Last updated on
2024年8月23日
How do you engage and collaborate with data users and providers to ensure data relevance and usability?
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The first step is to identify who are your data users and providers, and what are their roles, needs, and expectations. Data users are the people who will consume, analyze, or act on the data, such as managers, researchers, or customers. Data providers are the people who will supply, collect, or generate the data, such as employees, partners, or vendors. You need to understand their perspectives, motivations, and challenges, and how they relate to your data goals and objectives. You can use methods such as surveys, interviews, focus groups, or workshops to gather their input and feedback.
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Identifying data users and providers is akin to recognizing the diverse musicians in your ensemble. Understanding their roles and needs is like grasping the unique instruments they play, each contributing to the overall harmony. Delving into their perspectives is akin to appreciating the individual nuances that enrich the musical composition. By conducting surveys, interviews, and workshops, you are essentially orchestrating a dialogue, allowing each musician to share their insights and ensuring that the symphony of data aligns with the collective vision. In this collaborative musical endeavor, the conductor's role—akin to the data analyst—is to bring together the varied voices into a harmonious and meaningful arrangement.
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And what the users will get back out of inputting good data. Eg if they value a particular report to run their service, if we improve the data quality of X you'll then see a improvement in Y. Make it personal to their own objectives and requirements.
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Teams should support features and products are focused on helping define which information should be tracked, and then they translate that data into easy-to-understand core data sets. A core data set represents the most granular breakdown of the transactions and entities you are tracking from the application side. From there, some teams have different levels of denormalization they might want to implement. For example, they might want to denormalize if they remove any form of nested columns to avoid analysts having to do so. You can use cor data sets to identify your data users and providers
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1. Facilitate Open Communication: Regularly engage data users and providers through meetings, surveys, and workshops to understand their needs, challenges, and expectations, ensuring that data remains relevant and actionable. 2. Align on Data Goals: Collaboratively set data goals with users and providers, clarifying how the data will be used and what criteria define its usability, to ensure mutual understanding and relevance. 3. Continuous Feedback Loop: Establish ongoing feedback mechanisms to adapt data collection and processing practices based on evolving user needs and provider capabilities.
The next step is to define your data requirements and specifications, which are the criteria and standards that your data must meet to be valid and useful. Data requirements and specifications should be aligned with your data users' and providers' needs and expectations, as well as your data goals and objectives. You should specify the data sources, formats, types, attributes, values, ranges, rules, validations, and quality measures that your data must follow. You should also document your data definitions, metadata, and glossary to ensure consistency and clarity. You can use tools such as data dictionaries, data models, or data maps to define your data requirements and specifications.
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When engaging with data users and providers it’s essential to define your requirements and specifications. What kind of criteria’s your data should meet and what makes it qualified? Also, be clear with your data users and providers about your needs and expectations and foster transparency for anticipated outcome. Regularly refine your specifications through collaborative feedback sessions. And don’t forget to document your data definitions, metadata, and glossary for clarity.
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Just as an architect meticulously plans every detail, specifying materials, dimensions, and design elements, a data analyst outlines the parameters that ensure a robust and reliable data foundation. Aligning these specifications with the needs of data users and providers is akin to tailoring the architectural plans to accommodate the preferences and requirements of future occupants.Utilizing tools like data dictionaries or models is like employing sophisticated design software, allowing for precision and adaptability in crafting a resilient and purposeful data framework.
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Audits of how systems are configured against data dictionaries is a must. Sometimes incorrect configuration can cause more work trying to go back and make corrections. It's worth the effort and time in the testing phase before implementing a change to the system. Don't let a change be pushed through until you're sure it's not going to impact the data.
The third step is to implement your data validation methods and tools, which are the techniques and systems that you use to check and verify your data against your data requirements and specifications. Data validation methods and tools can be classified into two categories: manual and automated. Manual data validation involves human intervention and inspection, such as reviewing, sampling, or testing the data. Automated data validation involves software or hardware intervention and execution, such as applying rules, formulas, or scripts to the data. You should choose the appropriate data validation methods and tools based on your data characteristics, complexity, volume, frequency, and risk.
