Navigating the Four Types of Data Analysis: A Journey from Descriptive to Prescriptive

Intro

In the world of data analytics, one thing is abundantly clear: there's no one-size-fits-all approach. To excel in this field, it's crucial to understand the distinct types of data analysis and how they connect with each other.

My own journey in analytics began much like many others', examining historical trends in data, whether it was sales figures, financial budgets, or market share statistics. These analyses primarily aimed to answer one fundamental question: What happened or what is happening? For instance, we'd delve into queries such as, "Are we meeting our quarterly targets?" or "How does this year's revenue compare to last year's at the same time?" These inquiries fall under the umbrella of Descriptive Analytics, a type of analysis that sifts through historical data to provide insights into the past or present. However, they don't explain the "why" behind the numbers.

When you're working in a corporate context and discover, for example, that European sales have dropped by 5% compared to a 10% increase in the same period last year, the immediate follow-up question is, "Why?" Why did sales decline by 5% while showing such a significant increase last year?

This is where Diagnostic Analysis steps in to answer those "Why" questions. It often requires domain-specific knowledge to uncover compelling cause-and-effect relationships. From a data analytics perspective, we typically identify outliers, identify recurring patterns, and break down complex questions into more manageable components.

Taking Analytics to the Next Level:

Enter a new breed of data analysts known as Data Scientists. Armed with an understanding of "what" and "why," they aim to predict future outcomes. Predictive analytics harnesses statistical models and forecasting techniques to explore the future and address the question: "What could happen?"

Returning to our previous example, now that we understand why sales declined, predictive analytics can help us determine the expected revenue outcome in the coming months, quarters, or years.

The final frontier of data analysis is Prescriptive Analytics. Its goal is to prescribe specific actions to eliminate potential problems or fully leverage promising trends. Prescriptive analytics relies on advanced tools and technologies such as machine learning, business rules, and algorithms, making it sophisticated to implement and manage. This cutting-edge approach demands not only historical internal data but also external information due to its algorithmic nature.

In Conclusion:

More than ever, companies seeking analytical talent require proficiency not only in descriptive and diagnostic analysis but also in predictive and prescriptive analytics. For traditional corporate data analysts, I strongly encourage you to venture beyond your comfort zones and explore the world of data science, becoming proficient with tools like Python and R.

In this ever-evolving landscape of data analytics, mastering the spectrum from Descriptive to Prescriptive is your key to unlocking new realms of insight and impact. Are you ready to embark on this exciting journey?