Key Data and AI Trends for Technology Leaders

Ryan Jamieson
January 11, 2024

As we start 2024, it is a time to consider trends that are emerging in the data and AI space that will shape how organisations operate and create value this year. With the rapid pace of innovation around data infrastructure, analytics, and machine learning, technology leaders have a lot to keep up with in order to best position their companies for the future.

To cut through the noise and provide insight on the developments that matter most to drive competitive advantage, this piece highlights key focus areas for Chief Technology Officers, Chief Information Officers, and Chief Data Officers to consider in the year ahead. Specifically, it explores trends to note across three categories: technological innovations to leverage, operational use cases to enable, and organisational considerations to embrace.

Major Technological Innovations

New capabilities and architectures focused on supporting AI workflows and getting more value from data continue rising to prominence. Here are some of the most important tech trends on the radar right now:

  • Data lakehouses and Apache Iceberg: Combining elements of data warehouses and lakes for flexibility and performance at scale, data lakehouses are gaining traction as a centralised analytics hub with platforms such a Databricks being at the forefront. Apache has introduced the Iceberg table format which is emerging to help structure and query huge datasets efficiently across various storage systems.  
  • Vector databases: Purpose-built vector databases that support AI workloads by efficiently searching vectors and embeddings, these specialised databases will grow significantly.
  • Observability for AI: Dynamic, real-world AI systems require advanced observability tools to monitor data flows and machine learning models in order to ensure reliability and understand issues.
  • Streaming architectures: As organisations deal with increasing volumes of real-time data, streaming infrastructures to ingest, process and analyse event streams are becoming more critical.

Key Application Areas

In terms of how organisations are applying data and AI operationally to add business value, a few key use cases stand out:

  • Data products and enterprise AI: Leading data teams now build internal data products with requirements gathering, documentation and product life cycles. Similarly, AI models get operationalised as products via MLOps techniques.
  • Generative AI: With the multitude of generative AI platforms that are available, organisations need to consider how to embed these into the business in a trusted and controlled manner.
  • Optimisation and rightsizing: With cloud costs growing 30% yearly, analysing usage, minimising waste and right-sizing deployments helps slash budgets.
  • Monitoring and analytics: Tools that gather metrics on how infrastructure and services are utilised allow for usage-based cost optimizations.
  • Automation and self-service: Automating manual processes via ML pipelines and giving more users self-service access to data helps increase productivity and democratisation.

Industry Application

Trends are emerging in AI application in industry such as:

  • AI in Healthcare: The application of AI in healthcare is growing, with AI being used for drug discovery, personalized medicine, and to improve patient care through predictive analytics.
  • AI in Services: The application of AI in service industries is expanding, with AI being used in legal, finance, human resources and recruitment, to automate and optimise information production.
  • AI in Creative Industries: AI is being used in creative industries like music, art, and design, to augment human creativity and produce new forms of artistic expression.
  • AI for Cybersecurity: With the increasing threat of cyber attacks, AI is being leveraged to enhance cybersecurity measures. AI algorithms can detect and respond to security threats more quickly and efficiently than traditional methods.

Major Operational Considerations  

Finally, technology leaders also need to keep in mind evolving operational best practices and organisational trends around data and AI, including:

  • Converging team structures - Data teams now emulate software teams while software teams require more data skills for AI. This drives convergence of the two.
  • Responsible AI standards - Ensuring models are transparent, fair and avoid bias is crucial as regulations and societal expectations grow around ethical AI.
  • Data culture and governance - Treating data as a strategic asset requires governance frameworks addressing lineage, quality, privacy as well as developing and accessing data skills.
  • Rise of the Chief Data Officer - Appointing an executive-level Chief Data Officer is becoming best practice for unlocking greater value from data.

We encourage technology leaders to stay attuned to the technological, application and operational trends emerging in data and AI. With careful attention in 2024 to the trends and best practices covered across these three dimensions, significant value and differentiation can be unlocked.

The raw ingredients for gains through data continue expanding dramatically - we hope this article supports leaders in crafting and executing strategies to unleash potential within their unique constraints. all the best for 2024!

References:

5 Key Big Data Trends (2024 & 2025) (explodingtopics.com)

7 Top AI Trends (2023 & 2024) (explodingtopics.com)

Exploring The Future: 5 Cutting-Edge Generative AI Trends In 2024 (forbes.com)

AI predictions for 2024: What top VCs think | VentureBeat

opendatascience.com/7-data-science-ai-trends-that-will-define-2024/

Top Data Engineering Trends For 2024 (montecarlodata.com)

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