To ensure data accuracy and integrity when leveraging technology for decision-making, implement robust data validation processes, utilize reliable data sources, regularly audit data for consistency, and employ advanced analytics tools to detect and correct errors.

Ensuring data accuracy and integrity is a core business ethic at Marriott, where I’ve witnessed the commitment to this standard over my 35-year career. Leveraging technology for decision-making begins with trusting the data. Marriott champions this by verifying sources, regularly checking the credibility and reliability of information. Additionally, we implement validation tools to ensure data consistency and accuracy, and conduct routine audits to catch and rectify discrepancies early. This rigorous approach not only upholds operational excellence but also reinforces Marriott’s dedication to responsible, data-driven decisions across all levels.

Firstly you have a legal responsibility if you’re working with data to make sure any changes are automatically put in place in your company. Secondly how can you judge a piece of software? If you are not clear on the quality of the data you’re putting into it. It’s too easy to blame the software but it’s far more important to know the regulations, validity & the quality of the data you’re putting into the system. There are validation tools accessible as-well as audit tools, that you can use in between your external audits. The most important thing to do is to implement a software system that is consistent with your computer data requirements & manage it well.

Ensuring data accuracy and integrity in technology-driven decision-making is crucial. Here’s a structured approach to achieve this: Data Validation Data Source Reliability Regular Audits Data Integration Access Controls Data Backup Monitoring Systems Data Encryption Training & Policies By combining these strategies, you can significantly enhance the accuracy and reliability of data for decision-making. Have you faced any specific challenges with data integrity?

Trust is the root issue. Having recently 'been there and done that' with our IT/finance teams. Again, somewhat wasteful and old-school, but for the first few weeks we simply duplicated the work - on one side the newly leveraged AI analysis technology, and compared numerical analysis and modeling results done in our previous labor-intensive (but time-tested and trusted) manner. Within four weeks we felt sufficiently confident in the newly generated results that we regrettably let previous specialists go. Disruptive tech is not always pretty in some of its 'benefits' guises.