Introduction
In today's data-driven world, understanding and optimizing your data journey is super important. This guide provides a detailed questionnaire to help data teams gather essential info from stakeholders. We'll cover everything from data handling to visualization, with a focus on the 4 Vs of data: Volume, Velocity, Variety, and Veracity.
The flow
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General Information
Let's start with some basic info about your team.
- Team Name:
- Contact Person:
- Role:
- Email:
- Team WIKI:
Data Handling
Understanding the types of data and their sources is key.
- What types of data do you handle? (e.g., structured, unstructured, semi-structured)
- What are the sources of your data? (e.g., databases, APIs, files, streaming data)
- What is the volume of data you handle? (e.g., daily, weekly, monthly)
The 4 Vs of Data
- Volume: How much data are we talking about?
- Velocity: How fast is the data coming in?
- Variety: What types of data do you have? (e.g., text, images, videos)
- Veracity: How accurate and reliable is your data?
Data Extraction
Let's dive into how you get your data.
- What mechanisms do you use for data extraction? (e.g., ETL, ELT, data scraping)
- Do you use data push or pull methods?
- What tools and technologies do you use for data extraction? (e.g., Apache NiFi, Talend, Airbyte)
Data Push vs Pull
- Push: Data is sent to the destination system automatically.
- Pull: Data is fetched from the source system by the destination system.
Data Transformation
Transforming data into a usable format is crucial.
- What processes do you follow for data transformation? (e.g., cleaning, normalization, aggregation)
- What tools and technologies do you use for data transformation? (e.g., Apache Spark, dbt, Pandas)
- How do you handle data quality and validation?
Data Formats
- What data formats do you commonly use? (e.g., CSV, JSON, Parquet)
Data Analysis
Analyzing data to extract insights is the fun part!
- What types of analysis do you perform on your data? (e.g., descriptive, predictive, prescriptive)
- What tools and technologies do you use for data analysis? (e.g., Jupyter, R, Apache Flink)
- How do you ensure the accuracy and reliability of your analysis?
Data Storage
Storing data securely and accessibly is essential.
- Where do you store your data? (e.g., on-premises, cloud, hybrid)
- What storage technologies do you use? (e.g., Hadoop, PostgreSQL, MongoDB)
- How do you manage data backups and recovery?
Hosting Options
- What hosting options do you use? (e.g., baremetal, in-house, Kubernetes, cloud, SaaS)
Data Governance
Managing data availability, usability, integrity, and security is a must.
- What policies and procedures do you have for data governance?
- How do you ensure data privacy and security?
- What tools and technologies do you use for data governance? (e.g., Apache Atlas, OpenMetadata)
Data Lineage
- How do you track data lineage? (e.g., tools, processes)
Data Sharing
Sharing data across teams or organizations is important for collaboration.
- How do you share data with other teams or stakeholders? (e.g., APIs, data lakes, data warehouses)
- What tools and technologies do you use for data sharing? (e.g., Apache Kafka, Delta Lake)
Data Visualization
Presenting data in a graphical format makes it easier to understand.
- What tools and technologies do you use for data visualization? (e.g., Grafana, Apache Superset, Metabase)
- How do you ensure your visualizations are effective and accurate?
- What types of visualizations do you commonly use? (e.g., dashboards, reports, charts)
Automation
Automating tasks can save a lot of time and effort.
- What parts of your data journey are automated?
- What tools and technologies do you use for automation? (e.g., Apache Airflow, Jenkins, Prefect)
- How do you handle monitoring and alerting for automated processes?
Data Pipelines
Data pipelines are essential for moving data from one place to another and transforming it along the way.
- What data pipelines do you currently use? (e.g., batch, real-time)
- What tools and technologies do you use for building and managing data pipelines? (e.g., Apache Airflow, Luigi, Prefect)
- How do you monitor and maintain your data pipelines?
Open-Source Tools
Open-source tools are great for flexibility and cost-effectiveness.
- Which open-source tools do you use at each stage of your data journey?
- What are the benefits and challenges of using these open-source tools?
- Are there any open-source tools you are considering for future use?
Additional Information
Let's wrap up with some final thoughts.
- What are the biggest challenges you face in your data journey?
- What improvements or changes would you like to see in your data processes?
- Any other comments or suggestions?
Conclusion
By using this comprehensive questionnaire, data teams can gain a deeper understanding of their data journey and identify areas for improvement. Effective communication and collaboration with stakeholders are key to optimizing data processes and achieving success.
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