The importance of good data practises
In this post I will share how data can help us create more focus and speak the same language
Most companies are sold on the idea to adopt more data driven ways of working by now. They know that data allows teams to make more informed decisions, reduce bias and uncover new insights for the business.
Today I want to cover two aspects of having good data practises that are a lot less talked about, but in my experience just as powerful.
Metrics and Strategy
The reason I personally love the data and analytics space so much is not because I like to geek out over conversion rates or funnel drop offs (well, maybe a little bit). I've realised that talking about data always brings the conversation back to the wider question of what we're tracking and - most importantly - why we’re actually tracking those things.
I often speak with teams that actually do a decent amount of tracking and reporting. However, when I ask them to tell me about their most important metric, they typically name about ten metrics that are all "equally important".
While data doesn't magically create focus and alignment, it does reflect your strategy. In Michael Porter's words, strategy is about making choices and deliberate trade-offs. If every metric is priority number one, it means there are too many competing objectives that the company is trying to achieve at the same time.
Good data practises allow us to uncover gaps in our strategy and create more focus by defining our key objectives and most important metrics.
Business and Product Metrics
Talking about metrics is also a great way to find out whether teams have a holistic view of how their work contributes to the wider business. Especially in larger organisations, product teams often get very disconnected from the business goals.
This hugely reflects in the language teams use. For example business stakeholders would track financial and operational metrics, while the product teams focus more on customer usage patterns in the product.
Unfortunately in most cases, neither side has a good understanding of how those metrics impact each other.
I can recount countless examples, but I'll share one of the more recent conversations I've had to illustrate this problem.
I was part of an analytics panel discussion with a Product Manager of a large very well known technology company whose entire team was solely focussed on improving customer activation. Their activation metric has remained pretty much the same over the years: customers should perform a certain setup action within the first x days of signing up.
But when querying why this activation metric has been chosen in the first place, the Product Manager quickly deferred that "the commercials are not the team's responsibility", and that they're "only focussing on the customer".
I find this example so powerful as it not only highlights the problem of teams not understanding the full business context, but also uncovers a subtle bias against using financial metrics for product teams.
I believe this is a bit of an unwanted side affect of the (very necessary) shift in product teams to "get out of the building" and become more customer focussed. While I don't ever want to move away from these practises again, it did set a hidden message for many teams that spending time with business stakeholders is a bad thing.
While we always need to start with solving a real customer problem (or there won't be a business at all), I strongly believe we should not forget to also spend some time inside the building to understand the mechanics and the goals of the business itself. It goes both ways: if the products we build don’t work for the business, we can’t continue to solve those customer problems.
Ask yourself: how do certain customer actions in our product impact business outcomes? Looking at our most engaged and highest paying customers, what behaviour patterns do they have in common and how can we find more of them?
Good data practises allow us to bridge the gap between the business and the product and align around common goals.
Imagine the Product Manager from the panel discussion would get across the financial metrics and find new surprising links between customer activation and long term revenue. The Product Manager might even uncover completely new customer segments and activation metrics that the business can now go after.
Data helps us speak the same language across the organisation and break down silos. Use metrics to show the impact you’re creating on the customer, but also on the business.
Data also allows us to get a good understanding of a company’s strategy and key objectives. If there is no clear answer, use this discussion as a trigger to revisit the wider strategy. With a better defined strategy you can now align on the most important metrics to create true focus for the company.
Want to read more about data practises?
If you want to read more on the topic of data and analytics, here are some more articles you may find useful:
The big gap between product and analytics
Product Managers should drive analytics
Why we need to stop tracking vanity metrics