Data analytics is a key component of most business operations, from marketing to supply chain. But what does data analytics mean, and why are so many organisations utilising it for business growth and success?
What is data analytics?
Data analytics is all about studying data – and increasingly big data – to uncover patterns and trends through analysis that leads to insight and predictability. Data analytics emerged from mathematics, statistics and computer programming before becoming a field in its own right. It’s related to data science and it’s a skill that is highly desirable and in demand.
We live in a world full of data gleaned from our various devices, which track our habits in order to understand and predict behaviours as well as help decision-making. Algorithms are created based upon the patterns that arise from our usage. Data can be extracted from almost any activity, whether it’s tracking sleep patterns or measuring traffic flow through a city. All you need are defined metrics. Although much of data extraction is automated, the role of data analysts is to define subsets, look at the data and make sense if it, thereby providing insight that can improve everyday life
Why is data analytics important?
Data analytics is particularly important in providing business intelligence that helps with problem-solving across organisations. This is known as business analytics, and it’s become a key skill and requirement for many companies in making business decisions. Data mining, statistical modelling, and machine learning are all major elements of predictive analytics which uses historical data. Rather than simply looking at what happened in the past, businesses can get a good idea of what will happen in the future through analysis and modelling of different types of data. This can then help them assess risk and opportunity when planning ahead.
In healthcare, for example, data analytics helps streamline operations and reduce wait times, so patients are seen more quickly. During the pandemic, data analysis has been crucial in analysing figures related to the rate of infection, which then helps in identifying hotspots, and forecasting either an increase or decrease in infections.
Becoming qualified as a data analyst can lead to work in almost any sector. Data analysis is essential for managing global supply chains and for planning in banking, insurance, healthcare, retail and telecommunications.
The difference between data analytics and data analysis
Although it may seem like data analytics and data analysis are the same, they are understood slightly differently. Data analytics is an overarching term that defines the practice, while data analysis is just a section of the entire process. Once data sets have been prepared, usually using machines to speed up the sorting of unstructured data, data analysts use techniques such as data cleansing, data transforming and data modelling to build insightful statistical information. This is then used to help improve and optimise everyday processes with data analytics as a whole.
What is machine learning?
Machine learning – a form of artificial intelligence – is a method of data analysis that uses automation for analytical model building. Once the machine has learnt to identify patterns through algorithms, it can make informed decisions without the need for human input. Machine learning helps speed up data analysis considerably, but this relies on data and parameters being accurate and unbiased, something that still needs human intervention and moderation. It’s a current area of interest because the way that data analysis progresses and supports us is reliant on a more diverse representation amongst data analysts.
Currently, most automated machine learning is based on simple, straightforward problems. More complex problems still require at least two people to work on them, so artificial intelligence is not going to take over any time soon. Human consciousness is still a mystery to us, but it is what makes the human brain’s ability to analyse unique.
What are data analytics tools?
There are a number of tools that help with analysis and overall analytics, and many businesses utilise them at least some of them for their day-to-day operations. Here are some of the more popular ones, which you may have heard of:
- Microsoft Excel is one of the most well-known and useful tools for tabular data.
- Tableau is business intelligence software that helps to make data analysis fast and easy by linking with Excel spreadsheets.
- Python is a programming language used by data analysts and developers which makes it easy to collaborate on machine learning and data visualization amongst other things.
- SQL is a domain-specific programming language that uses structured query language.
- Hadoop is a distributed file system that can store and process large volumes of data.
Analysts also use databases that provide storage for data which is relational (SQL) and non-relational (NoSQL). Learning about all of these tools and becoming fluent in how to use them is necessary to become a data analyst.
How to get into data analytics
Working in data analytics requires a head for numbers and statistical techniques. But it also requires the ability to spot problems that need solving and the understanding of the criteria needed for data measurement and analysis to provide the solutions.
You need to become familiar with the wide range of methods used by analysts such as regression analysis (investigating the relationship between variables), Monte Carlo simulation (frequently used for risk analysis) and cluster analysis (classifying relative groups). In a way, you are telling a story through statistical data so you need to be a good interpreter of data and communicator of your findings. You will also need patience because, in order to start your investigations, it’s important to have good quality data. This is where the human eye is needed to spot things like coding errors and to transform data into something meaningful.
Studying for an MSc Computer Science with Data Analytics online
You can become a data analyst with the postgraduate course, MSc Computer Science with Data Analytics from the University of York. The course is 100% online with six starts per year so you can study anywhere, any time.
You can also pay per module with topics covered such as Big Data Analytics, Data Mining and Text Analysis, and Artificial Intelligence and Operating Systems. Once you’ve completed the learning modules you can embark on an Individual Research Project in a field of your choice.