How do you manage unclear data sources with different stakeholders?

1 Define the scope and purpose of the data sources

Before you start analyzing the data, you need to have a clear understanding of the scope and purpose of the data sources. What are the objectives and questions that the data sources are supposed to answer? Who are the owners and providers of the data sources? How reliable and accurate are the data sources? How often are the data sources updated and maintained? These are some of the questions that you should ask yourself and your stakeholders to define the scope and purpose of the data sources.

2 Communicate and collaborate with your stakeholders

Communication and collaboration are key to managing unclear data sources with different stakeholders. You need to establish regular and transparent communication channels with your stakeholders to share your progress, challenges, and feedback. You also need to collaborate with your stakeholders to validate, verify, and reconcile the data sources. For example, you can use data dictionaries, data quality reports, data lineage diagrams, and data governance frameworks to document and communicate the details and issues of the data sources. You can also use data visualization tools, dashboards, and reports to share your findings and insights from the data sources.

3 Use data preparation and integration techniques

Data preparation and integration are essential steps in data analytics projects, especially when dealing with unclear data sources. You need to use data preparation and integration techniques to clean, transform, and combine the data sources into a consistent and coherent format for analysis. For example, you can use data cleansing tools, ETL tools, SQL queries, and Python scripts to perform data preparation and integration tasks. You can also use data quality metrics, data profiling tools, and data testing methods to ensure the quality and integrity of the data sources.

4 Apply data analysis and modeling methods

Once you have prepared and integrated the data sources, you can apply data analysis and modeling methods to extract value and insights from the data sources. You need to use data analysis and modeling methods that are appropriate for the scope and purpose of the data sources and the expectations and goals of your stakeholders. For example, you can use descriptive, exploratory, inferential, or predictive analytics methods depending on the type and level of analysis required. You can also use statistical, machine learning, or deep learning models depending on the complexity and accuracy of the analysis required.

5 Interpret and communicate the results

The final step in managing unclear data sources with different stakeholders is to interpret and communicate the results of your analysis. You need to interpret and communicate the results in a way that is relevant, understandable, and actionable for your stakeholders. You also need to address any limitations, assumptions, or uncertainties that may affect the results of your analysis. For example, you can use data storytelling techniques, data visualization principles, and data presentation skills to interpret and communicate the results. You can also use data ethics principles, data privacy regulations, and data security practices to protect and respect the data sources and the stakeholders.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?