Data, analytics and humans: The insight equation
Jim Harris shows how data, analytics and humans work together to form the "insight equation."
Tag: data management for analytics
5 articles to read as you embark on data preparation
Learn how to get started with self-service data prep in these go-to articles.
3 ways to prepare data for analytics: How to choose the best option
Jeff Stander helps us understand the different options of preparing data for analytics.
6 tips to prepare data for analytics
Proper data prep means faster, better analytics. Guest blogger Jenine Milum shares tips.
Reflections on the data-related challenges of today and tomorrow
.@philsimon chimes in about data-related accomplishments and challenges.
The case against automating data management
Phil Simon flip-flops from his last post.
Data steward: The concierge of analytics
Jim Harris says data stewards are essential to analytics, providing life cycle management for data across the enterprise.
5 principles of data management for analytics
Joyce Norris-Montanari cites five things she considers essential to data management for analytics.
Making life easier for your data scientists
Phil Simon chimes in with some tips on how to set these folks loose.
Trust in the data: Ensuring quality analytics (continued)
How should a data trust process work? David Loshin elaborates.
Data management for analytics: Best practices and examples
Focus on data governance, quality and storage if you want to do data management for analytics right.
Growing importance of data lineage when managing data for analytics
Jim Harris says learn the lineage of the data that fed the analysis before you get dazzled by visualizations or algorithms.
Trust in the data: Ensuring quality analytics
David Loshin raises questions about what needs to be done to ensure quality analytics.
The relationship between data management and analytics: Lessons from the field
Phil Simon shares some lessons from his consulting career.
Data lake management for analytics
Jim Harris warns against allowing your data lake to become a poorly managed and ungoverned data dumping ground.
Data management strategy for analytics
Avoid frustrations by following these 5 tips from David Loshin to create a successful data management strategy for analytics.
Data preparation without analytics: Just well-mixed data
If you need more than just well-mixed data, take a look at data preparation from SAS.
Top data management trends among companies that succeed with analytics
@philsimon chimes in about how success in one area can lead to success in another.
The SAS platform – It all starts with data management
To get full value from analytics programs, Todd Wright says be sure you can first access, integrate, cleanse and govern your data.
Analytical data preparation
To do their jobs well, Joyce Norris-Montanari says users doing analytical data preparation need access to both standardized and raw data.
3 new outcomes you can expect from self-service data preparation
Phil Simon weighs in on the value of getting your own hands dirty using self-service data prep.
The pros and cons of data on demand
Phil Simon chimes in on the immediacy of enterprise data.
Need faster analytics? Modernize your data management platform.
Helmut Plinke explains why modernizing your data management is essential to supporting your analytics platform.
Governing data acquisition
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?
Harmonizing semantics for consistency in interpreting analytical results
One aspect of high-quality information is consistency. We often think about consistency in terms of consistent values. A large portion of the effort expended on “data quality dimensions” essentially focuses on data value consistency. For example, when we describe accuracy, what we often mean is consistency with a defined source
Why an agile mind-set will lead to better data prep and analytics
.@philsimon on the need to adopt agile methodologies for data prep and analytics.
Data preparation and data wrangling, Part 2 (yippee, bring your lasso)
In Part 1 of this two-part series, I defined data preparation and data wrangling, then raised some questions about requirements gathering in a governed environment (i.e., ODS and/or data warehouse). Now – all of us very-managed people are looking at the horizon, and we see the data lake. How do
The importance of a business glossary for data preparation
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
Better tech means better analytics and less data prep
.@philsimon chimes in on new data-gathering methods and what they mean for analytics.
Data preparation and data wrangling, Part 1 (yippee, bring your lasso)
I'm a very fortunate woman. I have the privilege of working with some of the brightest people in the industry. But when it comes to data, everyone takes sides. Do you “govern” the use of all data, or do you let the analysts do what they want with the data to