How do you navigate unexpected data challenges in your projects?

When the transactions data used for analytical purpose, many times some of the irrelevant data also assumed that important for the analysis. After stakeholders clarify they don't require that information, updated version is quickly loaded in the reporting system. Due to communication gap and not understanding the needs and wants of the customer and not freezing the requirements properly, this kind of pitfalls happens. PM's should take the responsibility of this, rather than depending anyother person in the project. When PM validated and ensured the data, it made easy and over come the pitfalls.

In the healthcare, clinical data is notorious for being incomplete and out of date. This is due to the fact that the data is distributed across multiple stakeholders and not all of them may use the same standards or connect to each other very well. Over years interoperability has become better and more standards are available but the timing of the data flow is still a challenge.

As an organization we deploy a lot of clinical rules to establish the sequence as well as the validity of data that should be considered by the model

future of navigating unexpected data challenges in projects which leverage AI, is dependent on the evolution of more sophisticated data management strategies and the integration of advanced technologies. As project managers and teams become increasingly data-savvy, the emphasis will likely shift towards proactive data quality assurance, real-time data monitoring, and the use of AI and machine learning algorithms to predict and mitigate data-related issues before they impact project outcomes and thus the organisations. With more intuitive buuilt-in, AI-powered data management tools will enable project teams to swiftly identify, analyze, and correct data anomalies, ensuring data integrity and reliability. Furthermore, as collaboration between AI experts and PM professionals deepens, we can expect a more seamless fusion of project management practices with data science, leading to enhanced decision-making processes and more resilient project frameworks. This not only minimizes the occurrence of data-related pitfalls but also enhance the overall efficiency of projects across various industries/ organisation. The role of ethics and privacy will become even more central as data becomes an even more critical asset, and guides the development of tech and methods that respect individual rights and data sovereignty.