Zone’s James Tyson describes the three key elements for a clear and successful data strategy…
With everything that has been going on in 2020, there has been an increase in the need for a clear data strategy in both the private and public sectors in the UK. Since the start of the pandemic the government have been turning to data to inform and underpin the decisions it makes, be it imposing lockdowns or lifting restrictions. It has been the same for businesses, who need hard-line metrics to understand their operational and financial dynamics in order to support making difficult decisions due to Covid-19.
As a data professional, I constantly see interest in using data to inform decisions, but it has always been a challenge for both data professionals and stakeholders to understand what it all means. Data is an extremely powerful tool for companies to utilise, but that doesn’t mean creating fancy dashboards for the sake of it. It requires strong data narration (see my previous blog) and clear actionable insights from the effort — answering the ‘so what?’.
To get the most out of your data I believe three key elements are essential to ensure your reporting and analytics efforts are successful:
- Collaborative planning
- Concise report creation
- Clear insight narration
The following demonstrates how these can be used in the context of a business change programme.
Collaborative planning involves defining a process that ensures the metrics you report on are actually of use and that they support the reporting development. A first step to achieving this is utilising a technique called value mapping (mapping outcome with value).
Value mapping sets out to establish how you can align the customer/company needs, strategy & goals (the value) with what you will end up reporting on. Only by defining a business’s long-term goals and strategies can you ensure what you will measure will be relevant, meaningful and answer the ‘so what’. A useful tool to ensure goals are achievable is to use the SMART acronym (Specific, Measurable, Achievable, Relevant, Time-based).
The first stage of value mapping involves workshop(s) with the end stakeholders to define the end goal of the project. While this usually happens on most programmes, it is important for the data or reporting owner to be involved in these conversations to ensure they are clear on these initial points.
The next step is the mapping portion of value mapping which uses a measurement framework. This enables you to easily map actual metrics you can track with the key outcomes you are looking for. Throughout this process it is vital to ensure constant playback of findings to key stakeholders to validate and verify the understanding, and to keep them engaged in the process.
With the defined needs and goals of the project in mind you can start by defining several hypotheses stating what effect of change you are looking to achieve.
For example, if the goal of the work is to increase meaningful site visits to a company website, a hypothesis could be: “By increasing our focus on a better SEO strategy we expect to see an increase in meaningful site visits and a corresponding uplift in product sales.”
From this hypothesis you can pull out outcomes you would expect to see if it were successful. Taking our example above, a more focused SEO strategy could lead to “an increase in search ranking on more relevant search terms to your company”.
You can next define the impact to the business of these outcomes to again ensure both the hypothesis and outcomes align with the business goals. Using the above example, the impact of a SEO strategy could mean an “increase in longer site visits due to visits from more relevant search terms”.
Finally, to close the loop, it is important to review what metrics will be monitored to track the proposed hypothesis, outcomes and impact (there can be several). Continuing with our example, by reviewing site visits from newly agreed search terms and/or monitoring the length of site visits vs benchmarks will show whether or not a hypothesis has been realised.
One note to point out is that for a measurement framework to be successful, not all hypotheses need to be correct. Part of the reporting process is testing and learning what is and isn’t working, as this enables a more agile strategy for improvement and is actually a big value marker for using this process. Each hypothesis can be adapted over time with more insight gathered.
Another useful point to make is that value mapping gives you a defined tangible process that can be shared and discussed with the end stakeholders. This allows for refinement, communication and, most importantly, collaboration. It is for the data professional to start this process off and use their experience but engaging the stakeholders and ensuring they are involved is crucial for long-term success.
Concise report creation
By now you have defined the requirements with the stakeholders and worked through ensuring the metrics you are tracking can be directly connected to the programme goals. The next step is to ensure all this background work is utilised in the reporting format to meet the desired reporting requirement.
Actionable insights are those in which there is a clear reason and next step for the audience to take away from the report, and is the determinant as to why it has been included. It is extremely important this is considered when choosing visuals for a dashboard or deck. In my experience, reporting should be kept as short and clear as possible, as usually the audience do not have much time to dig into the detail (that’s why they hire us!).
This is where value mapping helps as each metric you have defined can be clearly mapped to the goals, ensuring they can (with proper review and analysis) be classed as meaningful. Alongside this, it is important to remember that different visuals can convey very different information — for example, pie charts with more than 3 to 4 segments can become extremely hard to understand (something that is discussed a lot on the internet)
Clear insight narration
Finally, crystallising the ‘so what?’ from reporting and analytics is the final crucial piece to enabling insightful reporting. A pragmatic approach is advised when first producing a report which can evolve as the data and audience’s understanding evolves. If you want to convey a change over time, an obvious choice is a line graph. If you want to show proportionality of a metric, then treemaps are great. However, to the original point of this blog these visuals can mean nothing without delivering a clear, actionable message.
When I think of the ‘so what’, I have always used narration and/or brief commentary to convey what the audience should be taking from a particular visual. Unless it is extremely obvious (which it rarely is), a brief note provides the audience with a clear marker as to what they need to get from the report.
It is important to stress the brevity required in this commentary (think 1–2 sentences max), which should be structured using my ‘SSS’ process below
- Situation — Where we are (e.g. we have seen a 40% increase in meaningful visits this week from newly targeted search terms)
- So what? — What does this mean to the audience (e.g. this means we are on a positive path to sustaining meaningful engagement)
- Suggested next action — What do we do next (e.g. we will continue to monitor these search terms to ensure this is effect is long term)
Too often, data reporting can be swayed by the desire to generate complex visual dashboards or vanity metrics that provide little to no meaning or insight. But by ensuring a clear method for determining metric selection, confirming this process is clear to the client and developing a concise report that presents this meaningfully, it will make it easier for you, the team and the stakeholder to see the value of your work.