Cloud data: A new dawn for dormant data

In brief

• Data, as part of a strong digital core, has become a critical driver in Total Enterprise Reinvention.
• Companies now have an opportunity to break data free from silos, poised to activate more advanced AI/ machine learning (ML).
• That begins with building a modern data foundation for extracting data's intrinsic value, a precursor of accelerated & exponential value through AI.
• Our survey of 700 organizations shows that, right now, fewer than one in five companies are adept at unlocking the intrinsic value of their data.
• We’ve identified six foundational data practices to help use data as a differentiator and create measurable business value from its insights.

A new life for data

Every enterprise knows how important data is—and always has been—to running a business. But today’s enterprise data operates on a completely different scale.

Over the past decade, data’s importance has grown, and its value has accelerated. AI cannot reach its full potential until companies figure out data. And data-adept digital-native companies have shown the value in using data and artificial intelligence (AI) to unlock new products, experiences, and operational efficiencies, at scale.

Advances in cloud-native services now puts those same capabilities within the reach of every enterprise. In 2021, for instance, almost half of the CEOs of the world’s 2,000 largest companies mentioned AI in their earnings calls.^[i]

The complexity conundrum

But there’s a catch. The data landscape and the ways enterprises want to use it, is becoming ever larger and more complex. Put simply, there’s much more data to handle, at greater speed. Data complication is also evident in many more varieties and resides in many more places, not to mention the heightened need to access data from all points of the cloud continuum.

What’s more, most organizations are not effective at using real-time data, or ensuring connectivity and interoperability across different infrastructure components. Many also struggle to trace their data or find the information needed to answer even basic questions about their customers, assets, people, and partners.

Why is this so hard? Because knowing where the data resides is only part of the problem. There’s also the challenge of accessing it, extracting it, processing it, analyzing it, and distributing the insights to the right people or systems at pace. And that’s dependent on the data platforms, processes, skills and culture the organization has at its disposal.

Whether this data complexity comes from old challenges or is exacerbated by new ones, a majority of companies believe there’s a gap between where they want to be and where they actually are today.

Extract the intrinsic value

The good news? The breathtaking evolution of cloud and edge capabilities provides solutions that simply didn’t exist a few years ago. These capabilities allow companies to make a profound impact through three strategic objectives: extracting data’s intrinsic value, accelerating the value via pre-built cloud solutions, and creating exponential value with AI.

The value journey of data begins with the first of those objectives: extracting the intrinsic value of data, which refers to the data’s true value, reflecting what data is fundamentally worth. Enterprise leaders should instinctively look for that diamond in the rough data opportunity, that, for whatever reason, is currently being treated below its true worth, or intrinsic value.

This focuses on breaking down data silos, removing duplication, creating trusted data products, reducing the cost of data rework, ensuring more timely insights and cross-functional use cases, and improving user adoption.

The goal is to ensure the enterprise can tap into the full range of available data, faster, to radically enhance business intelligence and performance today.

Six key practices to extract data’s value

How? Our survey of C-suite executives at 700 of the world's largest organizations identifies a group of companies who excel at capturing value from data.

These companies get several critical things right. They understand their current and future sources of data. They bring together data from across the organization and external sources. Their data masterpiece is not only utilizing it for operational or transactional purposes but also analytically making better decisions. And they have data tooling infrastructure that’s both fit for today and a foundation for the future.

We’ve identified six key practices all companies can learn from these top performers. They are:

1. Extricate: Free data from silos

Break-free data trapped in legacy systems; silos, enabling enterprise-level data graphs, analyze data in one place, to activate advanced AI/ML, at scale.

2. Extend: Expand data mindsets across the Cloud Continuum

Capture, manage & process data across the full span of public and private clouds, and network edge to activate data in real-time & optimize data place.

3. Productize: Treat data as a product

Establish product attributes & bring proven product development practices, processes, and tools. Deliver quality, configurability, and reusability.

4. Automate: Adopt data-as-code

Evolve from DevOps to DataOps, applying rigorous approaches widely used in software development, spotlighting autonomous data management.

5. Democratize: Make high-quality data products available

Put high-quality data products in the hands of the people who need them, when they need them, in the way they need them.

6. Publish: Share data…the new norm 19%

Tap into more of data’s intrinsic value by ingesting and sharing data seamlessly and securely with the broader ecosystem with confidence.

Wake your dormant data

These six data practices are key to getting more out of today’s complex data landscape. But they’re not the end of the data maturity journey by any means.

Having mastered these practices, companies then need to move on to more advanced strategic data capabilities, such as building decentralized data meshes across the Cloud Continuum and creating exponential business value by embedding advanced AI throughout the organization.

But first, an organization needs to ensure it’s extracting the full potential of data’s intrinsic value.

That begins by rousing enterprise data from its state of dormancy—and releasing the huge volume of latent value waiting to be tapped.