David Menninger's Analyst Perspectives

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.

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

I often use the term “analytics” to refer to a broad set of capabilities, deliberately broader than business intelligence. In this Perspective, I’d like to share what decision-makers should consider as they evaluate the range of analytics requirements for their organization.

Topics: Business Intelligence, natural language processing, AI and Machine Learning, Streaming Analytics, Analytics & Data

Organizations are continuously increasing the use of analytics and business intelligence to turn data into meaningful and actionable insights. Our Analytics and Data Benchmark Research shows some of the benefits of using analytics: Improved efficiency in business processes, improved communication and gaining a competitive edge in the market top the list. With a unified BI system, organizations can have a comprehensive view of all organizational data to better manage processes and identify opportunities.

Topics: business intelligence, embedded analytics, Data Governance, Data Management, natural language processing, AI and Machine Learning, data operations, Streaming Analytics, Streaming Data & Events, operational data plaftforms

There is a fundamental flaw in information technology, or at least in the way it is most commonly delivered. Most technology systems are developed under the assumption that all people will use the system primarily in the same way. Sure, there are some options built in — perhaps the same action can be initiated by either clicking on a button, selecting a menu item or invoking a keyboard short-cut. The problem is that when every variation needs to be coded into the system, the prospect of providing personalized software programs to every individual is impractical.

Topics: Business Intelligence, Data Management, natural language processing, AI and Machine Learning, data operations, Analytics & Data

Natural language processing (NLP) is a field that combines artificial intelligence (AI), data science and linguistics that enables computers to understand, interpret and manipulate text or spoken words. NLP includes generating narratives based on a set of data values, using text or speech as inputs to access information, and analysing text or speech, for instance, to determine its sentiment. There are various techniques for interpreting human language, ranging from statistical and machine learning (ML) methods to rules-based and algorithmic approaches. In this perspective, we will focus on two aspects of NLP: natural language query (NLQ), which offers the ability to use natural language expressions to discover and understand data, and natural language generation (NLG), which uses AI to produce written or spoken narratives from a dataset. NLQ and NLG enable business personnel to communicate information needs with business intelligence (BI) systems more easily.

Topics: Big Data, Analytics, natural language processing, NLP

Despite all the advances organizations have made with respect to analytics, our most recent research shows the majority of the workforce in the majority of organizations are not using analytics and business intelligence (BI). Less than one-quarter (23%) report that one-half or more of their workforce is using analytics and BI. This is a problem. It means organizations are not enabling their workforce to perform at peak efficiency and effectiveness. It means the workforce in many organizations does not have access to the same information by which they are being measured. It means organizations must find other ways to communicate with, and manage, the workforce.

Topics: Sales, embedded analytics, Analytics, Business Intelligence, Data, Sales Performance Management, Digital Technology, Digital Commerce, natural language processing, subscription management, partner management, sales engagement, revenue management, Collaborative & Conversational Computing

Ventana Research recently announced its 2022 Market Agenda for Analytics, continuing the guidance we have offered for nearly two decades to help organizations derive optimal value from technology investments in order to improve business outcomes.

Topics: embedded analytics, Analytics, Business Intelligence, Digital Technology, natural language processing, Process Mining, Analytics & Data, Collaborative & Conversational Computing

Organizations today have huge volumes of data across various cloud and on-premises systems which keep growing by the second. To derive value from this data, organizations must query the data regularly and share insights with relevant teams and departments. Automating this process using natural language processing (NLP) and artificial intelligence and machine learning (AI/ML) enables line-of-business personnel to query the data faster, generate reports themselves without depending on IT, and make quick decisions. Some organizations have started using NLP in self-service analytics to quickly identify patterns and simplify data visualization. Our Analytics and Data Benchmark Research finds that about 81% of organizations expect to use natural language search for analytics to make timely and informed decisions.

Topics: embedded analytics, Analytics, Business Intelligence, Data Integration, Data, natural language processing, data lakes, data operations, AI & Machine Learning, data platforms

The annual Ventana Research Digital Innovation Awards showcase advances in the productivity and potential of business applications, as well as technology that contributes significantly to the improved processes and performance of an organization. Our goal is to recognize technology and vendors that have introduced noteworthy digital innovations to advance business and IT.

Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Internet of Things, Digital Technology, natural language processing, AI & Machine Learning

I am happy to share insights gleaned from our latest Value Index research, an assessment of how well vendors’ offerings meet buyers’ requirements. The 2021 Ventana Research Value Index: Collaborative Analytics and Data is the distillation of a year of market and product research by Ventana Research. See our prior post for a description of our methodology and included vendors.

Topics: business intelligence, Analytics, Collaboration, Data Preparation, Information Management, Data, Digital Technology, natural language processing, mobile computing