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Revenue Operations

8 Steps to Creating a Data Utopia

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Years of quick fixes and turning a blind eye to data quality concerns can have long term consequences. Most of us are struggling through our own personal data dystopias, dreaming of a utopia where perfect data leads to perfect strategies.  

In this webinar, Jasmine Chung, Director of Demand Generation & RevOps at Openprise, and Demar Amacker, Senior Director of Business Operations at Zift Solutions, share their eight essential building blocks for creating a data utopia.

Every company struggles to address their data quality issues. But turning a blind eye to this problem can cause more harm than good. Eventually, something major will break and you’ll find yourself living in a data dystopia. 

Jasmine Chung, Director of Demand Generation & RevOps at Openprise, and Demar Amacker, Senior Director of Business Operations at Zift Solutions, shared their 8 step strategy to ensure good data comes in, so good data can come out.

“When faced with data quality, it's like an iceberg. There’s more that lies beneath.” - Jasmine Chung

8 steps to creating a data utopia

  1. Know your sources

Where is your data coming from? Typical sources include events (in-person, field, anchor, partner, virtual), content syndication, paid social, and inbound. RevTech sources can be especially complex because they come through marketing and sales automation workflows, file transfers, databases/data warehouses, and data services. 

  1. Understand your format

How is your data coming in? Do you collect field type, field length, data parts, allowable values, and international data? Data compatibility is such an important feature of data storage and management that a missing field can throw off an entire data import or export between systems. 

  1. Correct your data 

How can your data management be better? You may have duplicate data, invalid values, or stale values built into your data records. Determine where your data quality practices can be improved. 

  1. Infer from your data + append and enrich

What information do you already have? If you have an email domain, you can infer a website. If you have a job title, you can infer a job function, sub-function, and role. You may have more information than you realize, by way of inference, so use what you have before finding data outside of your organization. 

What other information can you add to complete a data set? How often should you update data? What data should be updated versus left alone? Right now sales reps are struggling to understand their contacts and leads because of the Great Resignation. Can your enrichment vendor keep up with how often people change jobs? 

“When a contact leaves an organization, how do you handle it? Do you deactivate their record on an account? Or are they unique to your CRM throughout their work history?” - Demar Amacker

  1. Normalize and standardize

Can your data speak the same language across systems? Consider data scalability, variability, manageability, and how your current data quality is affecting system performance. 

  1. Deal with Duplication 

Do you have data doppelgangers? If so, you’ll need to deal with data ownership across sales reps, which system to dedupe against, which fields to use for deduping, determining the surviving records, and setting data verification rules.  

  1. Segmentation 

How can you personalize your data management and become ultra focused? Fields that can help include job level, persona, and territory/region. 

  1. Operationalization

How can you use your data effectively? Consider using it to determine team alignment, process flows, enablement and education strategies, and testing and measuring. 

 “RevOps is the execution and operationalization of your data and the process all coming together into one harmonious picture.” - Demar Amacker

At the end of the day, ask yourself, ‘what if I’m making decisions off of bad data? What if I'm using bad data to drive my whole strategy?’ 

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