if you’re solving a genuine problem.
So start by hunting down the biggest sources of anxiety from your manager, the marketing lead, the CEO, etc. When you have this list, work out where you’re best poised to make the biggest difference by providing some much-needed clarity.
When you find that issue, we’d also advise you to find someone senior in the organization to work with as they are much better poised to transfer the clarity you deliver through the rest of the organization.
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