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Top 5 Data Analytics Trends for 2023

By Aarti Dhapte, Senior Analyst, ICT, Market Research Future.

Start-ups, SMEs, and large corporations increasingly use data analytics to cut costs, enhance customer experience, streamline current operations, and achieve precise marketing. Big Data attracts interest from many businesses due to its capacity to enhance data security.

The Global Data Analytics market is estimated to register a CAGR of over 27.6% to reach USD 3,03,252.3 million by the end of 2030. The generation and consumption of data are continually increasing due to the introduction of mobile technologies such as smartphones and tablets and improvements in mobile networks and Wi-Fi.

Let’s take a quick look at five upcoming trends that will take the centre stage in the Data Analytics:

Artificial Intelligence: AI and Data Analysis are interdependent; Data management is complex without AI, and AI depends on data analysis. As AI can identify data types, find possible relationships between data sets, and recognize knowledge using natural language.

Gartner predicts that by the end of 2024, 75% of businesses will operationalize AI, spurring a 5x growth in streaming data and analytics infrastructures. The capabilities of AI are ready to support analytics activities and help businesses internalize data-driven decision-making while facilitating simple data handling for all employees. This indicates that AI aids in decentralizing data across the company and frees data analysts, scientists, engineers, and other data professionals from time-consuming manual tasks.

Data democratization refers to making corporate data accessible to all levels of the organization, not just the analytics teams and senior management. The idea also demands that the company give workers the tools to interpret the Data they access to affect business decisions and increase opportunities. Teams can decide more quickly if they have instant access to and knowledge of the facts. Managing big data and maximizing its potential requires a democratized data ecosystem. These days, companies that give their staff the proper resources and knowledge are better prepared to help customers and make judgments.

Augmented Analytics combines Artificial intelligence (AI) and machine learning (ML)- to increase human capacity for contextual data exchange. The term “augmented analytics” refers to the tools and software that enable more individuals to access analytical capabilities, such as recommendations, analysis, or resolution to a question.

Users may identify critical information, design the best queries, and promptly obtain insights into their company’s context with augmented analytics. Even though augmented analytics primarily benefits those who lack in-depth analytical knowledge, it also speeds up data preparation and analysis duties for analysts and professional users.

Data-as-a-Service: Every company needs to use data to be competitive as more Data is being produced daily. Data as a Service (DaaS) comes in handy as not every company will have the same ease of access, storage, and data analysis compared to the most prominent tech giants. The Data as a service market is expected to register a CAGR of 36.9% and reach 61.42 billion by the end of 2030.

The Data as a service model offers the companies customized software solutions. Remote workers are more susceptible to cyberattacks now that the pandemic has introduced work-from-home tendencies to the industrial sector. Data as a Service implements security features, including managing passwords and logging data to ensure that the system complies with the companies’ security regulations.

For large organizations, the workloads can be heavy. The flexible Data as a Service system offers higher scalability and permits more resources instantaneously. The processing costs and data management costs can be optimized since the data as a service model optimizes the utilization of the resources for the workload. Resource allocation can also be adjusted; accordingly, such practices have benefitted the organization that has deployed the DaaS system.

Edge Computing: The focus of edge computing in the market is to bring computing and data processing close to the data source. Internet activity has grown drastically recently, and a lot of software has been produced, which has fuelled the boom of the edge computing market. A study by Statista estimates that the world creates over 64 zettabytes of data every year—that is, 64 trillion gigabytes of data from a total of 23.8 billion connected devices. By 2025, this number will exceed 180 zettabytes of data from approximately 41 billion connected devices. Edge computing, according to experts, holds the key to the future of technology.

Conclusion: Data analytics is crucial in enhancing business processes and reducing data waste. This service mainly works on four significant characteristics: volume, variety, velocity, and veracity. Data analytics enables enterprises to configure deeper insights into unstructured data and discover data that will empower business assumptions. The analytical tools help business analysts and users create business value and experience a competitive advantage. Furthermore, it helps ensure efficiency in procurement, empowers organizations to develop their marketing strategies, supports business growth, and differentiates themselves from competitors.

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