Harvard Business Review says data is useless without the skills to analyse it. While there is enough and more data generated in real time, there are not enough professionals to analyse it. Data analytics has the biggest shortage of talent even though a lot of professionals are setting foot into this interesting domain.
Have you ever wondered what skill sets make a professional who can read from — or even better — listen to data? Do look at the list below, which talks about the analytical skills essential to excelling at a data science job.
Good analytical thinking ability
A person with analytical thinking and the capacity to couple organisational problems and solutions together who can solve puzzles, take technology head-on, and who enjoys teaming up with other people is on the way to becoming a successful data scientist. Obviously, you need to apply the power of your frontal lobes and work towards acquiring analytical skills. This is because if analytics is to be the valuable tool that it claims to be, you must know how to use it and having the above skills will ensure that you’re on your way there.
Knowledge of a business function or domain is necessary to even understand what part of the business process needs data-driven decision-making and how it can be solved using data analytics. Therefore, professionals with thorough domain knowledge can make the best of their analytical skills.
A data scientist who knows the business application of his quantitative capabilities is far more useful for an organisation than someone whose core strength lies merely in quantitative skills.
Understanding the relevance of data
When you are whirling in an ocean of data, you shouldn’t stumble — you need to be able to quickly separate the chaff from the grain, isolate it, and identify its relevance to the present context and make sense of it.
Multi-tool skill sets
Finding meaningful solutions requires domain knowledge and, more specifically, experience in domains that are bound with analytics — market research and data warehousing, to name a few. A few technical skills such as knowledge of SQL, Excel, SAS, and R are assets in this context as they are the ones that will strengthen your thought processes while analysing data. Clearly, then, what you need to be able to do is to leverage technology to its optimum level.
The ability to find, manipulate, manage, and interpret data which includes not only numbers but also text and images, is essential. Data literacy skills have spread far beyond their usual home — the IT function — and become an integral aspect of every business function and activity. Therefore, this becomes another core competency for a data scientist.
Einstein said, “Curiosity has its own reason for existence.” If you’re not curious about data — why and how it’s being used, and when — then you can’t hit the nail on the head and get the right solution.
Be hungry and keep learning
What doesn’t change and is obvious about acquiring any skill set is the need to be consistent and the ability to learn, and this holds true for excelling in analytical skills too. As analytics is evolving as a necessary business function and finding its applications in multiple domains, consequently the advent of technology is leading to the creation of new tools and technologies for performing analytics on raw data. The ever-evolving analytics industry demands a data scientist to be constantly “learning and evolving” in the changing times. This is the key skill any organisation would expect in a data scientist.
Data analytics finds its application in every business function, whether it is IT, HR, supply chain, or management, you name it.
It has become an absolute value-add for young professionals to learn analytical skills irrespective of the business function one belongs to. While data needs to be analysed even before making sense of it, it does guide the decision-making process and has stories to tell. Like Jim Bergeson once said, “Data will talk to you if you are willing to listen.”
(Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the views of YourStory.)