What is Data analytics?

Data analytics is a must-have for any business looking to grow.
This is what it means and why it is so important.

With the rapid development of business and information technologies, the word data gained an additional dimension. Data analytics, data science or Big Data are on everyone’s lips. But why, exactly?  

Over the last decade, the overall amount of data created worldwide has been increasing year after year. In 2010, there were around 2 zettabytes, or two trillion gigabytes, of data in the entire world. By 2017, this number was 26ZB, and by the end of 2020, 64ZB.  

The size of the Global Datasphere


Annual Size of the global digital data from 2010 to 2025

Source: Forbes


Estimates suggest that 2021 closed with 79ZB of data (information) created, captured, copied, and consumed worldwide. This number will most likely more than double by 2025, and there is really no end in sight. And if zettabytes confuse you, think something like one zettabyte is the same as having one billion 1TB hard drives.

It is safe to say the world is becoming more data driven. Companies generate, collect and store copious amounts of data, but struggle to put it to the best possible use. This is where Data analytics comes in.

What is Data analytics all about?

Data analytics is a discipline whose goal is to extract insights from data. Collecting, organizing, storing, and managing data are all critical tasks but there is more to Data Analytics. Much more.
This concept comprises all the processes, tools, and techniques used to apply statistical analysis on data, in order to find trends and patterns and draw conclusions.

Data analytics vs. Data Science vs. Big Data

Big Data refers to the data that is too large, too fast, and/or too complex to be processed by traditional systems and methods. It is used in customer, compliance, fraud and operational analytics, where large amounts of disparate data need to be collected and analyzed.

Data science and data analytics might sound the same but aren’t. In short, data science is the discipline that pulls data together to create questions; data analytics is the discipline that uses data to answer them.

To illustrate, search engines use data science algorithms to deliver the best results in Internet searches. Data analytics can be of use in healthcare to track and optimize patient flow, treatment, outcomes, or even equipment used in hospitals.

The four types of Data analytics:

There are the four basic types:

  1. Descriptive analytics: uses historical and current data from multiple sources to identify trends and patterns.
  2. Diagnostic analytics: uses data to understand what factored in or caused good or bad past performances.
  3. Predictive analytics: uses techniques such as statistical modelling, forecasting, and machine learning to make predictions.
  4. Prescriptive analytics: essentially uses testing and other techniques to recommend solutions that help achieve desired outcomes.

Data analytics: how and why?

Data analytics is a set of processes that allow businesses to extract insights from data. Every project is different, but the application usually follows these three steps.

The preparation

The first step is to understand what data is needed (for example, qualitative or quantitative), how it is grouped (by age, demographic, and so on), and what are the expected requirements.

Then comes the stage of collecting data. There are a lot of potential sources of data (isn’t everything a potential source?) like websites, blogs, online shops, cameras, environment, through customers themselves or through personnel.

Only then can companies organize data and make it ready to be analysed. Think spreadsheets as the most basic tool, but it can be any other software that can take and handle statistical data.

Finally, to wrap up the Preparation stage, it is essential to do a thorough clean-up. After all, the output of Data Analytics is only as good as the quality of its input. So, the data needs to be cleaned up, corrected of errors, duplication or incompleteness.

The Analysis

This is where the four types of Data Analytics come in. To do so, and depending on the type, format and purpose of the data, the data analyst may use one or several methods, such as:

  • Regression analysis: it is used to understand how variables affect one another. For example, how are marketing campaigns affecting sales?
  • Cohort analysis: used to divide datasets into smaller groups with common characteristics or cohorts. This is useful to, for example, understand customer segments.
  • Factor analysis: a statistical method for breaking down large datasets and finding hidden patterns. A potential use case is understanding customer loyalty.
  • Cluster analysis: a technique used in machine learning for finding similarities between data and grouping it in clusters. Commonly used for email marketing to identify consumers who use email in similar ways and then tailor the emails sent to different clusters of customers.
  • Time series analysis: used to identify trends, cycles, and systemic patterns in specific time intervals. Frequently used in automated stock trading, but also interest rates, quarterly sales and sales forecasting.
  • Monte Carlo simulations: very used in, for example, physics. Monte Carlo is a statistical method used to model complex probabilities. Risk analysis is one of its biggest applications.
  • Sentiment analysis: uses tools such as natural language processing, text analysis, computational linguistics, AI, etc., to analyse qualitative data. It is relevant for social media monitoring, brand monitoring, customer support, customer feedback analysis, or market research. For example, brands can use it to detect the tone used in social media comments.

Learn why human centric process automation is so important.

The Results

But what is the point of these methods, tools, and techniques?

Data analytics exists at the intersection of information technology, statistics, and business. No wonder it is so used in Business Intelligence and across all industries to help businesses optimize performance. Implementing Data analytics means your company can actively use the data that is currently not being analysed to improve operations, increase revenue, and reduce costs by identifying more efficient ways of doing business.

But the use of it goes beyond maximizing profits and ROI. Ultimately, the insights obtained through Data Analytics help business make better decisions, which can lead to further growth and development, or even new opportunities.

Planning Data analytics

Implementing phase starts with a plan. What if you don’t have a plan yet, but more like an idea? You came to the right place. We can help you with the choice of platform and tools, navigate the metrics, meet the demand for mobile data accessibility, and comply with regulations.

Data analytics is fast becoming a fundamental part of any business plan. Digital data is everywhere, from social media to mobile sources. It just needs to be put to good use.

At Near Partner, we have a wonderful Data Analytics team, lead by Filipe Sá, our Head of Data Analytics. And we’re ready to collaborate with you and get the best solutions for your business. Let’s brainstorm and accelerate your business together: get in touch!

Filipe Sá

I am a father and husband in love with my family. I always try to make the best of what life can give me. I like music, sports, travel and DATA. My willingness to learn has helped me a lot on this journey of more than 20 years working with BI. Come and be part of the world of DATA LOVERS, you won't regret it.