How to make a Successful transition in to Analytics of a same company.
By: Udayakumar D
Tags: Career Paths, Successful, Transitions, Machine Learning, Data Analytics

In the movie ‘Moneyball’ (based on true story) where Brad Pitt plays Billy Beane, the GM of Oakland A, Faced with a tight budget he goes for a trade negotiation with Cleveland Indians and he finds that their boss carefully listening to the suggestion of an odd guy who didn’t even get to seat. Billy comes out looks for the guy and asks him what just happened that how the entire room listened to him? The next 5min conversation with the guy sharing his analytical view on the Game, his player analysis and his statement on opening up new possibilities from a hit they recently took and his stats made Billy recruit him which was his one of the best decision in his career.

Image of Peter Brandt from Moneyball Movie

There are either Companies with problems or problems with the company. If you want to be in analytics then the First part is an opportunity and if you are working in such a company then your internal transition in to analytics is an easy gateway. This article is an attempt to streamline important areas to focus, key skills to develop and right doors to knock. Hoping that you have built your reputation already.

If you start doing research on How to do internal transition you would eventually come across many valuable advises from industry experts, educators, consultants etc, but one of the best advice to me is — Don’t doubt yourself. Don’t look at the competition, the ladder (Hierarchy) or anything else. Biggest role an Analyst plays is identifying new possibilities from the available resources, so Go for it, Aim to hit the core! Other obstacles are just easy to break through.

What is Analytics? First let us understand what is analytics and its role in a company. Analytics is basically what you do with the available data (or create the data) to derive insights that drive decisions, create strategies, and enhance processes which ultimately aim in increasing the earnings or other Beneficial’s to the company. There are different roles in different companies, for eg:

· Data analyst

· Business analyst

· Consultant

· Portfolio analyst

· Campaign analyst

· Financial analyst

· Online marketing analyst

· Statistician

· Fraud analyst

· CRM analyst

· CS calls analyst

By now you must have understood that there is analytics need in every function and so every company needs someone to carry the responsibility of deriving something meaningful from the data and handle the Stat part of the company. With that understanding below are the key steps in achieving the desired transition,

1. Position Yourself: While there are multiple roles and functions based on the objective of the companies, you have to first decide if Analytics is your cup of tea or not. Analytics basically hits the Business part of any company so whether you are an IT person looking to move in to Analytics or you are a Sales guy willing to move into analytics you must have a clear understanding of that particular business. I would break the Analytics roles into two large groups.

If you are having an Experience below 8 years you can start learning the Data engineering, Technical tools, becoming a part of a team building models, deploying them and that supports the Management and according to me people in this category with qualifications are seeing easy transition. If you have more than 8 years of experience you must be more of a Business focused rather than IT focused (though knowing IT is an edge). There are multiple demands on Consulting, Functional expertise kind of roles within the company but the potential in applying the right approach, how do you structure the problem and draw a clear methodology matters most. The years of experience mentioned is a broad distinction and it could differ vastly between sectors and companies.

2. There is always a Policy: Be sure to know the HR policies in your company from basics like eligibilities for the role, relevant skillset, tools, qualifications that are required as per policies to be in Analytics role. As the specifications differs very broadly between every company it is tough to summarise them but talk to your recruitment team to understand these requirements and let them know your willingness to move into Analytics.

3. Understand the Company’s structure and how the Analytics role fit in their hierarchy. Companies earlier had 2 major parts. 1. Business — Sales, Finance, Marketing and 2. IT — Cloud, Networking, Data base management etc, but now the third part which is the Statistics, Analytics, Machine Learning part is evolving and is gaining everyone’s need and attention. It evolves throughout all parts of the business. Some companies have already set up their team, but still many domestic companies are yet to build the Data Analytics team, in those cases you have more potential to prove and large part to play which is discussed broadly in this article.

