In 2021, data analytics continues to play a vital role in the business world. It’s a popular method for cutting down on operational costs, charting new strategic directions, and mitigating risk. The many benefits data analytics provides to an organization help them not just stay afloat, but thrive, even in a tumultuous economic environment.
This article presents an overview of the seven most important current trends in data analytics that are being used in 2021 by companies and organizations to more efficiently and effectively manage their data needs.
Seven Current in Data Analytics
- Increasing use of the Cloud: Currently, cloud data warehouses and data lakes are considered to be go-to options for collating and processing large data volumes. These options for data storage enable businesses and organizations to manage sharp workload surges without the need for physical infrastructure. By 2022, it is predicted that public cloud services will be the backbone of 90% of innovations in the field of data analytics. The increased use of public cloud services provides companies with a proven method for completing work faster.
- Decline of Dashboards: While dashboards remain a way of visually conveying stories, their use is likely to decline in the coming years. Because many dashboards are static, they lack interactivity and aren’t especially user-friendly. Data stories, however, are expected to prevail and are likely to become the go-to way to present data findings to an audience. In most dashboards, users must complete the majority of the manual work that is required to gather insights. However, machine learning and AI are becoming more commonly used in many BI platforms, which allows data stories to offer insights without the user having to conduct their own analysis. It’s projected that three-quarters of these stories will be generated automatically using augmented analytics by 2025, a movement that will empower businesses and organizations to take the data exploration process into their own hands.
- Incorporation of Advanced, Responsible, & Scalable AI: Currently, nearly half of all companies rely on AI for handling data quality. This powerful tool can be used to quickly and effectively predict investment outcomes, as well as to devise strategies or establish long-term goals. The use of new AI technologies like AI chatbots provides organizations with smarter ways to use data. In addition, AI can be used for extracting value from large datasets and spotting patterns or trends that would be difficult or impossible for a human to notice. The advent of responsible and scalable AI is expected to provide more effective learning algorithms, which will allow businesses to maximize the benefits from AI systems such as formulating processes.
- More prevalent use of Data as a Service: Data has traditionally been stored in data stores. Now, just as is the case with Software-as-a-Service (SaaS) apps, Data-as-a-Service (DaaS) apps incorporate cloud technology that provides users with on-demand access to information that does not depend on where the user is. DaaS relies on the cloud for delivering analytic services like data storage, processing, and integration using a network connection. This new concept is starting to see widespread adoption, as it combines low-cost cloud storage with cloud-based platforms that were designed especially for quick data management and processing for vast data stores.
- Increased Reliance on Natural Language Processing (NLP): The goal of NLP is to find new methods of communication between humans and computers. This technology strives to read and decipher the meaning of human language. NLP is often used for developing word processor applications as well as software for translation. This technology combines machine learning with computational linguistics, statistics, and deep learning models so that computers will be able to process human language from voice or text data and grasp its entire meaning, as well as the writer or speaker’s intentions.
- Prevalence of Quantum Computing: Most of the time technology currently used requires a lot of time for processing large amounts of data. Quantum computers were designed to cut down on this time by calculating the probability of an event or the state of an object before it’s measured, thus allowing a greater amount of data to be processed than a classical computer could handle. By compressing billions of data in just a few minutes, the processing duration is drastically reduced, which enables organizations to make quicker decisions.
- Further Augmenting the Data Management Process: AI technology provides businesses with new possibilities for augmenting data management with the help of auto-monitoring data governance controls and auto-discovery of metadata. This active metadata can be used along with data fabrics and machine learning to optimize and automate the entire process of data management, drastically cutting down the time required to deliver data.
These powerful data analytics tools are just a few of the dozens that are not only changing the current data analytics landscape but also providing snapshots of the future of this expanding field. Other technology such as data integrity tools, embedded analytics, self-service data analytics tools, and AIOps, are also available for those interested in finding dynamic ways to work with big data.
Beyond 2021: Where is Data Analytics Headed?
As the amount of data used on various platforms continues to increase, challenges are anticipated in the future with visualizing it all. AI-Driven Analytics is already helping automate data management by incorporating technologies such as natural language generation, analytics, and machine learning.
In addition, deep learning, which is a kind of machine learning, will continue to allow users to create a complex mathematical structure, or a neural network, which is able to learn from a data structure. Deep learning can pinpoint anomalies as well as offer predictions. These changes in the field of data analytics are increasing the speed at which data can be sorted, organized, and transformed into stories that provide valuable information for companies and organizations.
In the future, new departments devoted to data visualization are expected to be developed to meet the increasing demand for data extraction. Emerging technologies, along with AI, are likely to be used more often to deploy machine learning and automation for data analysis. More than ever, there is a growing need for those with training in data analytics to help take this field into the future.
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