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
I wrote recently about the role that data intelligence has in enabling enterprises to facilitate data democratization and the delivery of data as a product. Data intelligence provides a holistic view of how, when, and why data is produced and consumed across an enterprise, and by whom. This information can be used by data teams toensure business users and data analysts are provided with self-service access to data that is pertinent to their roles and requirements. Delivering data as a product...
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Topics:
Analytics,
Data Ops,
data operations,
data platforms,
Analytics & Data,
AI and Machine Learning,
GenAI,
Data Intelligence
Data and analytics have become increasingly important to all aspects of business. The modern data and analytics stack includes many components, which creates challenges for enterprises and software providers alike. As my colleague Matt Aslett points out, a better term might be modern data and analytics smorgasbord. There are arguments for and against using an assortment of tools versus a consolidated platform. For example, purchasing, integrating and deploying a variety of tools can be complex....
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Topics:
Analytics,
AI,
data operations,
Analytics & Data,
Generative AI,
Data Intelligence
The development, testing and deployment of data pipelines is a fundamental accelerator of data-driven strategies, enabling enterprises to extract data from the operational applications and data platforms designed to run the business and load, integrate and transform it into the analytic data platforms and tools used to analyze the business. As I explained in our recent Data Pipelines Buyers Guide, data pipelines are essential to generating intelligence from data. Healthy data pipelines are...
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Topics:
Analytics,
data operations,
data platforms,
Analytics & Data,
Generative AI,
AI and Machine Learning,
Data Intelligence
As enterprises seek to increase data-driven decision-making, many are investing in strategic data democratization initiatives to provide business users and data analysts with self-service access to data across the enterprise. Such access has long been a goal of many enterprises, but few have achieved it. Only 15% of participants in Ventana Research’s Analytics and Data Benchmark Research say their organization is very comfortable allowing business users to work with data that has not been...
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Topics:
Analytics,
data operations,
Analytics & Data,
AI and Machine Learning,
Data Intelligence,
Data Products,
Data Democratization
We live in an era of uncertainty, not unpredictability. Managing in uncertain times is always difficult, but tools are available to improve the odds for success by making it easier and faster to plan for contingencies and scenarios. Software makes it possible to manage ahead of any future event, connecting the tactical trees to the strategic forest. The purpose of planning is not just to create a plan: Enterprises spend time thinking ahead because it enables leadership teams, executives and...
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Topics:
Office of Finance,
Continuous Planning,
Data Management,
Business Planning,
data operations,
digital finance,
AI and Machine Learning
I have previously written about the functional evolution and emerging use cases for NoSQL databases, a category of non-relational databases that first emerged 15 or so years ago and are now well established as potential alternatives to relational databases. NoSQL is a term used to describe a variety of databases that fall into four primary functional categories: key-value stores, wide-column stores, document-oriented databases and graph databases. Each is worthy of further exploration, which is...
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Topics:
Data,
data operations
In recent years, many enterprises have migrated data platform workloads from on-premises infrastructure to cloud environments, attracted by the promised benefits of greater agility and lower costs. The scale of cloud data platform adoption is illustrated by Ventana Research’s Data Lakes Dynamic Insights research: For two-thirds (66%) of participants, the primary data platform used for analytics is cloud based. As the quantity and importance of the data platform workloads deployed in the cloud...
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Topics:
business intelligence,
Cloud Computing,
data operations,
robotic automation,
Analytics & Data,
Analytic Data Platforms,
AI and Machine Learning
Ventana Research recently announced its 2024 Market Agenda for Analytics and Data, continuing the guidance we have offered for two decades to help enterprises derive optimal value and improve business outcomes.
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Topics:
embedded analytics,
Business Intelligence,
Data Governance,
Data Management,
natural language processing,
data operations,
Process Mining,
Streaming Analytics,
Analytics & Data,
Streaming Data & Events,
operational data platforms,
Analytic Data Platforms,
AI and Machine Learning