We've used this analogy many times here at Experian Data Quality, but that's only because it makes a lot of sense when referencing data standardization. What analogy am I talking about? The one where we discuss how a robust data management strategy relies on a methodical, step-by-step approach—much like how you'd approach building a house.
Data standardization is but one step to building a good data management strategy, but I like to argue it is a fundamental step in making your data actionable.
Why is it in your best interest to invest in data standardization when undergoing a data quality or data management project? Much like how a solid foundation is essential for a strong house, data standardization is necessary to building a strong data management program. It's how organizations who strive to be data-driven can make decisions quickly and efficiently.
What exactly is data standardization?
Data standardization is the process of transforming or manipulating data into a consistent format. This data most likely lives in multiple, disparate systems, all of which may have slightly different rules and formats for how data is stored within them. These small differences can result in misunderstanding and misinterpretations of your organization's data, causing the people who rely on that data to distrust it and put multiple checks in place to make sure that the conclusions made off that data are actually correct.
If that sounds like time could be better spent acting on data rather than going through manually-exhaustive checks, you'd be right.
What does data standardization do?
Standardization takes the data that lives in disparate sources and, based on rules you define, is transforms it into a consistent, usable format. It gets rid of anomalies and outliers, so that you not can identify errors in your data and make it easier to analyze.
Due to differing requirements, along with typos and human error, the data that flows into your organization comes in a variety of formats: inconsistent capitalization, punctuation, obscure acronyms, alpha-numeric characters living in fields they shouldn't be, and so on. Data standardization tools eliminate these inconsistencies and helps you define how data should appear in your database.
I'm still not convinced that I really need to standardize my data.
If you're still in need of convincing, I'd be happy to share a personal use case.
My organization relies on a marketing automation platform, a CRM, Google Analytics, and more platforms that all collect and store data for business-wide forecasting and projections. As we strive to gain insight from our data, it makes it pretty imperative that everyone has a standardized view and understanding of the data being used to make these decisions.
If you're undergoing a data migration or modernization project, if you've been charged with cleaning up your customer database before initiating a loyaly program or KYC initiative, data standardization is really a non-negotiable first step in the process.
Why run the risk of letting inconsistent data sabotage your success? Data is one of the most important assets businesses hold today. It can either empower your organization to reach new heights, or it can be a deadweight that drags your team down. I think the choice here is clear.
You want to make sure your data is a strategic asset. We can help you standardize your data to increase its value.