Big Data Trends 2020

Although we already are in the second quarter of 2020, it’s valid to talk about big data trends that will emerge and grow in the coming months and possibly even years. Augmented analytics, cloud optimization, continuous intelligence, edge computing–all of these disciplines are strongly connected to the big data topic, and, currently, they attract more and more attention. And for a reason, as they can significantly improve the way we work with big data! In this article, we are going to talk about big data trends for 2020 and see what this discipline is going to be like in the near future.

Big data has been a hot topic for years now. Particularly because it significantly changes the way companies do business and take advantage of all the data they possess. As you already know from our past blog posts, big data analytics is a much quicker, more convenient, and cheaper way to deal with data that flows through your company.

Not only it noticeably speeds up the pace of work, but it also provides you with insightful knowledge that can be used in the decision-making process in your company. In other words, thanks to big data consulting, your company works smarter, faster, more efficiently.

Hardly surprising that people all around the world carefully observe the development of big data and track the way it grows and enhances. We do so as well, and that’s why we decided to write an article about the current big data trends for 2020. We managed to indicate seven essential big data trends that shape the big data industry. Let’s just dive in!

Big Data trends 2020

Augmented analytics

Big data is all about analyzing data. As a result of this analysis, you obtain useful, practical knowledge that can be used to grow your company. Augmented analytics goes even further because it combines data analysis with machine learning algorithms and natural language processing (NLP). This combination gives the ability to understand data and interact with it organically as well as notice valuable or unusual big data trends. Moreover, the machine learning algorithms, harnessed to work in big data analytics, can suggest insights pre-emptively. This technology has merged artificial intelligence and machine learning techniques to make developing, sharing, and interpreting data analytics significantly easier.

Furthermore, augmented analytics speeds up the entire process of data analysis. It is estimated that a whopping 80% of the data scientists’ work is strictly related to gathering and cleaning data[1]. It’s a time-consuming process, but thankfully, it can be improved with the augmented analytics, as many of the time-consuming tasks of data preparation can be done quickly, automatically, and with fewer errors. Thanks to this improvement, data scientists can focus on much more productive tasks.

Some experts believe that in 2020, augmented analytics will become the primary purchase of businesses dealing with analytics and business intelligence[2].

Optimization

Cloud computing has also been with us for years, and we find more and more applications of this technology. Today, almost every internet user uses this solution, just to mention Google Drive, which is no more no less a cloud data storage platform. Cloud systems are cost-effective, quick, and convenient, as employees all around the world have instant access to it, without the need to download heavy software or applications. Usually, all you need is Internet connectivity and a browser.

However, although this solution is already immensely effective, it doesn’t mean that cloud systems can’t be optimized even further. One of the possible ways of this optimization is called cold storage. It can be used to store older and unused data, facilitating the necessary space to more valuable or up-to-date data. It is predicted that cold storage can save up to 50% of overall storage costs![3] Giants like Google or Microsoft are already working on this technology.

What’s more, within this big data trend, we can expect to see more of the hybrid cloud (a mix of private cloud and third-party, public cloud services[4]) and multi-cloud (a blend of various tools and solutions available at different clouds) strategies, even in SMEs (Small Medium-sized Enterprises).

Big Data trends 2020: Edge Computing

You may have never heard of this technology, so short introduction first. Edge computing is a distributed computing technology which brings data storage closer to the location where it is needed, in order to improve response times and save bandwidth[5]. In other words, edge computing is all about processing the information as physically close to endpoints as possible. The result is reduced latency and traffic within the network.

Edge computing can brilliantly co-operate with the cloud computing infrastructure, as it can be expanded to work not only in centralized servers, but also in distributed on-premise servers, or even the devices themselves. The result? Again, even more, reduced latency. Moreover, as edge computing offers a decentralized approach, we can expect the improved cybersecurity as well, since it reduces the need to send data over networks or to other processors.

In-memory computing

It’s a technology designed to detect patterns within data and analyze massive amounts of data. In-Memory computing refers to the storage of data inside the RAM memory. It’s an alternative to data storage in relational databases. This technology is used to perform real-time data analysis. It allows you to work effectively even with large datasets. In-memory computing will gain more and more popularity due to its indisputable benefits (it offers immensely powerful analytics and the ability to work with large datasets with impressive performance) and the reduction of costs.

SAS is one of the companies which work on this technology. According to their website, ‘In-memory-enabled data discovery combined with machine learning techniques deliver deeper insights with speed and precision. That means you can act faster and perform better. In-memory computing allows you to analyze data of varied size and complexity, with unprecedented speed’[6].

We can be certain that this technology will grow rapidly over the coming months!

DataOps

Its full name is Data Operations. It’s an automated, process-oriented methodology, used to improve the quality and reduce the cycle time of data analytics[7]. It’s a relatively new concept, as DataOps was first presented back in 2014, in a blog post on the IBM Big Data & Analytics Hub. The DataOps technology originates from the Agile and DevOps methods and is applied to the entire data analytics discipline.

