Integrity of data can be compared to a 'person's individuality'. Undermining the place held by either of these can result in dire consequences to be dealt with. So, maintenance and preservation of databank and holding it the way 'it has been' is something that comes instantly to the organizational management. By selecting the appropriate areas whose fluctuations have significant bearing on the usefulness of database, one can successfully win over the threats posed by potential dangers. The perspectives can be- Data Loss Prevention Perspective, Technical Competence Assessment, Data Infrastructure Assessment and Failure Assessment.The KPIs for Data Loss Prevention Perspective are- 'number of back-ups used', 'number of security mechanisms deployed', 'user interface capability ratio' and 'software ability for error detection and correction'. Technical Competence can be had with indicators like 'inaccurate or inconsistent data index', 'capability score of tools and techniques used for accessing data', 'Techniques' syncing with organizational operations' and 'Implementation Timeframe'. Next, the Data Infrastructure can be had using parameters such as 'competence of data model used', 'unification level with stakeholders', 'cost: benefit ratio', 'fraction of infrastructure covered by security mechanisms' and 'vulnerability ratio'. Lastly, Failure Assessment is possible with KPIs 'proportion of unauthorized accesses', 'number of issues that come up in one year', 'time that goes into fixing of the problems' and '% drop in resolution time'.
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The importance of maintaining the integrity of data is well-known to organizations that have been using their databases and statistical collection for deciding their future course of action.
This is because highly detailed studies are often conducted on the basis of these figures and numbers, thereby compelling to keep the entirety and wholeness of data.
In such a scenario, compromising the integrity of data is like creating a trap for oneself on own. No organization that harbors wishes of staying competitive and serving its group of customers for long enough would think twice on implementing the right technological solution in this regard.
This sends managers on a look-out for effectual and appropriate answers to preserve data integrity. There are several kinds of operations through which the database of any company passes. Some of these are- storage, retrieval, transfer etc. In case the content is not maintained as it is throughout the process, distortions (in the form of data quality or data completeness) can sometimes fall severely heavy on the progress and mark an end to the growth prospects of both the workforce and organization.
This is the actual scorecard with Data Integrity Dashboard and performance indicators. The performance indicators include: data loss prevention perspective, number of back-ups used, number of security mechanisms adopted, user-interface capability value, software ability for error detection and correction, technical competence assessment perspective, inaccurate or inconsistent data index, capability score of tools and techniques used to access data, techniques syncing with organizational operations, implementation timeframe, data infrastructure assesment perspective, competence of the data model used, unification level with stakeholders, cost: benefit ratio, fraction of infrastructure covered by security mechanisms, vulnerability ratio, failure assessment perspective, proportion of unauthorized accesses, number of issues that come-up in one year, time that goes into fixing the problems, % drop in resolution time.
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