With the merge of Cloudera and Hortonworks in 2019, there was a massive uptick in Big data usage across the world. The year proved to be a big year considering the change in the big data landscape. More businesses around the world embraced Big data for business data operation. Moreover, the big data trends in 2019 witnessed the introduction of new concepts and the mix and merged of different technologies related to big data.

The big data trends 2020 also see continuous advancement in the big data space that impacts the way organizations perceive and adopt business intelligence. In this blog, we will discuss the emerging trends in big data in 2019 and the data trends in 2020 to distinguish the changing areas.

What were the emerging trends in big data in 2019?

More Operationalization of Big Data Analytics

Big data operationalization started in 2018. However, it became more prominent in 2019. As organizations found initial success with data operationalization, they found data scaling and orchestration time-consuming and a maintenance issue. In this space, we see the automation framework’s involvement that became very useful to put use cases into production.

More involvement of Big data analytics in AI space

One of the surveys conducted in 2018 revealed an overwhelming response from the tech giants regarding more interest in Big data analytics and AI investments. No doubt, today’s business is all about analyzing data. It could be IoT Big data or social media data, which has broadened the scope of Big data technologies as a whole. Interestingly, Big data analytics is not a solo player in this game. Furthermore, Big data analytics trends for 2019 indicate direct connectivity between big data analytics and AI for better big data insights.

Some of the identified big data trends in this area are as follows:

• Big data meets Artificial intelligence with the enhanced use of IoT

• Edge computing will play a significant role in Big data analytics and AI processing

• More use of Machine Learning for Big data analytics will grow Natural Language Processing in AI

• More use of Big data technologies in Cybersecurity will enhance AI space

• AI will be the key to analyzing Dark data set in Big data

I have discussed in detail regarding the above trends in my previous blog – 5 Big data analytics trends for 2019 expected to influence Artificial Intelligence

Artificial Intelligence and Machine Learning are more in application

Another key big data trend for 2019 has seen an increased alignment between big data analytics with artificial intelligence (AI) and machine learning (ML). To augment everyday operational analytics, organizations use ML and AI in their normal line of business activities. 

There is a noticeable change in team structure as well. Most of the organization’s separate teams work for traditional BI and data science streams, i.e., for AI and ML. But now, the skill sets and groups have started to overlap to work more thoughtfully.

Big Data trends to watch for 2020 -2025

Big data today is not only ‘big’ but also complicated due to diversity in data sources. With more AI applications in expansion, the upcoming years are set to be more innovative and evolutionary for big data.

Here are the top 10 big data trends that are going to take our attention.

Trend #1 The Cloud is the new Data Lake with Multi-cloud and Hybrid in place

In the last few years, we have seen the continuous development of cloud-based technologies, which is why businesses are increasingly adopting cloud in their daily data operation. However, moving existing data integration from on-premises to the cloud is time-consuming as well as complicated. This is about data migration and syncing the data sources and platforms at the same time. In this context, the recent trend is hybrid deployment.

Besides, workloads are being divided for the early adopters of cloud as they find complete cloud migration difficult. Instead, utilize the cloud storage for dynamic workloads and on-premises for stable workloads. Furthermore, most of the enterprises now have adopted multi-cloud technology, which is going to be a trend for late adopters also. Hence, we can say, data ecosystem strategy will include both hybrid and multi-cloud at the same pace.

Trend #2: A growing trend of abandoning Hadoop for Spark and Databrick

Though Hadoop played the pioneering role through the inception of Big data, due to its complexity, it has been criticized by many communities. With the entry of Apache Spark in the market, in many areas, we have seen a growing trend of adopting Spark to address Hadoop’s problems. Even many wanted to eliminate Hadoop from the scene for Spark. Hence, there is raising question on do we really need Hadoop to run Spark?

Not only Spark, but Databricks is also a booming area in this aspect, which is gaining traction among data science workers. This is because of their capability of in-memory computing along with a user-friendly interface.

However, adopting these newer technologies will not solve all problems in one shot as they also have their own challenges. Furthermore, you cannot completely abandon code data pipelines and operationalization. It still needs data workflows to be fully manageable, along with data governance.

The bottom line is organizations will find many choices between the above two platforms that they can choose based on their preferred capabilities and budget.

Trend #3: Continuous evolve of Machine Learning and Artificial Intelligence algorithms

Amalgamating Machine learning and Artificial Intelligence with Big data analytics has become an integral part since 2019. The use of basic Machine learning and AI algorithms within the data pipelines to show up results in data integration platforms and traditional BI system is a common practice now. More automated tools are being applied in production to put advanced AI and ML logic in data analysis.

