Integrate a Data Audit into the Data Management Plan
Data audits are not just about security: quality matters!
- Almost half of organizations either aren’t performing data audits at all, or are only performing them when issues arise.
- Data audits are often viewed as a time and resource drain, and organizations do not see the value in committing dedicated resources to performing them on a regular basis.
- Most organizations that don’t perform regular data audits don’t know how bad their data really is, and as a result, may end up with data security and compliance issues.
- Most organizations are unaware of the location of all of their data sources, and quite frequently have rogue data contained in phantom data sources that staff are using without IT’s knowledge.
- IT is often unaware of how data is being used, and by whom, which leads to low data trust levels within the organization, and the potential for misuse of internal data.
Our Advice
Critical Insight
- Any organization that is educated about its data assets can maximize those assets to their fullest extent.
- Taking a proactive approach to data auditing is something every organization needs to do. Data is one of the organization’s largest assets. In order to utilize it effectively and benefit from it, data requires effective management in the form of policies, procedures, standards, and ownership.
- Organizations who perform audits will understand what issues exist around data quality, security, compliance, and overall data management, and what steps must be taken to create higher levels of data quality.
Impact and Result
- Performing data audits on a regular basis results in higher quality data, and less data replication across organizations. In addition, those who do data audits know where all their data repositories are and the levels of risk (quality, security, and provenance) associated with each.
- Following the data audit framework generates higher levels of data audit success.
- Implementing techniques such as data cleansing, synchronization, validation, and profiling, and incorporating on-going quality data checks and monitoring data entry promote higher levels of data quality and integrity within systems and alleviate data debt.
- Data owners and stewards play a critical role in the overall management of departmental data sources. Data owners and stewards not only implement and enforce data policies and processes, but also take responsibility for overall quality and integrity.
- Communicating changes to data processes and policies resulting from the audit in advance will address the various concerns that departmental stakeholders may have.
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Speak With An Analyst
Get the help you need in this 1-phase advisory process. You'll receive 4 touchpoints with our researchers, all included in your membership.
- Call 1: Prepare by reviewing these pre-audit considerations
Discuss audit options, which data sources to audit, internal vs. external audits, staff resourcing, and what to do about rogue data. Review how the Data Audit tool works.
- Call 2: Interpret and act on data audit results
Review data audit results, discuss how to rectify any issues highlighted, who will take ownership of the clean-up process, a communication plan for end users and any automated tools that may assist in the clean-up initiative.
- Call 3: Assess data management policies
Discuss what types of policies your organization requires, review existing policies and drafts for new policies to ensure completeness, and plan how to effectively communicate policy changes to end users.
- Call 4: Maintain data quality
Discuss the various techniques that can be used to maintain data quality and integrity: types of automated technologies available, data quality and integrity maintenance checks, and data governance.
Contributors
- ING Direct DV (Italy)
- Refrew Family and Children’s Services
- Genworth Financial Services
- Open Technology Solutions
- Medical Mutual of Ohio