Choose the right cloud model, staff a great data quality team, and ensure that your big data project will create positive ROI.
- By Quint Turner
- November 17, 2016
In their work, many data and business analysts spend the bulk of their time on data preparation tasks using a patchwork of tools. I visited with Manan Goel of Paxata to discuss how to reduce this overhead, how to weave together basic and advanced data preparation capabilities, and how analysts can be more efficient and effective in their work.
- By Jake Dolezal
- November 4, 2016
Data quality on Hadoop is becoming more important as more critical data is being stored there. Consider the automation and performance advantages of an on-Hadoop data quality solution which cleanses data without it ever leaving the cluster
- By Jake Dolezal
- October 3, 2016
The next leap forward in improving information agility is about rethinking who does the preparation work. Self-service data prep increases throughput and allows you to leverage the collective wisdom of the organization.
- By Adam Wilson
- September 29, 2016
How much to realistically expect from deep learning, how effective metadata can help analysts, and how to choose the right big data tool for your enterprise.
- By Quint Turner
- September 29, 2016
Since it introduced Power BI publisher for Excel, Microsoft has been working to enhance it with new features and to fix bugs highlighted by users.
- By Steve Swoyer
- September 21, 2016
We often assume that data obtained from outside sources meets the same quality standards as data from our own operational systems. Unfortunately, this may not be true.
- By Mike Schiff
- August 30, 2016
Do "Band-Aid" approaches always seem to become the permanent solution? You need to avoid shortcuts to ensure a strong foundation in your data architecture.
- By Wes Flores
- August 10, 2016