BLOG

Data governance and compliance: it’s safe to say the two go hand in hand. Without proper data governance, how can you be confident your organization is adhering to regulations? On the other hand, when organizations are compliant, you can bet there is an effective data governance strategy in place. If you’re asking yourself, “how can I get started,” we are here to help! First let’s take a look at the terms data governance and compliance, and see how they are related. 

Data matching compares two sets of data to identify duplicate records. Learn advanced data matching techniques that enable efficiency when matching records.

As 2018 commences, customers have sky-high expectations when it comes to their experiences with every business they interact with: retail brands, utility services, and even their banks. We expect these businesses to anticipate our needs, know who we are, and always be relevant. Essentially, we want companies to read our minds. While this is impossible and unrealistic, businesses can make strides by enriching their customer data to improve their customer experience.

This blog post is part two of three in a mini-series we are calling The art and science of matching your data.

Matching data should be simple, right? Well, that depends on your perspective. As much as processes can be automated these days, when it comes to record matching, the results still depend on the context in which you want to view the relationships.

This blog post is part one of three in a mini-series we are calling The art and science of matching your data.

Matching is a term used commonly throughout data management, but it is also known by several other terms: linking, deduplication, joining, aggregation, and so on. For the purposes of this discussion, let’s define matching at the process in which I can determine a relevant association between two or more individual data records.

Data doesn’t have to be a plethora of digits stretched across an Excel spreadsheet; it can be transformed into colorful imagery or visualizations and still achieve its purpose—sometimes even more effectively than just numbers on a screen ever could. Data represents statistics that measure everything from revenue growth to employee engagement to weather patterns. It’s everywhere you turn. Although the numbers may be tedious to analyze, there is greater meaning to derive from those statistics and what they tell you. The key to making the numbers more appealing is to paint a picture with your data.

Data quality is something organizations know they need---eighty--nine percent of U.S. companies plan to make data quality solutions a priority in the next 12 months. But the question is, how do you get to a state of better data quality? What are the right solutions? Can’t data just magically appear accurate and fit for a given purpose?

In today’s data-driven economy, we all want data at our finger tips and we use it in a wide variety of ways across the business, from marketing automation, operations, consumer insights and so much more.