Call us Today +1 (888) 240-8088

Data Reintegration is the process of layering modified data over the original dataset; or combining data from various sources  into valuable, actionable information centralized under one database.

 

Data Reintegration infographic

Data integration processes help the user cleanse, dedupe, monitor, and transform data, so the information is consistent and trustworthy. The primary goals are to:

  • Keep critical data points unchanged
  • Add data points via appended information
  • Modify outdated data points from data decay


Why Data Reintegration? Why not just process your data in the CRM?

Processing data within your CRM is not realistic for large-scale data operations, and will likely impact the quality of your existing CRM data negatively. There are two reasons for this:

  1. API Limits: most CRM’s do not provide a sufficient number of API calls needed to do a full data refresh
  2. Speed: API-based access is a slow process when working on a large dataset

To run a proper data integration, the existing data set must be extracted from the CRM into a data sandbox.


Planning Stage for Data Reintegration

Here are the necessary steps to properly execute a data reintegration:

Initial Pull:

Include as many data points as possible; reintegration planning begins when the initial dataset is pulled from the CRM

  • Most data cleansing operations have a better chance of success when more fields are pulled
  • Example: even when appending Account level data, including Contact or Lead data can help the process. An email associated with a particular contact can provide the website of the company via the email domain.
    • Then, that website can be used to verify the company name to better match firmographic data on the company.

Include All Required Unique ID’s:

If your data records include both Account and Contact data, include both the Unique Record ID for Account and Contact.

Do Not Forget Date Values: 

It is a common practice to consider old data “unusable” because of the rate at which data decays (ex: a former company that went out of business).

  • Look at the original “created date” at the end of a data refresh process to help make decisions about your data
  • Remove data points that are too old to be usable

Performing a Data Reintegration: Step By Step


  1. Create a Data Reintegration Plan: a step-by-step attack plan based on your goals, and the recommendations in this section
  2. Make a backup first
    • Most CRM’s have either internal or external solutions that allow the user to save the “state” of the CRM
    • Do this before the reintegration starts
  3. Use a sandbox before live testing on CRM
    • A sandbox refers to a temporary location for testing new operations or products
    • Make a copy of your CRM to test reintegration in the sandbox
  4. Start with a small data sample
    • Before a full reintegration, begin with a sample of 50 or 100 records
  5. Meet with your team
    • Explain which fields will be affected and get them to buy-in to the process
      1. There may be marketing, sales, financial, or operations processes that depend on certain data fields
    • Make a list of each field and how that field will be affected
  6. Score fields by importance
    • Low = ok to overwrite
    • Medium = possibly ok to overwrite (case by case basis)
    • High = do not overwrite
      • Strategy for scoring fields is to create a new, temporary field that lives side by side with your existing fields.
        • Allows the team to adjust new values/fields without losing data
  7. Set old fields as Read-Only
    • Do not allow the user to work with old data fields
    • Consider the “read-only” fields as “reference” fields (they can exist until the team is trained to use the new fields)
  8. Train Your Team: explain how the original dataset was changed during the reintegration
  9. For major changes, change one, or a few critical fields at a time
    • Make sure to make periodic backups of the data
    • Rule of Thumb: the longer the CRM has been used, the more dependencies there are
      • Regular usage should uncover broken reports or functions after each change
  10. Rename old fields before deleting them
    • Deleting fields is dangerous to automated processes and reporting. If you rename a field and a report breaks, this is better than losing data.
  11. Use the One Year Rule
    • Do not delete any old, unused fields
    • Some reports are run quarterly or yearly. Test reports on your new CRM structure
  12. Create a Change Log
    • Visible to all CRM users: show the changes that have been made and the upcoming changes scheduled

