Big data scoping final

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Datafication of E-commerce Prospecting FrameworkDiscussion Document

Prepared by Aditya Madiraju

© adiyanth – Distribution Restricted

Indicative Scope Questions to be considered..

Conduct Big Data Discovery (User-Case) based on the specificity of the business model

Create Information Harnessing methodologies based on the existing processes

Assist to build its Proprietary Big Data Analytics Architecture

Goal: Do justice to the phrase Big Data’s true name is Datafication of everything

NB: Scope creep can occur due to the influences of 1 & 2

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Indicative Architectural Consideration

Social Media

Website Logs

Search Logs

Journal entries

Online Enquiries

Product feedbacks

Private cloud footprint

FIREWALLWeb Scrapping

Prospect Data footprint

Smart Insights Engine

Analytics Architecture


Datafication of E-commerce Prospecting FrameworkSolution Considerations

Prepared by Aditya Madiraju


Solution Considerations – Big Data Capabilities on a Hype Cycle

Part A

Solution Considerations – Data Stream

C-Sat Data

Agent Logs

CRM Data

Call Transcripts

Payment Data

Data Linking & Cleaning

Text Mining Framework

Derived Attributes Framework

Common Text Representation

Indexed XML/ CSV files

Data warehouse

Data SourcesData Processing & Conversion

StageData Storage Stage Analysis & Reporting Stage

Assisted Insight generation

Decision Matrix

Reporting & Automation

Social Signals

Digital Pathways

Part B

Enabling highest data quality and governance


Solution Considerations – Structuring the Unstructured !!

Cruising Altitude (Fitness Value):

1.Sum of mutual information between cue & environment2.Linear function of environment probabilities

Transition Altitude (Half-Life Value):

1. Qualitative Data Value =( Data Usefulness ) * ( Loss to Competitive Advantage ) * ( Timeliness )

2. No. of days it takes for Qualitative Data Value to Half itself

Landing Altitude (Quality Value):

1. Completeness2. Consistency & Integrity

Ground Level (Decision Value):

1. Quantitative Decision Matrix2. Behavioral Decision Matrix

Part C


Solution Considerations – E-commerce Decisions enablement

Part D


Datafication of E-commerce Prospecting FrameworkSolution Capacity


Solution Capacity – Integrated LTV view


How long does value in information persist?

How can we account for its value if its own utility, usefullness, quality, or fitness falls?

Does the value curve fall at the same rate as the fitness curve?

How does information fitness change, and how is it changed, once information assets are combined (think bills of material)

Solution Capacity – Information Excellence


Source: Gartner (December 2014)

Solution Capacity - Big Data Discovery


Solution Capacity - Addressing few myths out there

Big data scoping final

Data & Analytics

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