I’ve spent the past five years as head of analytics at SONY Professional, where I have been lucky enough to have access to some of the best analytics technology available, but needed to cater to the needs of a company with a vast and complex structure.
Creating a data-informed culture is a passion of mine, and to do this right I’ve learned you need to invest a lot of time shaping and informing the culture than just the data itself. Once good data has been captured and rendered into meaningful insights, it would be easy to assume the job of the analytics manager is complete.
But the reality is actually very different – producing a report should be just the first step of an effective analytics manager’s role.
Jessie Willson
The job of the analytics manager is to discover objective facts, not to tell people what they want or expect to hear.
That sounds obvious, but you’d be surprised how much trouble this causes.
If the audience isn’t prepared for the conclusions the analytics team produces, all that hard work can easily come to nothing.
Without careful preparation, conclusions are often misunderstood, and this can result in real damage: misdirection, poor decision-making, and poor internal relations.
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So despite what you might think, strong interpersonal skills are often an analytics manager’s most important assets, not technical knowledge.
To understand better the sorts of problems analytics managers constantly encounter let me give you an analogy. A good analytics report is like a referee making a crucial penalty call in the closing seconds of a goalless football match. Even if the call is spot on, half the players on the pitch will reflexively shout, throw up their hands, and complain that the referee blew the call. That’s just human nature.
Being in charge of analytics is a LOT like being a good referee – it is your job to be impartial and tell it like it is, especially when there are big consequences at stake. Just like football players, when some colleagues get information they don’t want, their first reaction is to question the data, the analytics, the people who compiled the report, assuming something or somebody got it wrong.
Analytics does not even have to produce ‘bad news’ to create friction among colleagues. All data-driven conclusions are potentially disruptive. For example, objective proof that a new initiative is driving sales is great news for the company overall, but this information may result in resources being allocated away from less successful initiatives. Finding out your own project is being curtailed because of someone else’s success can be very disheartening.
Knowing that an important report is about to be released can make many people in a company a bit anxious. And that anxiety can easily lead to defensiveness, and defensiveness in turn often leads to misunderstandings. And misunderstood analytics, which typically lead to bad decision-making and misdirection, are usually worse than no analytics at all.
Here’s my real-life example of how easy it is for analytics to be misunderstood when colleagues are anxious and defensive about its conclusions.
A few years ago I produced a report to evaluate the contribution of a key industry event with an international customer base, and the impact it has on brand awareness and product launches. Digital analysis showed that demand for the show, and the majority of the people who visited our stand in the previous three years, had come from outside of the UK.
When I produced my findings, however, the event manager thought I was criticizing her: “I can’t help it if an international audience likes our display,” she explained, and then proceeded to tell me all the things she does to encourage UK customers to attend.
She had misunderstood my point, and then I realised why. Her primary role at the company was to drive UK sales growth. A relatively low percentage of UK visitors sounded like criticism to her because her job is to focus on the UK, not the international market. If I had properly anticipated how her role might colour her reaction, I would have presented the data differently.
That is a relatively minor example of data being misinterpreted, and one that I was on the spot to correct immediately. But in a far-reaching international corporate environment, where different cultures and different departmental responsibilities are all factors, making sure everyone understands the conclusions analytics is meant to show is far from straightforward.
Taking responsibility for not just producing analytics information, but making sure they are correctly understood and properly implemented, should be an essential role for any analytics manager. Here are my top three tips for making sure this happens.
Know your audience. This is perhaps the most important skill for sharing reports and insights, and also perhaps the most difficult to quantify. Be aware that everyone looking at a given report will come with their own expectations and perspectives. Do not be afraid to invest time on this – every misunderstanding you can navigate around will save enormous amounts of time down the road.
Reach out in advance. Got a report that is going to ruffle some feathers? Tell key stakeholders in advance. No one likes bad news, but springing bad news on people in public just leads to unnecessary complications. Heading up analytics at a division of SONY, where Japanese business practices are part of our company culture, I’ve learned how time explaining and syncing messages in advance not only prevents any awkwardness, but provides an opportunity to get a better understanding of underlying causes and business priorities from your colleagues before a report is released. This makes the process more collaborative and actionable rather than disruptive and anxiety-inducing.
Vary your message. If you know that different constituencies inside your organisation will react to data in markedly different ways, send out different versions of your report. Use the same data, but tailor your message and your conclusions to your audience. Your reports will have greater meaning and ultimately be more useful and actionable.
And here’s one more important asset a good analytics manager needs to make all this possible. People who are attracted to careers in data analysis (like me) are typically a bit introverted. This makes it tempting to ignore the cut and thrust of company politics and pretend the issue of how analytics is received is someone else’s problem. This trap can actually be hard to avoid if you are doing too much of the work by yourself – if your head is buried too deep in the numbers it is hard to find the time and space for utilising reports and actually implementing the insights. .
The best way to address this is to get plenty of support. This can mean several things: getting advice from thought leaders and peers, having the support of your senior stakeholders and a strong team around you, I have found that working with an outside agency is also a great solution. Once I found one that shared my views and values I was freed up to focus on sharing and actioning insights, not just producing reporting.
