4 Elements That Can Make or Break Your Data Analytics Initiative

Over the years, I’ve helped organisations in a variety of sectors improve their performance and reduce inefficiencies through the use of data and analytics. Either as a member of an analytics team or leading a team of analysts, I’ve come across and worked on projects of varying degrees of complexity and thought I’d share some of the key elements that I believe can have a major influence in your data analytics initiative.

While all of these elements are non-data related, I’d argue they can have as much of an impact on your data analytics initiative as issues relating to data quality, choice of analytical tool, time spent fine-tuning algorithms, etc.

1. The Right Level of Executive Sponsorship

If there is one single element that I believe can have great influence in the success of your data analytics project is the level of involvement your manager or project sponsor has in your strategy or project.

Your sponsor must give you the necessary investment (not only capital investment, but time and presence) you need for the project to succeed. Your sponsor (which could be your boss and your boss’s boss) should be the conduit between your project goals and the organisational strategy; he or she should be providing strategic direction, oversight, establishing key milestones and signing off on an ideal communication rhythm.

Where I think it becomes less productive is when executive sponsors start to demand an unnecessarily big say on the tactical elements of your project, on the toolset you plan to deploy or how you’re going to be guiding your specialist team. He or she should provide the right conditions for you to overcome obstacles or resistance and, most importantly, the executive sponsor need to know how to play the role of sponsor for your team’s and project’s benefit - not only for their own success.

I have come across projects in which the level of sponsorship turned somewhat unsatisfactory due to a variety of reasons (priorities shifting, poor clarity of expectations, lack of knowledge on how to play the sponsor role downward, too many projects being sponsored, etc.). And when this happens, the chance you’ve got to get the best out of your project is dramatically affected.

“The best executive is one who has sense enough to pick good people to do what he wants done, and self-restraint enough to keep from meddling with them while they do it” - Theodore Roosevelt

A suggestion here is to make sure you constantly communicate with all involved, and that you are actually involved and help set the expectations from the beginning with the project team and your executive sponsor. This will allow you to have better influence on how your sponsor sees your project in light of all the other priorities and will help set your analytics initiative’s operating rhythm.

2. A Clearly Defined Set of Questions

More often than not, data analytics initiatives start and finish with unclear set of questions it intends to have answers for at the end of its endeavour. Requests for data analytics initiatives, for example, can arise from a set of people who are experiencing a specific customer, product or service issue.

In some cases, analytics requests are relatively small in the grand scale of the business and often become a small tactical effort relegated to an analyst in the team to “go and figure it out” with hardly any sense of whether that analytical effort can or will result in any appropriate response.

I have found that having clearly defined questions from the beginning, can make your analytics initiative a lot more tangible and allows it to be put into the larger perspective of the organisation’s strategy. But I have also found that one of the hardest things to get out of project stakeholders is the ability frame questions that are relevant to their issue in a clear, attainable, detailed, time-bound, and complete manner.

“The wise man doesn’t give the right answers, he poses the right questions” - Claude Levi-Strauss

Here, my suggestion is that you work in combination with both your team and all the stakeholders to help them clearly define their problems and questions. Analysts have a good sense of what the data they’ve got or need should look like and what sort of aggregations and transformations are possible in light of the problem being solved. This can help with necessary drill-downs on some of the more high-level questions you get thrown at from varying stakeholders. Sometimes, having an outside opinion of someone else’s problem can help better clarify it for them.

3. The Right Mix of Talent

In any analytics project, you’ll most likely need a mix of both generalist and specialist analytical skills. You’ll need as much of the Machine Learning, AI and Statistics gun as of the less technical individual with a deep knowledge of the domain you’re operating in.

In my experience, having a more generalist analytical resource in a team can also help broaden the view of the whole project. It can also serve as an important “sparring” partner for the specialists to bounce ideas off, often ending up with novel approaches to solving the problem.

When it comes to talent, you will also need to make an assessment of your current analytics resource relative to what your project requires. You may need, for example, a solution architect embedded in the team to help select the most appropriate technology for the problem. Or you may need a data visualisation expert that knows as much of front-end web development as analytics.

When hiring for a data analytics resource for your team, I’ve found rather helpful to gauge not only the candidate’s technical ability (obviously) but also their resilience and willingness to adapt and recover to ever-changing conditions, priorities and strategic direction.

“Be humble, be hungry. An always be the hardest worker in the room.” - The Rock

If you are hiring for a long-term resource to grow in your team or organisation, I’d also suggest focusing less on where that candidate is at the moment (i.e. school she attended, scores she achieved, experience, companies or projects she worked for, etc.) and more at how far she has come or evolved to be what she is today (i.e. what adversities she has been through, how she overcame those, how she spends her time, how does she continuously improve her analytical skills, etc.). To me, these can be powerful indicators of a valuable professional, now and in the future.

4. The Political Environment

Culture, power and politics are elements that can affect your data analytics initiative the most. This is because analytical problem-solving doesn’t create itself out of thin air (yet). It relies on humans, with all of its beauties and flaws, to get things defined, built, managed, fine-tuned. And people can be heavily influenced by the environment they spend most of their time in.

As you can imagine, one thing is being surrounded by a group of data savvy individuals who engage with you and incentivise your efforts on a frequent basis - even as outsiders to your project. The other is when you have to deal with indifferent, uninterested stakeholders showing a lot of resistance and obstacles to get agreement on very trivial tasks. The worse the culture and level of politics downward through to you and your analytics team, the more difficulty you’ll have in delivering useful, timely, insightful analytics solutions.

In my experience, the less customer-focused and complex the organisation, the more chances are you’ll find a culture of politics and power struggle. In this scenario, you may also find it harder to find and retain resources - in or outside your project team - to be engaged and interested in helping you achieve your analytics project goals.

My suggestion in dealing with this element is to make sure you do everything you can to know - preferably beforehand - what you’re getting into. Ask previous employers, research company reviews and don’t hold yourself back from inviting for a coffee (or as many as you think you need to know what you need to know) those interested in hiring you.

I’d also suggest clarifying the appropriate amount of formal authority you’ll need to deliver on your project objectives. You should assess whether the level of authority proposed - relative to your peers as well as relative to other areas of the business in the same context - is enough to make you succeed.

“Politics is the art of looking for trouble, finding it everywhere, diagnosing it incorrectly and applying the wrong remedies” - Groucho Marx

Lastly, it’s important to constantly monitor whether you are getting the right amount of support you need from your peers and boss to get your project across the line. Particularly, whether you are getting the right amount of exposure from your managers and giving the right amount of exposure to your team. You should aim at delivering upward results (to your managers and stakeholders) as well as getting downward support (for you and for your team).

Over the years, I’ve found that these more non-data related elements I mentioned above can have material implications in your data analytics initiatives. Yet, they are often not on the ordinary analytics team’s radar. So, for your next analytics engagement, make sure you keep these make-or-break elements in mind as much as your data quality concerns.

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