The topic of artificial intelligence using machine learning (AI/ML) used to conjure up images of PhDs in white lab coats using massive computers to solve arcane problems, but today’s technology is packaged to be used effectively by those not trained in data science to address daily chores more efficiently. AI will be valuable to finance departments in five general areas: forecasting and planning, analytics, task supervision, recommendations and automated commentary. Although the payoff from throwing massive brain power at solving difficult problems can be significant, AI also has the potential to create value by automating repetitive processes or augmenting human involvement to gain efficiency while improving performance.
Topics: Office of Finance, Analytics, Business Planning, ERP and Continuous Accounting, AI and Machine Learning, digital finance
Data is moving to the cloud. And as cloud applications are adopted, it means that many critical sources of data for analytics now, or soon will, live in the cloud. In fact, nearly nine in 10 participants in our research (89%) reported that they plan to use cloud-based analytics deployments, and the overwhelming majority of organizations (86%) expect that most of their data will eventually be in the cloud. But, as organizations look to embed analytics and reporting into their applications, they face many hurdles including high infrastructure costs, lack of domain-specific expertise and roadblocks that appear when scaling.
Topics: embedded analytics, Analytics, Business Intelligence, natural language processing, AI and Machine Learning, Streaming Analytics
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 that optimizes organizational resilience and workforce readiness.
Topics: Cloud Computing, Internet of Things, Digital Technology, blockchain, AI and Machine Learning, mobile computing, extended reality, Digital Innovation Awards, robotic automation, Analytics & Data, Collaborative & Conversational Computing
Organizations face a variety of data and analytics challenges resulting from growth and increased scale. Multiple tools and techniques are needed to derive value from various databases. But, adding more systems means adding more complexities, which can slow operations and add costs for maintaining additional systems. SQL databases have been very popular among organizations for storing and managing data. These databases enable workers to manage and analyze massive volumes of data quickly and reliably.
Topics: business intelligence, Data Management, AI and Machine Learning, analytic data platforms
A digital finance and accounting organization is one that uses software to enhance efficiency by eliminating manual operations and automating workflows, improving financial data quality. This is especially relevant to small to midsize organizations that need to minimize administrative overhead yet still have financial controls and operational visibility to achieve and sustain profitable growth. A continuous accounting approach can streamline tasks and processes, reducing time spent on repetitive tasks, ensuring data quality and providing a focus on financial outcomes and business performance.
Topics: Office of Finance, Analytics, Digital Technology, AI and Machine Learning, digital finance
With more and more data available to analyze, organizations are realizing the value that sophisticated analyses using artificial intelligence and machine learning (AI/ML) can provide. The benefits of these analyses are significant: our Dynamic Insights research on machine learning finds that organizations most often benefit through competitive advantage, but also improved customer experiences, increased sales, the ability to respond quickly to opportunities in the market, and lower costs. However, the need for specialized skills to deploy AI/ML models can stall data science initiatives. An organization’s efforts to scale data science and apply models are often complicated by a lack of self-service access to infrastructure, tools and data.
Topics: business intelligence, Analytics, Data Governance, Data Integration, Data, Business, Digital Technology, AI and Machine Learning
The virtualization of business and the evolution of digital transformation to applications and systems that operate in cloud computing — or the “as-a-service” environment — has fragmented enterprise and data architectures. The role of cloud computing has become a utility to provide elastic resources in support of operational needs. For example, data in the cloud requirements are provided by third-party vendors, managing security and storage of data outside the organization.
Topics: Cloud Computing, Data, Digital Technology, Digital transformation, data lakes, AI and Machine Learning, data operations, digital business, data platforms
The artificial intelligence industry is unique in that it has evolved to the point of making dramatic impacts across other markets. AI is used to help businesses scale, improve customer experiences, decrease waste and streamline cumbersome tasks. Ventana Research sees the need for AI innovation for the future of technology and the many other industries it influences, but making such tools available to everyone at fairly low costs can be difficult. It is no surprise that nearly two-thirds (62%) of organizations report using machine learning today, and nearly three-quarters (72%) of organizations participating in our research say they plan to increase ML use.
Topics: Analytics, Cloud Computing, Digital Technology, natural language processing, AI and Machine Learning
When you think about it, events are at the very core of computing, right down to the simplest if-then operation in a spreadsheet. Each event leads to a set of choices, often binary, which then become events in themselves. As computing has become more ubiquitous and has developed into the architecture upon which all business and commerce runs, the events themselves have become more consequential and more numerous. There’s still an event, and each "if" still leads to a "then," but now we evaluate at enterprise scale.
Topics: Analytics, Cloud Computing, Internet of Things, Data, Digital Technology, AI and Machine Learning, Streaming Data, Streaming Analytics