Digital Transformation Leadership

Successful AI requires not just lots of data but lots of high-quality data built on a well-executed data strategy.

Demand for high-quality data to train Generative AI algorithms presents new opportunities for organizations to monetize their data and protecting their privacy.

Creating a robust data strategy is not a luxury but a lifeline for navigating the turbulent tides of the rapidly evolving digital landscape.

A data monetization matrix can help leaders assess opportunities and approaches for converting data into revenue.

Enterprise data leaders must adapt to the way their orgs are structured and the strategies they pursue to drive tangible business value.

A strong, up-to-date data strategy is fundamental to your enterprise’s long-term success. In light of ongoing changes, it will probably need some serious updating.

Four ways to make sure your company is on the right side of the digital divide between data-driven companies and those struggling with data strategy.

Developing a data strategy that is both defensive and offensive may seem overwhelming but it can deliver the expectations from stakeholders while staying in compliance with privacy laws.

To foster a data-centric culture, adopt the right technology, improve data literacy, and don’t be afraid to disrupt the status quo.

There are game-changing opportunities on the horizon, but any enterprise that seeks to enable automation and leverage AI must first develop a robust data strategy that prioritizes quality and contextualized training data.

As an era, ‘Big Data’ may be over, but its underlying value (and tensions) live on, even as organizations seek to make the leap to an AI future.

Organizations without a solid data management strategy are on a collision course with catastrophe. Unfortunately, that’s most businesses, judging by the fundamental disconnect on the importance of strong data foundations.

AI and ML technologies are driving unprecedented advances in productivity in every industry and the data-driven decisions these technologies enable unlock major growth opportunities.

It’s not just about having vast amounts of data — it’s about managing, securing, and leveraging that data effectively. This is where data governance comes into play. It ensures that data doesn’t just serve as some kind of a digital fodder but acts as a strategic asset, driving informed decision-making and operational efficiency.

Today’s data strategy revolves around four key initiatives, including data democratization and data orchestration.

One of the many goals of digital transformation is to create insights and data points that will improve the customer experience with the intent of increasing sales, loyalty, and brand advocacy.

Data is essential to running a business. Here are four considerations to account for when developing digital transformation strategies to maximize data use.

Data has been the lifeblood of organizations for decades, and it’s never been a more crucial asset.

The maturation of the Data Science field has brought the realization of the importance of having a well-grounded data strategy. Why? The spell is broken: big investments are not enough for success in Data Science. The challenges in becoming a data-driven company, let alone even being able to harness the power of data and transform it into measurable outcomes, are well documented.