The skills you’ll need to be a great data analyst.

Yiannis Pitsillides | 10 July 2019

The world is exploding with data - there's never been a better time to be a data analyst - what are the 5+1 skills you are going to need.

The skills you’ll need to be a great data analyst

The world is exploding with data; more data that we can possibly handle. Organisations are seeking to employ more data analysts. Young, talented and ambitious individuals are keen to start a career in data. But what does it take? In this post, we will be discussing the 5 + 1 skills that an analyst needs to be successful in this field.

  1. Ability to turn data into insights

The most important skill that a data analyst (even a data scientist) must learn is the ability to turn data into insights. This is often overlooked, but the reality is, that if you are not able to translate your analysis into valuable and actionable insights then your organisation will not value you. You will need to analyse your data by measuring the right thing and asking the right questions.

To really master insights, you’ll need to be able to tell a story using the data. You’ll need to articulate the why and present your analysis in the right order with very clear visuals.

  1. Problem Solving Skills

In the real world of data, no problem is the same and no solution is the same either. There are no fixed rules that mean you can follow and solve every problem simply.

You will need to be able to solve problems efficiently and effectively. By efficiently I mean that we do not live in a perfect world where we have all the resources available to us; we need to solve the problem with what we have and ensure it’s the best possible solution we can deliver. By effectively I mean that our solution should solve all of the problem and not just some of it.

Analysts should clearly state the problem they are trying to solve.  They then need to identify the information they need to solve that problem, gather that information and join it together, they can then use their well-honed analytical techniques to model the data and start analysing the outcomes that arrive at a solution and so answer ‘why’ this has happened and ‘how’ we can tackle it – problem solving – done!

  1. Technical Skills

‘Technical skills’ are the most obvious skills a data analyst will need. Technical skills will help the analyst to be faster and better when it comes to problem solving, analysing data and providing insights. At an industry level, the most used software is Excel so whether we like it or not, data analysts should be masters of excel. Managers, Senior Managers, Directors and Executives will want a relatively simple software package they understand and that in some cases can give them the ability to manipulate the numbers themselves.

The next most popular technical skill is SQL; or other databases where you can still query using SQL. Looking at the fundamentals, all the data that you will be using would sit within SQL, so it makes sense to learn SQL in order to extract that data and start your analysis. SQL also integrates with the majority of visualisation tools (and big data tools like Azure, AWS, GCP) allowing you to automate all your reports.

I would suggest learning how to use visualisation tools like Power BI, Tableau & Qlik Sense. These visualisation tools have more functionality than Excel and are able to handle big data better than Excel.

Worth noting that if you use there tools as ‘databases’ and have your ‘modelling’ and ‘joins’ within the tool then the tool will be effectively useless as it will take ages to run/filter. I have personally experienced this, many times in my day job and it’s one of the main reasons people find those tools hard to use – this can often lead to a reversion to Excel.

Finally, I would suggest learning Python and/or R. They are really useful skills when it comes to machine learning (ML) projects such as making predictions, forecasts, run rates and other statistical based projects. Usually, these projects are run by more experienced data analysts or data scientists.

Please note that the ‘ability to learn new software fast’ is critical as it might be Python and R today but that could change tomorrow. As an example, digital data analytics is relatively new and has new tools and processes to measure the data.

  1. Communication Skills

Data analysts don’t just interact with computers, (although some of us wish that were the case) they also interact with people often for the following reasons:

  • You might have done a great piece of analysis and have to present your results to the business
  • You might need some data that is not in the database and you will have to go and talk to other people to get that data
  • You might have been asked to help a certain team analyse their performance and you’ll need to sit down with them, understand their business and map the project/analysis from conception to conclusion.

So yes you are going to need to have good communication skills too.

  1. Critical thinking

Critical Thinking is not the same as the problem solving described above. Critical thinking is when you challenge your own presuppositions/assumptions.

Questions like:

  • Maybe I am wrong and not thinking this through clearly?
  • Am I doing this right?
  • Is there another way of doing it better? Faster?
  • Is there a hidden reason why ‘x’ is asking me to follow this direction?
  • Why this way over another?

Problem solving will typically have an end date as problem while critical thinking is an on-going process that you should apply throughout your career. The reason I have included both is because as an analyst, critical thinking will help support problem solving and should mean your recommendations are stronger.

5 + 1. Creativity

Creativity is an art however and wherever you apply it. As a data analyst, you will be presented with multiple scenarios that often have no right or wrong answer. Hence, you will need to get creative when providing solutions. Creativity can be applied in the following scenarios:

  • You can get creative on the type of model or software you use for your analysis
  • You can get creative when designing new reports / visualisations for the business
  • You can get creative in including more data sources in your analysis to get a more complete picture
  • You can get creative on how to present your insights by telling a story
  • You can get creative by experimenting how to automate all your work, so making it faster

I hope you enjoyed this read! If you did, then please consider subscribing to my YouTube channel as I will be sharing more content on data analytics. If you have different views or there is something you want to comment on or even just ask me a question, please let me know in the comments section.

https://youtu.be/I1MOn8kyc14

 

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