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Top Big Data Trends to Affect Your Business in 2020

The global Big data market is growing year by year. Data-driven decisions have become common for numerous industries around the world.
In fact, 97.2 percent of companies invest in Big data projects. The reasons are clear: it helps to understand and anticipate customer needs, scan the hottest trends in social media, analyze all kinds of external and internal threats to your organization.
Big data transforms practically every business sphere. With Big data steady growth, we can foresee how its capabilities are going to expand in the coming years. Let’s look at the potential of Big data technologies and disclose the trends and challenges that are going to dominate in 2020.

Big Data Industry Trends

Machine learning to continue hunting for insights

Machine learning(ML) provides an opportunity to analyze the data flow coming from numerous sources be it sensors, applications, or Google search queries. Machine learning systems use their own mechanisms to extract insights and assure fast and robust problem-solving without manual assistance. It’s no wonder companies are ready to prepare their environments for machine learning algorithms in the coming years. Experts predict that the machine learning industry is going to draw almost $60 million in late 2021.

Demand for NLP systems to rise

According to Gartner, 50% of all analytical queries will account for NLP (natural language processing) by 2020. This means companies will have to develop a system that can capture customer voice and translate it into the language used by machines. With NLP, it will be possible to dig deeper into customer attitudes and preferences. This, in turn, will lay the groundwork for developing virtual assistants or chatbots with “conversational” capabilities. Using Big data analytics tools, further advancements are likely to bring the concept closer next year.

Big data to transform skillsets

Big data growth has given rise to the specialization of roles within IT departments. This creates an urge for companies to search for talent with industry-specific expertise rather than an MBA degree. Moreover, Gartner predicts that almost half of all manual data management tasks will be put to automation towards the end of 2022. Data analysts will have to master automation methods and new Big data analytics tools that help to extract reliable insights.

Data-as-a-service to grow in demand

Almost every company is going to recognize the benefits of data-as-a-service (DaaS) in the next couple of years. DaaS delivers data in streams that are tailored to subscribers’ needs. Now, relatively few businesses have in-house resources to access data streams and manipulate that data efficiently. DaaS can eliminate the need for internal data management teams as it offers a robust means to access external data sources quickly and easily. Data-as-a-service will give you more control over company-wide data and will fill the gap between departments. Data lake proliferation, as well as secure cloud-based solutions, can make relevant data available in an easy-to-use and secure format.

5G to speed up data transmission

Gartner predicts that there will be over 25 billion connected devices by 2021 producing large volumes of data. To integrate the data coming from various sources, the 5G technology will provide a high-throughput data pipeline that can handle extremely large data volumes. The technology will improve user experience by connecting household-level wearable tech, VR-assistants, and health-monitoring devices. At the industrial level, 5G will be powered by the Big data technology stack to provide faster and safer ways to collect and process data.

Edge computing to gain numerous adopters

According to Gartner, edge computing is about shifting the data processing infrastructure closer to the data generation source. Local data processing as well as real-time data analysis are likely to grow exponentially by 2025. In fact, Gartner predicts that edge computing will jump by 75 percent within the next couple of years. This happens because sending business-critical data to the cloud or centralized data systems is not so effective as it used to be in the previous years. The urge for fast near real-time processing of data streams goes hand in hand with Big data growth.

Big Data Challenges

Along with its potential, Big data issues and challenges can’t be ignored. This happens because more data than ever have been created in recent years. The advent of wearable devices and other IoT technology based gadgets has generated massive data influx from ordinary users. Only a small amount of this data is accessible for further analysis with the help of Apache Hadoop, Spark, or other analytics frameworks. Other concerns to be addressed are:

– Data complexity and reliability: those challenge numerous companies willing to make informed decisions. Companies need to learn how to work with vast amounts of data and develop their own solutions to process raw data.
– Data security: data is coming from various sources making it hard to pinpoint a compromised one. Organizations will have to work out appropriate practices to secure data collection and retrieval.
– Data protection: did you know that the Internet handles 3.8 million Google searches and 1.4 million Tinder swipes every 60 seconds? Experts predict  that the figures are going to rise significantly in 2020. Should those forecasts come true, there will be mountains of sensitive data that will require consistent regulation and protection. Organizations that process personal data have to comply with data protection regulations introduced by GDPR. The California Consumer Privacy Act is also expected to come into action in 2020. Businesses will be under pressure to keep up with the ongoing changes as failure to comply will result in heavy penalties.

In the recent couple of years, Internet users have already created more data than ever before. Big data is transforming the way businesses operate and there is no sign of it slowing down. We believe that 2020 is going to be another crucial year with Big data and analytics remaining at the forefront of decision-making.


Olga Demidenko

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