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Top 5 Trends in Embedded Analytics for Enterprises

By Héctor Cobo - SAS Mexico, Caribbean and Central America

Regional VP Mexico, Caribbean & Central America

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Amid an increasingly competitive and changing environment, with more and more data and insights for decision-making, analytical tools have been becoming increasingly important for industries and companies around the world. Analytics enables organizations to transform data into knowledge, providing the information needed to make real-time informed decisions, in addition to discovering trends, optimizing processes and enhancing personal experiences, while also helping to detect fraud and promote innovation, among many others.

Illustrations of how analytics works can occur when shopping, watching a streaming movie, using our mobile devices, and using virtual assistants, such as Google Assistant, Siri or Alexa, among many other actions. In these actions, we generate data. Analytics picks up this data and translates it into a language we can understand. It unveils things to companies that would otherwise go unnoticed.

Due to its growing relevance, it is very important to know what new trends this industry is introducing. Over the next few years, the following technologies are expected to be among the top key adoption trends for innovation, agility, and effectiveness within organizations:

Artificial intelligence (AI) and responsible analytics. ChatGPT and other generative AI technologies are all over the headlines; however, there is still a gap between the promise of AI and harnessing it – and your data – to solve important business challenges and improve people's lives. The important factor is to understand how a leading analytics and AI organization like SAS can provide a roadmap to put AI into action and responsibly deliver on the promise of analytics for everyone, everywhere.
Cloud-native hybrid platforms. Given the evolution that analytics is spawning, companies must have platforms and tools that allow them to adapt and meet all their needs. One example is SAS Viya, which enables the entire analytics life cycle to be carried out from a single graphical user interface, creating a seamless transition between stages of the analytics life cycle. A recently published benchmarking study revealed that the analytics and AI platform, SAS® Viya®, is, on average, 30 times faster than commercial and open-source software alternatives. It is also better suited to larger and more complex data.
Digital trust. Consumer trust in the expanding global digital payment ecosystem is an imperative, and that trust is built on companies that make effective use of advanced customer authentication and anti-fraud technologies, including AI, machine learning and biometrics, to detect and prevent fraud in all channels. Thus, AI and analytics-based solutions to reduce fraud and risk within organizations are vital.
From code to no-code: alternative paths for data analysts. Although programming provides the greatest flexibility in analytics, there are many tasks in the analytics life cycle (such as exploratory data analysis and preliminary machine learning models) where iterating quickly is much better than using programming and, in this way, providing the organization with greater agility, data integration and a better experience for business users.

Synthetic data for better interpretation of models and bias evaluation. This refers to the process of optimally using synthetic data to understand and analyze the results of machine learning models that determine how organizations make decisions and whether they are biased in certain directions. Interpretation involves breaking down the internal logic of a model and explaining how it arrives at its predictions. This is essential to understand why a model makes certain decisions and to gain confidence in its validity.