Trustable data is defined as data that comes from reliable sources and used according to its intended and delivered in the appropriate formats and time frames for the specific users.
Trustable data helps in effective decision making. The properties mentioned in the definition makes data trustworthy for effective decision making.
Trust Factors of data:
Trustable data are good only if they meet certain basic requirements. Here are some of the trust factors dimension:
- Accuracy
- Consistency
- Security
- Usefulness
- Privacy
- Reliability
- Interpretability
Most AI and Machine learning algorithms require their data aligned in a very specific way. This means that datasets usually require considerable preparation before yielding a useful objective. Some of the data sets contain invalid, missing, inconsistent, or in some instances, difficult for an algorithm to process. When the data is missing or invalid the algorithms are is not able to use it. If invalid, the algorithm will yield less accurate or misleading results. Some data sets could be clean, but they would need to be adjusted. Many data sets will also lack many useful business insights, therefore the need for feature enrichments. A good data preparation process produces clean and well-curated data. Clean and accurate data leads to more practical, accurate model results.
Conclusion:
Trustable data is a strategic asset for all enterprises. This is the reason why enterprises need to invest in expertise processes and technologies to make sure their data is trustable, accurate, and more reliable. Trustable data is used to maximize all the good for an organization while fostering trustable business relationships with its, clients, partners, and employees.
Trustable data can improve an organization’s outcome and lay the foundation for innovation and business transformation.
Credit: Data Science Central By: Indhu
