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“Boo!” Is that a ghost or ghoul? No—it’s something much spookier: bad customer contact data. Did you know that less than half of retailers trust their data to make important business decisions? In fact, 57 percent of retailers say that they rely on educated guesses or gut feelings to make decisions based on their data. While blood and guts may have a place in horror movies, gut feelings are simply not enough to go on for important business decisions. Accurate, reliable data to drive decision-making is a far stronger retail strategy.

In schoolyard terms, data migrations are the equivalent of the old “Telephone” game that you may have played as a kid. You get a line of people together, and the first person in the line whispers a sentence to the second person – “The quick brown fox jumped over the lazy dogs.” The second person then whispers this phrase to the next person, and so on, until they get to the end of the line. At that point, the last person says what the sentence is – in this case, “The slick clown’s socks slumped over the crazy bogs.” As you can see, the end result may be similar to the start, but it’s definitely not the same!

Data is at the heart of every organization, and data migration projects are important undertakings for many businesses as they strive to keep up with the pace of technological advancement. Data migrations to more updated systems underpin the success of many strategic initiatives. While 35 percent of organizations have a data migration project planned for this year, a staggering 80 percent of all data migrations fail!

Despite the fact that the importance of data is widely recognized among company executives, there is a gap between this recognition and the number of organizations that are leveraging data to empower business decisions. To close this gap, organizations are investing in data management practices to establish trust and control of their data.

Government agencies at every level—city, state, and federal—collect large amounts of data; that’s a fact. The challenge for many lies in the ways information is collected and processed. Much of it is locked up in departmental silos, on somewhat dated computer systems, and it’s hard to access for additional analysis or to share publicly. Much of it may have been collected on paper forms or input by busy staff with lots of other things to do. Yet in any commercial organization, your data might be considered like gold dust! Locked within those siloed systems are many nuggets of valuable information that can help improve the efficiency of your entire organization, help deliver better services, and help improve the lives of your constituents.

Beep! Beep! The American Association of Motor Vehicle Administrators (AAMVA) held their Region I Conference this week in Portland, Maine. Thought leaders and industry professionals seized the opportunity to network with each other, learn best practices from affiliate agencies, and share insights on what’s to come in the continually evolving world of motor vehicle administration and public safety. Portland, Maine provided a beautiful backdrop to the week, especially as conference attendees set sail on a sunset ferry cruise to Peak’s Island for a lobster dinner! 

In today’s data-driven world, you will hear buzzwords thrown around like MDM, big data, machine learning, data lakes, etc. But then you may think, how exactly do these concepts apply to my organization and my specific role? How can I deploy the most valuable data management processes? To answer these questions, it is important to understand what modern data management looks like at a high level, and then how are businesses are using modern data management to refine business processes and enable best practices. Understanding the concept of modern data management is the first step in deciding how to enable it in your organization.

This week I had the opportunity to join leading marketing and Ecommerce professionals at the Internet Retail Conference & Exhibition (IRCE) show in Chicago, Illinois. While the windy city may be known for its beloved Cubs, Blackhawks, and deep dish pizza, this week it became the pinnacle of Ecommerce. Thousands of industry professionals attended to collaborate and discuss some of the problems they face and some of the innovative things they’re working on. While this show had a multitude of businesses attending, it was clear that consumer-centric organizations were king.

In the world of data management, there is a lot of terminology that is used interchangeably. For example: validation and verification or fuzzy matching and identity resolution; these are similar terms that are widely used in our ecosystem. Data quality and data governance are two examples of terms that are not synonyms, but are often confused, and with good reason. These two terms are symbiotic, meaning they are interdependent on each other. You don’t really want to do one without the other.

If companies are collecting data, but it's inaccurate or outdated, they're only going to get themselves into trouble. Here are three examples of data quality best practices.