Software Asset Data Management – A Handy Glossary For Informed Software Asset Managersby ps
A continuously extending summary of important terms and definitions in software asset data management.
Cooked Data: SAM raw data that has undergone some form of processing – potentially ending up in a SAM database.
Dirty Data: A SAM database record that contains errors. Dirty SAM data can be caused by duplicate SAM records, incomplete or outdated SAM data and the improper parsing of record fields from disparate systems. Errors can be induced at any stage as data is entered, stored and managed.
Data Governance (DG): Overall management of the availability, usability, integrity, and security of the data used in a SAM organization. Includes a governing body, a set of procedures, a plan to execute them. Comprises the specification of who owns SAM data assets, who is accountable for various aspects of data, a definition of how the data is to be stored, archived, backed up, and protected from mishaps, theft, or attack, the development of standards and processes of how the data is to be used by authorized personnel, and specify auditing and controlling procedures allowing for ongoing compliance with external and internal regulations.
Data Hygiene: The collective efforts conducted to secure the cleanness of SAM data – whereat clean means relatively free of errors.
Data Life Cycle Management (DLM): A policy-based approach to managing the flow of an SAM information system’s data throughout its lifecycle – from creation and initial storage to the time when it becomes obsolete and is deleted.
Data Management: The development and execution of architectures, policies, practices and procedures in order to manage the SAM information lifecycle needs of a SAM organisation in an effective manner.
Data Profiling: also called data archeology. The statistical analysis and assessment of SAM data values within a SAM data set for consistency, uniqueness and logic.
Data Quality: the reliability and application efficiency of SAM data. A perception or an assessment of SAM data’s fitness to serve its purpose in a SAM context. Aspects of SAM data quality are: accuracy, completeness, update status, relevance, consistency, reliablity, appropriate presentation, accessibility. Within SAM organizations, acceptable SAM data quality is crucial to operational and transactional processes as well as SAM analytics and reporting. SAM data quality is affected by the way data is entered, stored and managed. Maintaining SAM data quality requires going through the the data periodically and scrubbing it which generally includes updating, standardizing, deduplicating records to get a single view of the data, even if it is stored in multiple and disparate systems.
Data Scrubbing: Often also called “Data Cleansing”. Process of amending or removing SAM data in a SAM database which is incorrect, incomplete, improperly formatted, or duplicated.
Data Stewardship: The management and oversight of a SAM organization’s SAM data assets to help provide SAM users with high-quality data. Maintains agreed-upon data definitions and formats, identifies data quality issues and ensures that business users adhere to specified standards.
Garbage In – Garbage Out (GIGO): The quality of SAM output is determined by the quality of SAM input. A faulty SAM decision made out of incomplete information. Originally coined by George Fuechsel an early IBM programmer. Also depicted by the saying: “A fool with a tool is still a fool” as a SAM tool can only process what is given.
Garbage in – Gospel Out: Tendency to put unwarranted faith in the accuracy of computer-generated data.
Master Data Management (MDM): Method of enabling a SAM organisation to link all of its critical data to one file, called a master file, that provides a common point of reference. When properly done, MDM streamlines SAM data sharing among personnel and departments. Includes training and teaching SAM personnel how data is to be formatted, stored and accessed, and updating master data on a regular basis.
Metadata: SAM data that describes other data. The prefix “Meta” connotes an underlying definition or description e.g. author, date created, date modified. Facilitates finding and working with particular instances of SAM data.
OLAP (online analytical processing): enables users to analyze multi-dimensional data from various perspectives. OLAP is usually a crucial part of business intelligence. It is based on a multidimensional data model and allows for complex analytical as well as ad hoc queries with a rapid execution time.
Raw Data: also Source or Atomic Data. SAM data that has not been processed for use.