Data Integrity
From OrgChart.net
Contents |
Overview
Data Integrity is an important aspect of modern day data architecture. Data Integrity implies credibility and is a feature which denotes the data has wholeness or completeness of structure.
Data Integrity is data maintained in the same state, without any distortion, even when an operation or function like a transfer, storage or retrieval is performed on it. In terms of database, Data Integrity means the database is an accurate representation of the scenario it is modeling.
The biggest threat to Data Integrity is data corruption. Data corruption can happen while the data is inert or when in transit. Corrupt data affects the entire organization and many of the critical processes, especially the data-intensive ones like customer service, transactions and decision making could come to a standstill, until the exact problem is identified and restoration of normal activity is done.
Data Compromise
Data corruption during routine backup can go undetected unless there are periodic maintenance and verification checks. Further, in today’s world, data is required to move from one system component to another and every movement creates an opportunity for data corruption. Systems are growing and applications can be added frequently creating more opportunities to move data from one system to another. Greater movement has increased the vulnerability of data several times over.
Data can be compromised due to several reasons:
- Human error while the data is being entered
- “Error creep” while data is transmitted from one system to another
- Corruption due to software bugs or viruses
- Hardware breakdown, e.g., a disk crash
- Natural disasters, fires and/or floods which can affect the physical storage unit
Need for Data Integrity
Businesses today thrive on global sharing and instantly available data. Developments like concurrent usage of applications, consolidation of data centers, virtualization and data replication require connecting multiple systems and applications together. The impact of data corruption could significantly affect an organization. The costs of down time until errors are fixed can be far greater than costs incurred for ensuring Data Integrity. Data Integrity is Mission Critical as data corruption can cost a company it’s reputation and credibility, cause a loss of business, a loss of critical financial data, or even create problems where compliance issues are concerned.
Some ways to reduce the occurrence of data compromise are:
- Regular, maintained, data backup
- Clear definition of responsibility and ownership for data within the organization
- Controlling access to data by implementing stringent security mechanisms
- Designing user interfaces which disallow input of invalid data
- Using error detection and correction software when transmitting data
Ensuring Data Integrity has costs associated with it. It is important to understand this cost is far less than what an organization would incur if data is compromised. There is an undeniable correlation between data quality and a successful business outcome. Data quality management is a fast growing area and there are Data Integrity Service Providers who have expertise in data capture, cleaning and validation who can assist in maximizing Data Integrity.
Data Integrity Management (DIM) encompasses data profiling and assessment, data cleansing and migration, process automation and Data Integrity Life Cycle Management (DILCM). DILCM not only resolves existing issues, it allows for the capture of data quality trend patterns, which helps to weed out repetitive mistakes.
Factors to consider when evaluating Data Integrity Service Providers
- The provider’s expertise is not limited to specific state or nation, but, rather has global expertise. Counties have different practices in data protection, compliance and legislations. If the provider has this attribute, the organization's competitive edge increases.
- The provider must be able to study and assess the organization’s particular needs before implementing a solution. As the volume and nature of data grows, one size fits all is a less reliable solution.
- The provider should render comprehensive services which ensure an end-to-end job.
