How important are soft skills in analytics

The field of data science is vast, and data scientists are expected to be skilled in a range of areas such as machine learning, data visualisation and data mining; at times, it can feel like you are only scratching the surface. It’s impossible to master all the tools and software available, but it’s important to understand the different technologies and developments within the field and tailor your skill set to your industry and role.

LinkedIn has put the role of a data scientist in the top ten most promising jobs, and statistical analysis, data mining and data presentation among the top 10 most in-demand skills in 2018.  There are a few key technical and soft skills that are essential for data and analytics professionals to have, which include (but are not limited to):

  • Quantitative analysis
  • Coding and programming
  • Technology savvy
  • Business acumen

While there is no denying that technical aptitude is integral to the role of a data scientist, balancing soft and technical skills is key to success. Jodie Sangster, CEO of the Institute of Analytics Professionals of Australia (IAPA) says that its ‘soft skills such like communication, influencing and advocacy, that will ensure businesses can take action on analytics-driven insights. Adding these soft skills to your analytics team is a business imperative for any analytics leader as it directly impacts the perceived success of analytics across the organisation – and the ability to act on insights.’

Soft skills in high demand

A term that is often used but not always understood, soft skills are defined as ‘desirable qualities for certain forms of employment that do not depend on acquired knowledge: they include common sense, the ability to deal with people, and a positive, flexible attitude’. The ability to effectively communicate with your colleagues and clients is imperative to a successful working environment and subsequently business success. This is especially true in the field of analytics and cyber security which are predominantly technical and often rely on technical jargon to communicate data patterns and/or project results.

Effective Data storytelling can help to convey messages that are meaningful to a range of audiences, from business leaders to clients and prospective clients. Creating a story that has a beginning, middle and end can be very effective in communicating project results, allowing your audience to understand why the analysis was originally undertaken, the end goal, the results and their effect on the business. Check out some great examples of data storytelling here.

What does the future hold for data science?

Data science and machine learning (and the technologies that assist them) will inevitably continue to evolve. It’s worth noting that the role of a data scientist is one of the main transformations set to occur in the field. There will be diversification in the job title ‘Data Scientist’; currently, the title is too broad and can often misrepresent someone’s role and responsibilities.

The future looks bright for data science professionals. According to Deloitte Access Economics in Australia, those who have a postgraduate degree are expected to earn an average of $130,176 p.a. Further postgraduate study can set you apart from your peers and enable you to choose a more streamlined career pathway, consolidating your previous experience and career goals.

By: James Cook University, Australia

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