Building Trusted Data Through Deep Profiling and Analysis with Machine Learning
TDWI Speaker: Fern Halper, TDWI VP Research, Senior Research Director for Advanced Analytics
Data environments are becoming increasingly complex. Many organizations are employing what TDWI terms a multiplatform data architecture to manage new and disparate data sources. This might include the data warehouse along with other platforms, such as a data lake or a streaming platform. The cloud might be an important part of this architecture as well. This data is often used to gain insights as organizations become more analytically sophisticated. In fact, analytics is the top driver for modern data architectures.
A first step to assembling trusted data for analytics is to perform deep profile and quality analysis to make sure the data quality, content, and structure is understood, sound, and fit for purpose. It is hard to start a meaningful analytics program without knowing what is wrong with your data. TDWI research indicates that poor data quality is an impediment to modernizing the data warehouse and a challenge for analytics. Big, disparate data sources can be especially noisy, and contrary to popular belief, data scientists like at least relatively clean data.
The good news is that the tools and technologies to profile data and identify quality issues have advanced along with the analytics market. Vendors are often utilizing machine learning and other advanced techniques across the analytics life cycle, including during the profiling and quality stages. These are operating behind the scenes to help organizations become more productive when dealing with data.
This webinar focuses on how machine learning and other analytics techniques can be used to ensure better data outcomes. Join Fern Halper, VP of TDWI Research, and IBM to learn about:
- The current state and challenges with data profiling and data quality
- Trends toward smarter data management tooling
- Machine learning for data profiling and data quality
- Tooling for data profiling, quality, and information governance
- Data classification as a way to identify sensitive data
Fern Halper, Ph.D.