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What are the data governance metrics for a successful transformation?

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Data governance is the process of managing the quality, security, availability, and usability of data in an organization. It involves defining the roles, policies, standards, and procedures for data creation, access, sharing, and analysis. Data governance is essential for ensuring that data is accurate, consistent, reliable, and compliant with regulations and business objectives.

However, data governance is not a one-time project, but a continuous journey that requires constant monitoring, evaluation, and improvement. To measure the progress and impact of data governance, you need to define and track relevant metrics that align with your goals and strategies. In this article, we will discuss what are the data governance metrics for a successful transformation, and how to choose and use them effectively.

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Talha T.

Manager-Data Engineer- | Informatica | ETL | Cloudera |SFPC | KIKF | Power BI | Tableau| SSRS | SQL| MES | Life sciences

1 Data quality metrics

Data quality metrics are essential indicators of how well your data meets the expectations and requirements of your users and stakeholders. Examples of these metrics include accuracy, completeness, consistency, timeliness, and validity. To measure data quality, you need to establish criteria and thresholds for each metric, and compare them with the actual data values. Data profiling, data cleansing, data validation, and data auditing are all tools and techniques that can be used to collect and analyze data quality metrics in order to identify and resolve any errors, inconsistencies, duplicates, or missing values.

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Talha T.

Manager-Data Engineer- | Informatica | ETL | Cloudera |SFPC | KIKF | Power BI | Tableau| SSRS | SQL| MES | Life sciences
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In my experience, data quality played a big role in developing my data pipelines. the initial step before even starting to develop the pipelines, i have to take care of the data quality metrics and have to make sure that data that I'm using must not have any errors, inconsistencies, blanks, string NULLs and any other discrepancy in data. First I have to resolve these inconsistencies and then start developing my data pipelines. Hence, my user always get a good quality of data at their end. :)

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2 Data security metrics

Data security metrics are the measures of how well your data is safeguarded from unauthorized access, modification, or disclosure. They can help you evaluate and reduce the risks and threats to your data assets, like cyberattacks, breaches, or leaks. For instance, availability is the percentage of time that the data is accessible and usable by authorized users and applications; confidentiality is the degree to which the data is restricted and encrypted according to the sensitivity and classification levels; integrity is the extent to which the data is precise and complete after being transmitted or stored; compliance is the adherence of the data to applicable laws, regulations, and policies. To measure data security, you must define and monitor metrics that reflect your data protection objectives and standards. You can use tools and methods such as data encryption, data masking, data backup, data recovery, and data breach detection to collect and report on data security metrics.

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Talha T.

Manager-Data Engineer- | Informatica | ETL | Cloudera |SFPC | KIKF | Power BI | Tableau| SSRS | SQL| MES | Life sciences
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Data encryption is one of the most important aspect in order to secure your data. An individual must not have access to any other outbound area or any particular columns that is not needed for that individual. for instance, if Ali is a developer, and has access to a table and but there is mother_name column and its not needed to Ali so, we should encrypt that column for developer.

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3 Data value metrics

Data value metrics are essential indicators of how much your data contributes to business outcomes and performance. They can help you justify and optimize data investments, such as data acquisition, storage, processing, or analysis. Examples of data value metrics include revenue, cost, efficiency, effectiveness, and innovation. To measure data value, you need to link and quantify the metrics that reflect your business goals and strategies. To do so, you can use tools and approaches such as data monetization, data ROI, data analytics, and data storytelling to collect and communicate data value metrics.

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Talha T.

Manager-Data Engineer- | Informatica | ETL | Cloudera |SFPC | KIKF | Power BI | Tableau| SSRS | SQL| MES | Life sciences
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Its important that you must justify the business that your data contributes and answer all business requirements. It’s essential to effectively communicate business value to stakeholders, with metrics such as cost savings, revenue growth, and cost avoidance. Tracking the progress of an ongoing data governance initiative, documenting the business benefits of data governance, and maintaining stakeholder buy-in by demonstrating program successes are some of the benefits of tracking business value.

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4 Data culture metrics

Data culture metrics are the measures of how well your organization embraces and leverages data as a strategic asset and a competitive advantage. To measure data culture, you need to capture and analyze the metrics that reflect your data maturity and readiness. Awareness, engagement, literacy, alignment, and satisfaction are all components of data culture metrics which can help foster and sustain a data-driven culture that supports and rewards data usage and collaboration. You can use tools and methods such as data surveys, interviews, assessments, and feedback to collect and improve these metrics. With the right measures in place, you can ensure your organization is equipped to leverage data for competitive advantage.

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5 Data governance metrics best practices

Choosing the right data governance metrics is essential for measuring and improving your data governance capabilities and outcomes. To ensure that your data governance metrics are relevant, reliable, and actionable, it’s important to follow some best practices. Align your data governance metrics with your business goals and strategies, and communicate them to stakeholders clearly and consistently. Select metrics based on the SMART criteria: specific, measurable, achievable, relevant, and time-bound. Balance quantitative and qualitative, lagging and leading, input and output, as well as process and outcome metrics. Automate collection and reporting of your data governance metrics using tools and platforms. Additionally, review and update your data governance metrics regularly based on performance and feedback. By doing so, you can ensure that your data governance transformation is successful and sustainable.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the data governance metrics for a successful transformation?

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1 Data quality metrics

2 Data security metrics

3 Data value metrics

4 Data culture metrics

5 Data governance metrics best practices

6 Here’s what else to consider

Data Governance

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What are the data governance metrics for a successful transformation?

1

2

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4

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6

1 Data quality metrics

2 Data security metrics

3 Data value metrics

4 Data culture metrics

5 Data governance metrics best practices

6 Here’s what else to consider

Data Governance

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