Over a decade ago, I coined the term NewSQL to describe the new breed of horizontally scalable, relational database products. The term was adopted by a variety of vendors that sought to combine the transactional consistency of the relational database model with elastic, cloud-native scalability. Many of the early NewSQL vendors struggled to gain traction, however, and were either acquired or ceased operations before they could make an impact in the crowded operational data platforms market....
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Topics:
Business Continuity,
Cloud Computing,
Data,
Digital Technology,
Digital Business,
Data Platforms,
Analytics & Data
I recently wrote about the importance of data pipelines and the role they play in transporting data between the stages of data processing and analytics. Healthy data pipelines are necessary to ensure data is integrated and processed in the sequence required to generate business intelligence. The concept of the data pipeline is nothing new of course, but it is becoming increasingly important as organizations adapt data management processes to be more data driven.
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Topics:
business intelligence,
Analytics,
Data Governance,
Data Integration,
Data,
Digital Technology,
Digital transformation,
data lakes,
data operations,
Digital Business,
Data Platforms,
Analytics & Data,
Streaming Data & Events,
AI and Machine Learning
I recently described the growing level of interest in data mesh which provides an organizational and cultural approach to data ownership, access and governance that facilitates distributed data processing. As I stated in my Analyst Perspective, data mesh is not a product that can be acquired or even a technical architecture that can be built. Adopting the data mesh approach is dependent on people and process change to overcome traditional reliance on centralized ownership of data and...
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Topics:
Business Continuity,
business intelligence,
Analytics,
Data Governance,
Data Integration,
Data,
Digital Technology,
data lakes,
Digital Business,
Data Platforms,
Analytics & Data
Data mesh is the latest trend to grip the data and analytics sector. The term has been rapidly adopted by numerous vendors — as well as a growing number of organizations —as a means of embracing distributed data processing. Understanding and adopting data mesh remains a challenge, however. Data mesh is not a product that can be acquired, or even a technical architecture that can be built. It is an organizational and cultural approach to data ownership, access and governance. Adopting data mesh...
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Topics:
business intelligence,
Analytics,
Data Governance,
Data Integration,
Data,
Digital Technology,
Digital transformation,
data lakes,
data operations,
Digital Business,
Data Platforms,
Analytics & Data,
Streaming Data & Events
As businesses become more data-driven, they are increasingly dependent on the quality of their data and the reliability of their data pipelines. Making decisions based on data does not guarantee success, especially if the business cannot ensure that the data is accurate and trustworthy. While there is potential value in capturing all data — good or bad — making decisions based on low-quality data may do more harm than good.
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Topics:
Data Governance,
Data Integration,
Data,
Digital Technology,
data lakes,
data operations,
Analytics & Data
I recently described how the data platforms landscape will remain divided between analytic and operational workloads for the foreseeable future. Analytic data platforms are designed to store, manage, process and analyze data, enabling organizations to maximize data to operate with greater efficiency, while operational data platforms are designed to store, manage and process data to support worker-, customer- and partner-facing operational applications. At the same time, however, we see...
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Topics:
embedded analytics,
Analytics,
Business Intelligence,
Data,
Digital Technology,
Data Platforms,
Analytics & Data,
Streaming Data & Events,
Natural Data,
AI and Machine Learning
The need for data-driven decision-making requires organizations to transform not only the approach to business intelligence and data science but also accelerate the development of new operational applications that support greater business agility, enable cloud- and mobile-based consumption, and deliver more interactive and personalized experiences. To stay competitive, organizations need to prioritize the development of new, data-driven applications. As a result, many have been encouraged to...
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Topics:
Analytics,
Cloud Computing,
Analytics & Data,
AI and Machine Learning
Breaking into the database market as a new vendor is easier said than done given the dominance of the sector by established database and data management giants, as well as the cloud computing providers. We recently described the emergence of a new breed of distributed SQL database providers with products designed to address hybrid and multi-cloud data processing. These databases are architecturally and functionally differentiated from both the traditional relational incumbents (in terms of...
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Topics:
Cloud Computing,
Data,
Digital Technology,
Digital transformation,
Digital Business,
Data Platforms,
Analytics & Data