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.
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 analytics and BI with other business processes. Participants also find that not all software is flexible enough for the constantly changing business environment, and that it is hard to access all data sources.
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.
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.
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.
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.
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.
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.