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I sat in on a very interesting meeting with a client today that I wanted to share.
My client is a large multi-national service provider. They maintain an enterprise wide automated marketing system that is used primarily for communications (as opposed to lead gen).
They have several web development groups who are responsible for various corporate web sites. All of these sites feed contact data to the marketing system. However, the group responsible for the data collected (my primary client) has no control over how the response forms or landing pages are built by the various departments which own the sites and none of the other sites employ the same upstream data quality and standardization tools used by my primary client. Some of the problems that result are:
Company name is the primary focus for corporate data analytics, but most of the response forms do not ask for company name;
Some of the response forms ask for delimited names (i.e., first name, last name) and others ask for the
respondents name in a single field;
Some forms ask for email and phone, some email only;
Some data points required on one sites form are omitted by other sites; and,
Without universal requirements and data standardization, duplicates become a problem in the marketing database.
The web development groups are focused solely (at least it was apparent to me from our dialog) on the user experience. They have very little interest in the data received from their respective response forms.
I’m sure other organizations with multiple web sites offering different types of content share these same problems. But there is very little discussion on this topic that I can locate on the web.
Can anyone steer me towards others dealing with this topic?
I have been involved in marketing and CRM data quality initiatives for a number of clients since early 2000. In all my encounters with companies trying to manage marketing and CRM data feeds from web sources, I have found they employ a number of individuals whose primary job roles is to manually edit and “cleanse” the data they have received after it is in the database. Clients who typically receive over 1,000 contact records per day often employ a number of people to manually cleanse data after receipt. So my question is this: does your organization dedicate staff to manually cleaning and standardizing data? If so, please share the number of people you use in this capacity and the average number of records you receive on a daily basis.
We are in the upstream data quality software business, so I keep wondering why data quality processes are still run once in a while, rather than as a normal part of the data capture process. Why do most companies start worrying about data only when it’s already dirty, already in the database, and in use? How come it doesn’t occur to them that the quality of data needs to be addressed when it’s actually captured? Since many data quality issues can be addressed at the point of data capture, why don’t more companies use upstream processes to improve their data? A recent Forrester paper titled It’s Time To Invest In Upstream Data Quality suggests that when companies realize short-term data cleanup ROI immediately, it’s hard to justify front-end investments that may take years. At the same time, Forrester says, IT budget planning committees tend to avoid the existing data quality (DQ) products that allow integrating downstream data hygiene rules into front-end processes, justifying this by solutions’ cost and complexity. The result? I&KM (Information and Knowledge Management) pros quickly reach diminishing return on data quality investments, requiring even more investments later on to catch up with missed opportunities like verifying customer contact information, standardizing product data, and eliminating duplicate records. Read the paper to find out more. f you are thinking about implementing an upstream data quality solution, or if you already have, chime in here and let us know your thoughts.
