Software license compliance and optimization is founded on knowing what you use
According to an ITAM Review survey, 85% of organizations are over-licensed1. In other words, these organizations own more software licenses than they need.
Most are deliberately overbuying licenses to provide some protection from vendor audits, with the thinking being that the predictable cost of over-buying is more palatable than the unpredictable cost of a bad audit.
But it doesn’t have to be like this. If you can get clear visibility of the software you use, versus the software you own, you can cut out the overspend and divert much-needed budget to other areas of IT. Gartner says that just a few improvements to your IT Asset Management program can save you upwards of 30% of your hardware and software spend2.
Good visibility and data are the keys to better Software Asset Management
Without a clear view of the software you use, you could be wasting money year-on-year. When you have a complete, accurate, and clear view of the software that is installed and used, you can apply a data-driven approach to software licensing and compliance. This allows organisations to make decisions based on data, instead of relying on guesswork and estimations.
Clean inventory information is the key to Software compliance and Software License Optimization
When talking about Software Licensing, compliance and optimization are two sides of the same coin. Where license compliance is about ensuring you have enough licenses to cover the software you use, Software License Optimization (SLO) is about ensuring you are compliant without overbuying. An accurate software inventory is a key piece of the puzzle—giving you a complete picture of where software executables exist in your IT environment.
However, simply having complete coverage of software discovery is not enough. Often, the raw data that comes out of discovery is fragmented. Vendor and software titles can be inconsistent, making it hard to group software together by vendor, application, and version. For example, Microsoft may appear in the data as “Microsoft”, “Microsoft Corp.” or “Microsoft Corporation”.
The scale of the Software Asset Management challenge
According to F5’s State of Application, 2016 report, 54% of organizations deliver up to 200 applications. 23% deliver up to 500 apps, 15% handle up to 1000, with the top 9% are handling 3,000 applications or more3. And that’s just the apps that they know about - not including Shadow IT (software installed without explicit permission by staff members). In a large organization with 1,000 distinct apps in their portfolio, a software discovery run may uncover 5,000 or even 10,000 “unique” titles—5 or 10 times more than the reality.
This type of messy data makes it difficult to report on your software footprint. In turn, this makes it difficult to match the software you have against the software you own. This would be displayed in the form of an Effective License Position (ELP) report which tells you precisely where you are overbuying and, crucially, where you are falling short of compliance. The challenge is to consolidate all these different title variations to de-fragment your view of your software estate. But with so many applications to track, and thousands of new software products released each year, the scale of the challenge and the pace of change make it impossible to manually identify and normalize software discovery data.
How assyst’s automated software recognition works
assyst’s Software Recognition Dictionary adds automatic identification to make sense of your raw software discovery and provide a simplified view of your ecosystem. The Software Recognition Dictionary uses a frequently updated database of software signatures to automatically recognize and normalize discovered software, turn messy data into clean information, and map each executable to the correct vendor and product. This enables automatic correlation between the installed software you use and the licenses you own, making it possible to create a near real-time view of your Effective License Position. When you have this visibility, it’s easy to see where you are overbuying, or falling short of compliance—so you can fix it fast. And with assyst, the combination of discovery, recognition, reconciliation and automation means that many of these issues can be automated—including detection and resolution. So, you can spend less time on reactive work and more time on driving cost-cutting and strategic initiatives.
Automation is the key to effective Software Asset Management
Clean data is the foundation of effective automation. With a sprawling software install dataset, polluted by multiple variations of the same software titles, it is difficult to line up automations with titles. When you have clean, rationalized data, it is easier to match up issues with automated workflows which correct those issues—for example, removing a blacklisted application. Likewise, when you can clearly identify software packages and versions, you can be more effective in your patch management efforts—applying automated remote patching from within assyst. In this way, automated software recognition helps you to achieve continuous software compliance, as well as reducing your patch backlog and your cybersecurity attack surface.
Making vendor audits easy
If you receive a vendor audit letter, you will need to quickly find out how many vendor applications you use and how many you own. Messy data can make this slow and difficult. Clean, normalized information gives you clear visibility, reduces the effort required to respond to an audit, and generally reduces the stress associated with Software Asset Management.
Benefits of automated software recognition
Through automated software recognition you can “reduce the noise” in your discovered data to get a clear, rationalized picture of your organization’s software footprint. With better information, it’s easier to achieve an Effective License Position (ELP) view—which shows precisely where you are over or under licensed—so you can systematically address risks and overspend, and reduce the stress of software asset management.