Top 4 Functional Pillars of Data Science

Is Data Science as useful as people make it seem to be? OK, so Data Science vs Machine Learning? Or, does AI has any use cases in my domain?

Have you been contemplating one or all of the above questions lately?

There's no doubt about it - Data Science is not just a way of the future, it's the need of the present hour!

The applications can be classified under 4 major flavours, all of which can be individual business problems to be addressed or they can be sub-components of a much larger business problem.

Let's explore what they are:

1. Reporting

This pertains to the practice of converting the raw bulk data into an easy-to-digest piece of information that helps in deriving insights.

This can be done with data summarization(numerically) or data representation(visually). It doesn't always have to be summaries, raw data visualizations can be an active source of pattern discovery or process fault alerts.

We can also define our custom metrics calculated from the raw data and report them to track the general health of various products, services, functions, and practices.

2. Hypothesis Validation

While reporting is a good first step to untangle what's going on, a crucial next step is to detect major changes or challenges to our business assumptions through data.

For e.g., there will always be inherent fluctuations in sales, and that slight bump or slump in sales might not necessarily be a cause of concern. We need to use statistical methods to comment on whether it's statistically significant.

Wherever we need to ascertain whether things have really changed for better or for worse, we can use statistics as a tool to determine the same instead of relying on wishful thinking or biased hunches.

3. Decision Automation

This is a big and most revered practical use for Data Science. Look for situations where there is continuous dependence on data to take similar and repeated business decisions.

Some examples can be:

People Clicking on Ads: People with different geographical attributes and browsing behaviours wander through online ads. They either act or don't.

In this case, interpersonal attributes, browsing behaviours, product and ad attributes make for a huge amount of data. Each triggered ad results in repeated action of being clicked or ignored.

Loan/Credit Card Approval: Millions of people across the world try to access different products and based on their financial health, they are either granted or denied access.

The key takeaway is that a person might not be predictable, but people are very much so!

4. Pattern Discovery :

Although decision automation is a big part of what data science can do for us, there isn't always an immediate outcome to be predicted to automate a decision.

Sometimes we need manual intervention before a decision is taken by identifying existing patterns to arrive at a concrete decision.

For example, before deciding what kind of new insurance product we are going to design, we would like to know what major kinds of consumer classes exist in the market and what are their characteristics as a group, what appeals to them.

There are tons of news articles coming up every day, every hour. How can I categorize them into various genres for my news app? How can I summarize articles in one hundred words and yet be faithful to the content? All these problems don't necessarily have an outcome associated with them; yet need pattern discovery to move forward.

Parting thoughts

These four types that we discussed account for a majority of data science use cases. However, you might always come across some use cases which don’t fit in any set definition.

But these distinctions are going to be very helpful when we are getting started with our career in Data Science.

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