With the digital transformations of many businesses of all shapes and sizes in full flow, their operations and services have become a non-stop data factory. However, for a digital transformation to fulfil on its promise, they need to be able translate all these 0’s and 1’s into something meaningful.
Hidden within this maze of data are insights that, if acted upon, could deliver huge value to the business. This could be anything from identifying a service bottleneck that boosts the productivity of your employees, to showing you how to go to market based on customer spending patterns. This means that data analytics, if applied effectively, could be central to establishing a lasting competitive advantage that cements your position in the industry.
Where’s the evidence of this? – Amazon and Google. These are two titans of the business world whose legacy is founded on their ability to turn data on millions, if not billions, of users into an increasingly consolidated competitive advantage. Amazon have been able to take data on their customers and turn that into services that we didn’t think we needed but, in a matter of years, expect. I can only speak for myself, but if I can’t get the product I’m looking at by tomorrow, I’m moving on.
Now this represents a gargantuan challenge for all businesses because they’ve got to provide services to their customers and employees that match the expectations set by the likes of Amazon and Google. These days, if a retail start-up can’t offer next-day delivery, it doesn’t last the year.
So, it’s no surprise that all companies big, small, new or old, are turning to data analytics.
Let’s start by drilling down to the basics - What actually is a data insight?
This is the DIKW model and it describes the functional relationships between data, information, knowledge and wisdom. It is best explained through the use of an example.
We have often seen the term ‘traffic insights’. Google, in particular, work very hard to give us the best route by drawing insights from the data they have available. This follows the pyramid.
We want to know the quickest route from Bath to London at 8am on a Monday morning:
At the bottom we have our raw, unprocessed data – the distance between Bath and London is 156 km. If we want to know how long it’s going to take us to get to London, that tells us nothing.
We start by applying a context to give us necessary information. We want to drive, and we have 3 options: via the M4 (185km) 2hrs 27mins, via Oxford and the M40 (207km) 2hrs 49mins, and via the M3 (195km) 2hrs 39mins. Based on this information we would take the M4.
What gives us our ‘actionable insight’ is the knowledge that we draw by applying our information to a given moment in time - today 8am Monday morning. There’s an accident on the M4, the traffic is tailed back 5 km and is adding an hour to the journey time. Suddenly, going via the M4 goes from the quickest to the slowest route. This is an ‘actionable insight’ because the traffic on the M4 causes us to take action by taking the M3 instead.
The peak of the pyramid is Wisdom. Wisdom uses our understanding of the past to make decisions in the future. In this case, the accident on the M4 is diverting hundreds if not thousands of people via the M3. The ensuing congestion has added 30 mins to the journey time in the past. As a consequence, today, the wise decision will be to go via Oxford and the M40 – we save 20 mins.
Bringing it back to business. To bring real value to your company, you need to make wise decisions that create change and progress. In a world where ‘actionable insights’ are becoming increasingly ubiquitous, it is the wise decisions taken in the context of the insights available that mark you out. Most people took the M3 because that was the obvious solution, if you really want to mark yourself out, you have to take that step further and predict that that’s going to happen.
The Identifiable Characteristics of Actionable Insights
To be able to make a wise decision we need to have the full picture and that comes from identifying as many insights as possible. It is widely accepted that insights have the following identifiable characteristics:
Alignment: To create value a data insight must align to the company’s goals and strategic initiatives.
Context: As mentioned above, an insight is information that is applied to the context of the system and its processes at a given moment in time. If your data is outdated and is drawn from processes that don’t exist anymore, it doesn’t provide any insight into today’s system or what it will look like in the future.
Relevance: Insightful data must be appropriate to the current time, period or circumstance.
Specificity: A data insight should be specific in how it aligns to the company’s goals. Where and when is change going to occur? How is it going to move the company forward?
Novelty: To create transformative change you need to look at the problems and challenges you are facing in a new light. That’s to say that old data can be relevant to the current objectives if it’s applied in a new way.
Clarity: Data must be presented in a way that makes it easily understood, interpreted and applied. Particularly as decisions are often made by people who are not data experts.
As always, these attributes should be taken with a pinch of salt. They don’t have to meet these criteria as a matter of principle, but they are a good guide and a good starting point.
For me, what learning about data insights has illuminated the most, is the potential not just to show us where we’ve been but to produce a map of where we’re going. Insights have the limitation that they only show what has been before, our role now is to extrapolate what we see before us to build an image of the future. And that’s the trick that Amazon and Google have mastered – the trick of knowing what we want before we know it ourselves.