Things to know about data science

As we move from BI to BA to DS, the work gets increasingly complex while the business impact increases, reports Tech in Asia

Imagine that you are running a fruit store.

You start by hiring someone to keep track of the stock level, profit and loss, top-selling fruits, and churn out reports. That’s Business Intelligence (BI), where you care about an accurate account of what happened and what’s going on in your business.

You then hire someone to take a look at the reports and work out new marketing strategies, pricing models, and new fruits to introduce to the market. That’s Business Analysis (BA), where you care about the discovery of trends and understanding what you can improve in running the business.

Finally, you hire someone to build an automated system to recommend baskets of mixed fruit to loyal customers, optimize the layout of fruit rack based on different metrics, and automatically set the optimal price based on market supply and demand. That’s Data Science (DS), where you care about how you can improve the business through automation and modelling of the real world.

How these work together

In general, as we move from BI to BA to DS, the work gets increasingly complex while the business impact increases exponentially.

We need to have the BA skills to interpret the findings and the DS knowledge to build the necessary data products to solve the problem.

Skills needed

Kevin Schmidt accurately summarized the skill set of data scientists as such:

 

Common misconceptions

Most data scientists come into the industry hoping to spend 100% of their time making cool models. Unfortunately, by the time most of us are done designing the data architecture and are starting on deploying the model, we would have filtered out most of the complex, “interesting” algorithms that couldn’t scale or was too expensive to operationalize.

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This is an excerpt from Tech in Asia. You can read the full article here