Blog
Modern organizations face a persistent challenge: balancing rapid insights with the need for trust, security, and compliance. Business leaders need fast access to data, but centralized data teams often become bottlenecks, delaying decision-making. Conversely, decentralized teams promote agility but can introduce inconsistencies in quality and governance. This tension, known as the "Speed vs. Trust Conflict," prevents organizations from fully harnessing their data.
Blog
Today, quality data can often spell the difference between business success and failure. In fact, Gartner projects that poor data quality costs the average business about $12.9 million each year. Small wonder, as poor data quality leads to flawed AI models, operational errors, and costly decisions – creating distrust between data producers and consumers. This lack of trust can severely hinder an organization's ability to make informed decisions and achieve desired outcomes.
Blog
Enterprises are navigating a profound data management crisis. Forrester Research found that "less than 0.5% of all data is ever analyzed and used" and projected that if the average enterprise could boost data accessibility by just 10%, it would generate more than $65 million in additional net income.
Blog
In professional sports, data is a game-changer—and nowhere is that more evident than at the NBA. The league’s data strategy team has embraced a modern approach to data management, transforming raw data into a valuable asset for internal teams, from marketing to finance to product development. At the heart of this transformation is a shift to treating data as a product, paired with technology and governance processes that enable discovery, collaboration, and trust.
Blog
What does it take to build a high-performing, compliant, and scalable data organization—especially in a highly regulated industry like sports betting?
Blog
For over a decade, data leaders have promised transformation: better decisions, stronger performance, and competitive advantage through data. But in today’s climate of tighter budgets and AI-driven urgency, data leaders are confronting a new reality: the need to deliver business outcomes – and fast.
Blog
The rapid rise of AI in the workplace is undeniable. In a recent McKinsey survey, 78% of respondents say their organizations are regularly using generative AI in at least one business function, up from 72% last year. AI offers immense value across a wide range of use cases, from automating repetitive tasks to generating creative content and powering data-driven decision-making.
Blog
The rapid evolution of artificial intelligence — and particularly large language models (LLMs) — has unlocked unprecedented opportunities for businesses to leverage their internal data in new ways.
Blog
As enterprises increasingly rely on data and AI for competitive advantage, aligning data initiatives to strategic business outcomes becomes critical. Marks & Spencer, the renowned British retailer celebrating its 140th anniversary, is leading by example. Phil Dale, Head of Data Governance, recently shared valuable insights on how Marks & Spencer ensures data-driven decision-making is central to their business strategy.
Blog
As AI adoption accelerates, so do the challenges of managing the vast computational resources needed to power it. Cloud inefficiencies, soaring compute costs, and a growing reliance on GPUs make optimization a critical need for enterprises.
Blog
Data duplication is a divisive topic—some see it as essential for flexibility and performance, while others view it as a source of confusion and inefficiency. The reality is that duplication itself is neither inherently good nor bad; its impact depends on the reasons behind it and how it is managed.
1 of 64