Five Steps to Data (Anger) Management
A New Year always means resolution time, and most of us are resolved as we turn the calendar to improve our health: eat right, exercise more, stop smoking— we all know the list. While this article has no personal advice for industry professionals, it will hopefully make them think about the health of their business and, specifically, the health of their business data — the lifeblood of business and business processes.
Unhealthy data leads to stagnant workflows, siloed thinking and bad decisions. If your data is sick, your business is sick. Sure, you can still make sales, attract audiences, manage risk and stay compliant, but think of all the extra effort that takes when your data is bad. Unhealthy data leads to stagnant workflows, siloed thinking and bad decisions. It’s kind of like if your company, metaphorically speaking, was huffing and puffing as it climbed a few flights of stairs. You made it, but it hurts.
Just like poor heath, bad data hinders growth.
Good data, like good health, takes good data management. It’s not easy. Let’s take your customer data in multiple departments (sales, marketing, finance operations), in different geographies (local, regional, global), and in a myriad of systems (ERP, CRM, MDM, martech, adtech): If you are like most enterprises, you have few healthy standards and could use a solid dose of well-mastered data.
Good data management can’t start until you realize you actually need it. I’ve identified the first five stages of any data (anger) management. (While the concept was focused primarily on master data about customers, prospects, vendors, suppliers, brands, products and services, it pretty much applies to any type of data):
1. Denial:“This can’t be happening!”
Being close to your customers does not mean that your customer data is close to right. Your head of sales will defiantly state, “We know our customers better than anyone else.” But can you easily answer questions like “who are my top customers?” Your master file is a slave to your own lack of consistency: How many ways do you identify “Walmart?” Wal*Mart, Wal Mart, Wal-mart, Wal-Mart, WMRT? It’s time to face this — head on.
2. Anger: “Who did this?” Trying to track multiple trade styles of a global relationship or seeing 100 variations of a customer name would get anyone upset. Before you tear your people apart, however, please realize they are torn between data maintenance and maintaining relationships. As their leader, what gets you angrier, anyway: low data quality, or low volume?
3. Bargaining: “We can fix it ourselves,” you think. Many others do, too. Go on and deploy your resources to formalize a strategic plan to ensure that everyone inputs the word “STREET” the same way. Now there’sa real value-added activity. Meanwhile, your competition is out increasing market share. Do you want good spelling –– or good selling?
4. Depression: “This is a disaster!” The more relationships you have and the more data inputs you gather, the bigger the problem. Nothing feels worse than customers telling you that your view of them is wrong. They want you to have a hierarchy that reflects the way they view themselves. But you don’t — and gee, it just never seems to be right. Bummer.
5. Acceptance: “OK, we need help.” Life is moving too fast. Thousands of companies open and close each month. Hierarchies and relationships change constantly. You can’t keep up. So, reach out to partners who understand your situation. They’ve been there. They know your pain.
If you think your situation isn’t really all that bad, prepare yourself for a data intervention. Healthy data makes for a healthy business. Accept it and get to Stage 5 as quickly as possible. Once you can start to apply a consistent data structure, one that connects across your business universe and is supported with quality your organization can trust, you begin you’ve begun journey to recovery.
About the Author
Scott Taylor is principal consultant at MetaMeta Consulting, a strategic marketing consultancy focused on partnering with innovative tech brands that seek to change the nature of enterprise data management. In a variety of strategic marketing, GTM, innovation and consulting roles, he has worked with some of the world’s most iconic business data brands (including Dun & Bradstreet, Nielsen, Microsoft and WPP/Kantar).