Improving Data Preparation for Business Analytics
TDWI Speaker: David Stodder, TDWI Research Director
Date: Thursday, July 14, 2016
Time: 9:00 a.m. PT, 12:00 p.m. ET
Data preparation is a hot topic today because modern technologies and practices are finally giving users and IT an alternative to traditionally slow, manual, and tedious steps for getting data ready for business intelligence (BI) and analytics. Data preparation covers a range of processes that begin during the ingestion of raw, structured, and unstructured data. Processes are then needed to improve data quality and completeness, standardize how it is defined for communities of users and applications, and perform transformation steps to make the data suitable for BI and analytics.
User requirements for integrated access to larger and increasingly diverse data sources are adding complexity to data preparation. Whether the data is brought into a data warehouse, a Hadoop data lake, or another environment, good data preparation is essential to gain business value sooner from BI, visual data discovery, data science, and analytics.
In a just-published TDWI Best Practices Report, TDWI Research finds that many organizations are mired in slow and unsatisfactory data preparation processes and are seeking new technologies and practices for improving them. Strong interest exists in self-service data preparation technologies that allow non-IT users and analysts to do more data preparation on their own.
Join this webinar and learn about data preparation, including the hot trend toward self-service data preparation. The webinar will detail insights from an extensive TDWI Research survey into current activities and future plans among peer organizations for improving data preparation. It will also highlight TDWI best practices that you can apply to your organization.
In this Webinar, you will learn:
- The most critical data preparation processes and how they fit together
- Where organizations are feeling the most pain in data preparation and how newer technologies and practices can help
- Self-service data preparation—what it is and best practices for deploying it
- Integrating data preparation with BI and analytics processes—challenges and how to overcome them