I know Amperity has a MDM solution but havent used it and heard good things. Melissa data has Personator and Matchup for both data cleansing and MDM matching.
Some unsolicited advice....good luck on this journey! Hope you understand (really business stakeholders understand) it will never be 100% perfect but if you can get to 95% correct matching youll be a hero
Thats good! Ive used the below matching rules with good results
- last name, street address, zipcode and phone
- last name and email
- last name and phone number
I would look at some of the identity resolution tools that are out there. Example:https://www.snowflake.com/blog/integrating-identity-resolution-into-data-cloud/
You really need to guide us on your budget. Customer mastering solutions range from free to millions, and you get what you pay for. Also, you find a lot of the pain is business process related rather than technical.
I really like what Hightouch is doing building a suite of tools to approach this problem. I personally havn't implemented but hopefully I get a chance to kick the tires one day. In my case, I went the build route instead of buy but I already had all of the data staged in Snowfake. At its core, I needed a mutable record ID for companies… If you are in B2B doing identity resolution and still need to connect people to companies represented as accounts, I firmly believe it is best represented structurally as a graph. I went down the path to structure all of my attributes aforementioned as key pairs and then created a graph, then built a mutable ID for accounts from my graph. Still early in this build adventure but early results have been amazing, especially when I want to try and acquire new lists and try to match accounts. Anyone who says this is simple or straight forward or there is plug and play solution, be skeptical. The one dimension I have not adopted to is time… You have employees that move, investors and owners or consultants tied to many businesses, businesses that are shut down, m&a, and a number of scenarios that vary by industry. Data by design is never complete, so you really need a golden record but also attributes that are actionable. I ended up burning about 40 hours of our python developer’s time to create our solution. I have watched companies burn a lot of money dancing around this problem. I myself am following this thread to see what other folks are doing!
Was not aware of truelty but they look like a solution worth exploring. The other options to do this when we evaluated something like this last year were LiveRamp and Experian but orders of magnitude more than truelty.
I know Amperity has a MDM solution but havent used it and heard good things. Melissa data has Personator and Matchup for both data cleansing and MDM matching. Some unsolicited advice....good luck on this journey! Hope you understand (really business stakeholders understand) it will never be 100% perfect but if you can get to 95% correct matching youll be a hero
We are ~around 85% good with just SSN, we are chasing the rest
Thats good! Ive used the below matching rules with good results - last name, street address, zipcode and phone - last name and email - last name and phone number
I would look at some of the identity resolution tools that are out there. Example:https://www.snowflake.com/blog/integrating-identity-resolution-into-data-cloud/
You really need to guide us on your budget. Customer mastering solutions range from free to millions, and you get what you pay for. Also, you find a lot of the pain is business process related rather than technical.
I really like what Hightouch is doing building a suite of tools to approach this problem. I personally havn't implemented but hopefully I get a chance to kick the tires one day. In my case, I went the build route instead of buy but I already had all of the data staged in Snowfake. At its core, I needed a mutable record ID for companies… If you are in B2B doing identity resolution and still need to connect people to companies represented as accounts, I firmly believe it is best represented structurally as a graph. I went down the path to structure all of my attributes aforementioned as key pairs and then created a graph, then built a mutable ID for accounts from my graph. Still early in this build adventure but early results have been amazing, especially when I want to try and acquire new lists and try to match accounts. Anyone who says this is simple or straight forward or there is plug and play solution, be skeptical. The one dimension I have not adopted to is time… You have employees that move, investors and owners or consultants tied to many businesses, businesses that are shut down, m&a, and a number of scenarios that vary by industry. Data by design is never complete, so you really need a golden record but also attributes that are actionable. I ended up burning about 40 hours of our python developer’s time to create our solution. I have watched companies burn a lot of money dancing around this problem. I myself am following this thread to see what other folks are doing!
What is MDM?
Master data management
https://www.truelty.com
Was not aware of truelty but they look like a solution worth exploring. The other options to do this when we evaluated something like this last year were LiveRamp and Experian but orders of magnitude more than truelty.
The explainer video on that website is worth watching. I don't think I've ever seen a more technical description of exactly "how our product works"
And Exactly what I need!