Markets have been more volatile than ever. It creates a need for decision makers to utilize technologies such as artificial intelligence and machine learning (AI/ML) to better understand the external factors that impact their business. By identifying these factors, organizations can better plan for changing market environments and seize market opportunities. However, manual modeling is a time-consuming process and results in a limited number of models and tests. Also, updating those models is...
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Topics:
embedded analytics,
Analytics,
Business Intelligence,
AI and Machine Learning
Ventana Research recently announced its 2023 Market Agenda for Analytics, continuing the guidance we have 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,
Data,
Digital Technology,
natural language processing,
Process Mining,
Analytics & Data,
Collaborative & Conversational Computing
Organizations conduct data analysis in many ways. The process can include multiple spreadsheets, applications, desktop tools, disparate data systems, data warehouses and analytics solutions. This creates difficulties for management to provide and maintain updated information across multiple departments. Our Analytics and Data Benchmark Research shows that organizations face a variety of challenges with analytics and business intelligence. One-third of participants find it difficult to integrate...
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Topics:
embedded analytics,
Analytics,
Business Intelligence,
natural language processing,
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
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
As I recently pointed out, process mining has emerged as a pivotal technology for data-driven organizations to discover, monitor and improve processes through use of real-time event data, transactional data and log files. With recent advancements, process mining has become more efficient at discovering insights in complex processes using algorithms and visualizations. Organizations use it to better understand the current state of systems and business processes. It is also used to enable ...
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Topics:
Analytics,
Business Intelligence,
Process Mining,
Streaming Analytics,
AI and Machine Learning
Process mining is defined as the analysis of application telemetry including log files, transaction data and other instrumentation to understand and improve operational processes. Log data provides an abundance of information about what operations are occurring, the sequences involved in the processes, how long the processes are taking and whether or not the processes are completed successfully. As computing power has increased and storage costs have decreased, the economics of collecting and...
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Topics:
Analytics,
Business Intelligence,
Process Mining,
AI and Machine Learning