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Top Data Science and AI Trends for 2023

The annual data science and AI trends report by Analytics India Magazine aims to highlight the top trends that will define the industry next year.
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The year 2022 has seen significant progress in terms of the adoption and expansion of AI and data science. The year has been rightly called the year of Text-to-Anything, with some phenomenal artwork being generated by AI-based tools like Dalle-2, Imagen, Midjourney and StableDiffusion. Striding on, we expect generative AI to become more accessible and expand into newer horizons.

As governments and enterprises have increasingly moved towards digitisation with data driving their operations and decision-making, very often, questions cropped up about data privacy and security. 2022 saw some development around this front. One of the prominent is the Indian Government’s initial scrapping of the Data Protection Bill to replace it with a more comprehensive Digital Personal Data Protection Bill. With more regulations introduced, it is likely that future developments in data science and the AI field will be based on the framework surrounding data privacy and security.

The advancements made in the field of data science have necessitated data automation which has been making rounds for quite some time now. Industry experts believe that automation will penetrate further with big IT firms attempting to automate internal processes.

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Finally, it is expected that the ongoing recession is likely to impact the data science and AI industry. The severity of this impact will unfold in the coming years. But industry experts have their opinions on it.

The annual data science and AI trends report by Analytics India Magazine aims to highlight the top trends that will define the industry next year. This report highlights trends for 2023.


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Determining the drift

  • Surveillance capitalism is driving governments across the world to put regulatory compliance in place.
  • Data privacy is now a strategic priority for organisations with more resources being devoted to arrest data privacy risk.
  • Flow of Insights with Trust (FIT) is enabling implementation of privacy framework.

Implications for enterprises

  • Increased trustworthiness and stronger relationships with customers and clients.
  • Increase in the operational cost and difficulty in accessing data leading from the compliance of data privacy frameworks.
  • Robust ethical frameworks for data, enabling inclusive growth for all stakeholders in the ecosystem.

Determining the drift

  • Organisations are increasingly moving towards fully automated value chains through hyperautomation.
  • Implementation of self-service analytics is enabling democratisation of intelligence and data within organisations.

Implications for enterprises

  • Implementing data-driven solutions within organisations will help create PoCs for solutions that Big IT can offer other clients.
  • Internal automation will enable Big IT to offer shorter development cycles with improved agility and faster time to market.

Determining the drift

  • Companies across sectors and regions are increasingly looking to make their data strategies more central.
  • There’s no established framework at present to assess the impact of data in the overall functioning of the organisation.
  • The role of CDO is being conceived to be more strategic—providing relevant insights for data-driven decision-making.

Implications for enterprises

  • Deployment of data at scale and improved technical maturity for the organisation aligned with the evolving regulatory landscape.
  • CDOs to transcend organisational and functional silos to establish an open and transparent data culture.
  • A top-down approach to lead the adoption of a uniform strategy that promotes the implementation of data-driven technologies.

Determining the drift

  • Enterprises are aggressively adopting hybrid data management solutions to optimise costs while avoiding vendor lock-in instances.
  • Adoption of containerisation and micro-services for cloud-native applications is enabling move towards multicloud.
  • Service providers want to build solutions agnostic of platforms to avoid limitations in terms of deployment.

Implications for enterprises

  • Flexibility to enterprises to use the best possible cloud for each workload.
  • Improved resilience to vendor-specific outages and configuration issues.
  • Better compliance with regulatory norms as multicloud permits storing sensitive data at specific locations as necessited by compliance requirements.

Determining the drift

  • As data-driven strategies take a more central role, access to the same data and insights across functions becomes fundamental.

Implications for enterprises

  • 360-degree view of business performance across different verticals leading to better value generation.
  • Better oversight over data, permitting ground-level business operations teams to identify and address issues immediately.

Determining the drift

  • At this point, we have reached saturation levels, and the chants of Moore’s law—which states that every 12-18 months, the processing power doubles—slowing down or nearing an end have been restored.
  • Chipmakers are also focusing on building libraries that enable accelerated computing/data science.

Implications for enterprises

  • Lesser time spent by ML professionals waiting for the models to train, and more on building the model.
  • Focus on macro architectural innovations as we move to distributed computing.
  • Supercomputer-as-a-service might become more affordable due to higher demand.

Determining the drift

  • The prevalent crunch of data science talent in terms of niche skills is driving organisations to bank on citizen data scientists who are trained to fulfill business needs.
  • The no-code low-code platforms are permitting amateurs to get into the business of data analysis and deliver insightful analytics, sometimes even without in-depth domain knowledge.

Implications for enterprises

  • Better use of data leads to greater efficiency and competitiveness.
  • Closing the talent gap without having to incur additional costs on hiring new talent.

Determining the drift

  • Exponential growth of big data has contributed to complexities like data silos, security risks, etc. Data fabric solutions are being used to address these challenges.
  • Rising importance of data privacy and security, data accessibility, and embedding data governance is leading organisations to integrating data by means of data fabrics.

Implications for enterprises

  • Holistic view into business performance by means of pulling data from legacy systems.
  • Facilitating accelerated digital transformation and automation for companies.
  • More fluidity across data environments making all data available across the enterprise.

Determining the drift

  • Demand for generative AI is growing by leaps and bounds. This is driven by the need for applications such as super-resolution imaging, text-to-image conversion and text-to-video conversion across sectors.

Implications for enterprises

  • Better representation and conceptualisation of theories and concepts.
  • Expanding applications in the realm of summarising data or information, saving resources and capital.

Determining the drift

  • There will be a domino effect of big technology companies resorting to mass layoffs and hiring freezes.

Implications for enterprises

  • Attrition rate is likely to go down with reduced opportunities.
  • A higher focus on digitisation might positively impact the hiring of data professionals.

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Zinnia Banerjee
Zinnia loves writing and it is this love that has brought her to the field of tech journalism.

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