David Menninger's Analyst Perspectives

Sisense is Sensible for Embedded Analytics and BI

Posted by David Menninger on Dec 7, 2022 3:00:00 AM

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 creating insights. On top of that, they are adding more data sources and information systems which in turn introduces more complexity. Our Analytics and Data Benchmark Research shows that organizations face various challenges with analytics and BI. More than one-third of participants (35%) responded that they find it hard to integrate analytics and BI with business processes and connect to multiple data sources. By embedding analytics and BI into business processes and workflows, organizations can enable users to make critical decisions fast, enhancing overall business agility.

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

IBM Builds on Analytics and BI Foundation

Posted by David Menninger on Nov 29, 2022 3:00:00 AM

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 find it challenging to access data sources and integrate data and analytics in business processes. Vendors such as IBM offer a broad set of analytics tools with self-service capabilities that allows organizations to reduce IT dependencies and enables decision-makers to recognize performance gaps, market trends and new revenue opportunities. Its technology can simplify data access for self-service applications, enabling users to make business decisions informed by insights and take the guesswork out of decision-making.

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

Cloud Computing Realities Part 3: Business Continuity

Posted by David Menninger on Nov 22, 2022 3:00:00 AM

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 disruptions. In this perspective, I’d like to address some of the realities of business continuity and cloud computing and how they impact the digital technologies of an organization. The cloud can be both advantageous and disadvantageous when it comes to providing business continuity.

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Topics: Business Continuity, Cloud Computing, Digital Technology, digital business

Mind the Gap Between Data and Analytics

Posted by David Menninger on Nov 16, 2022 3:00:00 AM

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: business intelligence, embedded analytics, Analytics, Data Governance, Data Management, data operations, Analytics & Data

MLOps: A Disciplined Approach That Increases Organizational Agility

Posted by David Menninger on Oct 20, 2022 3:00:00 AM

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 or planning to do so.

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Topics: Analytics, AI & Machine Learning

Celonis Improves Business Processes with Process Mining

Posted by David Menninger on Oct 14, 2022 3:00:00 AM

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 business process intelligence and improvement in any function or industry using events and activity models for data-driven decision-making. We assert that through 2024, 1 in 4 organizations will look to streamline their operations by exploring process mining to optimize workflow and business processes.

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Topics: Analytics, Business Intelligence, Process Mining, Streaming Analytics, AI & Machine Learning

Process Mining Improves Business Processes

Posted by David Menninger on Oct 4, 2022 3:00:00 AM

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 analyzing large amounts of log data have become much more attractive.

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

Cloud Computing Realities Part 2: Hybrid and Multi-Cloud Architectures

Posted by David Menninger on Sep 27, 2022 3:00:00 AM

In my first perspective on cloud computing realities, I covered some of the cost considerations associated with cloud computing and how the cloud costing model may be different enough from on-premises models that some organizations are taken by surprise. In this perspective. I’d like to focus on realities of hybrid and multi-cloud deployments.

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Topics: Cloud Computing, Digital Technology

Enhancing Data Catalog with AI

Posted by David Menninger on Sep 22, 2022 3:00:00 AM

Organizations are collecting data from multiple data sources and a variety of systems to enrich their analytics and business intelligence (BI). But collecting data is only half of the equation. As the data grows, it becomes challenging to find the right data at the right time. Many organizations can’t take full advantage of their data lakes because they don’t know what data actually exists. Also, there are more regulations and compliance requirements than ever before. It is critical for organizations to understand the kind of data they have, who is handling it, what it is being used for and how it needs to be protected. They also have to avoid putting too many layers and wrappers around the data as it can make the data difficult to access. These challenges create a need for more automated ways to discover, track, research and govern the data.

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Topics: Business Intelligence, Data Governance, Data Management, Data, data operations, AI & Machine Learning

Augmented Intelligence Reduces Dependency on AI/ML Skill Sets

Posted by David Menninger on Sep 15, 2022 3:00:00 AM

Business intelligence has evolved. It now includes a spectrum of analytics, one of the most promising of which has been described as augmented intelligence. Some organizations have used the term to describe the practical reality that artificial intelligence with machine learning is not replacing human intelligence, but augmenting it. The term also represents the application of AI/ML to make business intelligence and analytics tools more powerful and easier to use. It’s this latter usage that I prefer and I’d like to explore in this perspective.

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Topics: Analytics, Business Intelligence, natural language processing, AI & Machine Learning, Analytics & Data, Collaborative & Conversational Computing