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 and Data
I recently described the emergence of hydroanalytic data platforms, outlining how the processes involved in generating energy from a lake or reservoir were analogous to those required to generate intelligence from a data lake. I explained how structured data processing and analytics acceleration capabilities are the equivalent of turbines, generators and transformers in a hydroelectric power station. While these capabilities are more typically associated with data warehousing, they are now...
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Topics:
Analytics,
Data Governance,
Data Integration,
Data,
data lakes,
data operations,
Data Platforms,
Streaming Data & Events,
AI and Machine Learning
As I stated when joining Ventana Research, the socioeconomic impacts of the pandemic and its aftereffects have highlighted more than ever the differences between organizations that can turn data into insights and are agile enough to act upon it and those that are incapable of seeing or responding to the need for change. Data-driven organizations stand to gain competitive advantage, responding faster to worker and customer demands for more innovative, data-rich applications and personalized...
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Topics:
business intelligence,
Analytics,
Data Integration,
Data,
data lakes,
data operations,
Data Platforms,
Streaming Data & Events,
AI and Machine Learning
Ventana Research recently announced its 2022 Market Agenda for Data, continuing the guidance we have offered for nearly two decades to help organizations derive optimal value and improve business outcomes.
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Topics:
Data Governance,
Data Integration,
Data,
data lakes,
data operations,
Data Platforms,
Streaming Data & Events
Data lakes have enormous potential as a source of business intelligence. However, many early adopters of data lakes have found that simply storing large amounts of data in a data lake environment is not enough to generate business intelligence from that data. Similarly, lakes and reservoirs have enormous potential as sources of energy. However, simply storing large amounts of water in a lake is not enough to generate energy from that water. A hydroelectric power station is required to harness...
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Topics:
Analytics,
Business Intelligence,
Cloud Computing,
Data Governance,
Data Integration,
Data,
Digital Technology,
data lakes,
data operations,
Data Platforms,
AI and Machine Learning
As I noted when joining Ventana Research, the range of options faced by organizations in relation to data processing and analytics can be bewildering. When it comes to data platforms, however, there is one fundamental consideration that comes before all others: Is the workload primarily operational or analytic? Although most database products can be used for operational or analytic workloads, the market has been segmented between products targeting operational workloads, and those targeting...
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Topics:
Analytics,
Business Intelligence,
Data,
data lakes,
data operations,
Data Platforms,
AI and Machine Learning
It has been clear for some time that future enterprise IT architecture will span multiple cloud providers as well as on-premises data centers. As Ventana Research noted in the market perspective on data architectures, the rapid adoption of cloud computing has fragmented where data is accessed or consolidated. We are already seeing that almost one-half (49%) of respondents to Ventana Research’s Analytics and Data Benchmark Research are using cloud computing for analytics and data, of which 42%...
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Topics:
Data,
data lakes,
data operations,
Data Platforms
Enterprises looking to adopt cloud-based data processing and analytics face a disorienting array of data storage, data processing, data management and analytics offerings. Departmental autonomy, shadow IT, mergers and acquisitions, and strategic choices mean that most enterprises now have the need to manage data across multiple locations, while each of the major cloud providers and data and analytics vendors has a portfolio of offerings that may or may not be available in any given location. As...
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Topics:
Analytics,
Cloud Computing,
Data Governance,
Data Integration,
Data,
Digital Technology,
data lakes,
data operations,
Data Platforms,
AI and Machine Learning