In 2019, the world saw massive upticks in Big Data with companies flocking to embrace the importance of data operations. It was predicted that the big data industry, in 2019 was worth around $189 billion from it $20 billion from its previous years 2018. The industry is set to continue its rapid growth and expected to ready around $247 Billion by 2022. In 2020, the industry is predicted to observe some of the trends that will change the future of big data. Let’s see some of those trends:
In the year 2017, augmented analysis was named the future of data and analytics in research published by Gartner. Augmented analysis can provide clear results and present solutions in a simple format. The process of augmented analysis can automate the process of data analysis with the help of Machine Learning and Natural Language Processing (NLP). In this process, the data is prepared through a streamlined automation process from various sources like external portals, internal data, cloud data, and any other locations.
Analysts can prepare data for analysis by combining all the data, its process, and checking them for errors. These clarified data be used real-time analysis with sophisticated tools and then the data analysis is automated with algorithms. This helps incorrectly identifying trends and patterns to provide accurate results. Augmented analysis can be greatly beneficial for businesses for collecting and formatting data from different sources, managing tons of data simultaneously, improving daily functions in a business, prepare and analyze data on time-sensitive requirements, and providing analysts time to work on special projects.
Technology is pushing all of its tools to rise and the Internet of Things (IoT) is one of them. According to a Gartner forecast, by the end of the year 2020, IoT expected to reach around 8.4 billion. IoT devices are being used for refrigerators, parking meters, ovens, and several other home appliances.
In 2020, these devices are expected to play a major role in healthcare equipment, security apparatus, and home and retail devices. These devices will be increasingly used to their benefits of data and assistance in running big data analytics. For example, edge computing allows data storage in local storage device near the IoT device. This reduces the dependency of the devices on the cloud platform which in turn helps them to work faster as they do not require any wait time for accessing the data from the cloud.
Rising demand for real-time analytics requires fast CPUs and in-memory processing. Also, decreasing in-memory costs are expected to drive more and more analytics to real-time environments. Companies are looking for machines that can instantaneously respond to online sales activities, important alerts regarding their production infrastructures, and sudden changes in financial markets and others.
Over the past few years, voice-based applications and analytics have not observed any improvements due to the challenges of capturing voice in different intonations and accents with accurate NLR. However, today, NLR, interpretation, and mechanics technologies have evolved to a point where analytics queries can be posed by voice command. This has created fast-paced environments that do not require employees to work hands-free including warehouse yards, logistics, and others.
Graph analytics is expected to gain traction in 2020. This can be attributed to the fact that spreadsheets are instrumental and have kept companies engaged in analytics. However, companies are experiencing the problem that their data and complexity of analytics queries have increased beyond the point the common spreadsheet can handle.
Graph analytics have the potential to help these companies determine the connections between different data points. These data points even include those that at first do not appear to be connected. This technology can amplify the task of connecting people, times, places, and things, to speed up the market for business insights.
Corporate IT and data science departments have begun to integrate different pieces of analytics into a whole. Artificial intelligence and Machine Learning will learn from data analytics by observing repetitive patterns of processing their outcomes and then posing derivative queries. The developments in AI and ML will not only augment human creativity. This will be possible as they can rapidly perceive repetitive patterns and deliver faster times to market for business insights.
Scientists are spending as much as 80% of their time cleaning and preparing data. Businesses are looking forwards to data automation which can eliminate human involvement in such operations. By automating data automation, scientists can save more time and speed it to market for analytics. These analytics can obtain prepared and vetted data sooner.
Predictive analytics has helped companies to gain a crucial understanding of historical and current situations. Now, it is expected to grow a step further and shift toward assessing future economic conditions, infrastructure maintenance, climate trends, risk areas, and investment needs.
Analytics Life-cycle Development
The usage of analytics apps is becoming more and more common and very soon IT departments will start to look at them similar to traditional transactional apps. This is will encourage them to develop life-cycle management policies and procedures for analytics. The development will begin with application development and testing and then extending to launch, support, backup, and disaster recovery.
Big data is expected to create a proper central body that connects all medical records and medical data available can identify the cure, preventive measures, and other disease management solutions. It is expected to make a difference in healthcare services for various purposes as it has already played a crucial role during the times of pandemic in managing hospital equipment and other departments.
By the end of 2020, there will be a major revolution in the healthcare system backed by big data analytics and IoT. Researchers are working on discovering the applications of IoT devices in patient tracking and monitoring of different conditions. They are even working on robot designs with the help of big data to attend patients and perform operations.
R&D in Various Industries
Big data analytics has to improve the traditional ways businesses manage their operations daily. It has changed businesses from marketing to supply chain management by helping them gather customer preferences, behavioral insights, forecast industry trends, and customer expectations to create better products for customers.
Major industries are relying on their R&D department which in turn depends on big data analytics in various aspects such as simple social media analytics and management, improving product quality, manufacturing automation, location-based service decisions, providing better customer support, innovative tools to automate sales pipeline, and so much more.
The year 2020, is expected to mark the release of new applications of big data as the futuristic needs of big data are enormous. The year will also witness new research to include big data for the development of automated physical products. It will open up several more options and tools to help businesses innovate and discover their audience. Additionally, these developments will give way to fast pace development of the other technologies supported by data analytics.