The groundwork of any effective network analysis is data. The quality of that data permeates every other aspect of the network study and directly impacts results and recommendations. How can you ensure that the team is collecting the right data elements, processing, cleaning, and summarizing the data effectively? Here are some pressure-tested principles to building a strong data foundation.
Communication with Key Stakeholders: Effective communication with those interested in the results of the network analysis is needed. Clearly defining the goals and the underlying issues which drove the need for the study will help the team performing the analysis to understand what data needs to be collected and what are the critical points of focus. Thoughtful and open discussion amongst the team members will help the team to begin with the end in mind. Engaging the stakeholders throughout the process can steer the study and build trust in the eventual outcomes.
Attention to Detail: The science of data analysis is important but the team’s attention to detail is absolutely essential. Catching mistakes and errors early will make the remainder of the process free of significant rework. Like bricks in a wall, small details within the data will show in the end results.
Solid Assumptions: No data set is perfect. Certain elements requested for collection may be unavailable or incomplete. Making solid assumptions based on understanding the rest of the collected data in light of discussions with the team will help to effectively fill the gaps. Making too many assumptions may mean that not enough data was requested or collected. Therefore, an effort should be made to gather as much good data as possible before assumptions are made.
Data Review and Validation: Accurate and complete summaries focused on the key points comprise the critical final step in the data analysis process. The team should be careful to display the data in a straightforward manner and give others the time to digest the summaries and ask questions. A data review with the stamp of approval from key stakeholders will help gain confidence in the overall quality of the network analysis and results.
Poor data quality and lackluster data processing and validation will lead to a network analysis hastily constructed on sinking sand. Effective communication, complete data cleansing and summarization, keen attention to detail, all supported by extensive validation will be the solid rock upon which a robust network analysis is built. When engaging in any network analysis effort, all stakeholders should insist on data analysis steeped in all the requirements and characteristics discussed above—they are the rock solid foundations any quality network study needs.
—Eric Payne, St. Onge Company
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