The nature of your data is getting more complex and diverse than ever. If your data is incomplete, inaccurate and redundant, it affects all aspects of a business – from employee morale to loss of customers.
- Bad data quality costs US businesses $600 billion annually.
- Lack of visibility into data is one of the major reason for overrunning the project costs.
- Inaccurate data affects the bottom line of some 88% of organizations.
Data comes in from a large number of sources (such as Salesforce and Marketo) that is used for analysis. Implementing a Contact Washing Machine can be a strong initiative for your organization. It cleanses the bad data, normalizes it and enriches it into sales ready good data. The fully automatic Contact Washing Machine, built on Alteryx helps in improvising the lead data quality, ensuring that your sales team receives correct lead information.
3 steps to build a Contact Washing Machine using Alteryx:
Step 1: Cleansing the Bad Data
- Load your Leads data using the out-of-the-box Salesforce & Marketo connectors or in the form of flat files.
- Build the rule sets for cleansing the fields like Name, Company, Email using the Alteryx Preparation tools like Filter, Formula, Regex. These rule sets validate the data – if it is good or bad and in a customizable way.
- Flag the data as good or bad using the headers “Valid” and “Invalid” so as to separate them when required.
- Use the filter macro to partition the good and bad data.
- Output the bad data so as to examine it independently before you discard it.
Step 2: Normalizing the data
- Connect the good data output from the previous step to the Left of Join tool.
- Build a Normalized database that contains the Un-Normalized values corresponding to the Normalized values.
- Connect the Normalized database to the Right input of Join tool.
- Merge the Un-normalized values using Join tool and get the Normalized values as an output.
- Use the Union tool for joining the Left and Join stream of Join tool to get all the records.
- Use the Formula tool to get the non-empty normalized values, else take the original values.
Step 3: Enriching the Leads data
Data enrichment can be done by any of the following two ways:
- Using the Enrichment Data flat file, or
- Using the Data source API or Download macro available in Alteryx
Using the Enrichment Data flat file:
- Connect the normalized output from Step 2 to Left of new Join tool and Enrichment Data file (e.g. DnB) to the Right of Join tool.
- Configure the Join tool by Company Name and select the required fields from Enrichment Data file.
- Use the Fuzzy Match macro for the records that do not have the exact company name.
Using the Data Source API or Download macro:
- Connect the normalized data output to the Download macro.
- Using the Data Source API, fetch the company data dynamically through Download macro.
- Use the XML/JSON parser to parse the API response and get the required enrichment fields, merging them with the original normalized data.
As a result, the Contact Washing Machine delivers the data that is clean, consistent, correct and complete. Clean data is a competitive advantage and an investment that directly impacts the revenue boost as well as customer engagement.
About Grazitti’s Contact Washing Machine Expertise on Alteryx Platform
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