Successful CIOs, CTOs & executives from Forbes Technology Council offer firsthand insights on tech & business.
Data drives smart decision-making in modern industries, but the old saying still holds true: “Garbage in, garbage out.” The quality and completeness of the data pulled for analysis play a huge role in the accuracy and effectiveness of the results.
Focusing on data and analytics with a value-first drive is critical. To do this, a company must start with its business problem, not the data, and take a “decision back” approach to achieve a greater impact on the business. Value comes first, and data comes second. - The truth is in the data, and if the data is not trustworthy, the truth derived from it is impaired. The key consideration is balancing the criticality of the data source with the ease of integration with standard, out-of-the-box adapters. This balance can identify the low-hanging fruit that can enable you to prioritize and make fast progress. -
The most important thing about data source selection is to think about the total information content of your dataset, as well as the accuracy and timeliness of the data source. You will get inaccurate insights if your AI system does not correct for variations automatically or if you don’t do some data preparation outside the system. -
Ethical considerations regarding privacy, anonymization and consent must be considered when selecting data sources. Comply with ethical standards and regulations, such as the GDPR, the CCPA, HIPAA and similar regulations. The quality of the insights derived will depend on several other factors as well, but adhering to the law will ensure you do not encounter trouble later on. -
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