Analytics can get you answers from data. With an ever-increasing volume of data and new regulatory pressures, you need to know more than knowing where and how it is used. It's critical to comprehend the data: Is it relevant? Does it contain sensitive or private information? Can we detect bias?
SAS expert Leonid Batkhan demonstrates how to automatically replicate/clone metadata users’ groups and roles from one user to another.
In this SAS administration tutorial Leonid Batkhan presents various SAS programming solutions on how to identify and prevent duplicate LIBREFs in SAS 9 metadata.
Across all industries and sectors, the volume and scope of data continues to increase exponentially. It shows no sign of slowing down or reducing. UK Defence, and the wider public sector, collect vast amounts of data. They have a desire to innovate and exploit that data through analytics, artificial intelligence,
Learn why a data catalog is so valuable in helping you find and use big data at your business.
Jim Harris examines coronavirus terms that are crucial to data-driven decisions in the pandemic.
In the third post of his series, Jim Harris looks at types, terms and timing of coronavirus tests.
Could you pass a test on coronavirus testing terms? Jim Harris can help.
Dashboards and metrics support data-driven decisions – if you understand the terms.
Read about the value of data tagging and learn best practices for doing it effectively.
Jim Harris shares examples of how and why AI applications are dependent on high-quality data.
Jim Harris says learn the lineage of the data that fed the analysis before you get dazzled by visualizations or algorithms.
Kim Kaluba gives examples of the benefits of data governance for data lakes.
Get faster value out of your data by empowering business users to work with data on their own.
.@philsimon advises to be wary of those promising obvious and facile solutions to increasingly challenging governance and privacy issues.
As the application stack supporting big data has matured, it has demonstrated the feasibility of ingesting, persisting and analyzing potentially massive data sets that originate both within and outside of conventional enterprise boundaries. But what does this mean from a data governance perspective?
In the previous three blogs in this series, we talked about what metadata can be available from source systems, transformation and movement, and operational usage. For this final blog in the series, I want to discuss the analytical usage of metadata. Let’s set up the scenario. Let's imagine I'm a
As I discussed in the first two blogs of this series, metadata is useful in a variety of ways. Its importance starts at the source system, and continues through the data movement and transformation processes and into operations. Operational metadata, in particular, gives us information about the execution and completion
In the first blog of this four-part series, we discussed traditional data management and how we can apply these principles to our big data platforms. We also discussed how metadata can help bridge the gap of understanding the data as we move to newer technologies. Part 2 will focus on
Traditional data management includes all the disciplines required to manage data resources. More specifically, data management usually includes: Architectures that encompass data, process and infrastructure. Policies and governance surrounding data privacy, data quality and data usage. Procedures that manage a data life cycle from creation of the data to sunset
.@philsimon says that even seemingly useless information can be useful under the right circumstances.
Lately I've been binge-watching a lot of police procedural television shows. The standard format for almost every episode is the same. It starts with the commission or discovery of a crime, followed by forensic investigation of the crime scene, analysis of the collected evidence, and interviews or interrogations with potential suspects. It ends
In a couple of my previous blogs I discussed how to audit who made changes to data in a SAS environment. In the last couple of weeks I have been asked how to do the same thing for SAS Visual Analytics reports and explorations. The Visual Analytics administrator overview report
Love includes a range of strong and positive emotional and mental states, from the highest virtue to the simplest pleasure. An example of such a wide range of meanings is the fact that the love of a mother is different from the love of a spouse, which, in turn, is
It’s obvious that an enterprise data strategy involves data – but we sometimes disregard the fact that it should also involve metadata. Why? Because it’s key to unlocking the value of data. Metadata shows you what data is available and how people can use it. It also reveals which data
Jeff Stander passes along some of the lessons he's learned about third-party metadata collection.
I don’t know about you, but I'm asked every day where some type of data lives in our enterprise. I keep thinking that we have not done a good job of helping people learn to help themselves! A few things I have learned about corporate data assets are: The data
Integrating big data into existing data management processes and programs has become something of a siren call for organizations on the odyssey to become 21st century data-driven enterprises. To help save some lost time, this post offers a few tips for successful big data integration.
In a SAS Environment there is a lot of metadata, metadata about configuration such as server definitions, users, groups and roles and metadata about content like data, reports and jobs etc. SAS Administrators often want to report on metadata. They want to know what reports have been developed and where they are stored, what
In recent years, we practitioners in the data management world have been pretty quick to conflate “data governance” with “data quality” and “metadata.” Many tools marketed under "data governance" have emerged – yet when you inspect their capabilities, you see that in many ways these tools largely encompass data validation and data standardization. Unfortunately, we
