Do you trust your data? Are you confident that the reporting delivered to your business users provides the insight they need? Good quality data is a powerful tool for making business critical decisions. In the data-driven landscape of modern business, the quality of your data matters. Continue reading Understanding Your Enterprise Data Quality
Want to know what it takes to work in data and analytics? Every other Friday, our own Informationists will share their thoughts, experiences, and advice on what they do and what they did to get there. Expect to see a wide range of answers from individuals in the same lines of work; our Informationists come from all walks of life, which only shows that there’s more than one way to get on the right career track.
Our first post featured Dip Kharod, Big Data Architect. Continuing the series is Reed Bradford, Data Quality Architect. Reed earned his MBA in Computer & Information Sciences from Temple University and his BS in Information Management from Brigham Young University (BYU). He has worked in information management and analytics for 28 years across the financial services, life sciences, hospitality, manufacturing, and retail industries.
Data Quality Management (DQM) is a major concern in most data-driven organizations. But many organizations are challenged with improving and remediating data quality. From the beginning, they struggle with questions that impede the progress of their data quality efforts.
For example: What is DQM? How do I get started? Do I even need it? What metrics should I use? What data quality rules should I define? Continue reading White Paper: How to Build a Successful DQM Program
Whether mandated by regulatory considerations, driven by executive dashboards, or meant to enable personalized targeting of marketing messages to consumers, the rapidly increasing reliance on analytics has made Data Quality a higher priority than ever before. In turn, this new status has reshaped the very meaning of Data Quality. There was a time when Data Quality really meant one thing: a simple, binary assessment of the accuracy of data. That was the beginning and end of the Data Quality discussion. Today, however, the questions have grown more complex.
From “Is my data correct?” to “What does my data actually mean?,” the questions surrounding Data Quality are undergoing a rapid transformation. This change has been driven by four major factors:
Data Quality Management, or DQM, is an important component of data management. However, many Data Owners and Data Stewards stumble when it comes to implementing DQM successfully. Should you profile data or define data quality first? What can you use to report data quality metrics? What should you do if data quality issues arise when you’re trying to make improvements based on your initial assessment?
“How can clients trust us with their money when we can’t even get their name right?” With statements like this, the business stakeholders at our client, a major financial services organization, expressed their frustration with the lack of consistency, accuracy, and completeness of their data.
Previously, we noted that the three main attributes of data quality are accuracy, suitability, and cost effectiveness (in terms of the time, effort, and resources required to achieve the desired level of data quality). Assuming the data is accurate and cost effective, how do you determine if it is suitable for what you are trying to achieve?
Depending on your perspective and what you are trying to achieve, “quality” takes on different meanings. That being said, most people would probably agree that data quality relates to some degree of “correctness”. However, data can be correct in different ways.
So, what is “quality”? Many might say that quality is having the correct information, but that is only part of the data quality story. I like to think of quality as a three-legged stool, where each “leg” or characteristic is critical in supporting the overall structure.