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 Northern California Assessors Conference (NCAA) in South Lake Tahoe, CA. This conference provided an opportunity to network with county assessors across the region and understand opportunities to maximize success.
In the ever-increasing pace of today’s business environment, everyone is looking for ways to maximize the outcomes of their efforts and create more efficient processes. And more and more, organizations are looking to data to be the fuel that accelerates their business. According to our 2018 global data management benchmark report, 99 percent of businesses believe being data-driven gives their organization a competitive advantage. They believe that advantage includes better insight for decision making (57%), more efficient business practices (57%), and better customer relationships (56%). The thing is, though, that being data-driven only provides these benefits when the data driving the strategy is high-quality and accurate.
Business leaders today are using data to power all sorts of initiatives, from uncovering revenue opportunities to complying with regulations. More and more, data is becoming a mission-critical asset that many argue should be tracked on a balance sheet. In fact, nearly all of the C-level executives in our 2018 global data management benchmark report (95%) believe that data is an integral part of forming their business strategy—a sentiment that has increased by 15 percent over the prior year.
Last week, I had the privilege of attending the 2018 MIT CDOIQ Symposium in Cambridge, Mass. The event brings together data practitioners and business leaders from a variety of sector to advance the professional development of Chief Data Officers. Throughout the three days of workshops and classroom-style lectures, attendees were treated to a rich agenda with topics ranging from regulation to artificial intelligence. While I wish I could have attended all of the sessions, I wanted to share some common themes from the workshops I did attend.
After running email campaigns, performance metrics start flowing in. There is a gratifying feeling in seeing open rates and click-through engagement. But there are other metrics that indicate the success, or even lack of success, of your email campaigns; yes, I am talking about bounce rates. High bounce rates are the bane of any email marketer’s existence, but they are very common.
So, how you can reduce bounce backs and see better ROI from your email marketing campaigns? The answer may be simpler than you think.
Have you ever launched an email campaign only to find out that most of your emails never even made it to the intended target due to soft or hard bounces? Have you ever spent a large amount of your budget on syndicated content and then come to find out that your target audience is from a database that was collected from tradeshows and POS systems where a consumer’s email is captured with no verification? How can you determine if those email addresses were correctly captured? Whether the information was wrong at point of capture, or became outdated, these issues ultimately contribute to your company’s email reputation.
This past week I had the pleasure of representing Experian along with three of my colleagues in Chicago at IRCE (Internet Retailer Conference + Exhibition). The show ran the full gamut of the world of online retail with Ecommerce industry leaders representing many large online retailers as well as small retailers looking to embrace the next step.
If you ask senior leaders of your company if they consider data to be an asset, chances are that they’re going to say yes. If you ask them how confident they are in the accuracy of that data however, the answers quickly start to vary.
It’s not uncommon for challenges that are considered “just the way it is” to be truly simple fixes if you attack them from a data quality standpoint. Doing a cleanse of your customer database can make a world of difference for your customer communications, customer experience, and cost control.
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.