Scaling Customer Experience using Generative AI Chatbot for Major Manufacturing Company
Business Problem
The client is facing a significant business problem where the data being passed between different teams is not always error-free. This is causing major issues for the teams who are receiving the data, as they are unable to process it in their systems. The errors in the data are causing delays and disruptions in the workflow, leading to decreased productivity and increased costs. The client recognizes the need for a solution that will ensure the data being passed between teams is accurate and error-free, so that the receiving teams can work efficiently without any interruptions. The client is seeking a way to validate the data and identify any errors before it is passed on to the next team, so that any issues can be resolved in a timely manner.
Business Solution
To address the business problem, the client has decided to establish a dedicated Data Quality Solution (DQS) team. This team will be responsible for developing applications that can process the data and identify any errors or issues. By using business rules to validate the data, the DQS team will be able to ensure that the data being passed between teams is accurate and error-free.
The DQS team will also generate various reports that will indicate any problems with the data, allowing the client to quickly identify and resolve any issues. These reports will provide valuable insights into the data quality and help the client to improve the overall data management process.
As an intermediary between the data source and the data requesters, the DQS team will be able to monitor and control the flow of data between different teams. This will not only help to ensure that the data is accurate and error-free, but it will also help to streamline the overall process and improve the efficiency of the workflow.
Overall, by setting up the DQS team, the client will be able to ensure the data integrity and efficiency throughout their organization and minimize the disruptions caused by data errors.
Technical Solution
The technical solution for the client's business problem is to design and develop complex applications using Statistical Analytical System (SAS). The DQS team will use SAS to create applications that can process the data and identify any errors or issues. These applications will be coded in accordance with business rules, which will ensure that the data being passed between teams is accurate and error-free.
The applications will be scheduled on a scheduler that runs 24x7, which will automatically pick up the input data files and trigger the application as soon as they are received. The application will perform all the necessary checks and generate automated reports, which will be used to identify any issues with the data. These reports will be automatically sent to the appropriate stakeholders, who can then take the necessary actions to correct any errors.
If issues are identified with the data, the sourcing team will be responsible for correcting the data and processing the files again until there are no issues reported. This process will continue until the data is error-free and meets the necessary business rules.
By using SAS to create these complex applications and automating the process of data validation, the DQS team will be able to ensure the data integrity and efficiency throughout the client's organization. This will minimize the disruptions caused by data errors and improve the overall data management process.
Technologies
The technical solution for the client's business problem will be implemented using a combination of SAS technologies and other software. The DQS team will use SAS/BASE, SAS/SQL, SAS/MACROS, SAS/GRAPH, and SAS/ODS to design and develop complex applications that can process the data and identify any errors or issues. These technologies will provide the necessary tools for data validation, data manipulation, and reporting.
SAS Enterprise Guide will be used to create workflows that automate the process of data validation, and to schedule the applications on a scheduler that runs 24x7. This will ensure that the data is processed and validated as soon as it is received, minimizing the risk of errors.
The DQS team will also use MS SQL, MS-OFFICE, Word, Excel, PowerPoint and HTML to support the process of data validation and reporting. MS SQL will be used to store and manage the data, while MS-OFFICE, Word, Excel and PowerPoint will be used to generate and share reports with stakeholders. HTML will be used to create web-based reports that can be accessed by stakeholders.
Overall, the DQS team will use a robust and comprehensive technology stack, which includes SAS technologies and other software to ensure the data integrity and efficiency throughout the client's organization. This will minimize the disruptions caused by data errors and improve the overall data management process.
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