Varun is an ambitious architecture graduate who yearns to work in a multi-dimensional business environment. Even though he chose architecture as a major he was open to other career opportunities that might present themselves in the employment market. Varun talked to his friends and professors who mentioned that Data Scientists are in high demand right now and knowing his skills they felt that it might be a good fit for him. Now Varun wondered, “how can I as an architecture major, even consider a career in analytics? What skills would I need in order to even be considered and how can I acquire them?” Varun is one of the many trying to make a switch to this highly rewarding career in Data Science.
In this blog I share with you the Top 8 success factors for a successful career in Analytics.
1. Structured/Unstructured problem solving
“Problem solving” is a widely used term in the context of recruitment and this is often misinterpreted as the ability to mathematical problems. As true as that might be, problems present themselves in many shapes and sizes. Does a vegetable vendor solve mathematical problems every day? No, but he does have to find a solution, if his regular supplier fails to deliver the vegetables on a given day. Will he be able to obtain his stock from another supplier? If yes, could he possibly get a day’s credit until he has sold all the vegetables by the end of the day? He also has to consider the impact this will have on his profitability for the day. Will he be able to get the right quality of goods as is usually obtained from his regular supplier? So, problem solving…not so simple, is it?
2. Understanding business context
It is virtually impossible for every individual to have been employed in every possible industry there is and know something about every type of business. Data – both numeric and non-numeric is the deepest and widest form of knowledge there could be. It is very easy to get lost in the 1s and 0s only realizing much later – “Wait a minute, how did I get to this point?” A brief understanding of the context in which the client conducts their business enables an analyst to not only perform his role but also to understand what it means in the larger scheme of things. In the fast paced global environment it is not possible to take even a week’s time to understand the business in its entirety. Therefore, a quick thinking analyst should be able to identify what business information is relevant in the midst of all the noise.
3. Leveraging technology
Technology is a wonderful enabler. It makes our lives a lot easier. But there is a difference between being a user of a “tool” and leveraging “technology”. A tool helps you in your craft, but technology is set of tools that are utilized seamlessly in order to achieve a larger objective. A data analyst maybe proficient in SAS but there is also a need for the analyst to pick up on the business context (as noted above) and leverage all possible tools that set the course for problem solving.
4. A bird’s eye view
Why should an analyst know more than what he/she is designated to perform? Wouldn’t it confuse me if I had more information than is relevant for me to perform my tasks? Well yes, if you are only talking about performing “tasks”. Whatever be the motivation, don’t we all want to do more than just perform “tasks” monotonously? If one is looking to develop capabilities that advance from problem solving to solution building, it is certainly important that holistic thinking be part of one’s armory.
5. Open to learning
Data analytics is a dynamic landscape. No two datasets are similar and even those that seem similar when analyzed within the business context may provide different levels of insights. It is a continuous process of unlearning and learning and one has to be open-minded to be able to do this without too much difficulty. The phrase “Data Scientist” has been coined probably keeping in mind the exploratory and inquisitive nature that is required in order to succeed in the analytics space.
6. Agility in independent as well as collaborative work
Independence is always a sought after state of being, however, what good is independence if one is only shackled by one’s own thought process and not by anything else. Collaboration requires acceptance without boundaries. The Pyramid could certainly not have been built by one person. The ability to carve a single stone by oneself and collaborate to contribute to the whole is expected to be a key quality that a data scientist must possess.
7. Commitment to being detailed in every endeavor
As mentioned earlier, a data scientist is expected to have an exploratory attitude. This also means that from all the noise that could present itself in a given dataset, the key variables are captured and explored in detail. If one is comfortable with a superficial view of how things work, then probably data science may not be the right path.
8. Ability to articulate and communicate thought-process
Now what good is all this if one is not able to communicate it to the audience? Communication does not mean just writing e-mails or speaking over the phone. It includes the ability to visualize the data in various forms and tell a story that makes the audience sit up and listen.
The key aspect of the data analytics industry is that it requires a multi-disciplinary approach. Even though some concepts might be used repetitively, a monotonous approach to data will not help achieve the analytics goal. Not everyone might be a power horse of all the characteristics noted above, but a combination of these along with an unbridled quest to learn and hone one’s skills will definitely set a prospective data scientist on the right path.
Now Varun is one of those who did possess the right skills for the switch and was able to make a good career in analytics. If you think you possess the skills and are keen on analytics, it’s time you took some decisions.
This blog is written by Sindhuja Vasudevan, Analytics Project Manager at BRIDGEi2i
About BRIDGEi2i: BRIDGEi2i provides Business Analytics Solutions to enterprises globally, enabling them to achieve accelerated business impact harnessing the power of data. Our analytics services and technology solutions enable business managers to consume more meaningful information from big data, generate actionable insights from complex business problems and make data driven decisions across pan-enterprise processes to create sustainable business impact. To know more visit www.bridgei2i.com
The views and opinions expressed in this article are those of the author and do not necessarily reflect the official position or viewpoint of BRIDGEi2i.