5 Key Data Analytics Trends in 2021 That You Need to Know

It’s been a tough 18 months for businesses across industry sectors. To ensure that they keep their competitive edge in the wake of a global economic crisis, companies are becoming far more proactive where data and analytics are concerned.

Data analytics very often prove to be most useful as a means of driving cost efficiency, determining new strategic directions, and managing risk, all of which are more important now than ever as businesses fight to stay afloat.

The rise of data analysis is, in turn, bringing with it new business trends. Here are five of the biggest trends in 2021.

1. A migration to the cloud

With a new focus on data comes larger data sets that are more complex and more valuable. To better manage these data sets, companies are migrating their analytics to the cloud in droves, with 94% of all enterprises already using a cloud service.

It’s not hard to see why given that cloud computing comes complete with a whole range of benefits. For one thing, cloud computing can help to ensure the security of important information. Data that is migrated to the cloud is stored on servers outside of an organization, meaning that it is better protected from theft and easier to recover if something happens.

As well as security, businesses can enjoy additional scalability. So, when new resources are needed for effective analysis, it is easy to put them into place with cloud computing.

2. Increased DataOps

It is no longer possible for companies to get the results they want to see with manual approaches to data analysis. As such, there has been a new move towards DataOps, which is an area of automated analytics that is still in development.

What DataOps can offer is increased time efficiency when it comes to the gathering of data, as well as higher quality analytics as a result. In other words, it helps to make the whole process of acquiring and investigating data far smoother and ensures that any information generated during the analytic process is as valuable as it can be.

As it becomes possible for companies to acquire more and more data, speeding up the time it takes to collect, collate, and identify value within that data becomes increasingly crucial. Thankfully, for companies that want to keep their competitive edge, DataOps can assure both efficiency and accuracy from the outset.

3. More advanced AI

It probably won’t be news to you that AI is changing the business world for the better. From the Internet of Things making more data available to enterprises than ever before to AI chatbots using existing information to respond to customer inquiries in real-time, AI’s intuitive technologies offer companies the chance to put their data to much smarter use.

Other uses for AI in data analysis include finding value in large data sets, often in the form of patterns and trends that would be difficult (if not impossible) for the human eye to spot. AI can also predict outcomes of investments, strategies, and other courses of action, and quickly, too. As such, it is proving to be invaluable for planning and setting long-term goals.

So, it’s little wonder that, as of this year, just under half (48%) of companies use AI to address issues with data quality.

4. Risk reduction

As the field of data analytics gets more advanced, it becomes increasingly simple for businesses to mitigate risks in areas such as brand image and employee safety. However, one of the most common approaches to risk reduction in 2021 is break-even analysis. This involves looking at the numbers to determine the point at which a business will break even for a given investment.

Risk reduction strategies like break-even analysis are important for several reasons. Chief among them is the fact that understanding the point at which you will break even will help you to better plan for the long-term. In other words, by putting data to good use, companies can set more realistic goals and perform better in the future.

5. Data as a Service

Data as a service (or DaaS) tools provide businesses with all they need to better integrate, manage, and store their data in the cloud. It removes the need to install large, expensive software packages to manage large sets of data effectively. It also means that companies can be more flexible with how they use their data and scale it as needed.

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