- Dec 15, 2016
- Mathias Knops
Data Quality in License Management:
How Good is Good Enough?
An international operating company wants to review its license position for Microsoft Windows Server, another wants to reduce the license costs in 150 subsidiaries, and a third – a mid-sized company – wants transparency regarding their financial risk before the next round of negotiations with SAP. Each of these companies must confront the same critical issue: Data Quality.
And this means preparation. All companies share an objective: the accurate and comprehensive depiction of their license inventory, and a continuous comparison of actual usage with the contractual user rights.
SAM without data quality is meaningless.
Every analysis and optimization is only as good as the data on which it is based. The first step towards a functional license management system is transparency. Only after this is achieved can license demand be analyzed, and compliance and cost reductions discussed.
It is important to understand that software asset management platforms can only be as accurate as the underlying raw data. If companies process inaccurate data in license management, the consequences can be disastrous. Undocumented acquisitions incur double the costs, incomplete infrastructure data leads to incorrect demand assumptions, and incorrect organizational information impedes proper cost allocation.
Many tools tell you that something is missing. A smart tool tells you what is missing.
A good SAM tool prevents inaccurate data from getting into the current license inventory. Frequently, mandatory fields are not completed or data is counted several times in different places. Transactions are then always incorrect since they do not reference the master data. The reason for this is primarily missing or inaccurate information. An example would be an incorrect product number or incorrect contracts. It is imperative that only reliable data is collected.
How good is good enough for you? The better the data quality, the more automated the subsequent normalization can be. Only when licenses and usage rights are closely integrated will changes in software manufacture metrics automatically be updated in your SAM tool.
What’s missing is important.
To put it bluntly, everything that comes into a SAM tool is important. So, how does one measure data quality? It is crucial to identify your missing required data. An example of this would be the number of processors or cores, any virtualization technology, existing maintenance/service level agreements and much more. A good tool recognizes what is missing, and reports on the quality of that data.
When evaluating data quality, you must differentiate between:
- Completeness of the data sets: A good system provides warning. How many processors are there really? If there is a lack of accuracy, you have arrived in the land of estimations. This harbors risks.
- Completeness of the data content: Are the data sets consistent? A good system checks whether the information is correct. For example, are the number of cores less than the number of processors? If so, then an error has crept in.
A smart tool like SmartTrack takes care of all of this for you.
As many user rights as beer varieties - at the very least.
SAM tools must be able to display all the details that are required for the analysis. In other words, what do I have and what do I need? Nothing more. Sounds easy. But the reality is complex, because software products and their metrics are diverse.
Products with or without maintenance contracts cause huge differences in terms of licensing costs. The answer to the question, “which user rights must be used”, can ultimately lead to enormous differences in costs.
What you can do to make your SAM project a success:
- Only collect and merge reliable data! Prepare as accurate an overview as possible of all of your relevant business-related data. Very good preparation always means both a listing of your contractual, as well as your license data.
- You also need a complete database for your technical inventory. No discovery tool can do everything. The solution: Use the data sources you already have and expand with supplemental technology only where it is absolutely necessary.
- Take a step-by-step approach. Successful companies have incrementally progressed in improving data quality. Start with one or two manufacturers, and choose the one with the greatest potential for cost savings. Maintaining the quality of your data is the key to your success.
Does precision delay results?
Will such a focus on accuracy negatively impact the implementation timeline? Without knowing the size of your company, the implementation of a SAM tool with optimized data quality up to the first license inventory can be done within three months. Our three companies referenced at the beginning of this article can attest to this. They are all now capable of accessing squeaky-clean data.
In summary, data quality is usually accompanied by significant cost savings. You will, however, always reduce your compliance risk.