Framework to justify and prioritize Big Data initiatives. The deck will also help in developing roadmap for big data projects

1. BIG DATA INITIATIVE
JUSTIFICATION
Framework to justify and prioritize big data initiative
Authors -
Neeraj Sabhnani ( Enterprise Strategy Sr. Consultant in Microsoft Services)
Balarama V Raju ( Enterprise Strategy Sr. Consultant in Microsoft Services)

3. the amount of data generated by enterprises
is expected to grow by 48% this year and
90% of it will be unstructured data
Big Data is a top three priority at Walmart
70% of Big Data projects revolve
around customer facing ventures—
driving sales & boosting retention

4. Limited implementation of big data
projects
Source – Survey results published in IBM report – Analytics real world use of big data

5. Risks and Challenges for Big Data
Projects
• Big data technology is evolving and many organizations
are waiting for it to stabilize
• Big data solution might not be needed for all problems,
existing analytical solutions might be well equipped to
provide business benefits
• Organizations that have not addressed the more
traditional requirements of storage, processing and
information architecture need to carefully weigh the use of
big data solutions against more traditional ones
Organizations need due diligence on benefits and risks before initiating
big data initiative and not just go with market hype

6. Justification Framework
Step 1
Business
Relevance
Step 2
Technical
Complexity
Step 3
Economic
Viability
Step 4
Pilot
Success
Step 5
Implementation
and Adoption
Organizations can use this justification framework for due diligence of big
data initiatives and for developing the roadmap

7. Business Relevance
Step 1
Identify
beneficiary of
data analysis
(organization
department)
Step 2
Identify
Organization/dep
artment goals &
objectives
Step 3
Identify functional
use cases
Step 4
Map functional
use cases against
business goals

8. Typical Functional Use Cases
Customer
Insights
• Customer insights can help to identify valuable customers, help to attract more and better
customers, retain valuable customers longer. Successful enterprises are able to attract
more profitable customers as compared to competitors, drop undesired customers and
retain their best customers by knowing them better than their competitors do
• Typical sources for customer information :
Customer information through channels – stores, web, phone, catalogs
Web logs having customer click stream information showing customer preferences,
buying patterns, testing website features to attract more visitors.
Third party information
Publically available data
Information from social media
Product
Marketing
• Industries are facing challenges to reduce design cycle times and costs, satisfy global
regulations, and satisfy customers that expect high-quality, well-designed products
• Typical sources for product information:
Product use data
Product feedback sent to manufacturers
Customer reviews
Social media data
Publicly available data
Data from patents organization

9. Typical Functional Use Cases
Operations
Objective is to reduce operations cost through monitoring of devices and processes for failures
and problems, issuing SLA alerts for running out of capacity, or troubleshooting and
preventing application outages
• Typical data sources are :
Logs- web, application, transactions etc.
RFID data
GPS data
Fraud
detection &
prevention
• Social data is widely used to detect fraud. Medical claims, insurance claims, online retail or
Web click fraud are areas where big data analytics can play an important role through
social media data
• Big data technology gives a high-granularity view of the social networks and other
relationships, therefore resulting in a substantially clarified picture of fraud activities
Risk
Management
• Organizations can increase the sophistication of risk calculation by using more data (longer
time span) and additional data from multiple sources
• Typical data sources are :
• Social data
Credit history
Assets
Web logs, event logs
Publicly available data

10. Technical Complexity
1
2
3
4
5
Complexity Level
Single Dataset Simple Analysis
Single Dataset
Multiple type of
Analysis
Linked Dataset Simple Analysis
Linked Dataset
Multiple type of
Analysis
Linked Dataset with
transactional data,
unstructured data
Multiple type of
Analysis

11. Sample Use Case –
Customer Insights
Customer Insights
Use Case
Attracting
New
customers
Customer
Retention
Innovation Product
Expansion
Technical
Complexity
Mapping
Customer sentiment
analysis
X X X X 2
Customer segmentation X X X 3
Customer lifetime value X X 4
Customer churn X 4
Customer campaign X X X 3
Recommendation
engines
X X X 2
Personalized website
optimization
X X X 2
Business Use
Case Business Goals & Objectives
Business Relevance
Technical
Complexity
Unless there is business urgency for specific goal , prioritize uses cases meeting maximum
business goals and having least technical complexity .For above example roadmap can be :
Customer sentiment analysis, Recommendation engine and Personalized website optimization

12. Economic Viability
Different evaluation for initiative types
• Game Changers
• Business modifiers/extenders
Game Changers
• Have potential to provide
long term and significant
impact
• Impact of big data not
known in beginning
• Cost Benefit analysis might
not be appropriate method
for initiative selection
Business modifiers/extenders
• Cost benefit analysis can be
used for evaluation
• Typical initiatives can cover
efficiency improvement, cost
reduction, market expansion
etc.
• Rigorous analysis required
to determine if existing
technologies can work or
will require big data
For cost benefit analysis, consider all costs
• Infrastructure cost(hardware , software)
• Implementation cost
• Operations(ongoing) cost

13. Pilot Success, Implementation &
Adoption
• Success for pilot is not just about technical
implementation but more about realized business benefits
from insights provided.
• Pilot or POCs might be needed for different use cases as
analysis requirements might vary across use cases.