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Step 4: Reconciliation

The goal of reconciliation is primarily to harmonize contract, purchase and entitlement information with normalized inventory data to establish an ELP - the balance of licenses purchased to licenses consumed. ELP forms the basis of compliance, risk-reduction, audit defense, contract (re)negotiations, license "true ups" and optimizing software spend. Given the variety of SAM stakeholders, to accomplish these tasks, reconciliation merges normalized asset data with related information from other, often external, sources.


Reconciliation - The questions you need to ask yourself

  • How/where do you maintain entitlement data for key publishers?
  • What are the sources of entitlement data?
  • How much time do you spend weekly/monthly/yearly on reconciling entitlements to inventory?
  • What processes are triggered when a shortfall or over-license situation is discovered?
  • What processes do you have which are fed by reconciled data?


Reconciliation holds together Contract, Purchase and Entitlement Information with normalized inventory data to establish an effective license position, which is the balance of purchased vs. consumed software. In practice, reconciliation requires you to add license information into a Software Asset Management (SAM) solution and then assign it to users, machines or organizational units, depending on the metric of the application.


License information is extensive, including the number of licenses purchased, license cost, additional use rights, maintenance and support contracts and who or what the license is assigned to. The accuracy of this process forms the basis of compliance, risk reduction, audit defense, contract negotiations, license true-ups and optimizing software spend. Due to the various stakeholders within the Software Asset Management process, to accomplish the previous tasks, reconciliation combines normalized inventory data with related information from other data sources. These sources can include procurement data, license and entitlement details and information about users and organizational structures from Active Directory.


Reconciled data is very useful for planning, modelling and dependency mapping, which are critical for Software Asset Management and License Optimization. Reconciliation is also very useful for other IT groups such as Service and Support, who may be developing CMDBs which contain configuration data or ticketing information. It is always highly recommended that any organization regularly performs reconciliation tasks to understand their current compliance position. This proactive approach will help minimize the risk of exposure in the event of a vendor audit.


The Snow Way

Within Snow License Manager companies can manage all crucial licensing information within their estate. Once licensing information is entered into Snow License Manager, it will calculate a compliance position based on the normalized inventory data provided before. The result will be a compliance position for all software applications across the estate, giving involved stakeholders action steps to either reduce over-licensing or to prevent audit fines in case of under-licensing.



Step 3: Normalization

Enterprises often have multiple discovery and inventory solutions. Normalization is the consolidation of discovered inventory datasets to remove duplicated or conflicting information.


Normalization - The questions you need to ask yourself

  • How do you normalize your inventory data today? What inventory data is included?
  • Do you maintain your own catalog? How is it updated?
  • To what level is data normalized (publishers, title, edition, version, release)
  • What other tools (i.e. ITSM, CMDB) do you populate with normalized data? 
  • What processes do you have which are fed by normalized data?


Data can be extracted from many different sources which means it will not be consistent. The process of Normalization presents it in a friendly and easily recognizable format. It removes duplicates to present just one source of truth about each software asset.


The primary benefit of a normalization process is an accurate organized inventory across different datasets. For example: One dataset recorded an executable file name as ‘application.exe’, whereas the other dataset recorded it as ‘application’. Even though it is only one licensed application, it has two unique descriptions, but should not be counted as two separate installations.


Many approaches to normalization classify and categorize inventory automatically using databases of vendors, product and service names to standardize naming conventions of discovered inventory. Another good example of normalization is bundling identification. The inventory process might have discovered multiple applications including Microsoft Word, Microsoft Outlook and Microsoft PowerPoint installed on a Laptop. It determined that these are not three independent licensable applications, but instead a single suite.


Some normalization solutions may also provide default metric information, group applications into application families, add upgrade and downgrade paths, release dates and end of service information.


The Snow Way

Snow Software addresses these needs with its offering of the Data Intelligence Service (DIS), formerly known as the Software Recognition Service (SRS). It is available as a subscription service and provides our customers with up-to-date information for more than 500.000 applications from 80.000 vendors. If you would like to know more about our DIS/SRS, visit this blog post.