Data Quality and Beyond

Now, that you have had a chance to understand, assess and improve your Data Quality, it is time to keep it this way. It is time to make a plan, a Data Management Plan.

In the previous articles:

Understanding Data Quality (Episode 1)
Assessing the Data Quality (Episode 2)
Improving Data Quality (Episode 3)

-you have learned about Data Quality and what you can do to improve it.
To get it right, and being able to manage this on an ongoing basis this article contains a few suggestions to consider for your Data management Plan.

Develop a Data Management Plan

A Data Management Plan is a document, which outlines each of your data fields with respect to their importance for the various part of your business groups. You would need to know; who uses what; is the field used in validations, workflows etc. And how you address the question of Data Quality for each field.

And most importantly you also document which of the salesforce standard fields you DO NOT use.
Make a document for each Object in salesforce, and let it include detailed descriptions for:

  • Custom Field
  • Required Fields
  • Validation Rules
  • Workflow Rules
  • Picklists
  • Records Types

For each Object remember also to document each Page Layout, who uses what (by Profile) etc.

It is also recommended that you in such a collection of documents, note down:

  • Any external tool you use for data manipulation
  • Your plan/process for Duplicate Management
  • Your strategy and initiatives for Data Enrichment
  • Any consideration you may have for purging/archiving Old data.
  • Your back-up plan
  • Initiative regarding Data Privacy
  • Etc.

Implement your Data Management Plan

A plan isn’t worth much unless it is implemented, right?

Often the salesforce Admin also becomes responsible for the data. It doesn’t have to be this way. Just because you administer the system and know how to customize and adopt it to the business needs, doesn’t mean that you should be kept accountable for the contents.

But on the other side, if you can add “Data Steward” to your CV and Linked in Profile, and even get a raise, then why not.

“Being a Data Steward describes a relationship between a person and some data
– Robert S. Seiner:”

It may also be good to think in terms of relevancy. Why should the sales people be kept accountable and responsible for their data, and why should somebody in London take care of data for Germany?

We believe in a distributed model where multiple users can collaborate in improving Data Quality, whether it is with regards to Deduplication, Record Merging, Cleaning, Updating and Purging/Archiving.

Read more about being or becoming a Data Steward in this article: Data stewardship, a new skill for your Linkedin profile?

For more, check out these salesforce e-books:
Guide: Introduction to Data Governance and Stewardship
Guide: Get Data Strong: How How Data-Centric Teams Drive Business Success

Conclusion

Succeeding in a Data-Centric culture, bringing it all together in one global, consistent view for all to see requires a system, and thank you to salesforce for making this available on a global scale. AND data!. But not any data. Only Quality Data will make you succeed. Because the more workflow rules you create, the more marketing automation, the more process flow you creates. They will only work if the underlying data is there to support the decision process within the logic and rules you create.
Start by measuring your Data Quality and then go do something about it. We have seen it so many times, and we are sure you will discover and confirm: It can only get better!

At DataTrim…

We don’t have consulting resources to help you develop and implement a Data Management Plan for you. BUT we work with a range of salesforce consulting partners, who are competent in this area, and who we warmly recommend.

Data Management Plan - PartnerCloud

Your business success depends on your teams’ access to complete, consistent and reliable data. …
The DataTrim Data Laundry App is a cost-effective solution to assess and improve your Data Quality.