Trends 2024

Bridging the trust gap in GenAI

Transform from big to better data using the knowledge from our top 10 trends in AI. Until now, data access for GenAI has been a massive free-for-all, with no traceability of data origin or quality control. It’s time to step up a chain from Volume, Velocity and Variety (big data) to Validity and Value (better data). Will your data be ready?

Hybrid AI bridges the maturity gap

Whether ML or augmentations, it’s time to put traditional AI into production and scale, especially in well-established use cases like fraud analytics and churn analysis.

Analyst Prediction

Generative AI is expected to achieve ~30% share of the overall AI market by 2025.

Source: IDC FutureScape: Worldwide Data and Analytics 2024 Predictions, IDC #US51295223, Oct 2023
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Generative AI for insights: Supercharging the data consumer experience

This user base appreciates auto-generated visualizations and insights, enhanced with explanations in natural language.

Analyst Prediction

By 2025, 66% of G2000 will adopt AI-driven headless BI and analytics with chat, Q&A, and proactive notification functionality — quadrupling the number of users with access to contextual information.

Source: IDC FutureScape: Worldwide Data and Analytics 2024 Predictions
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The age of unstructured data is now

By using knowledge graphs and vector databases, complemented by RAG (Retrieval, Augmentation, Generation), the opportunities for combining structured and unstructured data in a trusted way are endless.

Analyst Prediction

By 2027, GenAI will help equalize spending on unstructured and structured data processing and analysis software, doubling unstructured data's productive use.

Source: IDC FutureScape: Worldwide Data and Analytics 2024 Predictions
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From BI to AI and back again, business analysis is changing

We'll toggle between these two different modes — enabled by embeddability, connectivity, and APIs — to get maximum benefits from each platform.

Analyst Prediction

By 2026, Generative AI will significantly alter 70% of the design and development efforts for new web applications and mobile apps.

Source: Gartner, Top Strategic Technology Trends for 2024, 16 October 2023 GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.
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Data origin matters: Understanding your data’s DNA

We need a mechanism to clearly label and signpost data, using techniques of provenance and cryptography alongside techniques we haven’t yet invented to create the equivalent of a "DNA test for your data".

Analyst Prediction

90% of online content will be generated by AI by 2025.

Source: Nina Schick interview with Yahoo Finance Live, January 7, 2023
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The rise of novice developers demands AI literacy

With an explosion of apps built by the ‘everyday developer,’ organizations must take steps to educate their workforce in the benefits and pitfalls of generative AI.

Analyst Prediction

To mitigate new risks created by the pervasive workforces use of generative AI, by the end of 2025, 60% of large enterprises will mandate formal data literacy and responsible AI training.

Source: IDC FutureScape: Worldwide Future of Enterprise Intelligence 2024 Predictions, IDC #US51293423, Oct 2023
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Data engineering, analytics, and data science are merging

This will empower business analysts, for example, who can now go back earlier in the pipeline to do data management and preparation tasks. That same analyst can also apply advanced statistical models to the data and tools they work with every day, without needing to export it to an advanced workbench.

Analyst Prediction

By 2026, 50% of organizations will have to evaluate ABI and DSML platforms as an all-in-one, composable platform due to market convergence.

Source: Gartner, Predicts 2023: Analytics, BI and Data Science Composability and Consolidation
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Automation and AI create a virtuous cycle

So far, LLMs and generative AI have mainly been used to support reasoning and conduct analysis, rather than for iPaaS and actioning. Now, there are several exciting efforts underway to support the latter, including an approach to LLMs that involves synergizing reasoning and action.

Analyst Prediction

By 2027, outlier detection and other augmented analytics capabilities will evolve into autonomous analytics platforms that fully manage and execute 20% of business processes.

Source: Gartner, Predicts 2023: Analytics, BI, and Data Science Composability and Consolidation
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Last-mile AI customization becomes critical for business

Your proprietary organizational data will be a valuable raw material here and “solution fabrics” will emerge where domain-specific data and apps can be shared and traded.

Analyst Prediction

By 2026, more than 80% of generative AI use cases in enterprises will leverage customized, specialized AI models rather than generic foundation models offered via public APIs.

Source: IDC FutureScape: Worldwide Future of Enterprise Intelligence 2024 Predictions, IDC #US51293423, Oct 2023
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Data as a product that can be traded

The concept of treating data as a valuable asset or product means it can be surfaced in a catalogue, used for various purposes internally, and even evolve into a tradable good. Like with music’s rise of streaming platforms, creators of quality data products must have more exchanges on which to trade them.

Analyst Prediction

By 2026, 60% of leading enterprise intelligence companies will have identified data products, and 15% will have attributed business value to the products with a data valuation methodology.

Source: IDC FutureScape: Worldwide Future of Enterprise Intelligence 2024 Predictions, IDC #US51293423, Oct 2023
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Where do we go from here?

Imagine a product fundamental to AI that can be traded and becomes more valuable the more it’s used. How do we get to this promised future? The path to trusted data is filled with challenges and opportunities that savvy organizations must be aware of.