5 easy to implement routines to always deliver actionable insights
5 easy to implement routines to always deliver actionable insights
Ready to learn how to create actionable insights, improve your data project success and business stakeholder satisfaction?
Backed by my 16 years of experience building and managing data teams, I give you 5 easy to implement routines to always deliver the right insight.
In this post on how to create actionable insights we go through the following:
- Defining project success – data project is not a success if it doesn’t produce actionable insights
- What are actionable insights and how they drive business action
- Benefits of creating actionable insights (infographic)
- 5 routines to implement to always deliver the right insights
- The 5 W’s of reporting
- Including business customers in data process.
- Standardising and systemising processes
- Embracing data storytelling
- Encouraging data-led culture
Let’s dive in!
Understanding where to start when it comes to data projects and providing actionable insights leading to desirable business outcomes can be troubling. Especially for managers, analysts, and researchers new to the Data & Insight industry.
What is the question?
What are the right insights?
How is a project’s success defined?
What resources are available to me?
With more and more organisations investing heavily in data collection, it is projected that global spending on the data infrastructure will reach $100bn by 2027 (report by Global Market Insights, Inc.)
However, with hight costs of data storage, collection, and organisation, companies need results from their investments. This has put more pressure on management to prove some ROI from their data assets.
Pressed-for-time, strategy-focused executives demand data more often and quicker which, consequently for data teams, often leads to a lack of context on requests.
An example of this would be a broad request with multiple answers:
‘What customers are likely to cancel their memberships?’
These often lead to results which do not offer a solution.
Data teams could also be asked to simply mine the data and produce some sort of result. However, without the right context for what they are looking for, the organisation will not benefit from any valuable findings and your project will be a failure.
So, how do you get the right insights from your data? First, let’s explore what it means for an insight to be right and what makes a data project a success.
Defining project success
Data project is not a success if it doesn’t produce actionable insights
Our latest research ‘Emotional Intelligence: Data & Insight job satisfaction and its impact on project success’ revealed that definitions of project success differ for data creators and their business customers.
While it’s been reported that 80% of all data project fail, providing no desirable business outcome, and stakeholders have been dissatisfied with results they get from data projects for a decade, 66% of data professionals surveyed by us said their projects were a success at least most of the time – with 11% saying their projects were successful every time.
Here’s the problem: while answering asked business questions means data project success for data people, their business customers don’t see projects as successful unless they create actionable insights driving business growth.
There clearly is a disconnect between the two groups.
We have a separate post about it, and you can read it here. Luckily, steps can be taken to significantly decrease the chances of project failure, but before we get into how to create actionable insights, let’s define what actionable insights actually are.
What are actionable insights and how they drive business action
Andy Grove said in his book, High Output Management, “Complex systems are black-boxes, and an insight is like a window cut into the side of the black box that ‘sheds light’ on what’s going on inside.”
Given the misuse of the word, let’s establish what an ‘insight’ is and what it isn’t.
Data is not insight.
Information isn’t insight.
Insight is insight.
According to agreed standard, data is raw figures and numbers that we capture.
Information is a collection of cleaned and processed data, from which we can understand something about the thing being measured.
Insight is gained by analysing data and information and can be used to drive business strategies and decision-making.
Insights help us bridge the gap between how we think organisations works, and how they really work. It’s the missing link between data and business value.
Actionable data insights help us clarify strategies aimed at benefitting the organisation by communicating urgency and encouraging action.
So, while data and information help understand behavioural patterns of any organisation, actionable data insights help translate those patterns into business actions.
Characteristics of an actionable insight
For an insight to be actionable, it must be all of the following things:
going hand in hand with business goals and strategies.
with background as to why it is important and what it means to a business.
delivered to the right person at the right time.
Explaining WHY a particular trend occurred.
Challenging our knowledge and actions, rather than reinforcing them.
Clear for purposes of easy adaptation.
While actionability of insights doesn’t guarantee their adoption, providing them reinforces data-led culture within organisations and encourages them to act.
If they do, great. If they don’t, we know we’ve done the best we could trying to add value.
Now that we know what project success means and what actionable insights are, we can dive into what this article is supposed to be about . That is how to create actionable insights from your data projects.
