Your modern data infrastructure isn’t delivering even after modernization? The limiting factor could be the processes and operations surrounding the systems. Learn how to improve your operating model to best leverage infrastructure and technology investments.
Cognitive enterprise planning is a key area where we generative AI is changing the game. We're seeing finance, supply chain and healthcare sectors lead the way by fusing GenAI, blockchain and IoT technologies to get real-time insights and make more informed, proactive decisions. Use cases include strategic forecasting for business expansion, workforce planning, inventory management, operational efficiency optimization and more. We help companies achieve the leapfrog benefits of a true cognitive enterprise.
Despite growing investments and sustained C-suite interest in generative AI, advanced analytics, BI and data infrastructure, businesses and their customers often are underwhelmed by the capabilities of their data systems. And the competitive pressure from nimbler competitors and new entrants is on the rise. We also can expect growing regulatory requirements in the data realm – from customer privacy, data sovereignty, compliance, cybersecurity, to fairness and inclusivity.
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Learn MoreI have previously described how data as a product was initially closely aligned with data mesh, a cultural and organizational approach to distributed data processing. As a result of data mesh’s association with distributed data, many assumed that the concept was diametrically opposed to the data lake, which offered a platform for combining large volumes of data from multiple data sources. That assumption was always misguided: There was never any reason why data lakes could not be used as a data persistence and processing platform within a data mesh environment. In recent years, data as a product has gained momentum outside the context of data mesh, while data lakes have evolved into data lakehouses. It has become increasingly clear that data lakehouses and data as a product are well matched, as the data intelligence cataloging capabilities of a lakehouse environment can serve as the foundation to enable the development, sharing and management of data as a product.
In an earlier Analyst Perspective, I discussed data democratization’s role in creating a data-driven enterprise agenda. Building a foundation of self-service data discovery, data-driven organizations provide more workers with the ability to analyze and use data. I’ve also examined how generative artificial intelligence (GenAI) could revolutionize business intelligence software by using natural language interfaces to lower the barriers to working with analytics software. Today, however, data democratization ensures that access is not limited to analytics software. Users in different roles should be able to use data through whatever applications or tools best align with their business workflow and objectives. This is the driver behind growing interest in a new category of products that enable headless BI.
There is a lesson to be learned for enterprises as they strive to make use of analytics and data to out-compete their rivals. Just as all 100-meter runners are fast, all enterprises strive to be data-driven. Making decisions based on data is not enough to be successful, however. The winners are those that can process and act upon data at the speed of business. This is easier said than done, given the overwhelming historical reliance on batch data processing. As I have previously stated, the execution of business events has always occurred in real time. Batch data processing is an artificial construct driven by the limitations of traditional data processing capabilities that require enterprises to process data minutes, hours or even days after an event. Processing and acting on data at the speed of business necessitates a change of approach by enterprises to make real-time data processing a central component of their analytics and data strategy, rather than an exception.
Enterprises are embracing the potential for artificial intelligence (AI) to deliver improvements in productivity and efficiency. As they move from initial pilots and trial projects to deployment into production at scale, many are realizing the importance of agile and responsive data processes, as well as tools and platforms that facilitate data management, with the goal of improving trust in the data used to fuel analytics and AI. This has led to increased attention on the role of data operations (DataOps) and its role in the application of agile development, development operations (DevOps) and lean manufacturing by data engineering professionals in support of data production. I assert that through 2026, more than one-half of enterprises will have adopted agile and collaborative DataOps practices to facilitate responsiveness, avoid repetitive tasks and deliver measurable data reliability improvements.
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