Organizations require faster analytics to continuously improve business operations and stay competitive in today’s market. However, many struggle with slow analytics due to a variety of factors such as slow databases, insufficient data storage capacity, poor data quality, lack of proper data cleansing and inadequate IT infrastructure. Challenges such as data silos can also decrease operational efficiency. And as the data grows, performing complex data modelling becomes challenging for users as...
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Topics:
Data Management,
Data,
Analytic Data Platforms
We live in a time of uncertainty, not unpredictability. Managing an organization in uncertain times is always hard, 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 quickly consider the impact of a range of events or assumptions and devise a set of plans to deal with them. Dedicated planning and budgeting software has been around for decades but is about to become all the more useful as...
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Topics:
Office of Finance,
Data Management,
Business Planning,
digital finance,
AI & Machine Learning
The market for data and analytics products is constantly evolving, with the emergence of new approaches to data persistence, data processing and analytics. This enables organizations to constantly adapt data analytics architecture in response to emerging functional capabilities and business requirements. It can, however, also be a challenge. Investments in data platforms cannot be constantly written-off as organizations adopt new products for new approaches. Too little change can lead to...
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Topics:
Data Governance,
Data Management,
Data,
data operations
Data observability was a hot topic in 2022 and looks likely to be a continued area of focus for innovation in 2023 and beyond. As I have previously described, data observability software is designed to automate the monitoring of data platforms and data pipelines, as well as the detection and remediation of data quality and data reliability issues. There has been a Cambrian explosion of data observability software vendors in recent years, and while they have fundamental capabilities in common,...
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Topics:
Cloud Computing,
Data Management,
Data,
Digital Technology,
data operations,
Analytics & Data
Organizations across various industries collect multiple types of data from disparate systems to answer key business questions and deliver personalized experiences for customers. The expanding volume of data increases complexity, and data management becomes a challenge if the process is manual and rules-based. There can be numerous siloed, incomplete and outdated data sources that result in inaccurate results. Organizations must also deal with concurrent errors – from customers to products to...
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Topics:
Data Governance,
Data Management,
Data,
data operations,
Analytic Data Platforms
Despite the emphasis on organizations being more data-driven and making an increasing proportion of business decisions based on data and analytics, it remains the case that some of the most fundamental questions about an organization are difficult to answer using data and analytics. Ostensibly simple questions such as, “how many customers does the organization have?” can be fiendishly difficult to answer, especially for organizations with multiple business entities, regions, departments and...
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Topics:
Cloud Computing,
Data Management,
Data,
data operations,
AI & Machine Learning,
Analytics & Data
Ventana Research recently announced its 2023 Market Agenda for Data, continuing the guidance we have offered for two decades to help organizations derive optimal value and improve business outcomes.
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Topics:
Cloud Computing,
Data Governance,
Data Management,
Data,
Digital Technology,
data operations,
Analytics & Data,
Streaming Data & Events,
operational data platforms,
Analytic Data Platforms
Data observability is a hot topic and trend. I have written about the importance of data observability for ensuring healthy data pipelines, and have covered multiple vendors with data observability capabilities, offered both as standalone and part of a larger data engineering system. Data observability software provides an environment that takes advantage of machine learning and DataOps to automate the monitoring of data quality and reliability. The term has been adopted by multiple vendors...
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Topics:
Cloud Computing,
Data Management,
Data,
Digital Technology,
data operations
The shift from on-premises server infrastructure to cloud-based and software-as-a-service (SaaS) models has had a profound impact on the data and analytics architecture of many organizations in recent years. More than one-half of participants (59%) in Ventana Research’s Analytics and Data Benchmark research are deploying data and analytics workloads in the cloud, and a further 30% plan to do so. Customer demand for cloud-based consumption models has also had a significant impact on the products...
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Topics:
Business Intelligence,
Cloud Computing,
Data Management,
Data,
natural language processing,
data operations,
AI & Machine Learning,
Analytics & Data,
operational data platforms,
Analytic Data Platforms
Ventana Research uses the term “data pantry” to describe a method of data storage (and the technology and process blueprint for its construction) created for a specific set of users and use cases in business-focused software. It’s a pantry because all the data one needs is readily available and easily accessible, with labels that are immediately recognized and understood by the users of the application. In tech speak, this means the semantic layer is optimized for the intended audience. It is...
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Topics:
Continuous Planning,
Business Intelligence,
Data Management,
Business Planning,
Data,
Financial Performance Management,
Enterprise Resource Planning,
continuous supply chain,
data operations,
digital finance,
AI & Machine Learning,
profitability management,
Analytics & Data,
Streaming Data & Events