Do More With Less: Data Management

Digital Pi's Do More with Less series, Data Management
This post is part 5 of 11 on setting up your marketing for scale using the Gold Standard—our signature best-practices marketing automation framework consisting of a series of foundational processes and corresponding deliverables focusing on strategic marketing as well as technical and campaign operational services. Need to catch up? Browse our Do More with Less series.

In this fifth installment in our Gold Standard blog post series, we’ll discuss ways to centralize operational processes to help ensure good data hygiene. In our last post in this series, we discussed marchitecture — the best-practice strategy for optimizing Marketo’s performance within the context of your martech stack. However, even with the best program integrations and processes in place, all else is moot if the data in the system is not accurate. Thus, with marchitecture set up, we move to our next critical piece of the Gold Standard: Data Management.

Govern data hygiene with good data practices

Data management includes defining, retrieving, and managing data, data formats, field names and record structures. Though it may not be every marketer’s favorite point of discussion, good data management strategy is mission critical to executing a successful marketing program. Poor data management practices create messy and inconsistent data, which can lead to incorrect reporting that precipitates poor business decisions. Bad data management can cost you, big time. Good data management, however, promotes better data quality.

Data management operationalized

The Gold Standard approach to data management is designed so that key demographic and firmographic data is always correct and automates other data cleansing processes. Some of the key elements of Gold Standard Data Management include:

  • Learning where duplicates come from and building processes to clean them
  • Achieving agreement between CRM and Marketing Automation systems – capture and manage data in the same ways (data values and standardizations – especially when it comes to the CRM and sales operations)
  • Mass cleanup and mapping of old to new data values once standardized values are defined and approved

Picklists promote good data

Example Gold Standard best practice data management operational program structure for Marketo Engage
Example Gold Standard best practice data management operational program structure for Marketo Engage

Gold Standard data management begins by standardizing and normalizing data types. This helps mitigate variations of the same value.

One simple tactic to use is to convert open text form fields to picklists. Using picklists also eliminates typos or false data points. For example, an open text field for “State” may include entries “CA,” “Calirfornia,” and “California” or even “asdf”. By converting this text field to a picklist containing two-letter codes, all of these entries would contain “CA” allowing for much more accurate reporting.

Audit field data and usage

Next, inventory existing data by field using reports and smart lists. The goal is to discover field usage, check for inactive or active smart campaigns that could affect field value. Audited fields include (but are not limited to): Acquisition Program, Job Title, and Industry. After fields are completely inventoried, items like blacklists, duplicate data, and test records and programs are also audited.

Data-driven recommendations

With auditing complete, the Digital Pi team then makes recommendations based on findings to key stakeholders. After stakeholder approval is provided, implementation of standardization begins. Some of the setup steps include:

  • Mapping old values to new values
  • Ensuring all leads have an associated acquisition program
  • Removing any existing campaigns that conflict with no cleansing campaigns
  • Setting up subscriptions to weekly reports to identify and data that appears incomplete or synced improperly

Set up for data success

Upon completion, Gold Standard Data Management establishes processes that regulate clean and complete data inflow, creating self monitoring-systems that can send alerts for data points that are problematic. In doing this, the primary role of a database manager becomes managing exceptions instead of regulating all data manually. Ultimately, Gold Standard Data Management contributes to complete and accurate reporting contributing to better business decisions and budget spend.

Now that best-practice Data Management programs are in place, we will look at an important piece of the data itself, namely Source Assignment. In our next post in this series, learn how Gold Standard Source Assignment allows you to better understand how net new leads enter the funnel to help you best put your resources to work for you.