Why Build Data Products UX

By creating a user-friendly interface for data, data products empower a wider range of people within an organization to leverage its potential. This can lead to more collaboration and a culture of data-driven decision making across all levels.

Who are you guys?

  • Jane Anderson

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  • James Doe

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Data Product Knobs

Enterprises are most successful when they treat data like a product. It means companies can use data in multiple use cases. However unlike traditional product data need to be changed according to use-case. Some use case need detail data, some use case need aggegate. Some use case focus on sensitive data, some use case focus on sharing and want to filer sensitive data. Another set of use cases may add more context for new use cases.
Managing data thru knobs enable you to use data into different context. In traditional method data is copied/shared and then pipeline are build for ingestion-transformation for each use case. Thru Data knobs we build data as product but allows you to filter/transform/enhance/secure/annonymize thru knobs.
Data products are built with knobs

  • Lever 1: Transparency and Consent

    The foundation of data privacy is user trust. By being transparent about what data we collect, why we collect it, and how it's used, we empower users to make informed choices. Consent mechanisms, like clear opt-in options, give users control over their data. This not only fosters trust but also ensures compliance with evolving privacy regulations

  • Lever 2: Robust Security

    Data breaches are a constant threat. Strong security measures are essential to safeguard sensitive information. Encryption scrambles data while in transit and at rest, making it unreadable to unauthorized parties. Access controls restrict who can view or modify data, minimizing the risk of human error or malicious attacks. Regular security audits identify and address vulnerabilities before they can be exploited.

  • Level 3 : Dara Minimization

    The less data we collect, the less there is to protect. A core principle of data privacy is minimization – collecting only the data essential for a specific purpose. Data governance frameworks establish clear guidelines for data collection, storage, usage, and disposal. This ensures data is used responsibly and minimizes the risk of misuse.

  • Level 4 : Data Quality Management

    Data quality directly impacts security and privacy. Inaccurate or incomplete data can lead to flawed decisions and expose vulnerabilities. Data cleaning techniques identify and rectify errors, while data validation checks ensure data accuracy from the outset. Data quality monitoring tools provide ongoing insights into data health, allowing for proactive maintenance.

  • Data Security thru Knobs

    The less data we collect, the less there is to protect. A core principle of data privacy is minimization – collecting only the data essential for a specific purpose. Data governance frameworks establish clear guidelines for data collection, storage, usage, and disposal. This ensures data is used responsibly and minimizes the risk of misuse.

Create value from data

Empower business users

Synergy Effect

These levers are not independent. Strong data governance practices, for instance, naturally lead to better data minimization and user consent management. By pulling these levers in concert, we create a robust data ecosystem that prioritizes privacy, security, and quality. This not only protects user trust but also empowers organizations to make better decisions based on reliable data.
In conclusion, by strategically manipulating the levers of transparency, security, data minimization, and quality, we can unlock the true potential of data while safeguarding individual privacy and building a more secure data-driven future.