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Creating a Profitable Data Lake

Since the last decade with the advent of big data, Data Lakes have been included in conversation. However Chief Data Officers have been reluctant to undertake the initiative. There are a few others of large Enterprises who have committed investments but are struggling to justify the spend so far. Barring a few use cases implementation here and there, the list to a successful data lake strategy lies in gaining a understanding of what is included and what is the structure.

What is a Data Lake?

In the new world of big data (trillions and billions of records, A data lake is a centralized layer for all the data in the enterprise. A comparative definition of a data lake could be enterprise data bus for the enterprise. It acts in different purposes for various types of unique needs for enterprises.

1. Inward Data lake:

A data lake which is in a receiving mode of data objects , ERP feeds, transactional data , social media data and combines and aggregates into one. Such a data lake is the ideally place to run self service BI and analytics and helps in providing a democratic view of the data eco system .

2. Outward Data Lake:

An Outward Data lake is something like that of a emanating source of data lake which becomes a source of data feeds for many a type of external and internal systems. Good examples of Outward Data lake would be the Data lakes of connected organizations like that of Facebook , Google Mail , Bloomberg, Stock exchanges etc. Such data lakes are in the business of funnel through data monetisation.

Designing the Optimum and profitable Data Lake:

The optimum data lake design for a enterprise would be in having a transient contour. The design considerations for such a transient data lake are

  • Combination of inward and outward data lake per degrees of data freedom
  • Modular and extendible
  • Searchable and indexed
  • Object-Path record memory
  • Governed by Information Catalogue mechanism
  • Possible Indexed Architecture

The above transient data lake view helps us in achieving the following objectives:

  • Making schema of Business data unit self-discoverable
  • Allows Self Service BI through democratization
  • Innovation layer as in and around the core data systems leaving space for innovation
  • Space for leveraging business and process maps to deliver real business value
Conceptual Technology Design:

Technology Implementation:

A data lake can be implementations using the one or combination of the below technology platform options:

  1. Hadoop data lake distributions
  2. Cloud based Data Blocks
  3. On Premise Big Data warehouse

Please do write to us at info@sixthsensedata.comfor a detailed discussion on journey design of your enterprise data lake.