About the Analyst
David Menninger
David is responsible for the overall research direction of data, information and analytics technologies at Ventana Research covering major areas including Analytics, Big Data, Business Intelligence and Information Management along with the additional specific research categories including Information Applications, IT Performance Management, Location Intelligence, Operational Intelligence and IoT, and Data Science. David is also responsible for examining the role of cloud computing, collaboration and mobile technologies as they affect these areas. David brings to Ventana Research over twenty-five years of experience, through which he has marketed and brought to market some of the leading edge technologies for helping organizations analyze data to support a range of action-taking and decision-making processes. Prior to joining Ventana Research, David was the Head of Business Development & Strategy at Pivotal a division of EMC, VP of Marketing and Product Management at Vertica Systems, VP of Marketing and Product Management at Oracle, Applix, InforSense and IRI Software. David earned his MS in Business from Bentley University and a BS in Economics from University of Pennsylvania.
Organizations are increasingly using data as a strategic asset, which makes data services critical. Huge volumes of data need to be stored, managed, discovered and analyzed. Cloud computing and storage approaches provide enterprises with various capabilities to store and process their data in third-party data centers. The advent of data platforms previously discussed here are essential for organizations to effectively manage their data assets.
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Topics:
embedded analytics,
Analytics,
Business Intelligence,
Collaboration,
Data Governance,
Data Lake,
Data Preparation,
Data,
Microsoft Azure,
AI and Machine Learning
Ventana Research recently announced its 2021 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 Preparation,
Information Management,
Data,
data lakes,
Streaming Data,
data operations,
Event Data,
Data catalog,
Event Streams,
Event Stream Processing
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
The industry is making huge strides with artificial intelligence (AI) and machine learning (ML). There is more data available to analyze. Analytics vendors have made it easier to build and deploy models, and AI/ML is being embedded into many types of applications. Organizations are realizing the value that AI/ML provides and there are now millions of professionals with AI or ML in their title or job description. AI/ML is even being used to make many aspects of itself easier. Organizations that...
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Topics:
Sales,
Customer Experience,
Marketing,
Analytics,
Business Intelligence,
Data Preparation,
Digital Technology,
AI and Machine Learning
Data is becoming more valuable and more important to organizations. At the same time, organizations have become more disciplined about the data on which they rely to ensure it is robust, accurate and governed properly. Without data integrity, organizations cannot trust the information produced by their data processes, and will be discouraged from using that data, resulting in inefficiencies and reduced effectiveness.
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Topics:
Analytics,
Business Intelligence,
Data Governance,
Data Preparation,
Information Management,
Data,
data lakes
Organizations are dealing with exponentially increasing data that ranges broadly from customer-generated information, financial transactions, edge-generated data and even operational IT server logs. A combination of complex data lake and data warehouse capabilities are required to leverage this data. Our research shows that nearly three-quarters of organizations deploy both data lakes and data warehouses but are using a variety of approaches which can be cumbersome. A single platform that can...
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Topics:
business intelligence,
embedded analytics,
Analytics,
Collaboration,
Data Governance,
Data Preparation,
Information Management,
Data,
data lakes,
AI and Machine Learning
Businesses are transforming their organizations, building a data culture and deploying sophisticated analytics more broadly than ever. However, the process of using data and analytics is not always easy. The necessary tools are often separate, but our research shows organizations prefer an integrated environment. In our Data Preparation Benchmark Research, we found that 41% of participants use Analytics and Business Intelligence tools for data preparation.
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Topics:
embedded analytics,
Analytics,
Business Intelligence,
Collaboration,
Data Preparation,
Information Management,
Internet of Things,
Data,
Digital Technology,
natural language processing,
Conversational Computing,
AI and Machine Learning
Traditional on-premises data processing solutions have led to a hugely complex and expensive set of data silos where IT spends more time managing the infrastructure than extracting value from the data. Big data architectures have attempted to solve the problem with large pools of cost-effective storage, but in doing so have often created on-premises management and administration challenges. These challenges of acquiring, installing and maintaining large clusters of computing resources gave rise...
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Topics:
embedded analytics,
Analytics,
Business Intelligence,
Collaboration,
Data Governance,
Data Preparation,
Data,
data lakes,
AI and Machine Learning
Organizations are always looking to improve their ability to use data and AI to gain meaningful and actionable insights into their operations, services and customer needs. But unlocking value from data requires multiple analytics workloads, data science tools and machine learning algorithms to run against the same diverse data sets. Organizations still struggle with limited data visibility and insufficient insights, which are often caused by a multitude of reasons such as analytic workloads...
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Topics:
business intelligence,
embedded analytics,
Analytics,
Collaboration,
Data Governance,
Data Preparation,
Data,
Information Management (IM),
data lakes,
AI and Machine Learning
A data lake is a centralized repository designed to house big data in structured, semi-structured and unstructured form. I have been covering the data lake topic for several years and encourage you to check out an earlier perspective called Data Lakes: Safe Way to Swim in Big Data? for background. Our data lake research has uncovered some points to consider in your efforts, and I’d like to offer a deeper dive into our findings.
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Topics:
Data Governance,
Data Lake,
Information Management,
Data,
Digital Technology