Author: Andy Hayler, Co-founder and CEO, The Information Difference
Have you ever been in a meeting where someone puts up a chart to make a point and someone in the audience says, “Those aren’t the proper figures”? You are not alone. Few company executives are confident that their data is good. A 2013 Information Difference survey found that only 39% of organizations even try to measure their data quality, and only 21% rate their data quality as “high.” A common problem for global organizations is to be able to confidently know their most profitable customers, products and suppliers. A 2012 PwC survey found that customer data was rated by respondents as the lowest quality of all data domains by a wide margin (finance and employee data was highest.
If you don’t know which customers or products are your most profitable, how do you know which ones to invest in?
One US manufacturing company I worked with found that a significant proportion of its larger contracts were actually unprofitable, but this fact had been hidden amongst a spaghetti-like mess of systems and interfaces. A global energy company that I worked with discovered that a major subsidiary had just one-fifth the number of commercial customers that it thought it had, once duplicates were removed. A major bank found that a significant proportion of its customers were aged over 150 according to its systems, which proved embarrassing (and expensive) when they tried to sell life insurance to them.
All of these issues are symptoms of poor quality master data. There are many reasons why this occurs, but one is the way that key shared data in large organizations, such as “customer” and “product”, is split amongst many systems. The median number of systems generating master data is 15, according to a 2013 Information Difference survey; this number was unchanged since a similar survey five years earlier, despite all the investment made in systems over that time. Different sources of master data lead to inconsistencies, making it harder to maintain quality.
For companies that haven’t invested in a MDM solution, getting to “one version of the truth” is like chasing a rainbow. Mergers and acquisitions, complex application architectures and conflicting priorities mean that it is very tough to eliminate overlapping data. Consider a customer order. The sales person that took the order cares about closing the sale; the finance team may be more worried about the credit worthiness of the customer, the distribution folks about the weight and volume of the order and where it is to be delivered, the marketing team about the brand involved and whether it was on promotion. These differing priorities end up spawning different systems and subtly different data regarding that order. No single person may be responsible for ensuring the quality of the data associated with it.
To diagnose whether you have a problem, ask yourself these three questions:
- Am I confident that I know my most profitable customers, product and channels?
- Am I confident in the quality of my organization’s data?
- Am I sure that the information that I need to make business decisions is timely, accurate, complete and consistent throughout the organization?
If you answered, “yes” to all three questions, then well done. In one Information Difference study, just 1% of companies claimed to have a unified source for their master data. You must be one of the happy 1%. If not, then you need to consider your master data and its quality within the context of how the business manages and governs that data. Admitting that you have a problem is the first step to a solution.
About the Author
Andy founded Kalido, which under his leadership was the fastest growing business intelligence vendor in the world in 2001. Andy was the only European named in Red Herring’s “Top 10 Innovators of 2002”. Kalido was a pioneer in modern data warehousing and master data management. He is now co-founder and CEO of The Information Difference, a boutique analyst and market research firm, advising corporations, venture capital firms and software companies. He is a regular keynote speaker at international conferences on master data management, data governance and data quality. Andy has a BSc in Mathematics from Nottingham University. He is also a respected restaurant critic and author (www.andyhayler.com). Andy has an award-winning blog www.andyonsoftware.com.