Today companies collect vast amounts of data, whether it’s from business systems, cameras, kiosks, robotics or frontline devices—but most organizations are not putting that data to good use. 

Some are holding petabytes of information and find the sheer volume overwhelming. Some suffer from technical debt and feel stuck between sunk costs and new investments in the cloud. Nearly all still see data as a reactive asset to make decisions based on the past through reports, dashboards and charts of things that already happened.

Doing more with data is at the center of discussions we have every day with customers, but that conversation is a lot different than it was just a few years ago. Today the true value of data is using it to understand patterns and predict what’s about to happen so the company can avoid a problem or take advantage of an opportunity. 

Things have changed in the data space, and results that formerly required a huge data science effort are now within reach. With cloud services, you don't need to process all that volume yourself. And with rapid advances in large language models, a machine learning solution can begin to spot anomalies after being trained on as few as 30 samples. 

Here are three core pillars that can transform the way you work with data and open up a new world of forward-looking analytics that can transform the way you do business. 

Govern your data lake to create a single source of truth. 

A data lake helps put structure around moving data from its source, applying business rules and calculations, and creating refined, consistent and secure data that is trustworthy and can be queried easily. 

The key is consistency. If the data lake is not protected with sound governance, it becomes a data swamp with dozens of channels dumping into it. In a world of department-based analytics, the same customer may have records in ten different systems, making it impossible to get that deep 360 view. 

Those organizational silos need to come down, and once they do, you can begin to curate it and create a single source of truth. Typically, we think about data lakes as three separate zones: bronze, silver and gold. As data flows into the data lake from the source system, it’s important to have a consistent template. 

After everything is consistent across that bronze layer, the data can be curated and refined into the silver layer and ultimately to that gold zone of trustworthy information. At the gold layer, the organization can make important decisions based on the data — because it's vetted, governed and secure. 

Use the power of cloud services to transform your capabilities. 

For years many companies were uncomfortable taking critical business data to the cloud, but today on-premises systems are at much greater risk from cyber threats. The cloud introduces layers of security that no organization could achieve on its own. 

Other benefits include ease of business, total cost of ownership, and the ability to rapidly scale up and down as needed. The plumbing is all there. The machines are spun up. You are not installing an operating system and software. You're not hiring people to manage the ecosystem. Everything from the foundation onward — security, hardware, power backup, restoring, redundancy — is provided. 

With the cloud, all of the capabilities for deep data analysis, AI and machine learning are already available. This frees the organization to focus on the outcome of their data initiative and how to achieve it. 

MLOps: the practice of using data effectively. 

With the data properly governed and secure, and the cloud infrastructure providing the fundamental capabilities needed to build a transformative data and AI solution, data can now be put into use by the organization to achieve results no one would have dreamed of just a few years ago. 

Machine learning operations, or MLOps, is an emerging discipline that helps turn all those resources into action — a set of practices that generates ongoing value from what was formerly a static asset. Machine learning is a continual process. MLOps practices turn machine learning into a loop where the system is constantly evaluating and improving the effectiveness of its models’ decision-making capabilities. 

For companies new to this arena, choosing the right partner can help facilitate a smooth transition to a data-driven organization. They’ll help inventory the systems in place, triage and solve any issues with data governance and management, identify points of integration, and put in place solutions and processes to create a seamless flow of data that derives the most value for the company.  

The value proposition with data is clearer than ever.  

Many companies are sitting on a wealth of data assets that they never imagined could be used in this way. Today almost any company can unlock the true value of that data and find those unconsidered, untapped value streams within. 

Today’s large language models, generative AI, machine learning and advanced tools are maturing to the point where they are stable and ready for business. There is a huge opportunity to catch up with the competition or even leapfrog competitors with new capabilities.

Companies that are not using data strategically today are missing out, but this is not a FOMO moment. This is what we call unconsidered business needs. Companies may not even know the value their existing data streams can bring to the organization — but by putting that asset to work, they can open up new revenue channels, optimize what they do, and avoid unseen pitfalls