The major consumer credit bureaus expect for data furnishers to report on their data in a single, standardized format, known as Metro 2®. While the Metro 2® standards are designed to make it easier to keep credit information up-to-date, many organizations still face many challenges with their Metro 2® reporting. From lack of resources to manual, time-consuming processes, many organizations currently struggling to comply with Metro 2® regulation take a reactionary approach to their reporting. As consumers become more well informed about their credit, through various ease-of-access channels, and as disputes grow exponentially, many data furnishers are looking for ways to ease their Metro 2® reporting.
Last week, I had the opportunity of attending the NASWA UI Directors’ Conference and IT/Legal Issues Forum in Orlando, Florida. The conference was a forum to collaborate and discuss innovative ways to improve customer service and business decision making, while fighting fraud within state workforce agencies. At this conference, I had the ability to connect with leadership to discuss the impact that quality data can have on their systems and processes.
In today’s highly competitive business landscape, the data an organization collects is expected to deliver insight and value back to the business. Therefore, there is an increased focus on the accuracy and reliability of data collected, while there is also the apparent need for business users to have direct access to their data. We are seeing organizations express their commitment to making data-driven decisions, and this is only possible when business users are directly able to understand and leverage data to make these decisions. Despite this growing need, a common problem presents itself when IT is the keeper of an organization’s data, and business users have to wait for insight from the IT that they can understand.
When you build something, the final product is only as strong as the foundation it was built upon. Building a company is no different. It’s not uncommon for startups, in the pursuit of rapid growth and higher valuations, to accidentally allow the basics to become an afterthought.
“Boo!” Is that a ghost or ghoul? No—it’s something much spookier: bad customer contact data. Did you know that less than half of retailers trust their data to make important business decisions? In fact, 57 percent of retailers say that they rely on educated guesses or gut feelings to make decisions based on their data. While blood and guts may have a place in horror movies, gut feelings are simply not enough to go on for important business decisions. Accurate, reliable data to drive decision-making is a far stronger retail strategy.
This week I attended the 50th Annual ISM show, which is the largest Health and Human Services (HHS) technology event of the year. Here, I had the opportunity to chat with some interesting people within the HHS space to understand their organization’s current data management practices. I was also able to listen to some informative sessions that explored ways to improve the HHS space using new, innovative technologies. I have personally spent the past couple years working in and learning about the HHS market, so I wanted to sit down and reflect on the most pressing topics in our world.
Businesses today continue to see data gaining in importance. As more and more organizations work to harness the power that their information can afford them, their underlying data is affecting every aspect of their operations. Departments like customer service, digital commerce, finance, compliance, operations, and more are all working to figure out how they can use data to better serve their customers, reduce risk, and become more efficient in their operations.
As a result of these trends, an increasing number of businesses are looking to improve their data management practices.
Data conversions are tricky undertakings. There is no way around that. They are time-consuming, expensive, and can feel downright overwhelming. For many agencies in the public sector, the constraints on budgets and resources are a challenge day in and day out. When it comes to data conversions or modernization projects, there is no exception. Across organizations in the United States, approximately 80% of data migrations fail. Why, you ask? Let’s take a closer look.
Extract, transform, and load (ETL) is the process of integrating data from multiple, typically disparate, sources and bringing them together into one central location. It is a key component to businesses successfully making use of data in a data warehouse.
Sure, the process itself is fairly straightforward, and when done right, ETL prepares an organization for powerful business intelligence initiatives. However, a lot goes into a successful ETL process. Let’s discuss the three steps involved and why data management practices are an essential foundation to carrying ETL out properly.
In schoolyard terms, data migrations are the equivalent of the old “Telephone” game that you may have played as a kid. You get a line of people together, and the first person in the line whispers a sentence to the second person – “The quick brown fox jumped over the lazy dogs.” The second person then whispers this phrase to the next person, and so on, until they get to the end of the line. At that point, the last person says what the sentence is – in this case, “The slick clown’s socks slumped over the crazy bogs.” As you can see, the end result may be similar to the start, but it’s definitely not the same!
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