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
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
The data and analytics sector rightly places great importance on data quality: Almost two-thirds (64%) of participants in Ventana Research’s Analytics and Data Benchmark Research cite reviewing data for quality and consistency issues as the most time-consuming task in analyzing data. Data and analytics vendors would not recommend that customers use tools known to have data quality problems. It is somewhat surprising, therefore, that data and analytics vendors are rushing to encourage customers...
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Topics:
Analytics,
Data Governance,
Data Management,
Data,
Digital Technology,
natural language processing,
AI & Machine Learning,
Analytics & Data
The shift from on-premises server infrastructure to cloud-based and software-as-a-service (SaaS) models has had a profound impact on the data and analytics architecture of many organizations in recent years. More than one-half of participants (59%) in Ventana Research’s Analytics and Data Benchmark research are deploying data and analytics workloads in the cloud, and a further 30% plan to do so. Customer demand for cloud-based consumption models has also had a significant impact on the products...
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Topics:
business intelligence,
Cloud Computing,
Data Management,
Data,
natural language processing,
data operations,
AI & Machine Learning,
Analytics & Data,
analytic data platforms,
Operational Data Platforms
When joining Ventana Research, I noted that the need to be more data-driven has become a mantra among large and small organizations alike. Data-driven organizations stand to gain competitive advantage, responding faster to worker and customer demands for more innovative, data-rich applications and personalized experiences. Being data-driven is clearly something to aspire to. However, it is also a somewhat vague concept without clear definition. We know data-driven organizations when we see them...
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Topics:
embedded analytics,
Analytics,
Business Intelligence,
Data Governance,
Data Integration,
Data,
Digital Technology,
natural language processing,
data lakes,
data operations,
Streaming Analytics,
AI & Machine Learning,
digital business,
data platforms,
Analytics & Data,
Streaming Data & Events