Surging ahead in the Data and AI age- 20 Insights from Gartner's Data and Analytics summit, Sep 2022

After 2 long years, i attended the physical edition of Gartner's data and analytics summit that on Sep 19th and 20th, 2022 representing my employer Cigniti technologies. Here are some curated insights that I found very interesting to share.

1) #AIengineering : By 2025, the 10% of enterprises who establish AI engineering best practices will generate at least 3x more value with their AI efforts than the 90% of enterprises who do not.? Ref : AI Engineering — Moving the AI Needle to the Next Level?by Chirag Dekate.

2)#Aiengineering : Primary components of AIengineering are Devops (Implementation and Deployment pipeline), MLops and Modelops (The Model pipeline) and Dataops (Data management and The Data pipeline).Ref : AI Engineering — Moving the AI Needle to the Next Level?by Chirag Dekate.

3)#Aiengineering : Interesting case studies (names excluded)- Focusing on speed to value with AI/ML and hence providing reliability, Remediating business value leakage from models in operation and Building trust with explainable AI and ensure reproducibility.Ref : AI Engineering — Moving the AI Needle to the Next Level?by Chirag Dekate.

4)Future of #Datascience and #machinelearning : Starting 2023, enterprises will compete on differentiating business ideas that require AI techniques. Ref: Svetlena Sicular

5)Responsible #AItooling : By 2024, 60% of AI providers will include a means to mitigate possible harm as part of their technologies. Ref: Svetlena Sicular

6) #EdgeAI : By 2025, more than 55% of all data analysis by deep neural networks (DNNs) will occur at the point of capture in an edge system, up from less than 10% in 2021.Ref: Svetlena Sicular

7)#CompositeAI : By 2024, 70% of organizations relying solely on ML for AI initiatives will spend more money per model than that leveraging composite AI techniques.Ref: Svetlena Sicular

8)#TransfomerModels : By 2026, transformer models will revolutionize AI, forming the foundation for 50% of NLP use cases, up from less than 5% in 2021.Ref: Svetlena Sicular

9)#SyntheticData : By 2024, 60% of data for AI will be synthetic to simulate reality, future scenarios and derisk AI, up from 1% in 2021.Ref: Svetlena Sicular

10)#FeatureStores : By 2023, ease of migration, interoperability and coherence will be deciding factors in 90% of data science and ML platform buying decisions.Ref: Svetlena Sicular

11)#FederatedLearning :By 2025, 80% of the largest global organizations will have participated at least once in federated ML to create more accurate, secure and environmentally sustainable models.Ref: Svetlena Sicular

12)#Graphtechnology :By 2025, graph technologies will be used in 80% of data and analytics innovations, up from 10% in 2021, facilitating rapid decision-making across the enterprise.Ref: Svetlena Sicular

13)Scaling Analytics and Automation: By 2025, data stories will be the most widespread way of consuming analytics, and 75% of stories will be automatically generated using augmented analytics techniques.?Ref: Rita Sallam

14)#Dataengineers the most in-demand skill : Create data engineers by upskilling your ETL developers, data analysts, DBAs or similar roles. Train them on software engineering, DevOps tooling, product development and soft skills. ref: Robert Thanaraj

15)#Lowcode and #Nocode in AI and DSML : By 2025, 70% of new applications developed by enterprises will use low-code or no-code technologies. Applications of the future will be assembled and composed by the people that actually use them.? ref: Anirduh Ganseshan

16)#GreenAI #CleanAI #AI and #ClimateChange : With ongoing adoption and complexity, AI will only become more power-hungry. By 2030, AI may consume up to 3.5% of the world’s electricity. Despite renewables, AI must become more energy efficient. ref: Source: AI Can Do Great Things — If It Doesn't Burn the Planet, WIRED. ref: Pieter den Hamer

17)#Datastories : By 2025, data stories will be the most widespread way of consuming analytics. 75% of stories will be automatically generated using augmented analytics techniques.?ref : Anirduh Ganseshan

18)#Datastorytelling = Visualisation+ Narrative + Context

19)#Dataintegration market tools : Market size of $3.9 billion end of 2021, +11.8% growth from previous year. Expected CAGR of 8.5% over next 5 years to reach $5.8 billion in 2026.

20)#Finops : Due to consumption-based & serverless metered pricing models becoming mainstream, and the notion of unlimited compute, FinOps has emerged as a critical capability for data integration tools.

All the above insights are based on my notes that I captured attending the sessions and insights from my conversations with respective analysts during the 2 days of the event. These do not have any binding on my employer. I am posting this blog as an industry observer, quintessential technology marketer, and sharing for a larger audience's benefit and knowledge.