Having Trouble Funding Gen AI?
Increase Innovation Investment Through Smarter Data and Storage Management

Every organization is managing through exponential information growth, much of which is driven by unstructured data.  

Since it lives in pdfs, videos, social media and other sources, unstructured data defies the easy classification organizations are used to with traditional SQL-based sources. This makes it harder to understand and manage from a usability/governance / security standpoint. Its expansive nature also quickly increases storage costs and adds to data sprawl challenges. 

We know unstructured data has incredible untapped value and potential to enhance any number of products and services, including helping to unlock the promise of GenAI. However, lack of understanding and classification of this data increases risk, especially with data that may be sensitive or stored at odds with the retention requirements for that class of data.  

Data and IT teams are looking for ways to get a better handle on unstructured data. They are also looking to free up budget to move GenAI from POCs and pilots into production. A strong data classification strategy, combined with storage tiering and automation, can improve performance and unlock crucial infrastructure and data management savings to fuel AI and GenAI efforts. 

First, Understand Your All of Your Data 

A well-structured data classification system helps organizations easily identify and access relevant data for any number of operational and innovative applications. This has taken on renewed importance since AI and GenAI applications rely on vast amounts of data for training and learning. 

Today, effective data classification means being able to access and understand all data, both structured data and unstructured sources including PDFs, blob files and media formats such as images, videos, audio, and more. Understanding the metadata around these sources and being able to score them on quality and reliability are vital to any customer-facing or decision-influencing GenAI or AI application.  

Data classification also plays an important role in governance and regulatory compliance. While there are already many industry-specific regulations such as HIPAA and Know Your Customer, there are also a wide range of laws already in place that relate to data handling and privacy that apply to AI. This doesn’t even include whatever new laws are coming, which are in various stages of implementation in different regions. Properly identifying sensitive information at scale gives organizations the power to apply the necessary rules and measures that reduce risk and help avoid potential fines while maintaining customer and stakeholder trust. 

Automating Storage: Right-Size Usage, Recapture Budget and Increase Bandwidth  

Once data is properly classified, you can implement tools that detect various aspects of the data lifecycle to score its value. The scoring of data’s value should be based on multiple attributes (size, usage rate, where it’s being used and for what purpose) to inform storage tiering policies that can then be automatically applied to every piece of data.  

Powered by automation and intelligence, this process creates cost savings in three ways. First is in overall storage costs. Since intelligent tiering and re-tiering of data allocates data location based on use and value, infrequently used data can be sent to lower cost environments. Secondly, with all data properly classified, it becomes much easier to quickly retrieve, and re-tier data only as needed for uncommon upstream application requests or new AI/GenAI asks. And with classification and policies established, an organization can better manage retention policies to ensure they are correctly implemented based on regulatory and corporate guidelines. 

Automated storage policies also scale with data’s growth, keeping costly manual processes at bay and protecting the hard-won agility and bandwidth teams need to keep up with AI and GenAI demands.  

A Winning Combination 

Integrating data classification with automated data lifecycle policy creation and enforcement creates a strong foundation for AI and GenAI success. This combination accelerates access to trusted and governed data, enhances data quality, and frees up precious budget that can be used to bring AI and GenAI projects to life. 

Request a demo to learn more about how Pentaho Data Optimization can enable your data classification and storage optimization needs and help your organization get data-fit.