Stuart Gentle Publisher at Onrec

Understanding Data Analytics: The Lifeblood of Digitising Industries

Digital transformation of industries has prompted the value of data to skyrocket over the past few decades in particular.

Nowadays, companies from all industries worldwide actively pay for access to industry reports created by analysing mass amounts of business data in order to improve on their own services. Simply put, the discipline of data analysis provides modern business owners with the information that they need in order to compete on a national or even global scale.

But business owners don’t necessarily have to wait for dedicated data firms to conduct their research and collate industry reports. As more and more tertiary institutions introduce data analytics courses into their course offerings, there is a growing population of educated data analysts, scientists, and other professionals whose technical and analytical skills are likely to be assets for any modern business owner.

But what is data analytics? What does this discipline entail? And why is it a worthwhile area of study to explore, both for business owners as well as students? Read on for some additional insights into this emerging discipline that is proving to be the lifeblood of digitising industries across the globe.  

What is data analytics?

We’ll start with a simple definition. Data analytics is essentially the science of collecting and processing data in order to draw educated conclusions about those data sets. Data analysts are tasked with identifying data sets that are likely to be most valuable for the business they represent as well as for the industries they operate in. 

In simpler terms, data analysts work to find patterns in business, industry, and consumer data. These patterns can then be used to create focused business development plans or even to improve on that business’ customer offerings, whether these be products or services. 

Despite data analysts being largely reliant on digital tools and software in order to collect, sort through, and analyse data sets, the process of data analysis is by no means simple or even linear, especially if an analyst is working with a business who may not have their own data collection systems in place. In these instances, data analysts often collaborate with programmers to create ETL (Extract, Transform, & Load) processes that support businesses in collating and organising data in a manner that best suits their organisational needs.

In other words, data analysis requires digital infrastructure. If that infrastructure isn’t pre-existing, your data analytics professional is likely to spend their first few weeks or perhaps even months of employment simply setting up data warehouses and other analytics tools that work best for your organisation. 

The four types of data analytics

There are generally thought to be four types of business data analytics. Each of the four types correspond to a specific question, the answers of which can be found in the types of data sets that are analysed, as well as the methods used to analyse those data sets.

The four types of data analytics and their questions are as follows:

  • Descriptive analytics - ‘What happened?’
  • Diagnostic analytics - ‘Why did it happen?’
  • Predictive analytics - ‘What’s likely to happen next?’
  • Prescriptive analytics - ‘What can we do next?’

As you can see, the four types of data analysis follow a collective journey of risk identification and problem solving. Despite all being interrelated, the processes behind each of these four types of data analysis do possess their fair share of differences. For instance, descriptive analytics generally uses historical data sets in order to analyse past situations. Diagnostic analytics is designed to delve deeper into these situations as a means of determining how they came about.

The patterns identified through the process of diagnostic analytics can help businesses design a ‘game plan’ of what to do vs. what not to do, depending on the impact that behaviour patterns held on their business or similar businesses in the past. This leads us to predictive analytics. By identifying the patterns that are most likely to trigger a specific outcome, data analysts can effectively ‘predict the future’, of course within a certain margin of error. 

And naturally, with a little bit of critical thinking, understanding the past can help us prepare for the future. That is the motivation behind prescriptive analytics, which utilises mathematical concepts like extrapolation and estimation in order to accurately pinpoint the best possible pathway forward for business owners and their enterprises. It’s common for prescriptive analytics to also utilise the computing power of artificial intelligence or machine learning technologies in order to better gauge the likelihood of success for development strategies created through the power of prescriptive data analytics. 

Although it’s common for data analysts to have their preferred type of analytics from these four main types, professional data analysts are likely to have engaged with all four of these different yet interrelated processes during their studies. This is because a strong understanding of all four processes is required in order to perform data analytics for modern enterprises. 

How to get started as a data analyst

If data analytics is starting to sound like an attractive career pathway for you, you’ll be happy to hear that there are actually a number of ways that you can get started in this field. Given the highly technical, information-heavy nature of this discipline, data analytics can easily be taught remotely and without the need for on-campus computer labs, meaning that prospective analytics students can select from either on-campus or online courses.

Alongside this, there are an abundance of learning resources available to motivated students, including free-to-access public data sets that can be used to trial ETL processes or to help students complete data science projects. Budding data analysts are encouraged to take advantage of resources and literature published online as well as the expansive data analytics community that exists online and on platforms like GitHub as well as social media platforms like Facebook and LinkedIn. 

Finally, it’s important to note that data analytics as a discipline is constantly evolving to incorporate new technologies, digital tools, and theories, as the industries that are being analysed continue to grow and change as well. For this reason, data analysts of the future are expected to stay in the loop with regards to industry trends and innovations, as is the case with any career in the technology sector.