This detailed analysis of the industry will help the reader develop a clear overview of the key trends and scope of the analytics industry. The report can be studied by recruiters, industry policymakers, companies, and data science experts or aspirants to get an overall view of the analytics industry.
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Data-driven insights have been an absolute must for companies, especially in a growing economy like India. This massive digital user base generates a wealth of data, with the volume of data previously projected to reach 2.3 zettabytes by 2023, according to IDC.
In a country as vast and complex as India, data analytics is a powerful tool that enables companies to navigate market dynamics, stay ahead of the competition, and unlock growth potential.
Owing to this, the analytics India industry study is seeing a growing expansion with new and different technologies coming into play every day. This report presents a comprehensive view of the analytics industry by analysing the different trends across multiple cities, geographies, sectors, salary brackets, experience levels, gender, and usage of key technologies in the industry.
The findings of this report highlight that the sudden explosion of the analytics market in the past few years is now coming to a grinding halt. The study also showed that the market for retail analytics is declining, while the market for analytics in the BFSI sector is continuing to grow.
As more organizations across various sectors lean towards data-driven decisions and automation, data science has become a key player in driving operational efficiencies and strategic insights. This shift has led to a significant increase in the number of data science service providers over the years. For enterprises, selecting the right data science partner can be a crucial factor in their success.
To aid businesses in making this critical choice, AIM Research presents the Penetration and Maturity (PeMa) Quadrant for Data Science Service Providers—a reliable industry standard to evaluate vendor competencies.
In the field of data science, the prominence of low-code/no-code solutions has grown rapidly. These tools have democratized data analysis and model development, enabling business analysts, domain experts, and citizen data scientists to actively participate in the data science process.
This report delves into the importance of building a robust prompt engineering capability for successful generative AI applications.
This report by AIM Research provides an evaluation of 12 Large Language Models (LLMs) based on various criteria to assess their performance and capabilities. The evaluation highlights the strengths and weaknesses of each LLM and provides insights into their potential applications in various domains.
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