Increased enterprise focus on artificial intelligence (AI) and generative AI (GenAI) has served to sharpen the focus on the need for trusted data and reliable analytics and data operations. The ISG State of Generative AI Market Report highlighted that elevated expectations and demands associated with AI are a forcing function for enterprises to take long-overdue steps to improve data and analytics processes to ensure that data that is clean, well-organized and compliant with regulatory...
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Topics:
Analytics,
AI,
data operations,
AI & Machine Learning,
Analytics & Data,
Generative AI,
Machine Learning Operations
Late 2024 saw the publication of the 2024 ISG Buyers Guides for DataOps, providing an assessment of 49 software providers offering products used by data engineers, data scientists, and data and AI professionals to facilitate the use of data for analytics and AI needs. The DataOps Buyers Guide research includes five reports which are focused on overall DataOps, Data Observability, Data Orchestration, Data Pipelines and Data Products. This is the first time in the industry when all software...
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Topics:
Analytics,
data operations,
AI & Machine Learning,
Analytics & Data,
Data Intelligence
I previously explained that data observability software has become a critical component of data-driven decision-making. Data observability addresses one of the most significant impediments to generating value from data by providing an environment for monitoring the quality and reliability of data on a continual basis. Maintaining quality and trust is a perennial data management challenge, the importance of which has come into sharper focus in recent years thanks to the rise of artificial...
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Topics:
AI,
data operations,
Machine Learning Operations,
Analytics and Data
The adoption of cloud environments for analytic workloads has been a key feature of the data platforms sector in recent years. For two-thirds (66%) of participants in ISG’s Data Lake Dynamic Insights Research, the primary data platform used for analytics is cloud based. Many enterprises adopted cloud-based analytic data platforms with a view to improving operational efficiencies by reducing the need for upfront investment in physical infrastructure as well as the ability to scale cloud services...
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Topics:
data operations,
data platforms,
Analytics and Data
I previously wrote about data mesh as a cultural and organizational approach to distributed data processing. Data mesh has four key principles—domain-oriented ownership, data as a product, self-serve data infrastructure and federated governance—each of which is being widely adopted. I assert that by 2027, more than 6 in 10 enterprises will adopt technologies to facilitate the delivery of data as a product as they adapt their cultural and organizational approaches to data ownership in the...
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Topics:
data operations,
Analytics and Data
I recently wrote about the role data observability plays in generating value from data by providing an environment for monitoring its quality and reliability. Data observability is a critical functional aspect of Data Operations, alongside the development, testing and deployment of data pipelines and data orchestration, as I explained in our Data Observability Buyers Guide. Maintaining data quality and trust is a perennial data management challenge, often preventing organizations from operating...
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Topics:
data operations,
Analytics & Data,
Data Intelligence
Enterprises are embracing the potential for artificial intelligence (AI) to deliver improvements in productivity and efficiency. As they move from initial pilots and trial projects to deployment into production at scale, many are realizing the importance of agile and responsive data processes, as well as tools and platforms that facilitate data management, with the goal of improving trust in the data used to fuel analytics and AI. This has led to increased attention on the role of data...
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Topics:
data operations,
Analytics & Data,
AI and Machine Learning
I recently wrote about the development, testing and deployment of data pipelines as a fundamental accelerator of data-driven strategies as well as the importance of data orchestration to accelerate analytics and artificial intelligence. As I explained in the recent Data Observability Buyers Guide, data observability software is also a critical aspect of data-driven decision-making. Data observability addresses one of the most significant impediments to generating value from data by providing an...
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Topics:
Analytics,
data operations,
Analytics & Data,
Generative AI,
AI and Machine Learning,
Machine Learning Operations
I recently wrote about the development, testing and deployment of data pipelines as a fundamental accelerator of data-driven strategies. As I explained in the 2023 Data Orchestration Buyers Guide, today’s analytics environments require agile data pipelines that can traverse multiple data-processing locations and evolve with business needs.
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Topics:
Analytics,
data operations,
data platforms,
Analytics & Data,
Generative AI,
AI and Machine Learning,
Data Intelligence
Enterprises are increasingly recognizing the need to streamline operations for efficiency, agility and innovation. This has led to various “operations” or “Ops” initiatives, each focusing on a specific aspect of enterprise IT. From software development and data analytics to IT and cloud management, these Ops groups are transforming the way enterprises operate and compete.
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Topics:
Analytics,
Cloud Computing,
Digital Technology,
data operations,
digital finance,
Digital Security,
Observability,
Analytic Operations,
DevOps and Platforms,
ITOps,
CloudOps,
Machine Learning Operations,
MLOps,
SecOps,
ProjectOps,
AIOps,
NetOps,
DevSecOps,
SecFinOps