Address data is semi-structured, making it one of the most challenging components in a data matching activity. For long now, manual data matching methods including extensive SQL programming and spreadsheet formulas have been used to match address lists. While this may have been workable and effective in the past, it is no longer a viable method to handle complex data from third-party sources. 

In this quick post, I cover key challenges to manual address data matching and how a…

Data, an organization’s intellectual asset, must be treated and regularly enriched to remain useful and valuable. Over 80% of companies we’ve worked with, — including Fortune 500 organizations — recorded up to 50% growth in sales and customer satisfaction as one of the many benefits of data enrichment. Those that enriched data in line with a company-wide data management plan recorded a 2X increase in ROI.

In this…

Poor address data is a complex data quality challenge that affects customers, businesses, and mailing service. Each year, millions of dollars get wasted in resolving the consequences of poor address data. Mailers spend over $20 billion on UAA mail, while direct costs to the USPS is over $1.5 billion/year. All this unnecessary cost is the result of poor, mismanaged, invalidated address data. 

Over the years, working…

Mergers & acquisitions happen when companies believe they are more valuable together than when operating separately. The companies join workforces, systems, infrastructure, and data to become a new, more powerful, more valuable, more effective entity. That is only until they realized they overlooked or underestimated the key issues with data, IT infrastructure & integration plans. In fact, most merger and acquisition plans fail miserably because of data…

Chance are you’re aiming to invest in a BI and analytics program to capitalize on the big data your company has been acquiring over the years. But before you spend millions on opting for expensive BI programs, take a step back and ask yourself three questions:

1. Do I have data I can trust?
2. Do I understand my data?
3. Do I have a data transformation & data quality framework in place?

A, ‘No’ to these questions indicates that you need to optimize…

What’s the #1 enemy to profitable machine learning, AI & data-driven initiatives?

Dirty data.

And who has had data on their agenda for a decade but are still not deeply involved in data projects?

The CEO.

In a 2019 Deloitte survey, 63% of…