4. Identify your comfort zone: If you are from a business domain you must get comfortable in identifying areas of improvement, important use cases where Analytics can be applied that could help in a big way. Next steps are to then collect the necessary data, transform them, remove the unwanted factors (by now you have achieved 75% of analytics role) and then comes the IT part which helps you execute the Analytics part. If you are a person with IT background who is comfortable in coding and making the computers do the work, you should focus on How to sell, gain the domain expertise. Basically start from your comfortable area so that you gain more confidence in what you are doing.

5. Companies’ perspective on Internal sourcing for Analytics: While each businesses are different, companies in same business are also different to its peers. Some companies move at its own pace, some are dynamic to competition and market changes, but all good companies value your experience with them. Everyone knows Data Science jobs are the hot cakes now but the hard truth is there is also high attrition with Data Scientists/Analyst reasons being many 2 most common reasons they cite are 1. They don’t get the data they want or they don’t get a proper data where too much time is needed in transforming them, 2. Their inability to collide with multiple departments to understand the process and interpretations from the data. This situation is expected everywhere because most people believe any statistical tools and techniques is threat to their jobs and thus posting a major threat to Anaytics person.

Once you have a good experience with a company, and if you go to new job interview the interviewer would only see your skills, the experience you gained with old company dies there isn’t? With current company you have understood their business, you can play around feature engineering, build the missing data and relationship with other departments could be easy and efficient. Companies do value this.

6. Get Qualified: As I mentioned about HR policies, most firms expect a qualification and a good exploration in the subjects already. I would strongly recommend to pursue a course with reputed institutions because these institutions offer variety of exposure to all domain functions and their applications. You would also get to collide with different minds from different levels and understand the different analytical mindset, applications of technical tools on the Use case etc. Institutions like GreatLakes make it a practice of assessing group assignments, capstone projects etc to enable students gain multiple practical knowledge on many real-time business problems. Below are the most important aspects any analytics person has to gain,

1. Understanding the problem/need clearly

2. Best approach/technique

3. Possibilities it can open or impact it creates

Institutions help you achieve all these skills.

7. Create a Portfolio: Mr.Sudalai Rajkumar (aka SRK) a famous Kaggle Grandmaster from India is a home grown data scientist and he said in a webinar that “Best way to learn to Data science is by doing it!” and that’s very true. Proving what you can do with available data is best way than telling them you can do something important. The credibility comes with portfolio that you have built and how well you can apply your knowledge in a practical problems. Below are few examples of building a relevant portfolio to your job,

a. Sales guys can build a model in understanding the quality of leads given to them by the marketing team.

b. If you are a Finance analyst you can build models in forecasting the returns of a portfolio.

c. Marketing person can predict the success of the campaign using previous data and the reach any new campaigns planned.

d. Do a Text sentimental analysis for your customers’ reviews in Social media websites, ecommerce websites and review platforms. Can do the same for competitors also.

e. If you are HR manager you can build models that help in identifying key variables that influence people to move out or to stay with company.

f. If you are a developer can focus on building automated predictive models and deploy them.

g. Society based open projects like Water management, population estimates, trends in education etc from the dataset you can get from many government websites.

These projects will help you understand all challenges in analytics and your creative ways to solve the problems. The above projects can be done in other means but with the data it is credible, can be applied in future cases, dynamic to future challenges and advancements and lastly they are quantitatively measured.

8. Blogging: Just start writing. Start explaining your work, streamline your thoughts so that you get better and better in presenting your ideas and learn how to convince people with your models. Analytics is one area where you must go solo initially, get deep into the problems & projects, create various ways to approach the problem and post it in public forums so you get feedbacks and inputs. Many platforms like for Eg: Medium have become a large source for exposing your Talent.

If you desire to achieve a transition into much promising role in Analytics inside your company the above steps are important and I would say given the competition this is Minimum. Transition is a large process it takes patience and there is a need to be focused and equipping yourself with relevant skill set. If someone achieve the transition in Analytics this way, then their pay checks would definitely surprise them in near future and the responsibility they carry would be invaluable.