I previously discussed the trust and accuracy limitations of large language models, suggesting that data and analytics vendors provide guidance about potentially inaccurate results and the risks of creating a misplaced level of trust. In the months that have followed, we are seeing some clarity from these vendors about the approaches organizations can take to increase trust and accuracy when developing applications that incorporate generative AI, including fine-tuning and prompt engineering. It...
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Topics:
Analytics,
Business Intelligence,
Data,
Digital Technology,
natural language processing,
AI & Machine Learning,
Analytics & Data,
analytic data platforms,
Operational Data Platforms
I previously described how Databricks had positioned its Lakehouse Platform as the basis for data engineering, data science and data warehousing. The lakehouse design pattern provides a flexible environment for storing and processing data from multiple enterprise applications and workloads for multiple use cases. I assert that by 2025, 8 in 10 current data lake adopters will invest in data lakehouse architecture to improve the business value generated from the accumulated data.
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Topics:
Analytics,
Business Intelligence,
Data Governance,
Data Management,
Data,
Digital Technology,
AI & Machine Learning,
Analytics & Data,
analytic data platforms
The data platforms market may appear to have little or nothing to do with haute couture, but it is one of the data sectors most strongly influenced by the fickle finger of fashion. In recent years, various architectural approaches to data storage and processing have enjoyed a phase in the limelight, including data warehouse, data mart, data hub, data lake, cloud data warehouse, object storage, data lakehouse, data fabric and data mesh. These approaches are often heralded as the next big thing,...
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Topics:
Cloud Computing,
Data Governance,
Data Management,
Data,
Digital Technology,
data operations,
AI & Machine Learning,
Analytics & Data,
Streaming Data & Events,
analytic data platforms,
Operational Data Platforms
I have written before about the rising popularity of the data fabric approach for managing and governing data spread across distributed environments comprised of multiple data centers, systems and applications. I assert that by 2025, more than 6 in 10 organizations will adopt data fabric technologies to facilitate the management and processing of data across multiple data platforms and cloud environments. The data fabric approach is also proving attractive to vendors, including Microsoft, as a...
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Topics:
business intelligence,
Analytics,
Cloud Computing,
Data Governance,
Data Management,
Data,
Digital Technology,
AI & Machine Learning,
Analytics & Data,
analytic data platforms
It is a mark of the rapid, current pace of development in artificial intelligence (AI) that machine learning (ML) models, until recently considered state of the art, are now routinely being referred to by developers and vendors as “traditional.” Generative AI, and large language models (LLMs) in particular, have taken the AI world by storm in the past year, automating and accelerating the development of content, including text, digital images, audio and video, as well as computer programs and...
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Topics:
business intelligence,
Analytics,
Cloud Computing,
Data Governance,
Data,
Digital Technology,
natural language processing,
AI & Machine Learning,
Analytics & Data,
analytic data platforms
As I have previously explained, we expect an increased demand for intelligent operational applications infused with the results of analytic processes, such as personalization and artificial intelligence-driven recommendations. These systems rely on the analysis of data in the operational data platform to accelerate worker decision-making or improve customer experience.
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Topics:
Analytics,
Data,
Digital Technology,
Streaming Analytics,
AI & Machine Learning,
Analytics & Data,
Streaming Data & Events,
Operational Data Platforms
The publication of Ventana Research’s 2023 Operational Data Platforms Value Index earlier this year highlighted the importance of incorporating analytic processing into operational applications to deliver personalization and recommendations for workers, partners and customers. This importance is being accelerated by interest in generative AI, especially large language models. The emergence of intelligent applications has impacted the requirements for operational data platforms with the need to...
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Topics:
Analytics,
Cloud Computing,
Data,
Digital Technology,
AI & Machine Learning,
Analytics & Data,
analytic data platforms,
Operational Data Platforms
Despite best intentions, many organizations still struggle with some fundamental aspects of data processing and analytics. Taking data from operational applications and making it available for analysis is a first step, but data management remains a perennial challenge. Data movement and transformation difficulties can lead to delays and data quality problems that prevent organizations from generating value from data. The inability to govern and integrate data from multiple data sources prevents...
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Topics:
Cloud Computing,
Data Management,
Data,
Digital Technology,
data operations,
Analytics & Data
I previously explained the arguments in favor of adoption of distributed SQL databases, the new generation of operational data platforms designed to combine the benefits of the relational database model and native support for distributed cloud architecture. It is critical for distributed SQL vendors to engage with developers to ensure they are considering the importance of resilience that spans multiple data centers and/or cloud regions as they choose the databases that will underpin...
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Topics:
Cloud Computing,
Data,
Digital Technology,
Analytics & Data,
Operational Data Platforms
Data fabric has grown in popularity as organizations struggle to manage data spread across multiple data centers, systems and applications. By providing a technology-driven approach to automating data management and governance across distributed environments, data fabric is attractive to organizations seeking to simplify and standardize data management. I assert that by 2025, more than 6 in 10 organizations will adopt data fabric technologies to facilitate the management and processing of data...
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Topics:
Cloud Computing,
Data Management,
Data,
Digital Technology,
data operations,
Analytics & Data,
analytic data platforms