Introduction Objective

Introduction

1. Objective

2. Overview

The Business Data Lake Conceptual Framework is described at the Enterprise Level. It means it provides both Technological and Business, organizational content. The new, disruptive technology that has emerged from the digital transformation of the Internet Giants can benefit to almost every enterprise (and ecosystem), but it also comes with a new, specific mindset that has to be addressed at the Enterprise level.

The content of the Business Data Lake Conceptual Framework has been selected to be relevant for any industry. It intentionally does not integrate sector-specific constraints or principle. Thus, obviously, it does not help regarding specific digital strategy objectives. The Business Data Lake will help you getting Insights from all kinds of data. It will not tell which new digital services you have to build.

3. Linkage to Other Open Group Standards

   1. Linkage to TOGAF

The Business Data Lake standard implements parts of the generic Information Sharing Environment (ISE) concept elaborated on in the TOGAF Information Architecture.

2. Linkage to Archimate

3. Linkage to IT4IT

4. Linkage to O-DEF

4. Conformance

5. Terminology

Can Describes a possible feature or behavior available to the user or application.

May Describes a feature or behavior that is optional. To avoid ambiguity, the opposite of “may” is expressed as “need not”, instead of “may not”.

Shall Describes a feature or behavior that is a requirement. To avoid ambiguity, do not use “must” as an alternative to “shall”.

Shall not Describes a feature or behavior that is an absolute prohibition.

Should Describes a feature or behavior that is recommended but not required.

Will Same meaning as “shall”; “shall” is the preferred term.

6. Future Directions

Thus the Business Data Lake Conceptual Framework may be updated to reflect this kind of major change or opportunity. At the time of its first release (November 2015) it is planned that the conceptual framework described in this document is to be completed (notably by Architecture Principles) in the near future.

Nevertheless, as an Enterprise-level Platform concept, the Business Data Lake is designed to be stable enough for organization to embrace change. In a volatile world, utmost solid architectures are required. The Business Data Lake is one of them.

2. Definitions

Definitions for the core Business Data Lake concepts are provided in Chapter 4.

For the purposes of this standard, the following terms and definitions apply. Merriam-Webster's Collegiate Dictionary should be referenced for terms not defined in this section.

1. Analytics

Analytics could be defined as "The extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions.²"

2. Batch, Micro Batch

Micro Batch processing is executed taking as input a group of events as they come in compact package frequently – usually every few seconds or few minutes.

3. Big Data³

• 
  Volume - referring to the sheer amount of data available (e.g. Walmart collects 2.5 petabytes from its customers every hour in 2012⁵);
  
• 
  Velocity - the speed of data creation is accelerating (e.g. in 2012 2.5 exabytes of information are created every day with the amount doubling every 40 months⁶)
  
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  Variety - the sources and types of data are varied (unstructured, semi-structured and structured) be it images, digital phone messages or the like.
  

• 
  advances of technology in the past 5-10 years where improvements in computing power and storage have made the processing and integration of the data feasible⁸;
  
• 
  a shift in mindset about how data could be used"⁹ ; and
  
• 
  the conversion of analog systems (e.g. telephones) into digital (Voice over Internet Protocol [VOIP] ones creating new information assets that have to be managed.
  
• 
  The use of digital platforms (such as mobile phones) for an increasing range of activities.
  
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  The use of Internet and cloud based services for collaboration and sharing of all types of information.
  
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  Open data movements in many countries.
  

• 
  Figure 1 - Big Data - An Architecture Perspective
  
• 
  From a business perspective Big Data refers to getting used to handling large amounts of data that is "messy" (i.e. varying degrees of quality) and "giving up our quest to discover the cause of things, in return for accepting correlations"¹⁰ (i.e. focus on correlations rather than causation).
  
• 
  This integration opens up critical infrastructure protection challenges derived from the ability to access SCADA systems through new channels such as social media¹¹.
  
• 
  Certain industry/government verticals organizations, notably power generation and defence/defense, have long coped with many aspects of Big Data, but the implications are that organizations are moving on from an emphasis on transaction processing to more decision support/analytics.
  
• 
  In TOGAF the question of Big Data will be addressed implicitly through its consideration in all of the IM Functions to be implemented. Big Data will be considered a normal way of doing business.
  

4. Ecosystem

At a certain level, one of the objectives of the Open Platform 3.0, the Business Data Lake and more generally Platforms is to foster the creation and the development of such ecosystems.

5. Enterprise Data Warehouse (EDW)

The five components of a data warehouse are:

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  production data sources
  
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  data extraction and conversion
  
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  the data warehouse database management system
  
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  metadata management and governance (standards, quality, lifecycle, protection)
  
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  data warehouse administration
  
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  business intelligence (BI) tools
  

6. 
   
   Directory: bigdata
   bigdata -> Use Cases from nbd(nist big Data) Requirements wg
   bigdata -> Use Cases from nbd(nist big Data) Requirements wg 0
   bigdata -> Nist big Data Public Working Group (nbd-pwg) nbd-pwd-2015/6a,DW. abbreviated rr (M0444) Source: nbd-pwg status: Draft Title: Big Data Use Case #6 Implementation, using nbdra author: Afzal Godil
   bigdata -> Nist special Publication 1500-4 draft: nist big Data Interoperability Framework: Volume 4, Security and Privacy
   
   
   
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