The Data Roundtable
A community of data management experts![Data prep considerations for analytics, Part 2 man considering data prep for analytics](https://blogs.sas.com/content/datamanagement/files/2016/08/249226486.jpg)
.@philsimon continues his series on data prep and anlytics.
![Clean-up woman: Part 2 cleaning supplies for clean-up woman](https://blogs.sas.com/content/datamanagement/files/2016/08/200553796.jpg)
In my last post, I talked about how data still needs to be cleaned up – and data strategy still needs to be re-evaluated – as we start to work with nontraditional databases and other new technologies. There are lots of ways to use these new platforms (like Hadoop). For example, many
![Data prep considerations for analytics, Part 1](https://blogs.sas.com/content/datamanagement/files/2016/08/42-28147658.jpg)
I'm hard-pressed to think of a trendier yet more amorphous term today than analytics. It seems that every organization wants to take advantage of analytics, but few really are doing that – at least to the extent possible. This topic interests me quite a bit, and I hope to explore
![Data cataloging for data asset crowdsourcing people studying data catalogs](https://blogs.sas.com/content/datamanagement/files/2016/08/327843788.jpg)
What does it really mean when we talk about the concept of a data asset? For the purposes of this discussion, let's say that a data asset is a manifestation of information that can be monetized. In my last post we explored how bringing many data artifacts together in a
![Clean-up woman: Part 1 cleanup woman thinking of data preparation for analytics](https://blogs.sas.com/content/datamanagement/files/2016/08/580501817.jpg)
If your enterprise is working with Hadoop, MongoDB or other nontraditional databases, then you need to evaluate your data strategy. A data strategy must adapt to current data trends based on business requirements. So am I still the clean-up woman? The answer is YES! I still work on the quality of the data.
![Data prep and self-service analytics – Turning point for governance and quality efforts?](https://blogs.sas.com/content/datamanagement/files/2016/08/538938021.jpg)
The demand for data preparation solutions is at an all-time high, and it's primarily driven by the demand for self-service analytics. Ten years ago, if you were a business leader that wanted to get more in-depth information on a particular KPI, you would typically issue a reporting request to IT