4 Ways to Spot a ‘Good’ Data Analyst

Hungry for growth? Your data does hold the potential to supercharge your business and catapult you kilometres ahead of the competition. But while data is delicious in theory, it’s the astuteness of your analyst that will determine how successfully you identify the true areas for impact in your business.

Got it? Good!

These days we see many executives patting themselves on the back for having tackled the ‘main challenges’ to becoming data-driven, by building data infrastructure and getting their analytics in order. Don’t get us wrong – this is a crucial step! But sadly having access to data and wielding it, are two very different things! Which is why a ‘good’ data analyst will also need to understand the business question at hand, interpret the answer to that question and of course know the means to retrieve the answer.

It should therefore come as no surprise that data analysts have been hailed as one of the most in-demand tech talents for 2021. But when it comes to what ‘good’ looks like, there are subtle nuances that are easily missed by those who are newer to the game. So stick with us as we break down the 4 ways to spot a ‘good’ analyst. Don’t be late, you’ve got a date with data!

The right fit

We’re just going to come right out and say it – team size matters.

We hate to break it to you, but in a small business you’ll need data engineering skills before you even bat your eyelashes in the direction of an analytics solution. Once you have access to your cleaned, organised and joined data, you can start unearthing meaningful trends and insights from the mix.

In the meantime, heed our warning and don’t be seduced by candidates based solely on their strong BI skills. In reality they are likely to waste time fumbling around with your data before they can actually use it. In fact, even Heads of BI are often not incredibly knowledgeable on the technical side at all. Which means that alone they are ill equipped to roll up their sleeves and get the data flowing.

In a larger organisation the dynamic will be different, with a team of engineers, data analysts and business analysts to take on different pieces of the puzzle. Here you need to be factoring in more of the soft skills such as excellent communication and the ability to manage projects and potentially other team members. The bigger the team, the more important teamwork is to make the dream work.

So ask yourself:

  • What does my team look like? Have you got engineering capacity in place? If not circle back to the starting line and rethink who you need to bring onboard first!
  • Can they code or do they build visualisations (BI)? The ability to do both is preferred but if your business is on the smaller side, then coding is king.
  • Are they an Expert in SQL with knowledge of python? No? If you require them to handle data processing then they don’t have what you need. Sometimes people call themselves analysts but really they only work with Excel, Alteryx, Tableau or PowerBI.

A curious disposition

You are likely to be familiar with the proverb, “Curiosity killed the cat” – often used to discourage children from asking incessant questions one after the next, after the next. The problem with this tactic is that children have an insatiable urge to understand the world around them and how they can move forward in it. After all, they’re new here.

Similarly, a good data analyst will be hungry for answers about literally everything. It shouldn’t be enough for them to just fulfill a brief and move on, instead they will think about why they are performing it to begin with and what will happen as a result. Data analysis can be as much of an art as it is a science and the deeply curious will consistently strive to paint a clearer picture of your business challenges. The deeper they dig, the more informed your assumptions will be and ultimately the juicier your insights.

They add additional value by mentally fashioning themselves into the business user and proactively pushing their feedback into the organisation. By putting their curiosity into practice they constantly self-validate their work and see first hand which assumptions hold up and which fall under the weight of it all.

Remember that data is not black, white and universal. It is coloured by context. So you want to surround yourself with talent that aims for the big ‘why’ in the sky.

So ask yourself:

  • How detailed are the questions they ask about your business and the role? Both the quality of their questions and answers will help you to glean clues as to their skill level as well as how they interrogate information.
  • How do they approach prioritisation? Whilst we love a detail-orientated analyst, watch out for those who get stuck in the weeds passionately coding the day away. This is a person you’re likely to have to hover over and direct to keep them on task.

An engineering mindset

Much like an engineer, a data analyst looks to solve complex problems as efficiently as possible with flexible solutions that can be reconfigured and refreshed as needed. In fact, they play the long game by separating striving from strategy and taking a macro view of what the business is trying to accomplish overall.

A ‘good’ data analyst will operate in a way that makes themselves “redundant” within 6-12 months by automating as much as humanly possible.

For example, if a marketing colleague were to ask your analyst to quantify customer repeat purchase rates, this piece of analysis should show historic trends, but also be created to automatically include future information and automatically refresh. Now the marketing department has what they need in place to view this information for the foreseeable future and everybody does their job better as a result!

Operating in this manner, an analyst can make their rounds through each business function automating and optimising as they go, before returning full circle to their original focus. The likelihood is that by this point expectations, goalposts and key metrics will have shifted somewhat and all processes will need updating accordingly.

So ask yourself:

  • Did they run any manual reports in their previous role – if so, it’s a no. They need to be of a fix-forward mindset that will make their problem solves even more valuable over time. If in doubt, automate yourself!

A strong communicator

Having the technical skills to access and unlock meaningful insights is a clear prerequisite for any data analyst. Next in your considerations should be their ability to translate complex output into a clear digestible format that even those less data savvy in the business can understand.

Whoever said that a picture is worth a thousand words was really onto something. Which is why data visualisation is a must-have arrow in your data analyst’s quiver. Afterall, a bunch of numbers in a spreadsheet is more likely to elicit exasperation rather than excitement for stakeholders whose time is money and attention span is fading fast.

When we think big picture though, it’s the analysts who can show a business how data can be used to make smart decisions and optimise certain processes that you desire. These gems can not only get hands-on with the data, but also bridge the gap between the numbers and the real-world implications for your business. Take it from us – if an analyst has an interest in how businesses operate and actively enjoys working closely with key stakeholders to connect the dots between the data and the business strategy – you’re hiring.

So ask yourself:

  • What are the metrics that they are interested in and why? A fine way to spot if they understand what metrics make a genuine impact in the real world.
  • Can they articulate the business value generated by their insights in previous roles? If not, they’ll have a tough time getting their findings off of their screens and into you team’s brains!

Hopefully you are now clear on the more nuanced boxes you will need to tick when searching for your match made in data. Bearing in mind that there is a sliding scale for what ‘good’ looks like and that one variety of analyst will not suit all businesses. However, if there were some general guidelines they would steer you towards an agile thinker, with technical expertise, a curious mind, good prioritisation skills and a certain knack for communicating complex findings to real live humans.

Remember that being skilled with Tableau…doesn’t mean you can wrangle data into a usable state and being skilled at problem solving…doesn’t mean you explain the results to a non technical audience. At the end of the day you need to assess your candidates on more than just the technical skills that they bring to the table – and if you’re lucky they’ll build you a story, not just a graph.

Don’t settle for less than a ‘good’ data analyst.