In our white paper “How to become a more data-driven organisation”, we wrote about the five steps that an organisation would need to take, which are:
- Outcomes: Defining goals and metrics to ensure clear and measurable outcomes
- Analytics: Implementing and sharing the analytics to improve data-driven decision making
- Innovation: Testing assumptions through hypothesis testing and learning
- Data Platform: Gaining new insights and enabling more intelligent features through improved data integration
- Culture, Skills and Capabilities: People who believe in and understand the new ways of working are as important as the technology
This deep-dive will focus on Analytics and how we can use metrics to track our progress against our well defined Outcomes to kick-start our approach to defining strong OKRs (Objectives and Key Results).
We will focus on Key Results which are a key element of OKRs and, if not chosen carefully, can become too complex to measure and too numerous to count that they become an afterthought.
It is important to create the right cadence for data and analytics to support team decision making, to embed data directly into teams and to democratise access to the data for everyone in the organisation.
What are Objectives, and what are Key Results?
Objectives are short, memorable descriptions of what it is you want to achieve in a given timeframe. They should be quick and inspirational to help motivate and challenge a team.
Key Results are a small set of metrics that measure your progress towards the Objective. There should ideally be two to three Key Results per Objective, but no more than five – more than that and no one will remember them!
Why are Key Results so important?
By having well thought through Key Results, we can ensure that we are creating healthy and sustainable OKRs. They help us to define what we mean in our Objective and allows different teams to have other metrics to work towards the same Objective.
While you can have different teams focused on the same Objective, it is important not to have multiple teams impacting the same metrics as it could cause conflicts and it would be difficult to attribute positive or negative impacts.
Shouldn’t we track everything?
“Not everything that can be counted counts and not everything that counts can be counted” – Albert Einstein
Just because we have the ability to track every click, view, conversion, action, behaviour and everything in-between, doesn’t mean that we should. A standard implementation of Google Analytics, for instance, gives you so much data that it can be overwhelming to most people – and in most instances, there are only about 3-5 actionable reports worth working with.
Making sure that you are using a tag management system like Google Tag Manager or Tealium along with a well thought through and easily extendable data layer, will enable you to adapt your analytics implementation quickly without impacting your engineering teams.
Focus your analytics implementation and reporting efforts on the needs of teams to perform against their OKRs. Everything else is just noise, distracting you from the crucial conversations that help product development teams to pivot or persevere on their various initiatives.
Real-time data vs In-time data
Many companies focus on building real-time data pipelines that are expensive to develop and maintain – yet they are unable to make real-time decisions.
Your data needs to support the cadence of your organisation’s decision-making processes. If you are looking at your data daily, then your reporting needs to be daily. If decisions get made at a weekly planning meeting, then it needs to be weekly.
Real-time data is only needed if teams or the systems we rely on continually need to make real-time decisions.
Democratise your data
Data needs to be so ingrained into our ways of working that everyone should be educated to gain meaningful insights from the data available to them. Historically, data and analysis have been a centralised function in an organisation where stakeholders request the data that they think they need and who are often asking the wrong questions.
If we want to become a more data-driven organisation, then we need to ensure that people have access to the data they need quickly and efficiently. They need to be able to analyse the data and answer the right questions themselves.
Embed data specialists in your teams if possible to ensure that they are tracking the right metrics, asking the right questions of the data, and therefore ensuring that data is at the heart of how your organisation works.