These automation frameworks allow data scientists to create data pipelines that are almost production-ready. Interestingly, this trend will increase the number of AI and ML algorithms to go into enterprise-level production.

Trend #4: Continuous Intelligence

One of the promising big data trends in the coming years is continuous intelligence. As per Gartner’s prediction, almost 50 percent of businesses will be using continuous intelligence by 2022 to get a competitive edge. To explain more, continuous intelligence integrates business operations with real-time analytics. In this system, both historical and current data are processed to provide automated decision-making support. Continuous integration is a combined form of different technologies like business rule management, optimization, event stream processing, machine learning, and augmented analytics. It directs action based on historical as well as real-time data.

As a result, continuous intelligence will provide more effective customer support and a customized offer for specific customers. We will see more use of it in industries like logistics and transportation. Furthermore, continuous intelligence is an evolution of augmented analytics and other technologies.

Trend #5: Augmented Analytics in use

Augmented analytics is more pervasive in 2020 as a part of big data trends 2020. This advanced analytics form uses advanced artificial intelligence and machine learning to automate business process insights. The augmented analytics engine automatically goes through business data, cleans it, and then analyze it. Finally, it converts the insights into actionable steps. Most importantly, the whole process needs minimum human intervention.

Trend #6: IoT and data analytics combined power

As the IoT gets prominence in day-to-day life, it has also combined streaming analytics and machine learning. Interestingly, this particular big data trend will grow further in the coming years. As the internet of things has merged with data analytics, at the same time, Gartner predicts the enterprise IoT and automotive IoT will expand up to 5.8 billion endpoints during 2020.

The large organizations already using IoT devices are also implementing assisting technology to support data analytics to achieve maximum efficiency. This combination of IoT with data analytics and machine learning also improves flexibility and accuracy. Besides, these systems are fine-tuned to improve interaction with human beings.

Trend #7: More use of In-Memory Computing

In-memory computing proved to be very useful when processing a massive amount of data. In this case, data is stored inside the RAM instead of relational databases. As a result, data is processed fast, and businesses can easily identify patterns while analyzing the massive amount of data. Not only that, but in-memory computing can also perform real-time data analysis of complex data. As memory cost has reduced, in-memory computing will gain more popularity and become one of the growing big data trends in 2020.

Trend #8: Implementation of the GDPR and other Regulations

With the proliferation of big data, there is a growing need for protecting consumer sensitive data. For this purpose, GDPR or General Data Protection Regulation came into effect in May 2018. Additionally, The California Consumer Privacy Act took effect from January 2020. These regulations significantly impact how data is processed and handled and security and consumer profiling.

However, many organizations, especially those who sell their data, don’t see these regulations as profitable for their business as consumer privacy is an integral part of these regulations. But regulations like GDPR and California Consumer Privacy Act places the power back in consumers’ hand by recognizing them as the owner of the information. Furthermore, GDPR enables consumers to remove their data from the organization’s control.

Trend #9: Use of DataOps and Self-Service Analytics

Today, many modern self-service tools are in use with data analytics, which is implemented on the business level. DataOps, which is an agile solution, is now involved in data management. This enables users to increase the quality and speed of data management significantly by using automated technologies. Different technologies, like real-time data integration, streaming data pipelines, and change data capture, commonly known as CDC, form the basis.

As a result, one of the major big data trends 2020 sees the use of DataOps and self-services to make effective use of scattered data throughout the organization.

Trend #10: More demand for Intelligent Metadata Catalogs

One of the growing big data trends in demand for intelligent metadata catalogs. Metadata is information about data. It is structured data that contains information about the characteristics of other data. Metadata allows localizing, capturing, synthesizing, and automatically processing huge amounts of data.

Nowadays, data preparation and collaboration are being done based on intelligent functions of machine learning. Such growing demand for intelligent metadata catalogs increasingly equips artificial intelligence to enable active, faster, and adaptive data provision.

Final Verdict:

The ongoing big data trends show that big data analytics will change the way businesses operate in different domains like finance, manufacturing, healthcare, and other industries. It is not surprising that big data’s overwhelming size may generate additional challenges in the future related to data privacy and security. Besides, there will be a shortage of data space like data storage and processing. Last but not least, there may be a shortage of data professionals.

However, most experts agree that the ongoing big data trends will shift most of the organization from data-generating to data-powered by using business insights and actionable data.