The 2020 year has left the world with recent advances and trends in predictive manufacturing systems in a big data environment.
Data and analytics permeate every part of the digital enterprise. Rita Salam, an analyst and vice president of Gartner, outlined their development trends for the coming years at the Gartner IT Symposium in Orlando, according to InformationWeek.
Recently, organizations have seen a tangible increase in interest in data and analytics, driven by the growing popularity of big data technologies as well as machine learning and other artificial intelligence technologies that are responsible for processing them.
While many businesses are still struggling to get a commercial return on AI software, they continue to experiment with the understanding that AI adoption will be critical to their business in the coming years. It is because data and analytics greatly expand the scope of digital business strategies.
They have become the main components of customer service, recruiting specialists, optimizing the supply chain, helping to balance finances, and solving many other important business problems.
AI is one of the Big Data Trends
Artificial Intelligence Services and a range of related technologies are driving trends and are the basis for deployment in the coming years, enabling businesses to run faster and more reliably. “Today’s pace of business change and technology cycles are changing faster than ever before,” Salam said. “You need a flexible data and analytics architecture to keep up with them.”
Big Data trends and analytics trends cover three key areas, she said. The first is intelligence. Machine learning and AI are increasingly intertwined with workflows by expanding employee responsibilities, reducing the skills required, and automating tasks so that they can work with clean, real-life data.
The second is the new data formats. Today AI and machine learning support more flexible and new data formats.
The third point is scaling. It will take three to five years for these trends to become reality.
In the list of promising technologies presented below, there are no self-service systems and cloud services – they are already everywhere, there is also no quantum computing – the prospect of their commercialization is far from reality.
Big Data Trends For 2021
Here is the list of the latest trends in the field of big data and analytics:
1. Advanced Analytics is on the list of Big Data Trends
Advanced Analytics is one of the latest Big Data Trends. It is a nexus technology that spans business intelligence, data science, and machine learning. It will be used to enable as many people as possible without analytical skills to obtain valuable information from the data. Over the next few years, advanced analytics will become one of the dominant positions in the enterprise, and will be useful, in particular, when conducting an assessment for the selection of suppliers.
Also, other technology providers include it in their products and services to make the user experience more comfortable. The purpose of advanced analytics is to democratize analytic tools. It allows you to reduce the time for retrieving and understanding information per unit of time, with fewer skills, which cannot be said about today’s tools.
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2. Advanced data management
In a few years, data will flow to the enterprise in a much more dynamic mode than it is now. By then, organizations will have learned to analyze them at higher levels of automation in near real-time. Data management has many tasks, such as pattern recognition, monitoring capacity management database (CDB), monitoring data usage, regulatory requirements/compliance, cost models, and more.
Many of these tasks will be shifted to advanced data management. By 2022, the volume of manual data management tasks will be reduced by 45% due to the active implementation of machine learning and automatic service level management.
3. Natural language processing and conversational analytics
NLP and conversational analytics go a long way towards complementing advanced analytics. They provide non-data experts with a new kind of interface for querying and analyzing. “Most people don’t understand SQL and can’t write their own queries, which is what NLP and conversational analytics tools will do. They will simplify the interaction between the user and the analytics to a minimum, ”says Salam. Next year, about 50% of analytical queries will be generated using search, NLP technology, or automatically. There is a lot of room for improvement here.
Most modern analytics and BI platforms have implemented basic keyword searches. They provide answers to simple questions (“What was my sales by product?”), But more complex questions (“What were my top 10 products or customers within 50 miles of New York compared to last year?”) to a dead end. It is difficult for a machine to answer such questions, for this it must support ranking options, synonyms, and other functions that are not supplied by every supplier today.
Another new feature in this area is conversational analytics, which will allow you to ask more specific questions. “Until recently, analytics was closely related to visualization, but conversational analytics will add another layer to understanding the data,” Salam said.
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Graph processing and databases allow you to explore data the way most people think, revealing the interactions between logical concepts and objects/actors such as organizations, people, and transactions. Gartner predicts that the use of graph processing and graph databases will grow by 100% annually through 2022. This technology is designed to continuously accelerate data preparation, making tools more adaptive and intelligent.
Graphs allow you to create new semantic and knowledge networks, to establish emerging connections between different data coming from sources such as sports applications or dietary advice with medical advice and news feeds on healthy lifestyles.
5. Commercial AI and machine learning products will dominate open-source projects
Like nothing else, Open Source has spurred the development of technologies such as big data, AI and machine learning, digital tech giants such as Google or Amazon have achieved particular success in this field. But most organizations aren’t digital giants, and they’re struggling to tweak AI and machine learning pilots so they can go into production. Big data Experts believe that sooner or later many companies will switch to commercial platforms to manage their AI programs.
Gartner big data trends predicts that by 2022, 75% of new end-user solutions using AI and machine learning technologies will be created using commercial rather than open-source platforms.
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6. Data Factory
This Big Data Trend is closely related to advanced data management and allows flexible data to be maintained at scale. Enterprises used to strive to store their data in a single repository, but now it has become more distributed. A data factory is designed for data that resides in isolated environments (data silos) and implements a logical storage architecture that allows seamless access and data integration in heterogeneous storage. Gartner predicts that by 2022 data factory projects will be custom-built and deployed as static infrastructure. All of this will lead to a new wave of costs – enterprises will have to change their data architecture to make it more dynamic.
7. Explainable AI
“We believe that sooner or later the massive proliferation of AI will make it harder for businesses to manage it,” Salam said. This is because models are becoming more complex and opaque, while organizations need to comply with regulatory requirements, understand the results of internal monitoring, and be aware of the risks of violation of confidentiality and bias that can be embedded in the AI model.
Vendors are currently working on AI solutions to help mitigate these issues. Gartner predicts that by 2023, more than 75% of large organizations will be hiring AI behavior, customer privacy, and trust analysts to reduce reputation risk.
In addition to big data and analytics trends, blockchain has penetrated many technological areas, but its importance in the field of trust in relationships and the reliability of information about transactions is difficult to overestimate. “This is really about cryptographic support for immutability in the network of trusted participants,” says the expert.
Many changes can be tracked using blockchain, but in the data realm, it can be used to validate sources of information (fake news) or fake videos (deepfakes). Gartner predicts that by 2021 most private and exclusive blockchains will be replaced by ledger DBMSs.
9. Continuous intelligence
Continuous intelligence is the ability to make better decisions with real-time data analytics and advanced analytics. It includes an understanding of the situation and prescribes actions to be taken. It is intelligent, automated, and result-oriented technology. Gartner’s studies on big data trends predict that by 2022, more than 50% of large new business systems will include continuous intelligence, which will use contextual data in real-time to improve solutions.
10. Servers with non-volatile memory
Over the next few years, the Big Data market will be flooded with non-volatile memory servers that provide more memory, more affordable performance, and ease of connectivity than typical servers. Some DBMS vendors are rewriting their systems to support non-volatile memory servers that allow real-time analysis of large amounts of data in memory. By 2021, nonvolatile memory will account for more than 10% of the total server memory used for in-memory computing.