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,
Analytics & Data,
operational data platforms,
Analytic Data Platforms,
AI and Machine Learning
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,
profitability management,
Analytics & Data,
Streaming Data & Events,
AI and Machine Learning
In previous perspectives in this series, I’ve discussed some of the realities of cloud computing including costs, hybrid and multi-cloud configurations and business continuity. This perspective examines the realities of security and regulatory concerns associated with cloud computing. These issues are often cited by our research participants as reasons they are not embracing the cloud. To be fair, the majority of our research participants are embracing the cloud. However, among those that have...
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Topics:
Analytics,
Business Intelligence,
Cloud Computing,
Data Governance,
Digital Technology,
Analytics & Data,
Governance & Risk,
AI and Machine Learning
Recently, I suggested you need to “mind the gap” between data and analytics. This perspective addresses another gap — the gap in skills between business intelligence (BI) and artificial intelligence/machine learning (AI/ML).
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Topics:
Analytics,
Business Intelligence,
Digital Technology,
Analytics & Data,
AI and Machine Learning
One of the most significant considerations when choosing an analytic data platform is performance. As organizations compete to benefit most from being data-driven, the lower the time to insight the better. As data practitioners have learnt over time, however, lowering time to insight is about more than just high-performance queries. There are opportunities to improve time to insight throughout the analytics life cycle, which starts with data ingestion and integration, includes data preparation...
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Topics:
Business Intelligence,
Data,
data operations,
Analytic Data Platforms,
AI and Machine Learning
Embedded business intelligence (BI) continues to transform the business landscape, enabling organizations to quickly interpret data and convert it into actionable insights. It allows organizations to extract information in real time and answer wide-ranging business questions. Embedding analytics helps tackle the issue of extracting information from data which is a time-consuming process. Our research shows organizations spend more time cleaning and optimizing data for analysis rather than...
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Topics:
embedded analytics,
Analytics,
Business Intelligence,
natural language processing,
Streaming Analytics,
AI and Machine Learning
In today’s data-driven world, organizations need real-time access to up-to-date, high-quality data and analysis to keep pace with changing market dynamics and make better strategic decisions. By mining meaningful insights from enterprise data quickly, they gain a competitive advantage in the market. Yet, organizations face a multitude of challenges when transitioning into an analytics-driven enterprise. Our Analytics and Data Benchmark Research shows that more than one-quarter of organizations...
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Topics:
embedded analytics,
Analytics,
Business Intelligence,
IBM,
IBM Watson,
AI and Machine Learning
Almost all organizations are investing in data science, or planning to, as they seek to encourage experimentation and exploration to identify new business challenges and opportunities as part of the drive toward creating a more data-driven culture. My colleague, David Menninger, has written about how organizations using artificial intelligence and machine learning (AI/ML) report gaining competitive advantage, improving customer experiences, responding faster to opportunities and threats, and...
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Topics:
Data Governance,
Data Management,
Data,
data operations,
Analytics & Data,
Analytic Data Platforms,
AI and Machine Learning
The starting point of an era is never precise and rarely conforms to neat calendar delineations. For example, the start of the 20th century is associated with the outbreak of war in 1914. So I expect that decades from now, the consensus will hold that what became known as the 21st century began in the year 2020, with the pandemic serving as a catalyst that accelerated already existing trends and forced changes to prevailing norms and practices. This and other disruptive events that have...
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Topics:
Office of Finance,
Business Intelligence,
Business Planning,
Financial Performance Management,
digital finance,
profitability management,
operational data platforms,
AI and Machine Learning
IBM Planning Analytics with Watson is a comprehensive, cloud-based business planning application that supports what Ventana Research calls integrated business planning. We coined this term in 2007 to describe a high-participation approach to business planning that integrates strategy, operations and finance. Our Next Generation Business Planning Benchmark Research demonstrated the value of IBP: Organizations that link planning processes get better results. Sixty-six percent of organizations...
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Topics:
Predictive Analytics,
Office of Finance,
embedded analytics,
Business Intelligence,
Business Planning,
Financial Performance Management,
Watson,
Digital transformation,
digital finance,
profitability management,
AI and Machine Learning