“Technical Debt” is a term most software and IT professionals are acquainted with, but it may not be as familiar with the marketing ops and sales ops crowd. As teams build out an application, a system, or a solution stack, they’re often required to make suboptimal decisions for reasons like: Lack of time or budget, […]
An ounce of prevention is better than a pound of cure. Data hygiene rules to keep your data clean.
People often ask us at Openprise why we just don’t call our platform “Data Management” like everyone else? Isn’t “Data Orchestration” just fancy marketing jargon to sound differentiated? The answer is no. There are very important distinctions between Data Orchestration and Data Management that can be summed up like this: Every dirty data problem is […]
Frighteningly, after 26 years, the MTV reality series The Real World still exists as a TV show. Even when it started, it wasn’t very “real” but these Marketing Operations scenarios are. Here’s a little dose of reality on how to solve real-world problems for Marketing Ops—with a lot less drama: Episode 1: Pulling a List […]
Common wisdom and best practice in marketing a dime a dozen. Many of them sound perfectly logical and intuitive, but the results often don’t validate the hypothesis. The data often tells a different story. Today we will discuss one of these “best practice,” the use of picklists on marketing sign up forms. The “Best Practice” […]
Too often Marketing Operations professionals are seen as the “plumbers” of the team. Their work is invisible until the shower backs up and then their colleagues only see them as fixers. When everything is running smoothly, their work goes unnoticed. It’s time for Marketing Operations to present itself as what it actually is —the foundation of the building, not the plumbing for the house.
The first step is define how you want to identify duplicate records in your MarTech database, whether that’s Salesforce.com, Marketo, Pardot, or Eloqua.
The Data Dedupication Blog Series covers all aspects of what you need to think about before, during, and after a data deduplication project.