Big Data and Data Science: Enterprise Paths to Success
TDWI Speaker: Fern Halper, TDWI VP and Senior Research Director
Date: Wednesday, January 11, 2017
Time: 9:00 a.m. PT, 12:00 p.m. ET
Big data and data science can provide a significant path to value for organizations. These technologies, methodologies, and skills can help organizations gain additional insight about customers and operations; they can help make organizations more efficient, be a new source of revenue, and make organizations more competitive.
TDWI Research finds that there are many paths to value. On the technology front, organizations are utilizing open source and commercial packages to drive big data value. Many organizations are using a mix of on-premises and cloud technologies; many use data warehouses together with newer technologies such as Hadoop and Spark to manage and process big data. They are deploying appliances, MPP (massively parallel processing) databases, and other solutions to meet their big data management needs. A new ecosystem is evolving to support big data and data science.
On the analytics front, data scientists and others needed to succeed in big data are often hard to find. Organizations are using several approaches to build data science skills. They are hiring from the outside as well as trying to grow talent internally, often looking to business analysts to become more sophisticated analytically and supplement data science expertise. Some are using a team approach. Many organizations are creating centers of excellence and building community to provide analytics and big data expertise and to disseminate learning.
Join Fern Halper, TDWI VP and senior research director for advanced analytics, to learn more about her new Best Practices Report about big data and data science. Fern will discuss her findings and present best practices and options for big data and data science. This will include organizational strategies for deploying data science as well as big data technology options.
Fern Halper, Ph.D.
Content Provided by TDWI and
IBM, MapR, OpenText, Snowflake