The transformative value of data analytics is well understood, at least in principle. Business leaders are investing heavily in recruiting large teams of analysts, data scientists, and furnishing them with expensive reporting and analytics licenses.

But investment and success are not synonymous.

All too often, data analytics teams fail to have the desired impact across the business. Gartner recently reported that over half of chief marketing officers (CMOs), for example, are dissatisfied with the impact of their marketing data analytics teams[i].

In this article, we look at why even good analytics teams can fail, and offer some suggestions for analytics managers designed to help your organisation get more value from the excellent work your team is doing.

Why Are Some Analytics Teams Failing?

I’ve written before about how poor data quality can hold analytics teams back, often causing individual projects to stagnate and fail.

But what about when analytics teams have access to the data they need and are producing good work? Why do analytics departments in this position so often still fail to deliver the promised value to their organisations?

The Walled Garden of Analytics

One common reason is that analytics teams, like any other, can become siloed from the wider business. The analysts and data scientists stop communicating effectively with their internal stakeholders, creating a disconnect between the quality of their work and the ultimate impact of their analysis.

In many cases, this type of misalignment occurs because of the speed at which many analytics and data science teams have grown in recent years. Rapidly expanding teams develop their own culture and their own momentum. The new analysts and data scientists are eager to do meaningful work and prove their value. And so they get on with it.

However, without careful alignment to the wider business, these efforts may not be directed in the most high-impact places. In short, analytics departments start working on the ‘wrong’ projects. Even when their projects go well, their work fails to have a meaningful impact on business objectives.

“The biggest waste of time is to do well something that we need not do at all.”  (Gretchen Rubin)

When analytics teams become siloed, it requires decisive corrective action to realign them with the wider business.

Fortunately, the way to fix the problem is simple. As we’ll see, it’s just not always that easy.

Course Correction: Realigning Siloed Analytics Departments

To realign siloed analytics and data science teams, the approach is three-fold:

1. Communication: Increase frequency and quality of communication at all organisational levels‍
2. Openness: Provide complete transparency over what the analytics team is working on‍
3. Balance: Ensure managers strike the right balance between engaging with stakeholders and hands-on analysis

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1. Increase frequency and quality of communication at all organisational levels

Effective communication is the cornerstone of every successful partnership. Analyst-stakeholder partnerships are no different. And yet, communication is often not held in high enough regard by analytics teams.

Communication is often undervalued for several reasons, including:

• Analysts’ projects are often intense and can be all-consuming, leaving little mental energy for the ‘softer’ aspects of their roles.
• Many analysts find comfort in working alone or in small groups, and enjoy burying themselves in data more than talking to stakeholders.
• Some believe that their non-technical stakeholders neither want nor need to understand the detail of their work.

Working closely with stakeholders can feel like a ‘necessary evil’ and analysts can easily underestimate the importance of communication in defining the success of a project.

As a result, analytics leaders must reinforce the importance of communication at every level in their department. For analysts and data scientists to have maximum impact, it’s not enough just to do the work.

A tightly-integrated analytics team communicates with its internal clients all the way through each project, regularly sharing analysis outputs and gathering feedback.

Over-indexing on communication helps to ensure the end-deliverable precisely meets the clients’ requirements. It also avoids the all-too-common situation where an analyst, working hard but in an isolated way, delivers the end-product and the client says “that’s not really what I wanted”. Or even worse – “thanks, but I don’t actually need that now!”.    

Processes must support effective, regular communication. Project kick-offs that enable analysts and their stakeholders to discuss objectives, priorities, timescales and potential barriers will help. Weekly status calls with project stakeholders should also be the bare minimum.

A former boss of mine tackled this problem by insisting that her analysts communicated with internal clients at least every two days, even when there wasn’t seemingly much to update. While this may sound like overkill it did ‘break the wall’. And the behaviours it encouraged quickly became second nature. In fact, communication became so ingrained that the ‘rule’ itself was forgotten.

Remember also that effective communication works both ways. It’s not just about the analysts explaining progress to their internal clients. When the analyst-stakeholder relationship is working well, this gives analysts the opportunity to hear about their stakeholders’ challenges and objectives. This perspective gives analysts valuable commercial insight that can only improve their work.

In summary, as analysts and data scientists we are close to the data – our stakeholders are usually not – so it’s vital that we take them on the journey with us. This means updating stakeholders on our progress and our challenges, as well as our breakthroughs. The ‘big reveal’ at the end of a project rarely lands well if there has been poor communication along the way.

2. Total transparency over what the analytics team is working on

Siloed analytics and data science teams likely feel remote from the day-to-day workings of the wider business.

Analytics departments therefore need to establish a routine of regularly sharing what they’re working on. Done well, this prevents misunderstandings and creates new opportunities to deliver impact.

Regular meetings between the leaders of analytics and different business functions should be viewed as opportunities to share details of what the analysts and data scientists are working on. Analytics leaders should develop a clear way of describing their team’s projects, along with an indication of the goals, who the stakeholders are, and what the status of each project is.

This also provides a perfect forum to flag issues and highlight successes.

If this sounds simple, it’s because it is. And yet many teams still don’t do this as well as they could.

3. Ensure analytics managers strike the right balance between engaging with stakeholders and hands-on analysis

Analytics leaders have often built their careers by solving complex data problems. Some come from academic backgrounds. They love working with data and can become absorbed by the intellectual challenge of their work. When they’re promoted to management positions, their tendency is often to continue doing what they do best. This leads many to continue spending the majority of their time performing hands-on analysis, at the expense of working with their stakeholders.

Managers continuing to perform analysis and support their teams has strong benefits, of course. Often analytics managers are promoted precisely because of their aptitude for analysis and their vocational data expertise. However, analytics manager roles also have different – and often competing – demands. As mentioned earlier, building effective relationships with their internal clients is a critical priority for the success of their team and department.

And analytics managers have a pivotal role to play in modelling exceptional stakeholder management behaviours for their teams.

Balance is key. In addition to building client relationships and supporting their teams, analytics managers will often still be required to roll their sleeves up to perform hands-on analysis as well.

Striking the right balance between analysis and communication will be dependent on things like:

• How many internal clients the analytics team supports;
• The nature of the problems the team works on; and
• The size and skills of the analytics team itself.  

In particularly large organisations with a large number of internal clients, it may be desirable that analytics managers dedicate themselves solely to supporting their team and communicating with stakeholders.

In most cases, however, a blend will be more appropriate.

One thing is certain – managers in the most impactful analytics teams find the balance that allows their teams to succeed. They get the work done while protecting sufficient time to communicate proactively with stakeholders around the business.

In Summary

Even analytics teams that are doing high quality analysis can still fail to deliver value. Often this happens when departments grow quickly and find themselves operating on a different rhythm to the rest of the business.

When this happens, the siloed analytics team loses credibility and receives criticism from its peers. The cultural gap widens and the analytics department ultimately loses motivation and delivers less valuable work. The wider business fails to extract the desired value from its data, all the while growing resentful of the high cost of their analytics departments.

The solution is simple, but not necessarily easy. Prioritise communication. Make it clear what the analysts are working on. Encourage analytics managers to spend less time analysing data and more time talking to their stakeholders. Do this well, and the great work the team is doing will translate much more accurately into tangible value for the organisation.

[i] Gartner’s Marketing Data and Analytics Survey2020: Optimism Persists as Results Fall Short of Expectations

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