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Are you effectively measuring your analytics teams?


Measure your data analysts qualitatively – not quantitatively.

Analysts work in numbers, so you would think that measuring their effectiveness is rather straightforward.


Unfortunately, in the real-life world of business, that’s not the case. Analysts can only make recommendations. It’s up to the company’s decision-makers to actually implement those recommendations and change the business for the better.

As such, it’s unfair to evaluate your analytics department, and the individual analysts who comprise it, against quantitative targets such as statistics and financial figures.

Instead, their effectiveness should be measured qualitatively, focusing on their degree of influence within the organisation and its various departments. And each one of your analysts needs to know that this is the case, with clear professional-development objectives to work towards.

In this article, we’ll cover four key qualitative criteria to measure your analysts against.


1. Clear technical expertise and confidence

Technical expertise alone does not make a great analyst, but it is and always will be essential.


Most businesses (the forward-thinking ones, anyway) are now also looking for soft skills, but they still have high technical expectations, because there is no substitute for expertise in areas such as...


● Programming languages (e.g. SQL, Python, etc.)

● Software platforms (e.g. Tableau, R, SAS, Power BI, etc.)

● Data collection and data cleaning

● Forecasting

● Statistical modelling (linear regression, logistic regression, etc.)

● Uplift modelling

● Campaign planning and analysis

● Web analysis


And as data analysis is such a broad field, with many disciplines and technologies, it’s important that your team contains a mixture of specialisms, and that your analysts share their specialist knowledge with each other.

As well as simply being experts, your analysts need to demonstrate their expertise to the wider business, specifically the decision-makers. Otherwise, they will have limited (if any) impact.


2. Commercial awareness and deep industry knowledge


Every analyst should have at least an understanding of their company’s overall purpose and its commercial objectives, as well as knowledge of historical performance against certain metrics. That should be a given.


Truly valuable analysts, however, fully immerse themselves in their employer’s industry and their stakeholders objectives and goals. Doing so means they can understand everything in its full commercial context, which is crucial to putting their results into context and for changing strategy for the better. Only then can the analyst’s recommendations be truly practical.


And of course, any analyst who wants to progress into management (either in the medium or long term) must clearly demonstrate commercial awareness. But that’s not to say that only the ambitious analysts need to sharpen their commercial minds – all analytical work is improved when it has proper context behind it.


3. Proactive communication and rapport with stakeholders


To do well in today’s business environment, analysts have to get up from their desks and become proactively involved in conversations, meetings and projects.

Rather than waiting passively to be called upon, they’re operating on a self-motivated consultative basis. And this is when they will begin to add true value, gaining the trust and credibility of decision-makers across the business, and building a rapport with all the different departments they might have to work with.


It isn’t about dazzling (or often bombarding!) non-technical colleagues with technical jargon. Quite the opposite. It’s about translating the technical jargon into the language of business – which is the only language that stakeholders and decision-makers are interested in speaking.


In addition to the ‘networking’ element, proactive communication means that the analyst is better placed to manage their workload, negotiate deadlines and – this one is a huge advantage – arrive at much more precise, focused briefs at the outset.


If the analyst can ascertain the stakeholder’s true needs at the very beginning, the resulting work is much more likely to hit the nail on the head first time round. This is a widespread problem that analysts come up against all the time, and there are two main reasons:


1) Stakeholders mistranslate their needs/objectives when making their requests for analytical work.


2) The Stakeholders themselves don’t actually understand what it is that they need out of the analytical work, and therefore can’t guide their analytical departments.


It’s therefore up to analysts to interrogate each brief they receive, and to work collaboratively with stakeholders to refine those briefs. The only way to do that is by standing up and speaking up.


4. Effective presentation and reporting


As we touched on at the beginning, analytics departments can only be considered ‘successful’ and commercially valuable if their recommendations are acted upon. So, analysts need to know how to sell their work to stakeholders.

Just like a classic story, a strong piece of analytical work, whether it’s a word-processed report or a slide-deck, has to be engaging and hit the right notes – which means highlighting the business benefits and really hammering those home.


However, a common mistake analysts can make is dressing their work up too much, either consciously or not, with verbose jargon and overcomplicated charts.


The solution is simple (quite literally): to use plain English, not mumbo-jumbo that sounds impressive. Along with that, effective data visualisation is key – such as using explanatory charts (as opposed to exploratory). These techniques mean that stakeholders can absorb the information quickly, which makes the ensuing recommendations seem more pressing.


The one thing analysts must bear in mind here, though, is that there are different types of stakeholders, with different motivations and objectives. Learning how to engage each one may take time and effort, but it will yield results.


Steve

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