Data Quality – How to make the journey from Aspiration to Realization

Even though the competence of Data Quality has been around for over a decade now – why does it continue to stay nascent and aspirational rather than a reality with tangible value for a lot of organizations in spite of having a fair spectrum of maturity as far as being data-driven is concerned.

In my session – I want to discuss with practical examples and scenarios – the common pitfalls that prevent Data Quality reach its full potential for an organization.

Key Takeaways

• Maturity path for Data Quality
• Common pitfalls of Data Quality implementation
• Strategy and focus areas for Data Quality
• Practical examples, business scenarios
• Data Quality Competence – skill set

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Bio

Ronak Jani | Domain Data Architect -Operations | Philip Morris International | Switzerland

Ronak Jani is currently based in Lausanne, Switzerland working as Domain Data Architect – Operations for Philip Morris International. She has worked across multiple data competence – Data Architecture , Data Management, Data Stewardship and Data Quality for companies like Shell, Target and now Philip Morris. She is passionate about enabling the organizations to deliver reliable and sustainable BI and analytics solutions. With customer centricity as a key value and approach, she is a strong advocate of informed decision making based on data insights.