Are you measuring these 4 qualitative metrics in your analysts?
How do you currently measure the effectiveness of your data analysts?
Gauging the real-world impact of any analytics department is difficult, because they don’t have the power to implement their recommendations. Frustrating as this may be, it’s the way the world of business works: the analysts will always navigate rather than drive.
Evaluating your analysts against numerical or statistical targets can therefore be problematic, so you have to measure an analyst’s effectiveness more from a qualitative perspective (as ironic as that sounds, given the nature of data analysis) – and you have to ensure that their personal development plan (PDP) reflects this.
So, here are four key questions to ask when reviewing an analyst’s performance:
Are they demonstrating technical expertise?
Numbers and statistics will always be the data analyst’s bread and butter.
It is true that businesses are now looking for their analysts to have more than just technical skills, but this doesn’t mean that technical expectations have lowered. Analysts still need to be experts in areas such as…
- Data collection
- Data cleaning
- Statistical modelling (linear regression, logistic regression, etc.)
- Uplift modelling
- Campaign planning and analysis
- Web analysis
And of course, this means using analytical software platforms such as SAS, Tableau, R, SPSS and so on. As different analysts tend to specialise in certain analytical areas and technologies, it’s important to encourage knowledge-sharing among your team, so that it becomes second nature.
Also make time to arrange external training wherever needed, to further the learning and fill any technical skills gaps – but do make sure your analysts are coming back and applying what they’ve learned to their roles.
Do they present findings and recommendations clearly?
Analytical work only has commercial value if decision-makers act on it, so presentation is everything. Your analysts have to know how to make their work engaging.
Getting stakeholders to buy into recommendations takes time – especially in organisations where the analytics team lacks credibility among senior figures in the business (which is a common scenario).
Analysts therefore need to make the business benefits shine in their reports and presentations. Making observations in plain English rather than corporate jargon is a good start, and perfecting the art of data visualisation and storytelling with data is equally important – getting into the habit of using explanatory charts instead of exploratory ones, for example.
That said, not all stakeholders share the same preferences and motivations. Your analysts have to learn how to position their outputs for different personality-types, pushing the right buttons with each one.
Do they communicate proactively?
When your analysts start to work consultatively rather than passively, that’s when you’ll know they’re building the right levels of trust and credibility with their stakeholders. And this will all be down to proactive communication. Instead of waiting to be asked to help, they’ll be integrated with stakeholders and other departments, offering their help upfront.
Working proactively means that an analyst can work with stakeholders to mould more detailed briefs – because stakeholders sometimes don’t understand what they actually need from the analytics department, and can mistranslate their perceived needs. A proactive analyst will drill down into the stakeholder’s request to establish the true requirements of the project, leading to better work, more quickly. Gaining a full understanding at the beginning of a project will also mean there is a much higher likelihood of getting it right first-time and help the analyst avoid re-doing any work.
Another benefit to proactive analysts is that they can better manage expectations, which ensures that trust and credibility are maintained once established. They give realistic timescales for pieces of work, pushing back where necessary to avoid disappointing, and they keep stakeholders informed on progress, flagging any potential delays well in advance.
Are they developing their commercial awareness?
Commercial context ensures that analytical recommendations are relevant and realistic.
Any good analyst will know what their organisation’s general mission and long-term objectives are, but a great analyst will take their commercial understanding to the next level – sharpening their knowledge of performance and strategy (short-term as well as long-term) and immersing themselves in the wider industry.
The advent of the ‘analytics translator’ is proof enough that modern businesses want their analytics departments to have deeper commercial awareness, but not every business is in a position to create such a role.
Data analysts at all levels benefit from being more commercially aware – first in terms of the quality of their work, and second in terms of preparing themselves for career progression (i.e. eventual promotion into a management role).
How to train and retain your data analysts
As the leader of an analytics team, keeping hold of talented analysts benefits you and your organisation. The longer your analysts serve and the wider their skill-sets get, the more effective your entire team will be.
Our new guide – How to train and retain your data analysts – explains all. Inside, you’ll find advice on…
- Managing analysts effectively
- Identifying skills gaps and development areas
- Making your analysts more commercially aware
- Supporting professional growth
And plenty more. Download the guide here.