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162 - In the Trenches: Automating Data Normalization with Scalable Solutions in Salesforce and Marketo

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İçerik CS2 tarafından sağlanmıştır. Bölümler, grafikler ve podcast açıklamaları dahil tüm podcast içeriği doğrudan CS2 veya podcast platform ortağı tarafından yüklenir ve sağlanır. Birinin telif hakkıyla korunan çalışmanızı izniniz olmadan kullandığını düşünüyorsanız burada https://tr.player.fm/legal özetlenen süreci takip edebilirsiniz.

Welcome to another insightful episode of the Revenue Growth Architects podcast, where we untangle the complex world of marketing operations. Today, we're joined by Grant Booth, a seasoned Salesforce admin with a passion for clean data. Buckle up for an intriguing ride into the depths of Data Normalization, and learn how you can save yourself hours and headaches down the road.

In this episode, we're demystifying:

  • The Basics: What is Data Normalization, really? Often referred to as data cleaning, we'll discuss its role in simplifying complex data and eliminating redundancy, ensuring you're not slowing down your processes with countless redundant values.
  • The Why: Why should you care about Data Normalization? We highlight its importance in facilitating easy and reliable reporting, fostering standardized data, and contributing to simplified automation downstream. We'll discuss why minimalism is key in retrieving and grouping particular datasets.
  • The How: How can you do Data Normalization efficiently? We'll explore strategies for future-proofing your data structure, determining the 'correct values', and how to categorize for simplicity. Most importantly, we will share detailed steps you can do to leverage CSV supported automation out of both Salesforce or Marketo to make this scalable and easy to maintain.

We're not about blocking data - we're here to transform it. We want to flip the script on the old adage, "Garbage In, Garbage Out" and make your marketing ops work smarter, not harder. And at the end of the day, it all comes down to your ABC's - Always Be CSV-ing.

  continue reading

201 bölüm

Artwork
iconPaylaş
 
Manage episode 363997597 series 2811318
İçerik CS2 tarafından sağlanmıştır. Bölümler, grafikler ve podcast açıklamaları dahil tüm podcast içeriği doğrudan CS2 veya podcast platform ortağı tarafından yüklenir ve sağlanır. Birinin telif hakkıyla korunan çalışmanızı izniniz olmadan kullandığını düşünüyorsanız burada https://tr.player.fm/legal özetlenen süreci takip edebilirsiniz.

Welcome to another insightful episode of the Revenue Growth Architects podcast, where we untangle the complex world of marketing operations. Today, we're joined by Grant Booth, a seasoned Salesforce admin with a passion for clean data. Buckle up for an intriguing ride into the depths of Data Normalization, and learn how you can save yourself hours and headaches down the road.

In this episode, we're demystifying:

  • The Basics: What is Data Normalization, really? Often referred to as data cleaning, we'll discuss its role in simplifying complex data and eliminating redundancy, ensuring you're not slowing down your processes with countless redundant values.
  • The Why: Why should you care about Data Normalization? We highlight its importance in facilitating easy and reliable reporting, fostering standardized data, and contributing to simplified automation downstream. We'll discuss why minimalism is key in retrieving and grouping particular datasets.
  • The How: How can you do Data Normalization efficiently? We'll explore strategies for future-proofing your data structure, determining the 'correct values', and how to categorize for simplicity. Most importantly, we will share detailed steps you can do to leverage CSV supported automation out of both Salesforce or Marketo to make this scalable and easy to maintain.

We're not about blocking data - we're here to transform it. We want to flip the script on the old adage, "Garbage In, Garbage Out" and make your marketing ops work smarter, not harder. And at the end of the day, it all comes down to your ABC's - Always Be CSV-ing.

  continue reading

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