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How do you navigate unexpected data challenges in your projects?

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Claudia Alcelay
PMI Team Member
Learning & Innovation Research Manager| Project Management Institute (PMI) Spain
Data quality and quantity is particularly important as we think about leveraging AI on projects. Considerations include the diversity and comprehensiveness of the data that is available to us. 
 
Have you ever encountered unexpected challenges or pitfalls while using data in your projects? How did you navigate the situation and find a resolution? 
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Dr Anand Lokhande Ph.D. Co-founder| Imagilytica Leadership Consulting LLP Bangalore, Karnataka, India
Data quality is key in any context of decision making, Seemingly structured data may not be so and needs to be appropriately managed to be able to use for further analysis. Bringing the data engineers and data providers (functional owners) is key to resolving this challenge and ensuring that the data represents the correct scenarios for which it was generated in the first place. The rest is then simple technology taking over. Ensuring stakeholders understand the importance of this activity is critical and perhaps a new found responsibility of project managers in today's context.
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Kottayam Murugan Portfolio Management| Blue Shield of California OAKLAND, United States
Data quality has been a regular challenge in many of my projects. Something as mundane as having an obsolete phone number or old mailing address for someone who has purchased health insurance has caused regulatory headaches when they log complaints about missing communications. We integrate with data brokers and third-party data aggregators to minimize such data obsolescence.
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Saadia Ahmad Dubai, Du, United Arab Emirates
It needs continuous monitoring and transparency right from the beginning with all stakeholders.
Locking the scope and going for delivery in Agile model so as to accommodate growing data and ensure incremental delivery.
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Patrick Henninger Senior Director, Divisional Pricing and Customer Master| Labcorp Indianapolis, In, United States
Through the various PMOs that I’ve inherited, a consistent challenge is the variety of types and formats accrued across generations of processes and documentation standards or lack thereof. That being said, it could strangely be an advantage in LLMs, assuming we can effectively de-structure (convert to narrative) the structured subset of that information.
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Jia Wu None Elgin, Ok, United States
As a PM we have to learn how to manage all unanticipated data complications in projects the may arise by developing strong problem-solving abilities, adaptability, and good communication skills. When issues pop up with any data interface, it is essential to first figure out the nature and scope of the problem. This entails collaborating with the project team, stakeholders, and data subject matter experts to be able to create a solid game plan to execute the solution to resolve any complications.
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Anonymous
I refer back to our documentation and frameworks when facing data challenges.
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Rajesh Manchikanti Project Manager| Tiger Hyderabad, Telegana, India
Recently we heard many terminologies like data governance, data quality, data products, data market these days, as we see decisions are taken based on data or reporting details. To consider the decision to be accurate we need to have or justify the quality of data. As we knew there are multiple products or tools available in market. Even my Organization can provide the information to take decision which can provide data quality.
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LIGIA-INEZ ZAMBRANO Project Manager| OmniPoint Consultants Orlando, United States
Some ways to navigate data challenges:
Locate the data needed (old school way).
Practice database hygiene, cleaning up bad data, removing or updating information.
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Kaushiki Roy Senior Project Manager| NZ Government Wellington, New Zealand
A Privacy Impact Assessment is vital in the public sector.
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NICOLAS GOMEZ PM Specialist| Occidental de Colombia Bogota, Colombia
In the production control of our biggest fields, we loose communication to capture the data or have to calibrate too often the equipment, so data is not precise, so we rely on tendencies.
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