You have an important decision to make within two weeks, but you’ve just received some new data that, when combined with existing data, could provide game-changing insights. However, you are unsure whether this new information is trustworthy. In such a situation, there are a few steps you can take to proceed with greater confidence or caution, depending on the quality of the data.
Evaluating Data Sources:
The first step is to examine the origin of the data. Check if it was generated within a high-quality data program that encompasses clear accountabilities, input controls, and efforts to mitigate the root causes of errors. If the data quality statistics appear satisfactory and the context in which it was created is reliable, trust the data.
Independent Data Quality Assessment:
To gain a better understanding of the data’s quality, conduct an independent evaluation. Identify the organization responsible for creating the data and investigate its reputation for quality. Perform research using both internal and external sources to form a comprehensive understanding of the data’s reliability.
Developing Personal Data Quality Statistics:
Utilize the “Friday afternoon measurement” technique to assess data quality by selecting 100 data records on a spreadsheet and scrutinizing essential data elements. Mark obvious errors and calculate the percentage of error-free records. If less than 5% of records contain obvious errors, proceed with caution when using the data.
Cleaning the Data in Three Levels: Rinse, Wash, and Scrub:
Rinsing involves replacing obvious errors with missing values or correcting them if it’s relatively simple. Scrubbing entails, a thorough analysis, potentially making corrections manually, one at a time. Washing is the middle ground between rinsing and scrubbing. Start by scrubbing a small random sample, making them error-free, eliminating uncorrectable records or data elements, and marking uncertain data as needed. After the initial scrubbing, proceed to wash the remaining data that was not in the scrubbing sample, ideally performed by a skilled data scientist.
Conclusion:
Deciding whether to use new data depends on its quality. Trust high-quality data but proceed with caution if it’s flawed yet potentially valuable. Conduct independent assessments and employ the “Friday afternoon measurement” to develop personal data quality statistics. Cleaning the data through three levels – rinse, wash, and scrub – can help identify and correct errors as much as possible.
Ultimately, base your decision to use or discard the data on the trust established through these steps. If still uncertain, consult a trusted expert or data analyst to help make an informed decision. With the right approach, you can transform game-changing insights into impactful actions that drive success.
Navigating the complexities of data assessment and utilization can be challenging. Anyon Consulting offers expert guidance and support to help you confidently assess and use new data in your decision-making process. Our team can assist with independent data quality assessments, data cleaning, and providing tailored recommendations for using the data effectively. By partnering with Anyon Consulting, you can ensure your organization makes informed decisions that drive growth and success. Contact us today to learn more about how we can help you harness the power of new data with confidence.
