July 16, 2015
Analytics, in general, refers to the collection and transformation of data for meaningful insights and business decisions. With big data and business intelligence rapidly gaining importance in today’s world, there is growing demand for analysts and data scientists. According to Mc Kinsey Global Institute’s Report on Big Data, demand for analytical talent in the United States could be 50% – 60% higher than its projected supply by 2018 – there could be a shortfall of around 1.5 million data savvy managers and analysts. This clearly highlights the fact that shortage of analytics professionals could become a serious constraint making analytics a lucrative career option for many. A career in analytics could be pursued in any industry – today organisations in most sectors and most fields of work have analytic divisions driving their strategic decisions.
First questions that pop up in everyone’s minds are regarding the educational qualifications required to build a career in analytics and skills that would help one excel in this world. In this post, we shall grow through these points along with other relevant details from a career standpoint.
Most of the analytic divisions prefer candidates who have an educational background in Economics, Statistics, Mathematics, Engineering or any other numerical discipline. A Masters degree specialising in any of these subjects or a professional degree like MBA along with a bachelors degree specialising in any of these subjects feature among the top selection criteria of prospective recruiters. For some specialised roles like roles of a data scientist, some organisations prefer candidates who have completed their Phd in a numerical discipline.
Along with educational background, the other key aspect that recruiters look out for are the analytical tools that a prospective candidate has knowledge or experience of. The most important tool that is predominantly used in analytics divisions is SAS. In fact, at one point of time the terms ‘SAS’ and ‘Analytics’ were being used synonymously. Although SAS still holds the highest share in terms of usage in the market, tools like R and Python are slowly catching up. Along with these, if one has knowledge of SPSS, SQL and VBA programming that is an added advantage.
The best way to develop your knowledge of these tools is by hands on experience – as they say, experience is the best teacher. However, prior knowledge of these tools is always an added advantage and gives an edge to a prospective analyst. There are several institutes that offer courses specialising in these statistical packages for training and development of prospective data professionals – one could enrol for these to get comprehensive learning which equips one better to face prospective recruiters.
You could also look up some free online courses being offered by top universities –www.coursera.org has a list of many such courses and one could choose any based on their requirement.
Reference to books like the ‘LittleSASBook’ and articles on the SAS website along with following blogs and posts by online SAS communities like http://blogs.sas.com/content/ and Linkedin posts on related topics is also very helpful in terms of keeping one abreast of the developments in the analytics world.
Once you’ve been recruited, the starting point of a career in analytics is generally as an Analyst. As an Analyst one starts learning techniques of data manipulation and analysis and quickly becomes a data professional. On the job training equips one with relevant knowledge and experience of working on data analytical tools and programming languages like SAS, SPSS, SQL, VBA and R.. From a career advancement point of view, in most organisations the hierarchy advances from an Analyst to Mid Management levels like Assistant Manager and Manager and one could move up to become the VP or Director as well. Different organisations follow different hierarchies – some relatively flat and some more layered. One could even choose to be a data scientist – data scientists need to have advanced analytical skills and have a more specialist role where they spend more time building predictive models and algorithms and working on data, software and systems.
Once you’ve settled in your role as an Analyst having become an experienced data professional, there are other skills which would make you stand out as a great analyst as compared to a good analyst. These skills basically relate to the ability to examine, analyse and understand in details exploratory data and present the findings and insights in the most effective way – often referred to as ‘Visualisation’ in the business world. Once the data has been deciphered, it is important to present the findings and key takeaways in the most lucid and structured way to senior management. Negotiation, creative and leadership skills are also very important to influence strategic decisions and drive change in an organisation.
Given the demand-supply scenario of professionals in the analytics industry, the salary packages being offered and future prospects, analytics is becoming a sought after career path very quickly. According to Harvard Business Review (October 2012 Issue), data scientist is “The Sexiest Job of the 21st Century” – clearly makes it a profession to watch out for.
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