Twitter Chat Announcement : Demystifying the Data Refinery on Oct 22nd from 1:00 pm – 2:00 pm ET

Organizations are storing large volumes of data in the hope of leveraging it for advanced analytics. Architectures that include data lakes, data reservoirs and data hubs are designed to help organizations manage growing volume, variety and velocity of data, and to make the data available for analysis by everyone in the organization. While a data lake can provide value, it alone isn’t sufficient for managing and analyzing disparate sources of data.

What are the additional capabilities needed to enable these centralized pools of data to deliver value? Is there a way to provide self-service access to everyone within the organization in a sustainable manner? What are the gaps that need to be addressed?  Join us in #makedatawork twitter chat to get answers for some of these questions and more.

Our special guests for the chat are R Ray Wang (@rwang0), Principal Analyst, Founder, and Chairman of Constellation Research, Inc.; David Corrigan (@dcorrigan), Director of Product Marketing for IBM InfoSphere; Paula Wiles Sigmon (@paulawilesigmon), Program Director of  Product Marketing for IBM InfoSphere; and James Kobielus (@jameskobielus), IBM big data evangelist, speaker and writer. Twitter handle @IBM_InfoSphere will be moderating the chat.

You can follow along and join the discussion using the hashtag #makedatawork. Here are the questions we’ll be discussing, as well as reference articles to help inspire the conversation on Wednesday, October 22, 1:00 p.m. ET.

#makedatawork chat questions

  1. Can the new paradigms for storing data – data lakes, data reservoirs etc., replace traditional platforms for managing data from disparate sources?
  2. How is a data refinery different from a data reservoir or a data lake? Is it a marketing gimmick?
  3. Where does a data refinery fit into an existing enterprise information architecture?
  4. What are the critical capabilities for a data refinery?
  5. What are the additional capabilities and services needed to make data in a data lake clean, relevant, and accessible to all?
  6. As we evolve toward self-service data refinement for the business user, what will be the role of IT?
  7. What advice do you have for people and organizations trying to streamline access to clean, relevant data for business users?

Looking forward to your participation!


Team IBM Data Warehousing

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