What are some best practices for data documentation and metadata management?

Create an enterprise level governance body who standardize and governs the entity and attribute naming, it's meaning, usage and associated business metadata across the enterprise. It will help to make naming to be self descriptive. For example all entity and attributes should start with class name (which identifies what the data is about), followed by prime words and modifiers which provides context to data and attribute type (for attributes) that describes the type of data element (for example amount, percentage, code, text, number, flag , etc). The list of these names to be used for entity and attribute naming should be standardized across the enterprise across all applications.

Identifying reliable data sources is essential, but it's equally important to be aware of potential biases and address them properly. Even so-called 'reliable' data sources can introduce bias into our models if not handled carefully. Regarding data storage and security, while they are important, they shouldn't hinder our ability to share and collaborate on data efficiently. I recommend using tools and technologies that facilitate secure, real-time data collaboration and versioning. These solutions will promote transparency and reproducibility in projects, making our work easier and more effective.

Today, the major challenge is identifying a reliable dataset from the sea of information. Metadata helps us ascertain the quality of data to an extent, but often critical details are missing. Most of the time, the data sources/processes are not described in detail. In general, the data are collected from different people using different methods and instruments; hence, have different uncertainty and bias. As the process by which data is obtained is missing, we cannot pinpoint the source of the uncertainty and biases in the dataset or the predictive model. Therefore, it should be a standard practice that the data documentation/metadata should contain sufficient information about how the data is collected or references to the methods used.

In my experience, a well-maintained data dictionary has proven to be an invaluable asset in any data-driven project. It not only provides a comprehensive overview of the data's structure and attributes but also facilitates efficient communication and collaboration among team members. When working with complex datasets or collaborating across different teams, a data dictionary becomes a crucial reference point, ensuring that everyone is on the same page regarding data definitions, formats, and sources. Furthermore, it aids in the identification and resolution of data issues, which is crucial for maintaining data integrity and reliability.

Some philosophies of creating a data dictionary should be: 1. Discoverability: A new person should be able to navigate and find what they are looking for without asking others. 2. Governance of Maintenance to ensure one always finds updated information. 3. How to use: links to standard parameterized queries for ease of getting started 4. Nuances: When to use a column from Table A vs. Table B, What can go wrong while using this data etc.

While utilizing metadata standards is crucial, it's equally important to establish clear and comprehensive documentation guidelines to ensure the accurate and complete representation of data in the metadata. Developing robust documentation practices enables data producers and curators to capture essential information effectively, such as data provenance, versioning history, and data usage restrictions. Furthermore, involving data users and stakeholders in the design and implementation of metadata standards can foster a more inclusive and collaborative approach to data management.

By leveraging tools like Knitr or Jupyter notebooks, we can generate reproducible reports that document every step of data analysis and modeling. These reports not only provide clear insights into the data processing but also serve as valuable references for future analyses and audits. Additionally, using Pydantic or Marshmallow to create schemas ensures that the data and metadata adhere to defined structures and are consistent throughout the entire pipeline. This helps avoid errors and inconsistencies in data representations and improves data quality overall.

Transparency has always been a crucial part in data science projects, particularly when dealing with data that has real-world implications. Sharing documentation and metadata not only enhances transparency but also increases credibility and trust among stakeholders and the wider community. It shows a commitment to openness and integrity, which is crucial in data-driven research and applications.

Tribal knowledge: When a data organization grows and the volume of data keeps growing exponentially, there are a few who know specific nuances about certain types of data. If one doesn't know what they should know about a data, and who knows what they should know, people commit the same mistakes and learn. Q: How to minimize impact of tribal knowledge in large Data Orgs?

Literally best practice for data documentation irrespective of whether it is Data warehouse ,data lake or data fabric is 1. Logical data model - ensure domain specific data model (which stores data source , column names and relationships) 2. Source to target mapping document (traceability matrix too) 3. Metadata extraction from data model , data lake or data fabric and store it in centralised database for quick extraction and find across organisation so you can reuse existing data.

Proper documentation could help reduce impact of tribal knowledge. Although, as an organisation grows it is observed that there will be overlap of work across teams, where 2 or more team starts documenting individually as part of discovery process. This ends up causing duplicate efforts and confusion. To have one source of truth for knowledge of a particular domain, with a regular cadence to track and monitor these, could help reduce redundancy. Have a certifying layer built on top of this source and having owner tagged to it might help reduce the same issues in future.