Automate car dealer software in Onboarding forms

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[Music] [Music] when our onboarding is a process that involves qualifying approving and contracting vendors for the business and therefore demands efficient data handling it is also known as vendor relationship management in the automobile industry efficient vendor management is a crucial responsibility of the purchasing department at REO vendor evaluation is required to choose and verify vendors for the company a well-designed vendor onboarding process can help avoid mistakes that may put the entire business at risk data integration tools are commonly used in such companies to generate error free databases this is Drake he works as a purchase manager in a car dealer company's head office the company's business spans across various cities and a massive amount of data from vendors is received at the head office daily from all over the country Drake manually assembles and verifies the vendor information that he receives for onboarding as with any manual tasks the risk of human errors and the scale collations in handling this data cannot be eliminated Drake has a crucial job as a purchase manager since the entire vendor onboarding process relies on whatever data he assembles of the company vendors once Drake compiles and verifies all the vendor records the data is written into the vendor management database which is the concluding step in the vendor onboarding process Drake has tried different tools to make things easy on his end and reduce the risk of human error he is in search of a tool that would help him orchestrate the entire process for automation Drake chooses to work but s teres Enterprise let's look closely at how s teres Enterprise can automate the process for Drake Drake has vendor data coming in from different regions in the form of semi structured sources a data flow needs to be designed for the data integration process we have the source data in a delimited file we'll get that into Center price through the delimited file source object in the toolbox once we have the data the first step is to sort the data inside the delimited file Center price supports several transformations that assist in data modification and applying different functions on the data we have a sword transformation through which we have sorted the data there is a field for dealer IDs in the source data set using this as the key field we have arranged the data in an ascending order of dealer IDs once the data is sorted it now needs to be cleansed to bring it into a standardized format that conforms to the enterprise's main database considering that this data is going into a main database we need to be sure that it validates for all the checks from the transformations will use the data comes transformation on the sorted data the data grants transformation offers multiple options to cleanse the raw data and prepare it for integration in this case we'll use the remove option from leading and trailing white spaces to eliminate any whitespace in the data before further processing once the data is cleansed the next step is to apply quality checks for data validation we'll use the data quality rules object provided by Center price and apply to data quality rules the first rule is to check if the records contain the number of miles of the car all records that matched that rule will be qualified whereas the records that fail to match the rule will return an error the second rule is applied to the transmission field that identifies whether the car transmission is automatic or manual if the transmission field is empty in any record the second rule will return an error on that record let's review the output to see the quality checks that we have applied to the data once the data is validated it's written into the main database using the database destination object and center price for unqualified records that fail to match the data quality rules we have used a record level log to capture and write them into a log file it captures warning or error status for each record and provides additional details such as error messages for the erroneous records in the data we can now orchestrate this entire process for automation for the rendered data coming in daily from different cities to do that in a new workflow we use a file system item source to provide the path to the location and directory where the vendor record files reside we'll set the file system item source to run in a loop to process multiple files on the data flow we designed in the previous step next to orchestrate the process will run the same data flow on these files to do that we use a run data flow task from the toolbox point the path to the data flow in the run data flow task properties the file system item source passes the source files one by one to the run data flow task to process each file on the data flow we created in the initial steps the final step is to schedule the job and deploy it on the job scheduler in s teres Enterprise this way when we run the workflow it automates and runs the data flow for all the other files that the folder path contains minimizing the time effort and risk involved in performing this task manually data automation is the key to improved clarity and efficiency faster turnaround reduced errors and paperwork and minimizing costs associated with handling large volumes of data manually it also ensures better performance and scalability of the data environment and saves a lot of time in handling tasks that are essential to data integration and management [Music] you