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by Aishwarya Banik


December 10, 2021

To find hidden patterns and links, data and analytics managers should explore the possibilities of adding graphical analytics to their analytics portfolios and applications.

Big Data has a bright future ahead: Improved technology and easier access to an ocean of data has enabled organizations to gain more insight, improve performance, generate revenue, and innovate more quickly. Responding to a global crisis and its consequences proactively and quickly, the data and analytics associated with artificial intelligence (AI) technology will be crucial.

Cloud management

Cloud computing is becoming more and more inventive, instantaneous and adaptable. Over the past year, there has been a significant movement from organizations keeping data on physical servers to companies storing data in the cloud or using a hybrid solution. Indeed, Gartner expects that by 2022, public cloud services will be needed for 90% of data and analytics innovations. Going to the cloud is a no-brainer: it can reduce IT spending, increase flexibility, improve efficiency, improve security, and enhance opportunities for creativity.

Artificial intelligence will get even smarter

According to Gartner, 75% of companies will have moved from piloting to operationalizing AI by the end of 2024, resulting in a 5-fold increase in streaming data and analytics infrastructures. Artificial intelligence (AI) is already making huge strides in the business world, and it will continue to increase its ability to learn algorithms and shorten time to market. Through approaches such as reinforcement learning and distributed learning, companies in 2022 will be able to tackle more complex business challenges using AI.

Data mesh

A data mesh is a conceptually comparable and useful architectural approach to an enterprise data fabric, which Gartner has named the top strategic trend for 2022. The latter is a holistic approach to integrating all of an enterprise’s data, regardless of whether whatever its location, so that they can be accessed on demand. Despite the many implementation methodologies, some skills to create a data factory have evolved. A data mesh extends this distributed architecture approach by integrating domain-specific data production, storage and cataloging information.

Self-service scanning will increase

Companies need to make factual choices regularly and across multiple departments. Many of these decisions are based on data, but not all business people are data experts. Self-service data analytics solutions allow anyone without technical training or a deep understanding of data analytics to access data and create or customize their reports and analyzes. In 2022, companies will likely adopt more fully self-service analytics solutions, which will allow non-technical business users to securely access and extract insights from data.

Decision intelligence

Decision intelligence combines several areas, such as decision management and decision support. It covers a wide range of applications in the field of complex adaptive systems that combine classical and advanced sciences. It provides data and analytics managers with a framework for designing, composing, modeling, aligning, running, monitoring, and tuning models and decision-making processes in the context of outcomes and business behavior. When judgments require many logical and mathematical procedures, they must be automated or semi-automated, or must be recorded and audited.

Customer personalization will be king

The COVID-19 outbreak has had a massive influence on consumer behavior. Consumers have turned to the Internet for all of their shopping needs because physical businesses have closed, forcing businesses to go digital more than ever. With digitization has come more data and better knowledge of customers, despite the obstacles. Businesses will develop a data-driven “personalized customer experience plan” that allows them to target customers in a way that delivers exactly the right offer at the right time.

Predictive analytics will increase performance

Data analysts had to prepare huge volumes of data to respond to problems. However, recent technological advancements and predictive methodologies have made it possible to analyze current data to identify future problems before they arise. Businesses can accurately predict what will happen in the future with modern predictive analytics, which enables any organization to dramatically improve performance by anticipating consumers’ next moves before they take them.

Augmented data management

To optimize and improve processes, augmented data management uses machine learning and artificial intelligence (AI). It also turns audit, provenance and reporting metadata into a power source for dynamic systems. Large samples of operational data, such as actual queries, performance statistics, and diagrams, can be examined using augmented data management systems. An enhanced engine can change operations and optimize configuration, security, and performance by using existing usage and workload data.

Markets and data exchange will ensure competitive advantage

35% of large organizations will be sellers or buyers of data in online data markets by 2022. Buying and selling data has never been easier, profitable or scalable thanks to these markets and exchanges. Organizations can use these markets to generate new revenue streams and obtain critical data from other companies without having to engage in lengthy negotiations. In the years to come, monetizing data will bring in huge sums of money.

Blockchain in data and analytics

Blockchain technology solves two problems of data and analysis. For starters, the blockchain records all the history of assets and transactions. Second, the blockchain guarantees the transparency of complex networks of actors. Besides the restricted use cases of bitcoin and smart contracts, general ledger database management systems (DBMS) will be a more attractive solution for single enterprise data source audits. By emphasizing the capacity mismatch between data management infrastructure and blockchain technologies, data and analytics could present blockchain technologies as a complement to their existing data management infrastructure.

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