Georgia Skinner – Marketing Intern, My Career Lab

With data being likened to that of oil and the 2018 Computerworld report stating that AI and data science have created 2.7 million new jobs forecasted globally by 2020, it’s clear that there is a supply and demand issue for talented and dedicated Data Analysts in the workforce.

Over twenty data sites were analysed to find the top six skills that Data Analysts should have to succeed in the industry, although these skills are not only relevant for Data Analysts. To have success in any role in the future of work everyone will need to master these skills.

The six top skills needed to up-skill into Data Analytics:

  1. Curiosity
  2. Statistical and technical expertise
  3. Creativity
  4. Communication
  5. Skepticism
  6. Detail Orientated


  1. Curiosity

What? When? Why? The possibilities of data are endless and the limits of what this information can do when applied to real-life scenarios is unknown. As a Data Analyst, you must be able to visualize the possibilities and be curious enough to explore them yourself. Data can be thought of as puzzle pieces that have an unlimited number of ways of merging together, and it’s your job to find new correlations, new solutions and be continuously stimulated by the hundreds if not thousands of possibilities. Data Analysts must be constantly asking and searching for the method behind the madness, which is found in the data.

  1. Statistical and technical expertise/ analytical

Don’t be disheartened if you’re interested in Data Analytics yet currently have no industry expertise. Yes, already being in the know of the ins and outs of the data world will provide you with an advantage over others, however it is not a necessity to begin with. Knowing programs such as MySQL, Python or SAS or having a background in mathematics or statistics will notably provide you with a wealth of knowledge when it comes to technical acumen, which of course is essential for this role. Yet technical flexibility and adaptability are just as important as the data environment continues to grow and change at an exponential rate. If you’re interested and motivated to constantly be learning, then the technicalities can be mastered. As they say, practice makes perfect.

  1. Creativity

To be curious and creative? We’re not saying you need an arts degree but having a creative flair is not only beneficial but arguably necessary. It’s not expected that you will be the next Picasso, but Data Analysts need to be creative enough to recognize patterns in unstructured numbers and draw difficult correlations between these data sets. Creativity is also key to effective communication. Having the skills to be able to represent data in easy to understand formats that translate across all business domains will distinguish a great data analyst from a good one.

  1. Communication

It’s all well and good if you can decipher data, but if you can’t translate the numbers into meaningful words then how can you show your stakeholders what you know? Data can be convoluted and confusing, and often a Data Analyst will be communicating their numerical analysis to a non-technical sales or marketing team. The need for analysts to be able to provide clarity and insight is essential as it enables the business to then take this information and activate strategic business plans.

  1. Skepticism

What many people fail to realize is that data can be deceptive, whether it is a flaw in the collection method or a misuse in application, not all data can be taken literally. Data Analysts must have an air of doubt towards results and consider possibilities or outcomes that may not be favorable to their end goal. Those who become complacent with surface-level results will lack the ability to identify great opportunities for further growth and be unaware of possible negative trends or factors at play within the data.

  1. Detail Orientated

Detailed data reports of business activities can be the catalyst for change and provide valuable strategic insights. However, all the conclusions drawn from said data are dispensable if the data reported isn’t accurate. Data is only as good as the method that is used to collect it – hence the more meticulous and thorough the method the more accurate and specific the results. Some may dread the thought of pouring over endless statistics and patterns, but it is important to remember the devil is in the details, or in this instance, the data.

So, do you have what it takes to up-skill into a Data Analytics career? If you have any of the soft skills listed above, then you’re already a step ahead at moving into one of the most in-demand roles of the future. Don’t forget though, mastering the hard skills in this industry is what will set great data analytics apart from simply the good.