Many DQ Global® clients are looking to achieve a single customer view (SCV) when they approach us for help. What does the concept mean?
Achieving a single customer view is a process where a business consolidates all of the data about a customer into a single record. That benefits everyone, as we’ll see in this article.
But first, let’s look at the basics. The single customer view sounds like a great idea, but how do we get there? Is it realistic to expect one record to hold absolutely everything we need to know? And what happens when we’ve created the database we’ve been planning for?
Is the SCV Achievable?
Sceptics believe that the single customer view is a utopian concept that few businesses will ever realise. That’s simply because every part of the business needs a slightly different type of data, and it’s sometimes difficult to see how the different parts of the puzzle fit together.
Experts in data quality disagree. The single customer view may be a complex endpoint for a data quality project, but that’s precisely why data quality software exists. And without a single customer view, the business never has the full picture.
Rather than writing off the single customer view as an undeliverable, unachievable goal, we simply need to accept that there’s a certain challenge in getting there. With investment in data deduplication, matching and merging, it needn’t be a fantasy.
The Consequences of Bad Data
How can a business come up with efficient processes, or make informed decisions, when each department only sees one side of the story? It can’t. Without a single customer view, the business cannot report on its operations properly, and it cannot allocate resources efficiently so as to capitalise on the data it has.
Without data deduplication and matching software, mailing lists become contaminated with out of date details; waste increases, costs skyrocket and staff are left frustrated.
Clearly, the single customer view is important if the business is to be efficient, compliant and customer focused. The good news is that no database is ever ‘too far gone’; data quality software can turn it around in a surprisingly short space of time.
Maintaining Data Quality Over Time
When a business works towards a single customer view, it will look at its databases, blend the different types of information it holds, then polish up the results so it has one comprehensive dataset. That means cross matching records, deduplicating information, merging data and standardising any errors. This may involve a certain amount of manual review, so it takes time and effort.
Once disparate sources of data have been merged, deduplicated and tidied up, the business has a clean, shiny, error-free database that will serve it for years to come, and everyone involved can breathe a sigh of relief.
Or does it?
The more likely story is that the database starts to decay again, and the business will quickly find itself wading through inaccurate records, non-existent customers’ data and records in duplicate or triplicate. It’s a natural consequence of holding information about people: the information will never be static. The business simply has to invest in ongoing data quality initiatives; everything from regular data quality checks to better ways of capturing and inputting data. Data health requires focus and investment from every part of the organisation to minimise decay.
