A dip into ‘Data Reservoir’

In the previous blog, we discussed in great details the limitation of a Data Lake and how without proper governance, a data lake can become overwhelming and unsafe to use. Hence, emerged an enhanced data lake solution known as a data reservoir. So how does a Data Reservoir assists the Enterprise:

  • A data reservoir provides the right information to people so they can perform activities like the following:
    – Investigate and understand a particular situation or type of activity.
    – Build analytical models of the activity.
    – Assess the success of an analytic solution in production in order to improve it.
  • A data reservoir provides credible information to subject matter experts (such as data to analysts, data scientists, and business teams) so they can perform analysis activities such as, investigating and understanding a particular situation, event, or activity.
  • A data reservoir has capabilities that ensure the data is properly cataloged and protected so subject matter experts can confidently access the data they need for their work and analysis.
  • The creation and maintenance of the data reservoir is accomplished with little to no assistance and additional effort from the IT teams.

Design of a Data Reservoir:
This design point is critical because subject matter experts play a crucial role in ensuring that analytics provides worthwhile and valuable insights at appropriate points in the organization’s operation. With a data reservoir, line-of-business teams can take advantage of the data in the data reservoir to make decisions with confidence.

Data ReservoirThere are three main parts to a data reservoir described as follows:

  • The data reservoir repositories (Figure 1, item 1) provide platforms both for storing data and running analytics as close to the data as possible.
  • The data reservoir services (Figure 1, item 2) provide the ability to locate, access, prepare, transform, process, and move data in and out of the data reservoir repositories.
  • The information management and governance fabric (Figure 1 item 3) provides the engines and libraries to govern and manage the data in the data reservoir. This set of capabilities includes validating and enhancing the quality of the data, protecting the data from misuse, and ensuring it is refreshed, retained, and eventually removed at appropriate points in its lifecycle.

The data reservoir is designed to offer simple and flexible access to data because people are key to making analytics successful. For more information please read Governing and Managing Big Data for Analytics and Decision Makers.


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