1.1: What is Data Analytics?

Introduction to Data Analytics

Data Analytics can be broadly defined as the process of ingesting, refining, and examining data sets in order to draw useful conclusions from the information from within. This generally involves a series of techniques and tools that enable any number of datasets that have been brought into context through ingestion, and subsequently indexed, to be able to be transformed, organized, and model data with the goal of discovering patterns and extracting actionable insights [2].

With Data Analytics, we can transform raw data into knowledge that can inform technical, non-technical, or business decision-making [1]. Core components to this process often entails statistical analysis, visualization, and other data manipulation methods that enable the ability to uncover trends or relationships that would otherwise be hidden in the raw data. Combining specialized software and algorithms, modern data analytics can handle large volumes of structured or unstructured quantifiable data (often referred to as “Big Data”) and increasingly complex analyses, enabling organizations small or large, to make more informed, evidence-based decisions.

Data analytics can also be referred to as a cornerstone of the current “data economy” and overlaps with related fields such as business intelligence and data science.

Figure \(\PageIndex{1}\): Business Intelligence, Data Science, and Data Analytics each have their own focus — but they all overlap in important ways. Where they meet, you'll find shared skills like visualizing trends, exploring data, and making smarter decisions backed by evidence. Think of them as three teammates with different strengths who work best together. (CC BY 4.0; Felix Amoruwa) Access a detailed description of Figure \(\PageIndex{1}\)

One way to distinguish these is scope: data analytics often focuses on answering specific questions by analyzing existing datasets, whereas data science may involve a broader process of collecting and experimenting with data (including unstructured data) to find new insights.

In practice, data analytics can provide numerous benefits – from increasing operational efficiency to optimizing marketing strategies – by basing decisions on data trends rather than guesswork.

Overall, data analytics is the discipline of making sense of data, using quantitative methods to uncover insights that drive informed decision-making.

Figure \(\PageIndex{2}\): The Four Foundational Pillars of Data Analytics. The conceptual framework shows the four essential pillars: Understanding Raw Materials, Sourcing Materials, Ensuring Structural Integrity, and the Management Plan. (CC BY 4.0; Felix Amoruwa) Access a detail description of Figure \(\PageIndex{2}\)

Types of Analytics: Descriptive, Diagnostic, Predictive, Prescriptive

We can classify data analytics into 4 distinct types based on progression and the type of question each classification can answer: descriptive, diagnostic, predictive, and prescriptive analytics. Each type builds on the previous one, representing a progression in value and complexity—from understanding what has happened to deciding what should be completed next.

Key Questions Framework

Each type of analytics addresses a fundamental question:

• Descriptive Analytics: What happened?

• Diagnostic Analytics: Why did it happen?

• Predictive Analytics: What is likely to happen next?

• Prescriptive Analytics: What should we do about it?