- Data Talent Pulse
- Data Governance: 5 Best Practices
Data Governance: 5 Best Practices
80% of a data scientist’s valuable time is spent simply finding, cleansing, and organizing data, leaving only 20% to actually perform analysis.
Organizations are faced with vast amounts of data that must be managed, protected, and leveraged to drive business success.
Data governance provides a framework for ensuring data quality, integrity, privacy, and compliance. It establishes policies, procedures, and accountability mechanisms to guide the entire data lifecycle.
Here are 5 best practices that can help unlock the full potential of an organizations data:
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Establishing data classification policies
Develop a clear and consistent set of criteria for classifying data, considering factors such as confidentiality, integrity, availability, regulatory requirements, and business impact. Create a tiered classification system to effectively prioritize and manage data based on its value and risk level. Data is typically classified into three categories based on its sensitivity:
Confidential data: Information which if leaked, could cause severe reputational and financial harm to your organization.
Internal data: Information that would cause moderate harm to the company if shared.
Public data: Any information or data that is intended for your corporate website.
The next step would be to create a series of training modules aimed at equipping current employees with the skills to proficiently classify and handle data within each respective data classification category.
Managing data in cloud and hybrid environments
If adopting a hybrid cloud strategy to store data, like two-thirds of businesses today, companies should strongly consider storing their data in a consistent manner within each cloud, leveraging the same technologies to reduce the sprawl, maintenance and expertise required to operate those technologies.
For example, use a relational database that all the cloud vendors provide—for data updated and changed frequently, it should live in the scope of the local cloud provider to reduce latency and network costs, because traffic within the cloud provider is generally free. If you were to write to a database outside the cloud provider, it’d cost more and be considerably slower.
A data index is essential in a hybrid environment, providing organizations with real-time visibility into the location of their data assets.
Quality checks of multi-structured data
After preparing a set of criteria for quality testing of data, implement 3 levels of testing:
1: Fact checking of data values
Quickly validating the accuracy of data by comparing it with known truth. Ex. age columns do not contain negative value, name fields do not contain numbers, etc.
To test your dataset quickly, you can generate a data profile. It's a simple compare and label process where you check your dataset against defined validations and known/correct values. You classify the data as valid or non-valid based on the comparison. While you can do it manually, using an automated tool is more efficient. It runs a quick profile test, showing you how well your data matches the validation rules you've set. This helps you assess your data quality and identify any discrepancies without much hassle.
2: Holistic dataset analysis
Holistic analysis requires that data is tested both vertically and horizontally.
Vertical testing involves calculating the statistical distribution of each data attribute and ensuring that all values conform to the distribution. By doing so, you can consistently verify that new incoming data aligns with the existing data in your dataset. This ongoing validation helps maintain data integrity and ensures the consistency of the dataset over time.
Horizontal testing involves assessing the uniqueness of records in your dataset, by examining each row individually and verifying that every record represents a distinct and identifiable entity, without any duplicates. This type of testing can be more intricate, particularly when there is no unique key available to determine record uniqueness.
3: Historic dataset analysis
The same as holistic testing but, It considers changes in data over time when validating data values, resulting in a more comprehensive assessment of data quality.
Note: Often the biggest challenge to implementing data governance strategies is getting stakeholders on board and getting the greenlight.
Encourage frequent communication between stakeholders
Effective communication is critical whether you're initiating a new data governance program or have been implementing one for years. Regular and consistent communication plays a vital role in demonstrating the impact of the strategy, highlighting successes, and adapting after setbacks.
A key aspect of communication is designating an executive team member, such as the Chief Information Officer (CIO) or Chief Data Officer (CDO), as the communication leader for the data governance program.
These leaders serve as the central point of contact for the organization's governance practices, providing updates on the current status. Team leaders and data owners can regularly report progress to the executive team member, who then relays important updates to the broader leadership team and the entire organization.
Implement quality control practices
Data quality testing should not be treated as a one-time event. Once you have established control over the quality of your dataset, it is crucial to implement a long-term plan for maintaining that quality. This involves undertaking various activities at regular intervals to ensure ongoing data quality. Some key activities to consider include:
Quality control for data integration
Incorporate data quality checks during data entry or integration. This ensures that new data introduced into the system is both accurate and unique, without duplicating any entities already present in the master record
Profiling data at frequent intervals
Perform regular quick profile tests on your dataset to promptly identify and address any errors. It is advisable to save the results of these profiles over time, as they provide valuable insights into when and how your data quality may have deteriorated.
Fixing root cause of errors
Pay close attention to the types of errors commonly reported in your data profiles. Are you frequently alerted about incorrect date formats or missing values in required fields? If so, it may indicate the need to address data entry form validations. By identifying and addressing these patterns, you can eliminate data quality errors at their root, ensuring a more accurate and reliable dataset.
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