While automated data cleansing is essential, there are data quality situations that need human intervention. In these situations, enabling business users through data preparation and data stewardship allows users to deal with such data quality issues. Learners will experience self-service profiling and data quality through data preparation, and play a role in record remediation efforts (from arbitration to resolution campaigns) in data stewardship.
This course helps individuals recognize and identify data quality problems. It enables individuals to prepare data for their own data analysis, and to be data stewards to ensure enterprise data maintains the highest level of quality and trust.
The Cost of Bad Data
The course starts with specific horror stories and continues with common statistics on the impacts of bad data. The group discusses their own bad data stories. This sets the stage for the importance of data preparation.
Relevance in Data Governance
Learners are introduced to how data preparation and data stewardship play a role in data governance.
Open Source and Commercial Tools
Review available open source options. Examine analyst reports on commercial data preparation and data stewardship tools, and understand what is being offered by some of the leading commercial vendors. Followed by a group discussion of their experiences.
Discuss data preparation methods and goals. Utilize Talend Cloud’s Data Preparation tool in a hands-on lab, learners profile data as well as create a recipe to correct data problems.
Operationalizing Data Preparations
Review how recipes created in data preparation can be operationalized by IT to act on other data sets. Participate in a hands-on lab with Talend Studio to embed a recipe into an ETL job.
Discuss the types of problems and situations where data quality routines should send records for human intervention. Experience a couple types of data stewardship, through hands-on labs using the Talend Cloud’s Data Stewardship tool.
At the end of this program, learners will be able to:
- Adapt these approaches to recognize and deal effectively with common data quality problems.
- Maximize the effectiveness of their data governance by helping select and utilize tools for data preparation and data stewardship.