The author:

capture,ringlead,sales rebuttals,data quality checklist,rebuttal sales,sales rebuttals list,bad data,ringleads,sales statistics,b2b diagram,crm customer satisfaction,sales stats,call voicemail,data quality analysis tools,data standardization,why is data management important,salesforce hacks,salesforce connections 2015,how to merge accounts in salesforce,salesforce merge accounts,rebuttals for sales,merge accounts salesforce,deduping,not always right,rebuttals in sales,importance of data management,salesforce phone number format,how to standardize data,salesforce implementation,sales motivation video,quality data management,salesforce sucks,motivational sales videos,standardize data,sales motivational videos,list of sales rebuttals,lost lead,sales rebuttal examples,data standardization process,data quality audit tool,best motivational sales videos,what is standardized data,standardizing data,contact capture,importance of data,salesforce customers,why data management is important,improving data quality,data accessibility,crm best practices,merge accounts in salesforce,salesforce administrator resume,salesforce data management,spooky lines,donato diorio,web to lead,data quality improvement strategy,sales team motivation video,dedupe tool,sales motivation,marketo address,sales rebuttals examples,sales motivational speech to sales staff,customers are not always right,data enhancement definition,data quality manager,data quality manager,salesforce certification,marketing automation expert,how to improve data quality,crm and customer satisfaction,database normalization,company swag ideas,salesforce chatter use cases,best motivational videos for sales meetings,sphere of influence definition,sales image,jewel restaurant,sales inspirational videos,salesforce implementation process,reasons why the customer is not always right,importance of quality audit,importance of data quality,data management companies,the customer is not always right,data quality audit,dms launch,why the customer is not always right,salesforce largest customers,staffing procedure,benefits of using a database management system,best sales rebuttals,what is a hot lead in sales,sales motivation youtube,standardize the data,salesforce web to lead spam,types of database management systems,types of database management system,standardising data,improve data,data quality checklist,quality data management,data quality analysis tools,duplicates in marketo,improve data quality,marketo duplicates,merge marketo duplicates,marketo duplicate leads,marketo duplication,data quality manager,data quality improvement,salesforce dedupe,deduplication in marketo,data mining techniques in crm,marketo deduplication,managing data quality,merge marketo contacts,merge marketo accounts,data preparation for salesforce,salesforce phone number format,data quality audit tool,data management for salesforce,salesforce deduplication,merge marketo leads,salesforce data cleaning,salesforce data cleansing,salesforce data management,data quality management tools,data enhancement,data quality platform,data management companies,salesforce merge,data wrangling for salesforce,capture,data enhancement software,salesforce lead capture,salesforce duplicates,lead capture tools,salesforce merge leads,data quality assessment tools,data cleansing,capture tool,best data quality tools,lead prospecting software,data quality management software,data quality plan,data quality management,how to capture leads,prospecting and sales tool,salesforce duplicate contacts,data quality tool,prospecting tool,data cleansing software,database normalization,marketo cost,data operations,salesforce data quality,prospecting tools for sales,data quality objectives,data quality salesforce,lead tool,list building tools,salesforce migration,data quality for salesforce,lead prospecting,list building,lead prospecting sales,website capture tool,prospecting lead,data quality management platform,salesforce chrome plugin,contact google sales,google sales contact,salesforce wrangling,prospecting leads,preparation for salesforce,sales prospecting tools,data cleansing platform,salesforce tool,tool capture,salesforce data migration,sales lead prospecting,prospect tool,prospecting on linkedin,linkedin prospecting,capture emails,data quality software,linkedin salesforce,list builder,data management,data quality,data solutions,web crawling tool,prospecting for sales,how to prospect for sales leads,prospecting tools,salesforce chrome,web crawling tools,smart prospecting,linkedin tools,salesforce linkedin,linkedin for salesforce,research tool google docs,sales prospecting software,sales prospecting,prospecting in sales,sales prospecting sheet,data management platform,data management software,data enhancement platform,database management,sales prospect,marketing automation pricing,data operations software,salesforce chrome extension,prospecting sales,sales lead sheets,prospect research,data base management,sales force tool,marketo price,marketing automation,sales force tools,database management systems,salesforce tools,data operations platform,salesforce preparation,data management solutions,salesforce crm tool