Think of classic combinations: peanut butter and jelly, summer and beach days, mornings and coffee. Though the components that comprise these pairings do just fine on their own, they are somewhat incomplete without the other. That’s how you should think of data governance and data quality.
As many states continue to undergo customer relationship management (CRM) system modernizations, one key driver is open data. Open data is the process of granting information access to the public, which includes converting data to a format easily consumable by citizens. What data are we talking about? Maybe your citizens are interested in Census data, the location of available retail parcel space, or the trending price of produce. The topics vary widely and states need to figure out how to support all of it in a scalable and organized way. If residents can access that data online in an easy-to-consume fashion, that’s one less person calling into the agency or adding to the in-person queue at your office. As the trend sweeps across the public sector, more and more agencies are trying to figure out how to grant access to open data.
Data has quickly become one of the most valuable resources for agencies across the United States public sector. In fact, 87 percent of agencies consider it one of their greatest strategic assets. This year, Experian conducted our first-ever study focused solely on the public sector to gain insights in the primary drivers behind their data management practices. We surveyed 200 professionals from across the United States who work for the federal government and state and local agencies including health and human services, law enforcement, departments of motor vehicles, labor and unemployment, and tax collection.
Data is quickly becoming the currency of the digital economy. The organizations that are able to best leverage their data for strategic decisioning will be well-poised for success in the years ahead. Nearly all of the C-level executives in our study (95%) believe that data is an integral part of forming their business strategy—a sentiment that has grown by 15 percent over the prior year.
Data quality is objectively a good thing—I doubt there’s a business that doesn’t agree with that fact. But how do you know what quality data looks like? How can you assess your data quality to determine how your data stacks up?
Without data quality testing, you won’t know what potential data issues you may have, or how significant they are, making it extremely difficult to work toward the right solutions. That’s why it is essential to create a data quality checklist for your business before attempting to conduct a data quality audit.
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.
This blog post is the third and final post in a mini-series we are calling The art and science of matching your data. In the previous matching articles, we talked about the fundamentals of data matching, and both the art and the science of building matching rules based on the context of your end goal. In this final section, I want to discuss some of the more advanced aspects of record matching, and how they can provide business value.
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.