Data Integrity and Management
Data integrity has become a serious issue over the past few years and therefore is a core focus of many enterprises.
What is Data Integrity?
"Data integrity refers to the fact that data must be reliable and accurate over its entire lifecycle" (Finestone, 2018), meaning that the data lifecycle provides a high level overview of the stages involved in successful management and preservation of data for use and reuse.
Data integrity and data security go hand in hand, even though they’re separate concepts. Uncorrupted data (integrity) is considered to be whole and then stay unchanged relative to that complete state.
Maintaining or keeping data consistent throughout its lifecycle is a matter of protecting it (security) so that it’s reliable. And data that’s reliable is simply able to meet certain standards, with which compliance is necessary.
Data is expected to be (Finestone, 2018):
Attributable - Data should clearly demonstrate who observed and recorded it, when it was observed and recorded, and who it is about.
Legible - Data should be easy to understand, recorded permanently and original entries should be preserved.
Contemporaneous – Data should be recorded as it was observed, and at the time it was executed.
Original - Source data should be accessible and preserved in its original form.
Accurate - Data should be free from errors, and conform with the protocol.
Data Lifecycle:
There are many versions of a data life cycle, depending on domains or communities. But they all have certain things in common (DataOne, 2018):
Plan - description of the data that will be compiled, and how the data will be managed and made accessible throughout its lifetime
Collect - observations are made either by hand or with sensors or other instruments and the data are placed a into digital form
Assure - the quality of the data are assured through checks and inspections
Describe - data are accurately and thoroughly described using the appropriate metadata standards
Preserve - data are submitted to an appropriate long-term archive (i.e. data center)
Discover - potentially useful data are located and obtained, along with the relevant information about the data (metadata)
Integrate - data from disparate sources are combined to form one homogeneous set of data that can be readily analyzed
Analyze - data are analyzed
What is the Difference Between Data Integrity and Data Security?
It's very important to not confuse these terms. Data security refers to the protection of data, while data integrity refers to the trustworthiness of data.
Data security focuses on how to minimize the risk of leaking intellectual property, business documents, healthcare data, emails, trade secrets, and more. Some data security tactics include permissions management, data classification, identity and access management, threat detection, and security analytics (Ng, 2018).
So how do you know when your data has integrity?
Verify if your data follows these standards.
Retrievability and accessibility – It’s important to have accurate data in the proper locations at the right time when anyone is working on projections, a deal, or presentation. Without proper and easy access and retrieval, it can be detrimental to the business, yielding the way for your competition to win.
Traceability –Today, you can trace every touchpoint you make with a prospect or customer. How? With a data point. The data can inform decision makers, highlight red flags, deficiencies, or limitations. Make sure these touchpoints are accurate.
Reliability – Having reliable, consistent business metrics against company goals and the competition is what will take an organization to the top. (Ng, 2018)
References:
Cindy, Ng. (2018). What is Data Integrity and How Can You Maintain it?. Various. Recovered from: https://blog.varonis.com/data-integrity/
DataOne. (2018). Data Life Cycle. DataOne. Recovered from: https://www.dataone.org/data-life-cycle
D, Firestone. (2018). What is Data Integrity? 12 ways to reduce data integrity risk. Global Vision. Recovered from: https://www.globalvisioninc.com/blog/12-ways-to-reduce-data-integrity-risk/
Data integrity and data security go hand in hand, even though they’re separate concepts. Uncorrupted data (integrity) is considered to be whole and then stay unchanged relative to that complete state.
Maintaining or keeping data consistent throughout its lifecycle is a matter of protecting it (security) so that it’s reliable. And data that’s reliable is simply able to meet certain standards, with which compliance is necessary.
Data is expected to be (Finestone, 2018):
Attributable - Data should clearly demonstrate who observed and recorded it, when it was observed and recorded, and who it is about.
Legible - Data should be easy to understand, recorded permanently and original entries should be preserved.
Contemporaneous – Data should be recorded as it was observed, and at the time it was executed.
Original - Source data should be accessible and preserved in its original form.
Accurate - Data should be free from errors, and conform with the protocol.
Data Lifecycle:
There are many versions of a data life cycle, depending on domains or communities. But they all have certain things in common (DataOne, 2018):
Plan - description of the data that will be compiled, and how the data will be managed and made accessible throughout its lifetime
Collect - observations are made either by hand or with sensors or other instruments and the data are placed a into digital form
Assure - the quality of the data are assured through checks and inspections
Describe - data are accurately and thoroughly described using the appropriate metadata standards
Preserve - data are submitted to an appropriate long-term archive (i.e. data center)
Discover - potentially useful data are located and obtained, along with the relevant information about the data (metadata)
Integrate - data from disparate sources are combined to form one homogeneous set of data that can be readily analyzed
Analyze - data are analyzed
What is the Difference Between Data Integrity and Data Security?
It's very important to not confuse these terms. Data security refers to the protection of data, while data integrity refers to the trustworthiness of data.
Data security focuses on how to minimize the risk of leaking intellectual property, business documents, healthcare data, emails, trade secrets, and more. Some data security tactics include permissions management, data classification, identity and access management, threat detection, and security analytics (Ng, 2018).
So how do you know when your data has integrity?
Verify if your data follows these standards.
Retrievability and accessibility – It’s important to have accurate data in the proper locations at the right time when anyone is working on projections, a deal, or presentation. Without proper and easy access and retrieval, it can be detrimental to the business, yielding the way for your competition to win.
Traceability –Today, you can trace every touchpoint you make with a prospect or customer. How? With a data point. The data can inform decision makers, highlight red flags, deficiencies, or limitations. Make sure these touchpoints are accurate.
Reliability – Having reliable, consistent business metrics against company goals and the competition is what will take an organization to the top. (Ng, 2018)
References:
Cindy, Ng. (2018). What is Data Integrity and How Can You Maintain it?. Various. Recovered from: https://blog.varonis.com/data-integrity/
DataOne. (2018). Data Life Cycle. DataOne. Recovered from: https://www.dataone.org/data-life-cycle
D, Firestone. (2018). What is Data Integrity? 12 ways to reduce data integrity risk. Global Vision. Recovered from: https://www.globalvisioninc.com/blog/12-ways-to-reduce-data-integrity-risk/
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