Are you satisfied if the asset and license data in the tool is "approximately" correct? Companies worldwide invest considerable resources in the procurement and operation of their SAM and ITAM tools. The creation of license balances and the determination of license compliance are fundamentally based on the data in the SAM tool, so they are decisive for the result.
The quality of the data in the tool, its usability and informative value are often taken for granted. That's what the tool is there for, isn't it?
However, effective risk management in license management is not possible without high data quality in the SAM tool.
So, you should ask yourself: is the data in the tool correct? Is it up-to-date and complete? What characterizes the term "complete" and when is it complete?
Are there procedures in place to determine the status and maturity of the SAM tool data and how can work be done to improve data quality?
Does data quality even play a role in the SAM development plan and the SAM organization?
Most SAM organizations will answer these questions: the "SAM tool" will ensure the necessary quality of the data, that's its job, right?
No tool, not even an AI-based tool, can compensate for systemic and/or conceptual errors in the data model and the data sources supplying the data.
Yes, it is good to believe that the data from the connected master data sources cannot be faulty. Even if they were, the license management is not responsible for errors in upstream master data sources.
No, license management is not responsible for errors in IT data sources, but it is responsible for ignoring poor data quality and the resulting consequences.
Assessments and decisions based on invalid data are not only bad. They are not worth the effort required to determine them and, in the worst case, they lead to incorrect decisions.
What's more, ignoring weaknesses and mistakes leads to no result, except that the company's limited funds and resources are used pointlessly.
Recognizing the weak points in the master data sources and identifying the reasons for incorrect processing in the tool and rectifying these, on the other hand, helps the company, and also license management to become better and more effective overall.
A SAM organization and SAM tool management must therefore constantly ask itself:
The tasks and responsibilities for determining data quality and managing SAM tool data should be described and defined as part of SAM governance in the areas of risk management and data quality management.
The answers to these questions should be determined as automatically as possible, from KPI reports and test procedures, from regular data validations carried out by SAM Risk Management as the result of a standardized quality management process.
Data deficiencies in upstream data sources and processes that are found using these checking methods must be rectified, not ignored. This increases the overall quality of IT data and improves the business value of data and processes.
Actively checking and managing tool data quality with the aim of generating business value for the company and identifying and avoiding potential risks at an early stage should be the task and goal of every SAM organization.
Evaluating the maturity level of a SAM tool and the SAM tool data stored in it is not rocket science - you just must want to do it and execute.
Every SAM organization should become aware of the value of the SAM tool data it maintains and actively manage it. Above all, see this data as a means of production and make a much greater effort to "market and use this data" within the company.
Do you have questions? Then let’s talk and contact us for a free consultation.
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