This article is part of a VB Lab Insights series paid for by Capital One. Most people are familiar with major technology platforms like iOS, Windows and AWS. Platforms, in their essence, are a group of technologies that serve as a base from which to build, contribute, experiment and scale other applications. They enable much of today's most advanced technology capabilities and cutting-edge customer experiences. In order to keep pace with the scale and complexity of the technology capabilities brought by big data, AI and machine learning (ML), many companies are developing sophisticated internal platforms of their own.

There's a reason it's called "big data." The growth in the volume of structured and unstructured information is exploding, literally exponentially, just as Moore's Law predicted. In fact, by 2025 there will be more than 180 zettabytes of data created and consumed worldwide, per Statista, helping to catapult the global data market to $103 billion by 2027. Since most banking products and services have become commodities, financial services executives are anxious to analyze even a tiny sliver of those zettabytes in order to differentiate themselves from competitors. As Capgemini puts it, banks and credit unions must evolve from capturing and managing data to using data to deliver hyper-relevant content, products and customized pricing based on customer and member behaviors, lifestyle, personality and preferences. Artificial intelligence (AI) applied to big data provides this range of insights.

Generative AI, distributed enterprise and cloud-native platforms are amongst the top strategic technology trends for 2022, Gartner has predicted. David Groombridge, research vice president at Gartner, says with CEOs and boards striving to find growth through direct digital connections with customers, the priorities of a CIO must reflect the same business imperatives, which run through each of Gartner's top strategic tech trends for 2022. "CIOs must find the IT force multipliers to enable growth and innovation, and create scalable, resilient technical foundations whose scalability will free cash for digital investments," Groombridge says. "These imperatives form the three themes of this year's trends: engineering trust, sculpting change and accelerating growth." Gartner says one of the most visible and powerful AI techniques coming to market is generative AI – machine learning methods that learn about content or objects from their data, and use it to generate brand-new, completely original, realistic artefacts.

CIOs are tooling up an assembly line of technologies to get back to business in 2022, including a mix of solutions leaned on heavily to weather the pandemic and new offerings aimed at making the most of emerging opportunities as the pandemic subsides. At its virtual IT Symposium/Xpo this week, Gartner identified the top tech strategies it sees CIOs embracing next year, including the "distributed enterprise," advanced AI, hyperautomation, cloud-native platforms, decision intelligence, and advanced security, among others. Tying together these trends is the C-suite's ongoing recognition of IT as an engine for business transformation. "The two top business priorities for CEOs going into 2022 are scaling digitation and building ecommerce, with the aim of getting back to business," said David Groombridge, research vice president at Gartner, noting that CIO priorities will vary depending on whether they are tasked with driving consumer revenue or building products. But all CIOs will have common set of technology priorities, the analyst predicts.

Subex has launched HyperSense, an end-to-end Augmented Analytics platform that helps enterprises make faster, better decisions by leveraging Artificial Intelligence (AI) across the data value chain. Developed based on Subex's extensive data analytics experience, HyperSense contains all the Augmented Analytics capabilities enterprises need in one flexible and modular platform. HyperSense's unique no-code capabilities allow users without a knowledge of coding to easily aggregate data from disparate sources, turn data into insights by building, interpreting, and tuning AI models, and effortlessly share their findings across the organization. First defined by Gartner, Augmented Analytics uses enabling technologies such as machine learning and AI to assist with data preparation, insight generation, and insight explanation. It empowers experts as well as non-data scientists by automating many aspects of data science, including model development, management and deployment of AI models.