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Manual validation is like stationing vigilant guards—analysts who inspect and scrutinize the data for irregularities, providing a human touch to the oversight. Automated validation is akin to installing advanced security measures—sophisticated algorithms and scripts that tirelessly patrol and ensure compliance with established rules. Choosing between manual and automated validation is like deciding on the optimal blend of human intuition and technological precision, aligning with the unique characteristics and risks associated with your data "treasure." In this way, treating data validation as a security protocol ensures the integrity and reliability of your valuable data assets.
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Make connections - Data Analysts need to be in sync with the teams that train how to use a system, the guides provided, the teams that input the data, system suppliers and local IT configuration teams. We have a role to play in stating clearly what is needed from a data perspective.
The fourth step is to monitor and report your data validation results and issues, which are the outcomes and problems that arise from your data validation process. Data validation results and issues should be tracked, measured, and communicated to your data users and providers, as well as other relevant stakeholders. You should use metrics and indicators to evaluate your data quality, accuracy, and completeness, such as error rates, completeness rates, or validity rates. You should also use formats and channels to report your data validation results and issues, such as dashboards, tables, charts, or emails. You should provide clear and timely information and feedback to your data users and providers, and address any questions or concerns they may have.
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Think of monitoring and reporting data validation results as captaining a ship through unpredictable waters. Just as a captain steers the ship and reports on its condition to the crew, a data analyst navigates the data landscape and communicates outcomes to stakeholders. Using metrics and indicators is akin to relying on navigation tools and instruments, providing a clear picture of the data's quality and accuracy. Reporting through dashboards, tables, and charts is like displaying a weather map to the crew, ensuring a transparent overview of the data journey. Addressing questions and concerns is akin to the captain explaining navigation decisions, fostering trust and understanding among the crew.
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A Data Quality Policy and strategy is needed. How will you progress in your Data Quality journey and what is the minimum expected from all staff?
The fifth step is to resolve and prevent your data validation issues, which are the actions and measures that you take to fix and avoid your data validation problems. Data validation issues can be caused by various factors, such as human errors, system errors, or external changes. You should identify the root causes of your data validation issues, and implement corrective and preventive actions to resolve and prevent them. You should also involve your data users and providers in the resolution and prevention process, and seek their input and support. You should document your data validation issues and actions, and update your data requirements and specifications accordingly.
The sixth step is to review and improve your data validation process, which are the activities and initiatives that you undertake to evaluate and enhance your data validation performance. Data validation process should be reviewed and improved periodically, based on your data validation results and issues, as well as your data users' and providers' feedback and satisfaction. You should use methods and tools to review and improve your data validation process, such as audits, assessments, or surveys. You should also use frameworks and models to guide your data validation process improvement, such as PDCA (Plan-Do-Check-Act) or DMAIC (Define-Measure-Analyze-Improve-Control). You should involve your data users and providers in the review and improvement process, and solicit their suggestions and recommendations.
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A robust data validation process is essential for maintaining data integrity. Key components of an effective review and improvement strategy include: Comprehensive Assessment: Conduct regular and in-depth evaluations of data validation procedures to identify potential shortcomings. Data Quality Profiling: Utilize advanced analytics to assess data accuracy, completeness, consistency, and timeliness. Root Cause Analysis: Investigate validation failures systematically to uncover underlying issues and implement corrective actions. Stakeholder Collaboration: Foster open communication with data owners and users to gather feedback and align validation efforts with business objectives.
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Never forget the importance of metadata. Timestamps are just one example. I've noticed throughout my career that metadata characteristics are often completely overlooked within any given data set. And some of those metadata characteristics can easily be utilized to provide the stakeholders with much needed analytical insight.
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