It makes automated data testing and delivery accessible, which is used for higher data and analytics quality. DataOps can be used to automate as many stages of the data flow as possible, including:

• Business Intelligence
• Data science
• Data analytics

This technology is rather new, and we can assess that its development should be counted from 2017. It was a significant year for DataOps because this was the year when the major ecosystem development happened. Without a shadow of a doubt, it’s a technology worth watching.

Big Data trends 2020: Chief Data Officers (CDOs)

Maybe it’s not a new technology, but it’s still a real big data trend, clearly visible in thousands of companies, especially in Europe. The CDO is a corporate professional, who’s responsible for governance and utilization of information within a company, particularly through data processing, analysis, data mining, and other means[8]. The need for the CDO’s, along with Chief Protection Officers (CPOs), emerged in Europe with the coming of the General Data Protection Regulation (GDPR)[9] in May 2018.

Currently, more and more companies decide to employ a CDO or a CPO. We can state that the GDPR  has a significant influence on big data analytics because companies adapt to the more efficient and streamlined big data analytics solutions. Partly, as a result of this, according to the report from IBM, there would be as many as almost 3 million data science jobs available by the end of 2020[10].

Continuous Intelligence (CI)

This is another big data trend we can expect to grow in 2020. It’s another way to improve the decision-making process in companies. Long story short–CI integrates real-time analytics with business operations. This technology processes historical and current data alike in order to support the decision-making process, or–in some instances–automate it as well.

What’s particularly interesting, CI uses another technology mentioned in this article–augmented analytics. Continuous intelligence is a new technology, which is made possible by augmented analytics and the evolution of other big data and AI technologies. The potential applications are still ahead, but we can predict that CI can help in:

• Providing more effective customer support
• Designing special offers and discounts tailored to each customer’s need and expectations
• Optimizing the corporate decision-making process

Gartner predicts that over 50% of new business systems will be using continuous intelligence by the end of 2022[11].

Big Data trends 2020: conclusion

As you can see, when it comes to big data, there is a lot going on right now. We can expect that every month, more and more novelties will emerge. So it’s crucial to stay focused on the big data issue. It’s one of the real game-changers in the AI industry. A multitude of professionals and data scientists continuously work on improvements and new applications within this niche. We will keep you updated about the important discoveries and advances, so we encourage you to drop by here regularly and read about big data, artificial intelligence, machine learning, and business intelligence.

After all, Addepto is your obvious choice when it comes to these disciplines! Would you like to talk to us about implementing AI in your business? Nothing simpler, just drop us a line, and let’s chat!

References

[1] TechTarget Contributor. Definition augmented analytics. Nov, 2018. URL: https://whatis.techtarget.com/definition/augmented-analytics. Accessed Apr 7, 2020.
[2] Keith D. Foote. Big Data Trends in 2020. Jan 28, 2020 URL: https://www.dataversity.net/big-data-trends-in-2020/. Accessed Apr 7, 2020.
[3] Lynda Partner. TOP BIG DATA ANALYTICS TRENDS HOLD TRUE AS WE LOOK TOWARD 2020. Oct 4, 2019. URL: https://blog.pythian.com/top-seven-big-data-analytics-trends-2019/. Accessed Apr 7, 2020.
[4] Sarah Neenan, Sarah Neenan. What is hybrid cloud? Everything you need to know. Dec, 2020 URL: https://searchcloudcomputing.techtarget.com/definition/hybrid-cloud. Accessed Apr 7, 2020.
[5] Wikipedia.org. Edge computing. Apr 2, 2021. URL: https://en.wikipedia.org/wiki/Edge_computing. Accessed Apr 7, 2020.
[6] Sas.com. In-Memory Analytics. URL: https://www.sas.com/pl_pl/solutions/in-memory-analytics.html. Accessed Apr 7, 2020.
[7] Wikipedia.org. DataOps. Nov 27,2020. URL: https://en.wikipedia.org/wiki/DataOps. Accessed Apr 7, 2020.
[8] Wikipedia.org. Chief data officer. Jan 24, 2021. URL: https://en.wikipedia.org/wiki/Chief_data_officer. Accessed Apr 7, 2020.
[9] Ec.europa.eu. EU data protection rules. URL: https://ec.europa.eu/info/law/law-topic/data-protection/eu-data-protection-rules_en. Accessed Apr 7, 2020.
[10] Will Markow, Soumya Braganza,Bledi Taska, Steven M. Miller Debbie Hughes. THE QUANT CRUNCH HOW THE DEMAND FOR DATA SCIENCE SKILLS IS DISRUPTING THE JOB MARKET. 2017 URL: https://www.ibm.com/downloads/cas/3RL3VXGA. Accessed Apr 7, 2020.
[11] Gartner.com. URL: https://www.gartner.com/en/conferences/apac/data-analytics-india/gartner-insights/rn-top-10-data-analytics-trends/continuous-intelligence. Accessed Apr 7, 2020.