Headhunter vs Recruiter, like Data Analyst vs Data Scientist – by Bob Norton
Still need to focus on the business problem, not parsing keywords in a job description!
In keep keeping current in the technology domain I regularly read. When I saw this post I had to smile about the parallels of a Data Analyst and a Headhunter. I make a large distinction here between a headhunter – who does relationship recruiting – typically of passive candidates, and a Recruiter who mills and parses resumes to match keywords in a job description. I will save the rant on quality vs quantity in staffing process for other blogs. The fun parallel for me and maybe you will glean the metaphor; read this article and substitute ‘Headhunter’ for every instance of ‘Data Analyst’
In the last couple of years, I am hearing this nice word ‘data scientist ‘being used in the industry to describe a data analyst. The picture that comes to my mind is of a person who works in an isolated environment doing things that may not be understood by the common man because it is so ‘high funda’ or cutting edge. At some point, maybe a few years down the road, his innovations / experiments will be part of the common man’s life but today, his work is understood by only a handful of people. In his cold, air-conditioned lab the scientist works away, day and night, for the love of his subject of research.
However, my view is that Analytics is a ‘skill’ – an amalgamation of data management, statistical analysis and business concepts. The output of Analytics is a line of thought which helps a business make tactical or strategic decisions. A ‘skill’ as per Wikipedia –is the learned ability to carry out pre-determined results often with the minimum outlay of time, energy, or both. Analytics is the discovery and communication of meaningful patterns in data.
To do this, we need to:-
1. Understand the business context: – The Analyst needs to provide a solution to a business question / problem. To understand the desired output, the analyst needs to understand the business problem and the business context. In fact, people with business exposure who move into analytics will do much better than people who do not understand the business. They especially understand what type of solution can be implemented and how. After all, a solution which is not implementable is not of much use.
2. Understand data / ability to access data –through codes or GUI driven softwares. Why do I stress on coding? Because there is a fair amount required especially if you are looking for working in spaces of SAS, SQL, Hadoop .In the area of data cleaning and management, data visualisation and reporting coding is extensively used.
3. Understand the implications of Statistics- with SaaS , the science of the maths is done by the software. The Analyst needs to concentrate on:
a) Business problems that have to be solved and to identify the possible outputs
b) Understanding the statistical procedure that needs to be used to deliver the output required. Here the familiarity with statistics will be tested. Now-a- days, understanding a statistical technique is like an open book exam with so many details available on the internet.
c) The dimensions of the data and the data management / cleaning/ transformations that need to be done to make the data ready for the procedure he has identified. Managing missing values and outliers, creating dummy variables etc. as well as creating development sample for the analysis is a big part in a successful analysis
d) Interpreting the output that the statistical procedure produces.
e) Validating the output and confirm consistency over data.
f) Creating an understanding of the output in the language of the business manager so that decisions can be taken. And creating a report in power point etc. for easy comprehension of the output by the business manager and the final conclusions on the same.
g) Monitoring the implementation and track performance.
Thus, I prefer to go with the term ‘data analyst and consultant’ so that we focus on the interaction of analytics with business. A successful analysis is one which solves a business problem efficiently…and a successful analyst is one who can conduct a successful analysis! Thus, the focus is firmly on the business problem and not on the statistical complexity of the process of analysis.
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