Monthly Archives: July 2017
Best practices in data governance
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.
Using data to improve city services
Government agencies at every level—city, state, and federal—collect large amounts of data; that’s a fact. The challenge for many lies in the ways information is collected and processed. Much of it is locked up in departmental silos, on somewhat dated computer systems, and it’s hard to access for additional analysis or to share publicly. Much of it may have been collected on paper forms or input by busy staff with lots of other things to do. Yet in any commercial organization, your data might be considered like gold dust! Locked within those siloed systems are many nuggets of valuable information that can help improve the efficiency of your entire organization, help deliver better services, and help improve the lives of your constituents.
Top takeaways from the MDM & Data Governance Conference
It’s 8:30 in the morning on Day 3 of the MDM & Data Governance conference and Aaron Zornes, Chief Research Officer of The MDM Institute, is about to introduce a panel of three experts who will address an intimate room of 100 or so attendees.
Now hiring: Data professionals
Organizations everywhere are looking to do more with their data assets, as well as better leverage open data and third party data sources for additional consumer insight. The good news is that there is no shortage of information available. The bad news is that wrangling and making sense of all that information can be very challenging. That is why we see 61 percent of U.S. companies stating inaccurate data is undermining their ability to provide an excellent customer experience.
According to a new Experian Data Quality report, Investing in digital transformation: This year’s most sought-after data roles, businesses are hiring a mix of business- and regulation-focused data positions. Below is a chart showing the top roles being hired according to U.S. respondents and also c-level executives specifically.
Top data quality values for DMVs from the AAMVA Region I Conference
Beep! Beep! The American Association of Motor Vehicle Administrators (AAMVA) held their Region I Conference this week in Portland, Maine. Thought leaders and industry professionals seized the opportunity to network with each other, learn best practices from affiliate agencies, and share insights on what’s to come in the continually evolving world of motor vehicle administration and public safety. Portland, Maine provided a beautiful backdrop to the week, especially as conference attendees set sail on a sunset ferry cruise to Peak’s Island for a lobster dinner!
The importance of modern data management
In today’s data-driven world, you will hear buzzwords thrown around like MDM, big data, machine learning, data lakes, etc. But then you may think, how exactly do these concepts apply to my organization and my specific role? How can I deploy the most valuable data management processes? To answer these questions, it is important to understand what modern data management looks like at a high level, and then how are businesses are using modern data management to refine business processes and enable best practices. Understanding the concept of modern data management is the first step in deciding how to enable it in your organization.
What is ETL? (Extract, Transform, Load)
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.
Process management versus data management
When it comes to storing and accessing information for an organization, there are several processes involved to help users get the data they need in a timely fashion. As one of the most valuable business assets, managing data and leveraging it to improve business operations is of paramount importance. As more and more business decisions become data-driven, organizations continue to place greater emphasis on effectively managing their data. Since most processes are now informed by data and constantly monitored, it’s easy to see how process management and data management go hand in hand. But has it always been that way? Not exactly.
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