Como você prioriza a qualidade dos dados no desenvolvimento do seu produto?

Prioritizing data quality in product development is akin to a chef prepping for a grand meal. Initially, gather accurate and relevant data just as a chef selects fresh, quality ingredients. Identify the true source of your data to ensure authenticity, akin to knowing where your ingredients are sourced. Establish a Single Source of Truth (SSoT) - your master recipe book, ensuring everyone refers to the same, updated information, avoiding culinary mishaps. Regular upkeep of data is like ensuring your ingredients remain fresh. Lastly, present your data on a dashboard, laying out a visual feast, making it easily digestible for all, ensuring a successful, well-informed product development journey.

Integrating data quality checks into ETL pipelines to maintain data integrity and support analytics is always a good idea. In order to ensure, comprehensive quality assurance, the six dimensions model can be followed which includes, Consistency – Ensure that data values do not clash with other values throughout your datasets. Accuracy – Strive for error-free data. Validity – Maintain data adherence to specific formats. Completeness – Avoid any data gaps or missing information. Timeliness – Keep your data current. Uniqueness – Prevent any instances of data duplication. These measures are crucial, as poor data quality can lead to significant financial losses and inaccurate decision-making.

As they say: "Garbage in, Garbage out". It's imperative that data quality checks are implemented at every stage of the product development process, with a major focus on incoming data. Qualitative data checks: - Are you getting requirements or feedback from a representative sample of customers? (Remember that the early majority or power users, might be the most vocal, but their needs are not the same as the "silent majority" Quantitative data checks - Source data from a high quality source - Run all incoming data through some basic validation rules - Run a larger range of quality checks on a random sample - Check your outputs to ensure all errors, gaps, are detected before publishing or using in a model

I remember the Total Quality Management (TQM) framework from grad school which said: A company's processes and procedures should be a direct reflection of the organization's vision, mission, and long-term plan. I find this to be very true for not just manufacturing, but any Analytics or AI/ML product as well. For data products in particular, misleading insights, estimates or predictions can erode trust very quickly and cause customer churn. Therefore, a product roadmap must have items focused on improving data quality (Availability, completeness or coverage and accuracy). These are seen as "must-haves" by customers and not something anyone should compromise on.

1. Continuous Alignment: Regularly align data quality efforts with the evolving product strategy. 2. Vision and Value Alignment: Ensure data quality aligns with the product vision and delivers value to users. 3. Integration in Development Phases: Integrate data quality considerations from discovery to delivery in product development. 4. Shared Responsibility: Foster a shared responsibility for data quality among the entire product team, stakeholders, and users.

1. Set Clear Expectations: Clearly communicate data quality standards and criteria to the product team, stakeholders, and users. 2. Highlight Importance: Emphasize why data quality is critical for the product, outlining its impact on insights, customer satisfaction, and resource efficiency. 3. Continuous Improvement: Establish a feedback loop for ongoing improvement, actively seeking input on data quality from the team, stakeholders, and users. 4. Integrate Communication: Weave data quality discussions into product-related communication, ensuring alignment with the overall product development process.