People and processes play key roles in the success of data governance programs. Leading organizations also turn to technology to supplement and automate governance processes and help ensure data delivers business value. (According to our State of Data Governance survey, about 80% of companies depend on technology for managing governance.) What are the governance issues facing your organization? Can they…
A few weeks into the new year, my calendar is already filling up with the industry events, webinars and podcasts I plan to join this year. I’ll show up (or dial in) to share my thoughts on all things data — strategy, management, quality, governance, architecture, analytics, et al — and field questions from hundreds of people. Some will be…
This holiday season has me thinking about gifts for family and friends and also for my friends and colleagues in the information management industry — like the many senior executives I’ve worked with over my 30+ years. What do you give the executive who has everything? Better quality data, of course. Because data quality is that perfect present for today’s…
“Data debt” is a term based on the concept of “technology debt,” which comes from the Agile software development world. Technology debt refers to the cost of deferring a software feature, or choosing an easy and/or quick solution instead of a more thoughtful one that would take longer (or be more difficult) to achieve. As a concept and metric, data…
By definition, Big Data means a large volume of data. When it comes to the topic of data quality, you may hear that large volumes of data can, in effect, dampen out any data inconsistency. This could lead to a line of thinking that the data quality of Big Data isn’t as important. While conceptually this may be accurate, in…
Welcome back to our CIO.com series on managing the data landscape and making sure you get the most value out of your data. In our first article in the series, 5 Critical Success Factors to Turn Data Into Insight, and the ones that follow it, we seek to define these five capabilities that play key roles in the success and…
Today, buyers have many options for data governance software. There are new vendors and established players both vying for your business. Each offers tools boasting a variety of features. With so many choices, though, it can be quite confusing. My four-part Buying Decisions series concludes with a look at the top tools on the market.
Organizations turn to data governance software to help ensure their data delivers business value. Selecting a tool can become challenging, however, when you’re looking at features that are attained by wildly different tools and vendor philosophies across several distinct product categories. In some cases, several vendors may provide the same functionality in their products ─ but through very different means.
Corporate purchases of most any size require research, planning, justification and funding. Buying data governance software can add more “pre-work” to the mix, especially when senior leadership must sign off on something they may not fully understand. The second article in my “Buying Decisions” series, How to Know if Your Organization is Ready for DG Software, offers several scenarios for your organization to consider.
Our first two articles in this series introduced the essential capabilities that contribute to the success of analytics-based initiatives: business alignment (determining context and value); data understanding (seeking to better understand data assets and manage accordingly); data quality (defining accuracy for data’s use); data-centric processes (increasing understanding as new data is created, used, managed and measured); and data-centric resources (embedding data-oriented knowledge and skills).
It’s often said that a successful data governance initiative comprises people, processes and technology — and DG software is used to supplement and automate the processes put in place by organizations. With organizations accumulating more and more data and using it to inform decision-making, there’s an increasing need to ensure data integrity so business executives and other users can be confident they’re working with accurate, consistent and high-quality data.
Our recent article written by our CEO Kelle O’Neal, “5 Critical Success Factors to Turn Data Into Insight,” indicated there should be concern around the not-so-successful outcomes of many analytics and big data projects. In her article, Kelle also covered five capabilities that contribute to the success of analytics-based initiatives: business alignment; data understanding; data quality; data-centric processes; and data-centric resources.