How can you balance data accuracy and accessibility in reporting?

One of the major thing in defining data standards and sources is maintaining enterprise wide data dictionary which has all the details of data source systems as well as business definitions for data sources and data columns which helps everyone in the organizations to maintain the consistency with data definitions which eliminates the ambiguity

One of the principles I like that came out of the Data Mesh architecture is the definition of a data contract. It's a document that contains the structure, format, quality, semantics of the data, access policies, and the like. Having a document like this can help bridge business users and engineering teams in terms of communication as they both have a common understanding of the "should-be" state of the data. Furthermore, business users and engineering teams both contribute to this document. A good starting point in your documentation is to use the 5W1H Framework: 1. What 2. Why 3. Where 4. When 5. Who 6. How

By adding the necessary data quality checks in stages. Add the basic formatting and data check while loading the data in staging zones. Perform the business validation/checks before loading the data into final reporting layers. Create a feedback loop cycle to make the corrections in loaded data by allowing some users access to modify the data.

After setting data sources and standards, focus on a data governance plan that includes easy-to-use tech tools to keep data safe and accurate. Use automatic systems to check data quality, find mistakes, and fix them quickly. Put security measures like encryption in place to protect data. Make sure you're following important rules like GDPR for data handling. Regular checks and simple guides for users on how to use data tools and rules will help keep data clean and secure for everyone's use.

Balancing data accuracy and accessibility in reporting requires strategic data governance, continuous monitoring, and user training. Implement access controls, maintain comprehensive documentation, and use agile reporting tools for flexibility. Conduct regular data quality assessments, employ automated validation checks in ETL processes, and establish data lineage with auditing. This integrated approach ensures reliable data while providing timely access for effective decision-making.

Over time, as I worked with various tools and technologies, I've learned that there really is no one-size-fits-all solution. It's essential to look beyond the marketing claims and be critical in evaluating if a tool aligns with your specific needs and use cases. Always be aware that there are trade-offs. Consider factors like the adaptability and scalability of the tools to future business needs, ensuring they can evolve as your data requirements change. One thing that you can also do is to seek feedback from others given a certain tool, regarding its performance and useability given a certain scenario.

To complement the right tool selection, it's crucial to train your team on these technologies to maximize their capabilities. Regular updates and maintenance are necessary to keep tools running smoothly. It's also vital to have a support system in place for troubleshooting. Integrating these tools with your current systems should be seamless, promoting efficient data flow and minimizing bottlenecks. Consistent review and adaptation to emerging tech ensure your data practices remain current and effective.

Its really important to pick the right platform for databases, data engineering and data visualization. We need to make sure data and data schedules are in sync so that we are providing correct data to the users. In my experience, i have seen some of tables are not refreshed for weeks and some tables are refreshed every few hours which leads to data inconsistency. Its always good idea to pick the data eco systems which supports business need.

If possible, automate and standardize this process. By doing that, you get two main benefits: you save time and you win trust. As a data engineer, you should always try to automate as much as possible, which saves you a lot of time you'd spent on repetitive tasks otherwise. The second benefit you get is trust in your data. If you have a standardized process that stakeholders can challenge and understand, they'll be much more inclined to trust the data you're providing.

In addition to simplifying data presentation and tailoring reports to your audience, it's crucial to establish a feedback loop with your end-users. Collect and analyze user feedback to continually improve data accessibility and usability. This iterative process helps you fine-tune your reports, ensuring that they evolve to meet changing needs and expectations, ultimately enhancing the value of your data-driven insights.

Try to make your presentation as understandable as possible! There is absolutely no need to throw around all the fancy buzzwords. Keep the language simple, on point and if you need to use technical terms, explain them accordingly and verify that everyone is still on board.

After refining the data presentation, focus on streamlining delivery. The simpler, the better. Opt for interactive dashboards that allow users to delve into specifics as needed. Use clear visuals and avoid jargon to make reports user-friendly. Ensure accessibility on various devices and platforms for wider reach.

Accountability for data accuracy from source system will ultimately lay with the business owners of the data. As data from source system is typically an output of a business process. Data engineers will need to liaise with business process owners and source system owners to gain and understanding of the data and potential hotspots for data errors; for example data entry points. The data organization should encourage implementation of data control upstream to reduce errors in the data. This will reduce reporting delays due to time spent on data cleansing and correction in the reporting pipelines. Engineers can then focus on data transformation and publishing to support faster reporting

For Accuracy: -Data Quality Framework: Define accuracy, consistency, completeness, and timeliness requirements to guide data validation. -Data Profiling: Early data analysis identifies anomalies, outliers, and inconsistencies. -Validation Rules: Set rules for data type, range, format, and referential integrity checks. -Automated Validation: Use automated tools and scheduled checks for continuous data quality. For Accessibility: -Data Catalog: Create a centralized catalog with metadata and data definitions for comprehensive inventory. -User Training: Educate users on data quality standards, empowering them to validate and provide feedback. -Data Governance: Establish governance with data stewards overseeing quality and accessibility.