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? Saving Changes...
Great discussion item! I would honestly say "it depends". It depends on the nature of the project, the objectives, goals and anticipated outcomes - including the timelines. In some cases, for the essence of timeliness, the best course of action when facing challenging or bad data is to simply 'draw a line in the sand'. Accept that the old data will present too big of a challenge to bother 'cleaning' up in the short term.
In this case, the project team can work to establish governance, standards and policies for effective data management, (handling, collection, organization and retention) moving forward. This positions the project and team to build new 'history' to use in future analytics, reports and AI. It may simply not be worth the effort to sanitize prior data - particularly if / when industry may be available as 'stand in' data.
When necessary to review historical data, I still find it makes sense to establish the new standards, apply, put audit and control procedures in place for current and future use to stop the growth of bad data. This work can be done applied to any defined or required historical subsets to be sanitized in an incremental fashion (i.e. 1 month back, or 3 months, or 1 year and so on). The older the data is, the less relevant it is in future decision making. Saving Changes...
Sarah WisemanProject Manager| JR AutomationSaint Clair Shores, Mi, United States
We navigate unexpected data challenges in projects through team effort to overcome those challenges. Normally, there is a lot to learn so we continue those challenges through lessons learned and continuous improvement projects. Saving Changes...
In my previous project, the objective was to reduce obsolescence for database technologies across the globe aka many regions. The timeframe for communication with immense amount of data took two weeks cycle for response and plan of action. However the data kept growing as many assets were not listed in a particular region as API failed to retrieve information at the right time. Hence I took that region as a subset project with dedicated resources and fast tracked to bring the region at par with other regions. It was immensely challenging but at the end it was rewarding in terms of achievement and stakeholders appreciation.
In my project manager roles, I gather detailed requirements and do my pre-project research to understand data requirements and work with Data SMEs seamlessly. I assigned responsible owners to different / individual data elements but due to un-foreseen circumstances or other project demands, they do not deliver at due dates. When there are late updates or no updates to utilised data, I'm able to pick up the piece of work based on my knowledge and set timelines for data quality for efficient usage. I also assigned deputy owners who deal with uncertainties and pitfalls. These process help mitigate the issue. Saving Changes...
Anonymous
I have a predicatively vision of the unexpected data challenges' Saving Changes...
Anonymous
Challenges with data:
Availability
Accessibility
Understanding (unstructured) / Getting into a format that could be understood
Outdated / not refreshed as frequently as needed
To get around data challenges, I've led efforts to accelerate data availability and refresh cycles, leveraged tools to get data into a usable format. Essentially, problem-solved as blocks occurred. Saving Changes...
Khalid Al-RasheedCommercial and Business| Tasnee National Petrochemicals Marketing Co.Riyadh, Saudi Arabia
I noticed in Pascals video segment in the Data Landscape course. Was talking about limitations to the results after sanitization of the documents. For example removeing the customer names removed the ability to get recommendations on a specific customer account.
This brought up an idea and question if the customer names were replaced with a customer reference code, that could be linked with an internal list of customer names. The code would allow us to talk with Ai and get recommendation for a specific customer using only a reference number. Would this type of action be questionable ethically regarding data governance? As the only individuals knowing the customer name would be those with an internal access to the customer list made specifically for this purpose?
Share your comments and ideas?
Saving Changes...
Eba'a MobydeenProject Manager | Senior Motion Graphics Artist and Video Editor| Self-employedAmman, AM, Jordan
Navigating unexpected data challenges involves proactive planning, agile problem-solving, and clear communication. My approach includes:
- Risk Management: Identify risks early and create contingency plans.
- Agile Methodology: Adapt quickly and implement iterative solutions.
- Communication: Keep stakeholders informed and encourage team collaboration.
- Expertise and Tools: Consult data specialists and use advanced tools.
- Continuous Improvement: Learn from challenges and train the team.
This ensures effective management of data challenges and project success. Saving Changes...
DORA LUZ MejiaCEO| IT ExploreEnvigado, Antioquia, Colombia
IN my experience Ithas been more importante good number of data specially in historical data and behavior data. Since, the projects I have implemented have been from financial transactions perspective the real transactions are available for trainning different models specially for fraud topics
IN a case for KyC (know your customer implementation) we implemeted a model to detect fraud face pictures , and the model was very sensitive to both to quality and to the amoung of data available to provide good result. for navigating these issues I have learnt that the data science professional is really a key to start early to get results and analyze the model and proactively determine if we need to activate new sources of data. For instance, in the face pictures project once we identified the big number of bad pictures to feed the model we activate an strategy with a large number of employes to generate pictures with the conditions we defined. Encription is very critical in some cases for the model itis notimportant to have the specific customer identification but we do need to know the demographics. So it is not for taking a fast approach the decision of whatto do for any case. I have found a very good tools to customize these needs. Saving Changes...
Francisco GarciaPMP, Scrum Master & DA, ITIL-OSA.| EDPMToluca, Mexico, Mexico
Tough data needs are reviewed previously by architects and the Security Officer, challenges sometimes come in, and when it happens, it is needed to clearly identify the challenge and estimate the impact not only looking at project scope, also with correlated solution so it is presented to CABs where architects, Security Officers and Data responsible determine next steps. Saving Changes...
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