I was fortunate to get early opportunities of working on numerous data science projects. I enjoyed this part the most. Even more, when I realized that my efforts will add value to some company.
However, the miserable part was, less than 30% of the data science projects actually got implemented to their potential. I got shattered realizing that my efforts got wasted. But, I wasn’t the only one. Almost, every other analyst had the same feeling of disappointment.
Even today, the real challenge which data science industry faces is the lack of coordination between business folks and analytics folks. To my surprise, I’ve even noticed that these people prefer to sit away from each other in same offices.
Only if both these skills set were common in professionals, we would have seen a much higher project implementations. Over last four years, I’ve spent lots of time thinking of best practices to make a project successful.
I found that, If you have right people sitting in your office, have clearly defined business problems and a culturewhich motivates out-of-box thinking, you have a break-through in the pipeline.
Therefore, to become successful at your data science / analytics jobs, I’d recommend you to follow the tips mentioned below. These are tried and tested tips. To reap the maximum benefits, I’d suggest you follow them with discipline. I’ve benefited from them. Its your turn now!
I know you are an analyst and all you care about is numbers. But, what differentiates an awesome business analyst from average data analyst? It’s their potential to understand business. You should try to understand business even before you take up your first project. Here are a few things you should definitely explore:
a. Customer level Information: Total number of active customers, month on month customer attrition, segments defined by business on portfolio.
b. Business Strategies: How do we acquire new customers, what are the channels. How do we retain valuable customers.
c. Product Information: How does your customer interact with your products? How do you earn money through your product? Is your product a direct revenue maker or is just an engagement tool?
If you can answer all these questions, you are in a good shape to start your first project.
I have observed that analysts aim for objectives which are not even the main concern of the problem. For instance, lets imagine we found that more a customer calls at customer care, higher will be his propensity to abandon the services.
Now, if we begin solving for method to minimize the calls at customer care, we probably won’t reduce the attrition rate. Rather, I already see higher dissatisfaction in your customers if you do not have a human justifying your faults. This is probably an easy kill, and you will refuse to get into such easy traps. But, real life problems are much difficult to find. I will say, it is much easier to solve a defined problem than finding what is the right problem to solve.
This probably is the easiest puzzle to solve for an analyst yet a simple trap to fall in. Let me explain it using few examples.
Suppose, you are trying to build a targeting model for a marketing campaign. Which of the metric will you choose to gauge your model:
I will always choose KS in this case, given that Lift will only give you estimate on a particular decile. Hence, it probably won’t help us to find the total target population and the break point. AUC-ROC will be an estimate for the overall population, which is not our intent in this case. Log-Likelihood is probably the biggest misfit in this case, as all matters to us is the rank order and not the actual probability.
I have seen this as the biggest problem in many functions/industries. Today business leaders seek innovation in every thing they do.
To truly innovate, you need to follow a systematic way to diverge and converge.
