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What methods can you use to ensure the data you collect is useful for your organization?

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Data is a valuable asset for any organization, but only if it is collected, stored, and analyzed properly. Poor data quality, inconsistency, and irrelevance can lead to wasted resources, inaccurate insights, and missed opportunities. How can you ensure the data you collect is useful for your organization? Here are some methods you can apply to improve your data management skills.

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Sujay Dutta

Helping Enterprises Accelerate Business Outcomes with Data & AI | Author | Podcaster | Investor
Sagar Navroop

Data Architect | AI | MLOps | AWS | SIEM | Observability | Technologist
Jacob Attah

Monitoring, Evaluation and Research | Analysis and Evidence | Program Adapting and Learning | Accountability to…

1 Define your data goals

Before you start collecting data, you need to have a clear idea of what you want to achieve with it. What are the questions you want to answer, the problems you want to solve, or the decisions you want to support? How will you measure your success and impact? By defining your data goals, you can align your data collection strategy with your business objectives and avoid collecting unnecessary or irrelevant data.

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Sujay Dutta

Helping Enterprises Accelerate Business Outcomes with Data & AI | Author | Podcaster | Investor
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In my experience, start with data consumption needs to meet business goals and then work backwards to define your data goals. It is critical that the data goals are aligned to business goals for the organization, i.e. the business goals of the data consumers - who would leverage the data to make data-driven decisions. So, it would be important that you engage with the business functions (like marketing, sales, manufacturing, fulfillment, sourcing) stakeholders to understand their business needs, and how data is going to support their business needs. Based upon those inputs, develop a prioritized list of data goals, in collaboration with the business stakeholders - this would ensure that you have the buy-in from the business stakeholders.

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Odunayo Rotimi

AI, LLM Engineer | Data Manager | Business Intelligence/CVM Manager @ Smartcash PSB | Data Engineering
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And it's necessary that the activities above is carried out by the data team in relation with the various stakeholders within the organization. Failure to carry them along with proper orientation leads to frustration or even disuse of the data. This is often a wate of investment of time and other organization's resources.

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2 Choose the right data sources

Depending on your data goals, you may need to collect data from different sources, such as internal systems, external platforms, surveys, interviews, or sensors. You need to evaluate the quality, reliability, and availability of each source, and ensure they are compatible with your data collection tools and methods. You also need to consider the ethical and legal implications of collecting data from certain sources, and obtain the necessary permissions and consents.

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Sagar Navroop

Data Architect | AI | MLOps | AWS | SIEM | Observability | Technologist
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1. Develop a data strategy aligned with business objectives 2. Implement data quality controls 3. Document and maintain metadata to provide context, lineage, and understanding of data elements 4. Put a data governance framework. Define roles, responsibilities, and processes to ensure data accountability 5. Implement data lifecycle management practices. Update, expire stale data 6. Encourage collaboration between data professionals and end-users 7. Classify data, and implement measures to safeguard sensitive information 8. Adopt effective data integration strategies 9. Plan for scalable and flexible data infrastructure to accommodate data volume growth 10. Implement Continuous Monitoring and Improvement Strategies

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3 Implement data standards

Data standards are rules and guidelines that ensure the consistency, accuracy, and completeness of your data. They include data definitions, formats, naming conventions, validations, and transformations. By implementing data standards, you can reduce errors, anomalies, and duplicates in your data, and make it easier to integrate, share, and analyze. You can also document your data standards and metadata to facilitate data understanding and governance.

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4 Use data quality tools

Data quality tools are software applications that help you monitor, assess, and improve the quality of your data. They can perform various functions, such as data profiling, cleansing, matching, enrichment, and auditing. By using data quality tools, you can automate some of the data quality processes, identify and fix data issues, and enhance the value and usability of your data.

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5 Apply data analysis techniques

Data analysis techniques are methods that help you extract insights, patterns, and trends from your data. They can range from simple descriptive statistics and visualizations to advanced predictive modeling and machine learning. By applying data analysis techniques, you can transform your data into actionable information, and generate reports, dashboards, and recommendations that support your data goals.

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Jacob Attah

Monitoring, Evaluation and Research | Analysis and Evidence | Program Adapting and Learning | Accountability to Affected Population
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Analysis of data provides usable information. Applying the right framework for analysis ensures that the data collected provides insight and patterns as required. It is useful to define frameworks for analysis in relation to the types of data that are collected, the information needs, and the target audience. This will provide a roadmap to ensuring that data is translated to people and is then used for improving systems based on evidence.

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6 Review and update your data

Data is not static, but dynamic and evolving. As your data goals, sources, standards, tools, and techniques may change over time, you need to review and update your data periodically. You need to check if your data is still relevant, accurate, and complete, and if it meets your current and future needs. You also need to archive or delete any outdated or obsolete data, and ensure your data is backed up and secured.

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7 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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Siddharth Rajagopal

Chief Architect at Informatica | Executive MBA at Quantic | Author |
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Start thinking and evaluating your Data as a Product, identifying the right Data Producers and Data Consumer Demand. Having standardized User experiences and Trust on the Data behind the Products are essential.

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What methods can you use to ensure the data you collect is useful for your organization?

1

2

3

4

5

6

7

1 Define your data goals

2 Choose the right data sources

3 Implement data standards

4 Use data quality tools

5 Apply data analysis techniques

6 Review and update your data

7 Here’s what else to consider

Data Management

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What methods can you use to ensure the data you collect is useful for your organization?

1

2

3

4

5

6

7

1 Define your data goals

2 Choose the right data sources

3 Implement data standards

4 Use data quality tools

5 Apply data analysis techniques

6 Review and update your data

7 Here’s what else to consider

Data Management

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