CTOs and IT managers at all levels will be defining and testing terms like “data as code” and “just-in-time” data analytics for their own production use cases. AI will be working overtime in the data management space, enabling call centers to mine more cogent information from customers, patching gaps in supply chains, and bolstering healthcare services, both locally and in the cloud. Here are some cogent predictions about what we can expect to see on the data management side of IT in 2022:
We will begin to hear ‘data as code’ frequently
– Stephen Manley, CTO, Druva
AI will be reading between the lines with customers
– Max Ball, Principal Analyst, Forrester Research
Supply-chain failures will fuel the meteoric rise of ‘just-in-time’ data analytics
– Matthew Halliday, EVP Product, Incorta
AI services will play a major role in generating revenue
– Zakir Hussain, EY Americas IoT Leader During the next few years, we can expect to see the emergence of federated machine learning, which enables high-traceability technologies and allows researchers to train predictive models on sensitive data transparently. This approach includes everything from support for the evolution of disease prediction to faster responses for autonomous vehicles.
New privacy-focused legislation will shift attention to data sovereignty clouds
– Danny Allen, CTO, Veeam
New data management approaches at the edge will come to the fore
– Bruce Kornfeld, CPMO at StorMagic
The data science industry is making the mistake of putting models before clean data
–Dr. Ron Bekkerman, CTO, Cherre
We’ll embrace data fabrics
– Stefan Sigg, Chief Product Officer, Software AG
Graph databases: A must-have component of the 2022 data landscape
– Richard Henderson, Technical Director at TigerGraph Throughout 2022, more companies will apply the power of graph analytics to support advanced analytics and machine learning applications, including fraud detection, anti-money laundering (AML), entity resolution, customer 360, recommendations, knowledge graph, cybersecurity, supply chain, IoT, and network analysis. Graphs will become even more linked with ML and AI. Gartner even reports that “as many as 50% of Gartner client inquiries around the topic of AI involve a discussion around the use of graph technology.”
Get value from data, AI or lose out to competitors and be shorted by investors
– Adam Wilson, CEO of Trifacta Thus, low-code and no-code solutions will become increasingly widespread, especially when they enable coders to do their work in the same space as business users. These more sophisticated, next-generation tools will have automatic programmer assistants and embrace modern techniques that allow non-coders to create custom programs without realizing it essentially. Finally, AI engineering is changing: Think “machine-learning operations.” This field will explode in growth as many start-ups make components of this more accessible and practical.