Benefits of creating actionable insights
5 routines to implement to always deliver the right insights
The 5 W’s of Reporting
Every data project starts with a question.
From my 15+ years’ experience, I know full well that the initial question is rarely the right one. A lot of the time, business customers don’t quite know what to ask for, and why would they? They’re not data professionals! They don’t have expertise in data analysis. Most the time they don’t even know what resources are available to them.
We’re the ones who are supposed to point them in the right direction.
It’s not about WHAT your business customer wants to know. Remember the 5Ws you learnt at school? Yes, the Who, What, When, Where and Why. It’s a simple yet effective formula to structure your project around.
Make sure you ALWAYS know the ‘why’.
If your report isn’t formed by context, then you’re just going to present arbitrary numbers. Being well-informed allows you to create a more focused plan for your analysis.
Tip: If the person making the request hasn’t outlined why they need the data, ask them what they’re planning to do with it. Understanding what they’re hoping to achieve brings your project a step closer to success.
Don’t exclude your business customers from your data process
This is due to low collaboration, the gap between technical expertise and organisational leadership and your business customers’ reluctance to use complex systems like Jira.
Project scope can change repeatedly throughout the duration of the project. It is important to keep your business customers involved in the process and communicate with one another to avoid starting over and redoing the work.
Staying engaged means you stay on track and reinforce outcomes at every stage of your project.
Tip: To avoid low collaboration, implement systems that are easy for both your data teams and business customers.
Standardise and systemise your processes
I’m genuinely shocked that most data teams still haven’t systemised their workflows.
Having the right Data project management tool to manage the entire process of delivering data insights and serving the needs of both data teams and their business customers is vital to drive organisational performance.
Yes, some teams already use programs like Jira or DevOps to manage parts of the insight process, but these kinds of platforms are way too complicated for your business stakeholders, leading to disconnect.
Using email for requirements gathering is chaotic and, often, prolonged. And messing up that part of the process means no valuable business outcomes.
Tip: Automate and systemise as much of your processes as possible. It’ll truly improve productivity and efficiency within your data teams. I’m going to do it…ready? Why not try Brijj? ; )
Embrace data storytelling: communicate insights in ways easily digestible to your target audience
Whenever you can, apply charts and graphs instead of data-centric tables.
In the immortal words of someone who’s long gone: a picture is worth 1000 words. In fact, 65% of our society are visual learners.
That aside, 9 out of 10 times, your business stakeholders are much less technical than you are. Chances are, presenting numbers on their own will cause confusion.
Combining data, narrative, and visualisations is how you create actionable insights.
Communicating in ways that are easily digestible to won’t create barriers and ensure insights are easily understood and can drive action.
We’ve recently attended CDAO UK, a networking event for data & analytics leaders. One of the first talks we attended was Kate Platanova’s presentation on creating a data-led culture. The Group Chief Data Officer at HSBC said:
“It’s up to us, data professionals, to make our discipline understood.”
Tip: Visualisations provide an easier way to present information to clients that might not be as ‘technical’ as data teams.
Encourage data-led culture
Let’s imagine an organisation as a huge mechanism of constantly moving cogs, each playing its part for the organisation to survive – its relationship with stakeholders, the environment, functionality, etc. It is impossible for a single person to fully comprehend and understand every aspect of every mechanism.
There will always be a gap between how you think the organisation works and how it really works but data help us understand organisational functioning better.
Promoting a data-led culture requires the vast majority of the organisation to have access to data teams.
By engaging and collaborating with your business customers closely, you can give them the confidence to seek out the underlying truth as to what is driving performance.
Data projects aligned with business goals and involving all project stakeholders allow them to link data to outcomes, encouraging the ultimate organisational data culture.
If you and your organisation are already doing the above, great! I’m sure your data projects drive organisational growth.
If you don’t, it’s time to think whether your definition of project success mirrors the one of your business customers. I am confident in saying, it does not.
Implementing the above into your data process can increase the impact your work has as well as your stakeholders’ satisfaction.
That wraps up our post on how to create actionable insights. I hope you found the data interesting and useful.
With that, it’s time to hear what you have to say.
What do you think of these easy to implement routines for actionable insights? Have you got any questions?
Either way, let me know your thoughts through our socials!