Last week, I, along with two other members of the Experian team, took our talents to sunny San Diego to soak up some knowledge and spread some insight at the Enterprise Data World (EDW) conference. The conference was packed with some of the best and brightest representatives from organizations across the U.S. and around the globe.
Data lineage charts the full life cycle of data: the path from its creation through consumption, and everything that happens along the way. For organizations interested in achieving strong data management programs, data lineage is a key component. It provides a more granular view of your data, allowing you to gain insights from the ways in which data is manipulated and transformed from collection to application.
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.
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 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.
Bright and early on a Thursday morning, hundreds of data professionals convened for the 2017 Data Governance Financial Services (DGFS) conference. Hosted in Jersey City, New Jersey, DGFS brings together likeminded and passionate data professionals from across the country with a common mission: to share best practices and overcome challenges related to their data governance programs.
Despite the fact that the importance of data is widely recognized among company executives, there is a gap between this recognition and the number of organizations that are leveraging data to empower business decisions. To close this gap, organizations are investing in data management practices to establish trust and control of their data.
An effective data management strategy is good for your business. As organizations today rely on their data to help drive business initiatives, the quality of that information is growing increasingly important. But you probably already knew that. It turns out that data professionals spend a lot of time talking about data in terms of its accuracy and a lot less time talking about its accessibility and readiness. While the importance of accurate data is undeniable, organizations should understand that having accurate data is only a benefit if you can access that information when it’s needed.
Let’s face it, depending on your institution’s resources and how you go about it, credit reporting can be a huge pain! Whether you provide consumer data to one or several Credit Reporting Agencies (CRAs), whether or not you use a third party to submit data, and whether you test data proactively or reactively, (or not at all) dealing with bureau rejects, data monitoring, and disputes can require significant time and resources.