Data and Analytics have emerged as two of the key metrics that define the success or failure of a company. With every company wanting to become a data company, it has thus become important for companies to not only become capable of using the data in the right way.
From using automation to deploying artificial intelligence, data science is integral to the success of a company. However, it is clear that not everyone has been successful in making that transition. With so much riding on implementation of data and analytics, Gartner has revealed the biggest trends or predictions for 2026.
The annual report from Gartner takes a look at how data and analytics leaders are approaching the industry. It also reveals important programs and practices, BI solutions, data management solutions, AI implementation, among others. Here is a look at some of the key takeaways from the report.
Chief Data Officers and their important role
The Gartner report shows that chief data officers (CDOs) will establish value-stream-based collaboration to outperform their peers by 2025. Such a system will lead to improved cross-functional collaboration and value creation. By 2026, Gartner predicts over 50 per cent of commercial organisations will have established initial efforts for formal data monetisation.
The data and analytics leaders will also rely on augmented financial operations (FinOps) to deliver improved cloud cost optimisation by 2026. Such a move will lead to reduced budget planning efforts by up to 40 per cent. Gartner also says ethical reviews will be as common as privacy reviews by 2024.
Impact on Data and Analytics Programs and Practices
Alan Duncan, Distinguished VP Analyst at Gartner says 70 per cent of public companies outperforming competition on key financial metrics will also report “being data and analytics centric” by 2025. In the same time period, 95 per cent of decisions currently using data will be at least partially automated while 80 per cent of organisations will have deployed multiple data hubs.
By 2026, 20 per cent of high-performing organisations will use connected governance to scale and execute on their digital ambitions. These organisations will also apply automated trust metrics across internal and external data ecosystems and will replace most outside intermediaries by 2026. This will also reduce data sharing risk by 50 per cent, predicts Gartner.
Deciding factor in data science buying decisions
Duncan predicts that 90 per cent of data science, machine learning, and AI platform buying decisions will be decided by ease of migration, interoperability, and coherence by 2023. A year later 60 per cent of organisations will have adopted a distributed delivery model.
Gartner also alerts organisations lacking a sustainable data and analytics operationalisation framework that their initiatives will be set back by up to two years by 2024. Another big prediction is that 60 per cent of analytics activities will be initiated by 2025 and 30 per cent “will be completed entirely within digital workplace applications.”
Cloud Data Ecosystems will be implemented in multiple environments
The report also reveals that 90 per cent of data management tools and platforms failing to support multi cloud and hybrid capabilities will be set for decommissioning within three years. By 2026, 20 per cent of large enterprises will use a single data and analytics governance platform.
While those changes will happen by 2026, Gartner sees 90 per cent of new data and analytics deployments happening through an established data ecosystem by 2025. The effect of such a move will be consolidation across the data and analytics market. Another change will be data fabric designs using active metadata will reduce human-driven data management and metadata management tasks.
Artificial Intelligence and major predictions
The most important predictions from Duncan and Gartner come in the field of Artificial Intelligence. In terms of core technologies, federated machine learning, transformer models, active metadata, and composite AI will dominate. AI regulations, digital literacy, compliance and security, and synthetic data will have an impact on consumers and workers.
The first important prediction being that 70 per cent of organisations relying only on ML for AI techniques will spend more money per model by 2024 than those leveraging composite AI techniques. By 2024, the report suggests 40 per cent of all organisations will offer or sponsor specialised data science education to accelerate upskilling initiatives.
That number is up from 5 per cent in 2021. Gartner predicts 80 per cent of the largest global organisations will have participated at least once in federated ML to create “more accurate, secure, and environmentally sustainable models” by 2025. It is also expected that 20 per cent of AI-automated decisions in an organisation will leverage active metadata to drive data and analytics automation.
One of the big trends in AI right now is making algorithms ethical, transparent, and privacy focussed. Gartner predicts that regulations will necessitate focus on AI ethics, transparency, and privacy by 2025. This will stifle “trust, growth, and better functioning of AI around the world.”
Gartner also predicts that synthetic data will reduce personal customer data collection. This will allow companies to avoid 70 per cent of privacy violation sanctions by 2025. The last prediction is that 5 per cent of workers will routinely use AI against their employer’s wishes to complete tasks by 2026.
Cloud technology to disrupt business
Gartner predicts that cloud infrastructure will move from disrupting technology to disrupting business. It expects more than 50 per cent of enterprise-managed
data to be created and processed outside the data centre or cloud by 2025. By the same time, 30 per cent of enterprises are expected to have implemented an AI-augmented development and testing strategy.
This will be a major change from just 5 per cent of enterprises having such a strategy in 2021. By 2025, Gartner sees sovereign clouds address specific compliance demands of highly regulated public-sector workloads. Over 65 per cent of edge use cases are expected to include machine learning (ML) in the form of deep learning (DL) by 2027.
Cybersecurity and IT Risk
The average annual budget for privacy among large organisations will exceed $2.5M by 2024 and will allow a shift from compliance ethics to competitive differentiation. Gartner also predicts that privacy lawsuits and claims related to biometric information processing and cyber-physical systems will have resulted in over $8B in fines and settlements by 2025.
Gartner also sees major changes among cybersecurity leaders with 50 per cent of them having unsuccessfully tried to use cyber risk quantification to drive enterprise decision-making by 2025 and 30 per cent will have adopted a data security platform. Lastly, organisations mishandling personal data are predicted to suffer 3x more financial damage through 2026.
Opportunity to build the digital future
The culmination of all these predictions will turn into major technological innovations necessary to build the digital future. Gartner estimates that 25 per cent of global enterprises will have embraced process mining as a step-up to autonomic business by 2024.
It expects to see 30 per cent of global enterprises use AI to “accurately measure and analyse the impact of climate change on their business” by 2026. Gartner also predicts 25 per cent of people spending at least one hour a day in the metaverse for work, shopping, education, social and/or entertainment by 2026 while the annual shipments of tiny IT tags and sensors will exceed 100 million units.
Gartner also predicts half the world’s B2C businesses will stop retaining customer data by 2030. This change will be driven by unmanageable compliance costs, and attempt to regain customer trust. The participation in metaverse is expected to triple healthcare costs by 2040.
The research and consulting firm sees significant decrease in societal productivity due to increase in personal and social disorders. While these changes will keep evolving, AI will forever be used first to automate every type of decision making before people “revert to making daily decisions themselves.”