Data continues to play a crucial role in shaping the landscape of sectors and industries. Rapid advancements in technology have led to data being generated and collected at an unprecedented rate, resulting in the rise of data science and data-driven decision-making, as well as increased attention to data privacy and security concerns.  

Last year has seen an increase in demand for skilled data professionals and a focus on utilizing data for improving decision-making, enhancing customer experiences and driving innovation.  However, the increasing amount of data also brought attention to ethical considerations regarding data privacy and security. All of these factors have contributed to a need for better data management and analytics. 

In this article, we cover three data analytics trends in 2023 and how they will impact the industry.  

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Data democratisation  

The democratisation of data enables many companies to streamline their processes across every aspect of the business, effectively utilising the free information for different purposes across different departments.   

When data democratization wasn’t common, users used to spend a significant amount of time searching for data and requesting access in order to use it. The data, which was usually placed in one location, was often provided to analysts in a messy spreadsheet. This method hindered and even prevented organisations from becoming data-focused, causing them to fall behind in a data-driven environment. 

With this trend, information is made accessible to everyone within the organisation. This eliminates the inefficiencies caused by a lack of data access and allows data analysts to easily create outcomes and strategies that can bring further efficiency, profitability and success. 

Integration of AI 

Artificial intelligence (AI) has been a hot topic in the last few months as most organisations struggle to manage and analyse their raw data. Recent studies and reports from different companies have revealed that AI technologies can aid companies in analysing raw data more intelligently and efficiently while uncovering data patterns and trends that may not be easily identifiable. 

Integrating or merging AI technologies with data analytics can help companies effectively address their most intricate data types while uncovering the potential of unstructured data on a large scale. 

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Adaptive and real-time data analytics 

Data analytics will no longer be confined to historical data thanks to AI technologies. As more and more data are being collected from internet of things (IoT) and industrial internet of things (IIoT) devices, data analysts can now process information as it is being provided. 

The primary advantage of adaptive analytics is that companies can use real-time data to make decisions with a very high level of precision. Since data is being analyzed continuously in real-time, the system is less likely to become obsolete or outdated. 

Eliza Sayon

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Eliza is a content producer and editor at Public Spectrum. She is an experienced writer on topics related to the government and to the public, as well as stories that uplift and improve the community.

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