Organizations of all sizes are dealing with exponentially increasing data volume and data sources, which creates challenges such as siloed information, increased technical complexities across various systems and slow reporting of important business metrics. Migrating to the cloud does not solve the problems associated with performing analytics and business intelligence on data stored in disparate systems. Also, the computing power needed to process large volumes of data consists of clusters of...
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Topics:
Analytics,
Business Intelligence,
Data Integration,
Data,
data lakes,
data operations,
Streaming Analytics,
AI and Machine Learning
How does your organization define and display its metrics? I believe many organizations are not defining and displaying metrics in a way that benefits them most. If an organization goes through the trouble of measuring and reporting on a metric, the analysis ought to include all the information needed to evaluate that metric effectively. A number, by itself, does not provide any indication of whether the result is good or bad. Too often, the reader is expected to understand the difference, but...
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Topics:
business intelligence,
Analytics,
Internet of Things,
Data,
Digital Technology,
Streaming Analytics,
AI and Machine Learning
Our research shows that nearly all financial service organizations (97%) consider it important to accelerate the flow of information and improve responsiveness. Even just a few years ago, capturing and evaluating this information quickly was much more challenging, but with the advent of streaming data technologies that capture and process large volumes of data in real time, financial service organizations can quickly turn events into valuable business outcomes in the form of new products and...
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Topics:
Analytics,
Internet of Things,
Data,
Digital Technology,
Streaming Analytics
Access to external data can provide a competitive advantage. Our research shows that more than three-quarters (77%) of participants consider external data to be an important part of their machine learning (ML) efforts. The most important external data source identified is social media, followed by demographic data from data brokers. Organizations also identified government data, market data, environmental data and location data as important external data sources. External data is not just part...
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Topics:
Analytics,
Business Intelligence,
Internet of Things,
Data,
Digital Technology,
Streaming Data,
Streaming Analytics,
AI and Machine Learning
Ventana Research recently announced its 2021 market agenda for Analytics, continuing the guidance we’ve offered for nearly two decades to help organizations derive optimal value from technology investments to improve business outcomes.
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Topics:
embedded analytics,
Analytics,
Business Intelligence,
natural language processing,
Process Mining,
Streaming Analytics,
AI and Machine Learning
