What is Data Analytics?

What is Data Analytics?

As the world becomes increasingly reliant on technology and information, almost every company is collecting data all the time. But in its raw form, this data is incoherent.

That’s where data analytics comes in — It is the science of analysing raw data to find trends and make conclusions about the information they contain.

This information can then be used to inform and drive forward-thinking business decisions to personalise the customer experience, optimise operations, improve efficiency, and more.

A data analyst and/or data scientist extracts raw data, organises, analyses, and then transforms it from incomprehensible numbers into coherent, factual information. They will then pass on their findings in the form of recommendations about what a company’s next plan of action should be.

How does it work?

Data analytics involves a series of steps to gain an accurate analysis.

  1. Data collection

The first step is to identify the data you need and then collect it from the source systems.

  1. Adjusting data quality

Data quality problems include inconsistencies, errors, duplicates, and more. To ensure the quality of the data, this step entails running data profiling and data cleansing.

Then, the data must be organised according to the requirements of the analytical model the analyst intends to use.

  1. Building an analytical model

An analytical model that can run accurate analysis must then be built using software like predictive modelling tools and programming.

This model is continuously tested with an experimental data set, reviewed and refined until it works as intended.

  1. Presentation

The final step is to present the model’s results to the relevant stakeholders in a way that is easy to understand, such as with charts and infographics. This is where it’s important for the whole company to be data literate.

Types of data analytics

Data analytics is broken down into four core types.

  1. Descriptive analysis

This focuses on what has happened over a specific period of time, such as whether sales have increased this month.

  1. Diagnostic analysis

This focuses on why something has happened, such as whether the weather affected sales.

  1. Predictive analysis

This focuses on what is likely to happen in the near future to locate patterns and predict risks and opportunities.

  1. Prescriptive analysis

This involves selecting the best course of action from the available choices.

The Bottom Line

When harnessed correctly, data analytics can hold a competitive advantage. Companies that leverage this power can make informed decisions that drive business success and accelerate growth, even in a challenging market.

Learn more about how our data experts at Polar Packet can turn your raw data into actionable information that you can rely on.