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.
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.
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.
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.
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.
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.