Data is now at the heart of the digital transformations occurring across the economy and building an advanced data analytics team is one of the keys to competitive advantage. Whether you plan to simply disrupt your own industry or expand into an adjacent segment, data is the key to unlocking opportunity. Data has become a critical corporate asset, and business leaders want to capitalize on the information they hold. But its value is tied to how it’s analysed and by whom. One dataset may be of little value, while another may contain the key to launching a new product line or cracking a challenging marketing question. One might affect only a small percentage of a company’s revenue, while another could reveal an opportunity for significant future growth. How do firms find out? Analytics.
So you convinced your business leadership that investing in a data governance program is in the best interest of the company. Now what? While embarking on a data governance program is an exciting time for any enterprise data management team, it can also be a big undertaking. With little example to follow, those beginning a data governance program, and even those who have implemented one recently, would be wise to follow industry best practices and learn from some of the cautionary tales out there. Knowing what works, and what doesn’t will help you get your program stood up faster and with greater efficiency. Here are the data governance best practices that we saw throughout the 2018 Data Governance and Information Quality (DGIQ) conference:
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.
Data governance refers to the set of processes to ensure data meets precise standards and business rules as it is entered into a system. Data governance enables businesses to exert control over the management of data assets. This encompasses the people, process, and technology that is required to make data fit for its intended purpose.
It’s no secret that the quality of your data matters. Your organization’s data is not some mysterious entity that exists only in the realm of technology and analytics, but is in fact a competitive differentiator increasingly used to influence broader business decisions around things like operations and marketing.
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.
Last week, I, along with two other members of the Experian team, took our talents to sunny San Diego to soak up some knowledge and spread some insight at the Enterprise Data World (EDW) conference. The conference was packed with some of the best and brightest representatives from organizations across the U.S. and around the globe.
Welcome to Share your success, a monthly series of interviews featuring successful people within the Experian family. I wanted to take a closer look at those who are thriving in our company to keep a pulse on everything happening in the data quality space from the people who know best: the professionals who live and breathe all things data management day in and day out. I recently sat down with VP of Finance and Operations, Garrett Kelleher, to understand how he got to where he is today.
Last week I attend the Utility Analytics Summit in Irvine, CA, which was sponsored by Southern California Edison. I’ve been to a lot of data conferences in my day, (let’s just say this was not my first rodeo) however, it was my first foray into the world of utilities. What I noticed right away was that data-related challenges run rampant regardless of the industry. The analysts, data scientists, and engineers that I met at the summit were all struggling with similar issues that organizations in finance and government have been challenged with for years.
Data lineage charts the full life cycle of data: the path from its creation through consumption, and everything that happens along the way. For organizations interested in achieving strong data management programs, data lineage is a key component. It provides a more granular view of your data, allowing you to gain insights from the ways in which data is manipulated and transformed from collection to application.