The number of jobs related to Big Data is growing by the day, as more and more companies become aware of the benefits data analysis could have on their competitiveness.
Many of these jobs come with very attractive six figure salaries, and for anyone interested in data and analysis, could provide extremely rewarding careers. Demand is likely to rocket, so getting into the industry in its early days could set you up for a future-proofed career.
The one question I get asked a lot is: What are the key skills required? My clients are asking me this question so they can select the right candidates for crucial data-centric roles in their organisations. And students and data professionals ask me the same questions to ensure they develop a rounded skillset. So here’s a look at the 6 skills I consider key if you are thinking about working in this industry or recruiting for data and analytics jobs.
Perhaps the most obvious skill you will need is to be able to make sense of the reams of data that your newly deployed data strategy is piling up for you.
Analytics involves the ability to determine which data is relevant to the question that you are hoping to answer, and interpreting the data in order to derive those answers.
If you have a knack for spotting patterns, and establishing links between cause and effect, then these skills will prove invaluable if you’re tasked with turning a business’s data into actionable plans of operation.
There are no hard and fast rules about what a company should use data for. Data science is an emerging field, which means the ability to come up with new methods of gathering, interpreting, analysing and – finally – profiting from – a data strategy, is a very valuable skill.
The corporate data superstars of the future will be people who can find new data to solve business problems and come up with new and innovative methods of applying data analytics.
Mathematics and Statistics
Good old fashioned number crunching. Despite the growing amount of unstructured data being incorporated into data strategies, much of the information being gathered and stored, ready for analysis, still takes the form of numbers.
And even when dealing exclusively with unstructured data, the objective of the exercise is often to reduce elements of the data – emails, social media messages etc. – to figures which can be quantified, in order for definite conclusions to be drawn from them. This means candidates with a strong background in maths or statistics are ideally placed to make the leap into big data enterprise.
Computers are the workhorses behind every big data strategy, and programmers will always be needed to come up with the algorithms that process data into insights. This is a very broad category which covers a whole range of subfields, such as machine learning, databases or cloud computing, which will be great additions to any budding data scientist’s arsenal. In particular, you should be familiar with the range of open-source technologies – Hadoop, Python, Pig etc. – which make up the foundations of most data science projects.
An understanding of business objectives, and the underlying processes that drive profit and business growth are also essential. The idea that a company will hire an “egg head” data scientist who will be locked away in a basement lab, to work their magic on data fed to them through a slot in their door, is dangerous and wrong. Anyone working with data should have a firm grasp of the company’s business goals and objectives as well as an understanding of the key performance indicators which let them know if they are heading in the right direction.
Both inter-personal and written communication skills are essential parts of a data scientist skillset. The ability to communicate the results of the analysis to other members of their team as well as to the key decision-makers who need to be able to quickly understand the key messages and insights are vital.
This also includes the skills of visualising and reporting data in the most effective manner. You can have the best analytical skills in the world, but unless you are able to make your findings understandable to everyone else you work with, and demonstrate how they will help to improve performance and drive success, they will be of little use to any business