The ITAM Review announced at their annual Wisdom Conference that ServiceNow® Software Asset Management (SAM) wins the 2021 Technology and Service Innovation of the Year Award for Machine Learning Normalization, and we could not be more excited and humbled for this honor. Our goal at ServiceNow is to make the world of work, work better for people which is why we heavily focus our efforts on listening to our customers and delivering creative, practical solutions to their biggest ITAM challenges.
With a single-architecture platform and data model, we are proud to provide our customers with the unique experience of running SAM on the same platform where the rest of IT operates. This helps create a simplified (and automated) way to manage their technology assets across software, hardware, and cloud resources. Our mission is to connect teams and streamline processes in a way that drives collaboration and efficiency at every stage of the asset lifecycle.
“We are extremely honored to receive ITAM Review’s annual innovation award for a second time since we launched the product 4 years ago. We are always looking for opportunities to innovate and help customers achieve their business outcomes faster. In this case, we are using machine learning to solve a challenging problem in IT environments where software titles are in constant evolution.”
~German Bertot—Sr. Director and GM, ITAM
Check here for the full list of The ITAM Review 2021 Award winners
What is Machine Learning Normalization?
Machine Learning Normalization identifies software even when there are no library patterns. This feature delivers mapping rules in real-time for unrecognized software, making it the first known use of machine learning in the SAM space. Machine Learning Normalization helps address partially normalized software, where software publisher and the software product may have been recognized, but software version or edition is unrecognized. Machine learning provides predictions for Version, Full Version, and Edition in scenarios where there are no content rules or patterns already in place. ML (Machine Learning) predictions are validated against constantly evolving new rules and are 100% accurate to date. Using machine learning technology to normalize software significantly helps our customers quickly uncover compliance or security risk.
The value of machine learning: quickly make sense of unrecognized software
Machine Learning Normalization helps our customers manage their dynamic environments where new software products and versions are constantly being deployed. SAM tools typically normalize most of a customer’s software estate. For example, ServiceNow SAM typically recognizes 85-98.5% of software right away. However, there is still the last mile of unrecognized or partially recognized software. Even while organizations receive content updates weekly, there still exists a need for gaps to be solved more quickly. Machine Learning Normalization addresses partially normalized software, where software publisher and the software product may have been recognized, but software version or edition is unrecognized.
Our customers can now leverage machine learning to normalize software and receive predictions for Version, Full Version, and Edition in these scenarios where there are no content rules or patterns already in place. Machine Learning Normalization identifies new software product versions and editions in real-time—shortcutting waiting periods for new rule creation and deployment, reducing company exposure to license compliance and security risk. As an added bonus, customers using Machine Learning Normalization are able to provide value to the greater ServiceNow community by opting in to push normalization findings to the content service for vetting and content library. This means our content library is constantly being updated with new mapping rules that every SAM customer can leverage.
The benefits of machine learning in a SAM tool
ServiceNow SAM offers our customers a unique experience of leveraging machine learning to greatly reduce traditional human-led efforts when trying to make sense of unrecognized software and fill in crucial gaps in software data. Traditional approaches to solving for normalization involve manual research or low-confidence generic rules in an attempt to scale. Removing manual effort while maintaining extremely high accuracy helps our customers save time and resources getting to value outcomes.
With machine learning, you can expect to:
- Improve time-to-value by automatically identifying unrecognized software
- Speed up coverage by delivering mapping rules in real-time
- Gain a more complete and accurate record of the estate, quickly
To put these benefits into perspective, a customer with 20 million software records at 95% normalization has 5% unnormalized software, or 1 million records. If we assume it takes 10 minutes per record, that comes out to 19 years of manual work saved.
Let’s face it – our IT teams may never be able to hire an army to manage our growing environments. With machine learning doing the heavy lifting, we are excited to see our customers able to shift their efforts away from manual work and processes toward a more efficient, automated way of managing their software estate. We believe our customers deserve great experiences and our continued goal is to deliver more value to cross-functional stakeholders with native workflows. We plan to continue to create a market where leading AI/ML technologies can elevate the SAM experience and create value for our customers. This is the first of several AI/ML use cases on our product roadmap.
“Applying machine learning to software normalization elevates the sophistication of our normalization engine, which can respond to customer demands in real-time. Customers benefit from higher software visibility as they manage the risks of operating in an ever-changing environment.”
Julian Flores—Director, Product Management, ITAM
If you are interested in learning more about what ServiceNow Software Asset Management can do for you as part of a complete IT Asset Management solution, see a demo today.
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