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Now more than ever, effective data management is crucial to enable decision-makers to better assess information and take calculated actions. It is also important to keep up with the latest trends and technologies to derive higher value from data and analytics and maintain a competitive edge in the market. However, every organization faces challenges with data management and analytics. And as...

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Topics: Analytics, Data Governance, Data Management, Data, data operations, analytic data platforms


Data analytics provide valuable insights and enable organizations to make better decisions, improve performance and gain a competitive advantage in the marketplace. Analytics can change frequently depending on the data being analyzed and the methods used to gather and process it. Factors such as new data, changes in the underlying systems or updates to algorithms can all contribute to differences...

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Topics: embedded analytics, Analytics, Business Intelligence


For years various types of systems have produced log files to help with monitoring, debugging and performance management. Often, this information was used in forensic analyses of why interruptions in service or other problems occurred. In many cases, log files are still used this way. But systems have grown more complicated, and many more devices are instrumented. Systems have been decomposed...

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Topics: Business Continuity, Digital Technology


I’ve previously written about the analytics continuum, which spans a range of capabilities including reporting, visualization, planning, real-time processes, natural language processing, artificial intelligence and machine learning. I’ve also written about the analysis that goes into making intelligent decisions with decision intelligence. In this perspective, I’d like to focus on one end of the...

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Topics: Analytics, Digital Technology, Analytics and Data, AI and Machine Learning


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...

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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, Collaborative & Conversational Computing, Analytics and Data


I’m proud to share Ventana Research’s 2023 Market Agenda for Digital Technology. Our focus in this agenda is to deliver expertise to help organizations prioritize technology investments that improve customer, partner and workforce experiences while also increasing organizational effectiveness and agility.

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Topics: Analytics, Cloud Computing, Internet of Things, Data, Digital Technology, blockchain, mobile computing, extended reality, robotic automation, Collaborative & Conversational Computing, AI and Machine Learning


Consumer and mobile applications have influenced our expectations. Nearly all of us carry a smartphone, and we interact with a variety of applications on our devices. Those applications have forever influenced what we expect from computing systems. When I search the web for a gas station, I’m not searching for all gas stations. I’m searching for those stations that are near me. Not just near my...

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Topics: Digital Technology


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...

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Topics: embedded analytics, Analytics, Business Intelligence, natural language processing, AI and Machine Learning


For far too long, business intelligence technologies have left the rest of the exercise to the reader. Many of these tools do an excellent job providing information in an interactive way that lets organizations dive into the data and learn a lot about what has happened across all aspects of the business. More recently, many of these tools have added augmented intelligence capabilities that help...

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Topics: Analytics, Business Intelligence, Digital Technology, Analytics and Data, AI and Machine Learning


Analytics processes are all about how organizations use data to create metrics that help manage and improve operations. Yet, the discipline applied to analytics processes seems to be lacking compared to data processes. I’ve pointed out that the weak link in data governance is often analytics. Organizations can also do a better job tying AnalyticOps to DataOps and do more to define and manage...

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Topics: Analytics, Business Intelligence, Data Governance, Data, Digital Technology, Analytics and Data


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...

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Topics: Analytics, Business Intelligence, Cloud Computing, Data Governance, Digital Technology, Analytics and Data, 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 and 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...

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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...

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Topics: embedded analytics, Analytics, Business Intelligence, IBM, IBM Watson, AI and Machine Learning


In my previous perspectives on cloud computing, I addressed some of the realities of cloud costs as well as hybrid and multi-cloud architectures. In the midst of the pandemic, my colleague, Mark Smith, authored a series of perspectives on considerations for business continuity in general, beginning with this look at some of the investments organizations must make to mitigate the risk of business...

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Topics: Business Continuity, Cloud Computing, Digital Technology


If you’ve ever been to London, you are probably familiar with the announcements on the London Underground to “mind the gap” between the trains and the platform. I suggest we also need to mind the gap between data and analytics. These worlds are often disconnected in organizations and, as a result, it limits their effectiveness and agility.

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Topics: embedded analytics, Analytics, Business Intelligence, Data Governance, Data Management, data operations, Analytics and Data


Artificial intelligence and machine learning are valuable to data and analytics activities. Our research shows that organizations using AI/ML report gaining competitive advantage, improving customer experiences, responding faster to opportunities and threats and improving the bottom line with increased sales and lower costs. No wonder nearly 9 in 10 (87%) research participants report using AI/ML...

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Topics: Analytics, 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...

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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....

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Topics: Analytics, Business Intelligence, Process Mining, AI and Machine Learning


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