What’s Next for Big Data?

In a previous article, we discussed the top 10 technology trends that will be shaping 2020. Based on studies by Gartner and other reputable entities, technologies such as artificial intelligence and hyperautomation are going to make 2020 a very exciting year for the tech industry in general. We also have technology like computer vision and edge computing being implemented more widely.

But what about big data? Big data used to be a buzzword that everyone used. It has since been overshadowed by AI implementations and new technologies. Don't think the days of big data analysis are over though, because the presence of AI, distributed ledgers (blockchain) and other technology trends that are shaping up right now will make big data analysis even more exciting in 2020.

More Data Sources

The ability of machines is only increasing, so expect to see businesses and other entities relying on a wider source of data in the future. AI can scrape data from sources that weren't available to computers before, making the ability to collect and process data on a massive scale within reach of more businesses.

The use of AI in acquiring more data sources also relates to how data is being distributed. Instead of relying on fixed sources, big data analysis will soon be able to acquire data from a dynamic set of sources. When applied to something like social media, for instance, big data analysis can collect deeper, more dimensional insights from different users.

The availability of new data sources means more businesses can use big data for data-driven decision making. With a growing need to understand markets, target customers, and key stakeholders, more businesses will turn to big data as a solution.

New Opportunities

Careers in big data is another interesting trend to watch out for in 2020. Data scientists are in high demand due to the growing application of AI, and more data specialists will be needed as companies turn to big data to amplify their use of AI further. Fortunately, businesses are also investing in training and development programs to equip their team members with sufficient big data-related skills.

Big data courses are not difficult to find. There are courses that focus on the technology side of big data analysis. There are also programs designed to help you master the business side of the equation, with courses covering subjects such as business intelligence and market analysis becoming more common.

Distributed Data

When multi-cloud was becoming a thing, many experts believed that big data was going to be replaced by something better. In reality, multi-cloud and the increasing efficiency of cloud environments only strengthen big data analysis. You now have more computing power, affordable cloud storage, and wider options when it comes to data frameworks and processing logics.

We also have technologies like blockchain and distributed ledgers making big data more powerful. Rather than relying on a cloud cluster or a physical server for storing and processing a vast amount of data, businesses can tap into existing blockchain networks for more resources. The most complex analysis can be performed in a shorter period of time through a blockchain network.

Sure, technologies like Hadoop are not as popular as they used to be, but that doesn't mean big data is not gaining traction. Hadoop is still one of the most accessible ways of storing and processing large amounts of data. Tools like Hive and Spark are still useful too. They may appear slow or outdated, but they represent the accessible nature of big data analysis.

Machine Learning to the Rescue

Big data is closely related to artificial intelligence – much closer than many anticipated. Both machine learning and deep learning benefit from big data, and the opposite is true as well. Data analysis models can be developed faster when the process of machine learning is further supported by efficient data collection and labeling.

On the other hand, big data analysis can be far more effective with the help of machine learning. Less human input is now needed to convert unstructured data into structured data. More importantly, AI can perform in-depth analysis and generate insights even without human operators doing the manual processing of the structured data.

Let's not forget that machine learning is contextual, and it makes big data analysis more relevant. AI can take context and other factors into consideration when processing vast amounts of data. The immense data processing speed is another big advantage of integrating machine learning into big data analysis processes.

Predicting the Future

The biggest trend of them all, however, is predictive analysis. As computers – with the help of AI – consume and process more data, they become better at making predictions. Insights based on historical data aren't the only outputs to expect from big data analysis. It will not be long before big data is used to make predictions with immense accuracy.

We're already seeing implementations of predictive analysis using big data in various fields. In manufacturing, big data analysis is now being used for predictive maintenance. Rather than waiting for a machine in the manufacturing line to fail, computers can schedule maintenance based on continuous data streams and variations.

The same is true with big data in advertising. If you find ads displayed by ad networks to be uncannily accurate to your personal preferences, that's because ad networks can now predict behavior with incredible accuracy. They have a large amount of data about the audience and can process that voluminous data into insights and values.

Big Data Personalization

That leads us to the last trend we are going to discuss in this article: big data for user experience. In the end, businesses are trying to deliver a pleasant user experience in today's increasingly competitive market. More and more businesses are opting for a personalized experience, and the way they deliver that personalized experience is by understanding their customers better.

Big data is the perfect technology for the job. Intelligent decision making is made possible beyond the limits of granular data. This is where other technologies get really amplified by the use of big data. AI-based customer service, for instance, can be incredibly personal when handling different customers. Ecommerce sites can promote their products better.

Big data becomes the glue that ties everything together. That brings us to the top 10 technology trend article we mentioned earlier. If you take a closer look at the trends that will shape 2020, you will see that 8 out of 10 of them rely on large data streams and rapid data processing to work. Even the ability to capture momentary markets depends highly on how well data is processed.

So, what's next for big data? From these trends, it is easy to see that the future for big data is brighter than ever. More businesses will use big data, more data specialists will seize market opportunities, and more exciting developments are to be expected.