The explosion of cloud technologies means that, for many companies, transactions, inventory and even IT infrastructure itself already exist in a purely virtual state. With such large and complex data being generated by many sources, making sense of it all is more important than ever before.

Clearly the world is already powered by big data, but the big question is, what comes next? Can data volumes continue to grow at an exponential rate or will the big data boom slowly grind to a halt? These are the experts’ predictions about what the future of big data has in store.

Data Volumes Will Continue to Grow

90% of the data that’s in existence today has been created over the last couple of years – that’s an astonishing rate of 2.5 quintillion bytes per day. The advent of the internet, social media, text messages, media files, IoT devices and sensors have prompted that data explosion, and it’s not going to slow down anytime soon.

Most experts agree that big data will continue to grow exponentially for the next few years at the very least, with the International Data Corporation predicting that global data will reach 44 zettabytes by 2020. This will be driven by the digital transformation process and businesses continuing to do almost everything online. There will also be a rise in the number of connected and embedded devices that share a wealth of data every day.

Demand for Data Professionals Will Increase the Skills Gap

With so much data to process, the boom in data creation will go side by side with an increased requirement for data analysts, data scientists, big data developers and chief data officers (CDOs). The skills required to become a big data analyst will be in increasingly high demand as data volumes soar. That will lead to a widening gap in the availability of data professionals and the demand for their skills.

IBM has forecast that data science roles will account for 28% of all digital jobs by 2020, with the firm’s Quant Crunch Report already identifying machine learning, big data and data science skills as the most difficult to recruit for. In the UK, demand for workers with specialist data skills grew by 231% over the last five years, compared to a 36% increase in demand for general workers.

Although the skills gap will be a difficult one to close, the future certainly looks bright for the data scientists and data analysts who will be in extremely high demand. Businesses that are striving to improve their operations and gain a competitive advantage will be willing to pay generously to secure top talent. We can also expect that 90% of companies will want to fill a chief data officer position by 2020.

AI Will Drive Automation

With more data to be crunched and not enough data scientists to do it, there’s going to be an increase in the level of big data automation over the next few years. Artificial intelligence will play an increasing role in data handling and analytics and take more of the tedious data cleansing tasks away from data scientists.

Although artificial intelligence and algorithms will be able to accelerate those processes by removing much of the manual hand-cranking, it still requires a good deal of human development and monitoring to create those systems and make sure they run smoothly. That will bring an increasing reliance on software engineers, data engineers and DevOps specialists.

Big Data Will be More Accessible

A key challenge for many larger organisations over the last few years has been trying to unify all of their data. Building flexible storage environments such as data lakes have been a priority for many. However, in the future it’s predicted that more of this critical data will be stored in systems that are much more accessible for analysis, predicted modelling and visualisation tools. Rather than data simply being gathered, processed and stored, these tools will make it much easier for data to be understood within companies and impact the decisions that are made.

In today’s landscape, businesses rely on expensive infrastructure to capture, prepare and analyse big data. They then need data scientists to be able to use it. In the future, the landscape will evolve to have more self-service, on-demand platforms and apps that provide the necessary data analysis resources, with organisations only charged for the tools they use.

There’ll Be an Increasing Reliance on Data Ecosystems

The tech industry, specifically Silicon Valley, has already switched its attention away from big data and onto data ecosystems. Data ecosystems combine big and small data to help businesses maximise the value they generate. The challenge that businesses face when trying to operate a data ecosystem is finding a tool that can manage and analyse those different data types.

Currently, many data analytics applications run on the open source Apache Hadoop framework, which handles data processing and storage. However, one piece of software does not solve all the data challenges businesses face. To make the most of their data ecosystems, businesses will have to diversify and use a greater number of niche solutions and technologies to deal with specific data types. Each solution will perform a different job and deliver a complete solution when used in combination.

Big Data Will Generate Value for Businesses That Know How to Use It

One of the biggest misconceptions about big data is that there’s value in simply collecting vast amounts of data and interpreting it. In reality, there’s not. The ultimate value for a business derives from the actions it takes after analyzing the data.

With a widening skills gap and more data to process, it will become increasingly difficult to sift through enormous data lakes to generate insights that can improve decision-making. That’s where AI automation and niche solutions and technologies will come into play. Stream processing where data is processed in motion by companies such as Ververica (view full pricing) will also become increasingly important.

All of this will help businesses make more sense of big data without the requirement for expensive infrastructure. That will allow businesses of every size to use big data more effectively in the future.