Dreamforce and the Road to Data Quality

Data has been given a lot of attention this year, especially by thought leaders and visionaries at Dreamforce. Although Big Data receives the attention, it’s becoming clear across many industries that Dirty Data is the real big news. If companies want to set themselves up for success in the 2020 marketplace, they better get a handle on how to fix…

Why Do You Need Data Quality Improvement

Every month, a portion of your contact data becomes outdated as people change jobs, move up and down the organizational ladder, etc. To learn more about data decay, check out this post by Donato Diorio. To quickly summarize, the rate of data decay in your CRM and marketing automation platform depends on two things, (1) the state of the economy and (2) your industry. When the economy is good, contact turnover is relatively low. When the times are tough, there is a higher job turnover. Certain sectors have lower-than-average job turnover, while others (like the high-technology sector) has much higher job turnover.In a year, depending on the state of the economy and the industries represented in your CRM database, as much as half of your leads and/or contacts can be rendered useless.

How to Make Your Data More Effective with Data Normalization

A colleague once said something to me that changed the way I think about data. “Data is the fuel that runs your marketing automation engine.” Data is truly a passion of mine, and after hearing that metaphor, I’ve never been able to think of data any other way! So every day it drives me crazy to see marketers developing programs and initiatives that can’t be supported with the data they have, and then asking “Why isn’t my reporting showing the success of this campaign?” 

Data drives all the major initiatives of marketing automation: lead scoring, segmentation, nurturing, and measuring marketing’s contribution to revenue. Here’s the secret formula: 

Normalized Data = Effective Lead Management 

In this post, I’ll look at why marketers have to be passionate about data accuracy, and how data normalization is a key tactic in good lead management. 

Dirty data costs the U.S. economy $3 trillion+ per year

Estimates show dirty data is a big problem for the U.S. economy. How big? About 2x the national deficit.

Software expert Hollis Tibbets, the Global Director of Marketing at Dell, estimates that duplicate data and bad data combined cost the U.S. economy over $3 trillion every year – which is just about two times the national deficit.

In his post “$3 Trillion Problem: Three Best Practices for Today’s Dirty Data Pandemic,” Hollis points to a few key facts and figures to back up his estimate.
Healthcare is a breeding ground for duplicate data
The U.S. Attorney’s office recently revealed that they believe about 14% of healthcare spending is wasted due to dirty data – which includes duplicate and/or incomplete data. With 16% of the U.S. Gross Domestic Product attributed to healthcare spending – or $2.14 Trillion total spend – that would mean that duplicate and dirty data costs the healthcare industry over $300 billion every year.

Eye-Popping Facts About Duplicate Data

Most business to business marketing executives complain bitterly about the status of their databases, but have difficulty convincing senior management of the gravity of the problem.
If you are experiencing this challenge yourself in your organization, these eye-popping facts from Jon Block, the Vice President and Service Director of Sirius Decisions should help you win your senior management over to your point-of-view quickly.

Fact #1: Your Data is Doubling Every Year

The amount of data in the average b-to-b organization typically doubles every 12 to 18 months, so even if data is relatively clean today, it is usually only a matter of time before things break down.