Top 5 trends of data analytics in 2023

With the growing influence of technology, data analytics is all set to reinvent itself for offering more utility and growth potential

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With the growing influence of technology, data analytics is all set to reinvent itself for offering more utility and growth potential to businesses across categories and domains


According to a Forrester Analytics report, up to 73% of the data that companies collect is never subjected to any kind of analysis. This is surprising, given how much efforts businesses put into gathering data from various sources, including consumers, partners, and employees, among others. Leveraging data and applying advanced data analytics (DA) to gain meaningful insights is always a key driver for unlocking business value. It helps to monitor and improve profitability, customer acquisition and more.

As the organisations become aware of this, they are now actively working to integrate the best methods for making the most of the data obtained at various levels. Here in this article, we have tried to summarise the key data analytics trends that organisations will adopt and that will be prevalent in the year 2023:

1) Concepts like Big Data Fabric and DataOps: Big data fabric is more of a platform concept that aims to accelerate business insights by automating ingestion, curation, discovery, preparation, and integration of data. Such concepts are extremely important for large enterprises where data is generated at higher velocity from disparate systems.


DataOps is more of a practice and architectural framework to drive faster insights. It can be considered as one of the components of the wider “data fabric” platform. It has evolved from the traditional concept of DevOps used in software development and utilises the concepts of agile development, DevOps, and statistical process control.

2) Data as a Service (DaaS): Industrializing data and developing modularised, re-usable data engine is one of the foundations for high speed-to-market for analytics and reporting needs. Customers can drive complex analytics transformation journeys with DaaS as a fundamental pillar which involves business to play a larger role at the beginning of the analytics transformation journey.

The benefits from DaaS adoption are incredible as it helps the businesses in maintaining the agility of their data management processes, reducing time-to-insight, and increasing reliability and integrity of their data. In fact, DaaS supports the entire lifecycle of data analytics and enables companies to build, execute, and manage modularised, re-usable data engines which can be one of the foundations for high speed-to-market for analytics and reporting needs.

3) Utilising AI for MetaData Management: MetaData management is not a new concept and is a framework for cataloguing data assets within your organisations throughout its lifecycle. As managing data in large enterprises is becoming increasingly complex, this concept is moving beyond its conventional boundaries of compliance and risk management and now enabling firms to improve on critical areas of performance.


Given that data is now becoming available in several formats extracted from disparate sources with higher velocity and volume, it is imperative to have a process to create metadata at a much faster pace and that’s where utilising AI/ML becomes more important and a key differentiator.

4) Democratisation of AI/ML: Business context is always missing in AI/ML use cases and that’s where business and data transformation leads must play a significant role to bridge this gap. Business is always struggling to understand the complex nuances of data science/ AI algorithms which makes it difficult to implement AI/ML for larger audiences. For example, for the finance function, building the AI/ML layer in order-to-cash, procure-to-pay for customer segmentation, vendor clustering and driving many other complex decisions is going to be the key differentiator for predictive outcomes.

5) Augmented Analytics: Augmented analytics is a concept to enable usage of AI/ML for data preparation, insight generation and insight explanation. Traditionally, the insight generation process is very ad hoc, and it needs an army of professionals to make sense of the information/ data. However, with augmented analytics, exploration and synthesis of data is much faster than before. This is of particular importance to large organisations generating data from disparate systems in heterogenous formats. With Augmented analytics, analytics professional would focus more on delivering business values rather than just counting the beans.

The article has been written by Anshuman Bhar, CEO and Co-founder, Aays Analytics