Across nearly every industry, data has become a key driver for critical business initiatives. At the same time, data will only add value to your business when it is trustworthy and actionable. That means there should be a robust and reliable data quality management strategy in place that aligns with the needs of your entire organization. The best way to justify the need for investing in a data quality management strategy is to identify the areas in which it will benefit the business as a whole. Here are the top four reasons to invest in data quality management.
This past week, I attended the National Health and Human Services Summit in Arlington, VA. Over the past three years, I have met and worked with many state Health and Human Services (HHS) agencies across the United States. Through my work with these agencies, I have encountered a common goal is “improving outcomes.” What this means is ensuring constituents are served in the best possible way, and the focus of this conference was no different.
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 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 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!
Data is at the heart of every organization, and data migration projects are important undertakings for many businesses as they strive to keep up with the pace of technological advancement. Data migrations to more updated systems underpin the success of many strategic initiatives. While 35 percent of organizations have a data migration project planned for this year, a staggering 80 percent of all data migrations fail!
So you’re tasked with your organization’s next big data migration. Maybe you’re moving to a new CRM system. Maybe you’ve just acquired another company and need to integrate their data into your system. Whatever the reason, data migrations are critical processes that many businesses go through. The continuing explosion in the volume of data businesses collect, store and use suggests that the trend of most companies engaging in some data migration project isn’t letting up anytime soon. According to a recent data migration study, 91% of companies engage in data migration projects.
Whether you’re working on a large data migration project, or simply trying to answer business questions with your data and having issues, Experian Pandora should be part of the conversation. We often hear from customers that they have disparate databases, leverage excel to house important data, or just have no idea how bad their data really is. This often leads to us running data tests on a subset of their data, which typically cannot tell the whole story, or is not statistically significant based on the total size of their database.