Keynote "Out of the box -- Thoughts on Data Evolution" at the plenary of Icfai Business School's BIAKM-2013, April 18th.

1. Out of the Box
--
Thoughts on Data
Evolution
-- Mahboob Hussain

2. Contents
• whoami
• Analytics
• BI
• Big Data and Logistics
• The Box
• Future directions : some thoughts
• Things I want to explore
• Q & A

3. whoami
• VNIT Nagpur, Webster University
• Vice President (Technology), Four Soft Limited.
Previously with Mukand, Parametric Technology
Corporation, FedEx
• http://bit.ly/mahboob

4. Tom Davenport
University of Houston ISRC
November 15, 2007
Analytics : Starting source

5. Definitions and Insights
• What are analytics?
• Comparison with DM, BI
• How is it different from before?
• Is the claim valid?

6. 6 | 2007 © All Rights
Reserved.
The Planets Are Aligned
for Analytics
• Powerful IT
• Data critical mass
• Skills sufficiency
• Business need
Source :
http://bauer.uh.edu/uhisrc/ppt/ISRC_CompetingonAnalytics_T.Davenport.ppt

7. 7 | 2007 © All Rights
Reserved.
What Are Analytics?
Analytics
What’s the best that can happen?
What will happen next?
What if these trends continue?
Why is this happening?
What actions are needed?
Where exactly is the problem?
How many, how often, where?
What happened?
CompetitiveAdvantage
Degree of Intelligence
Reporting
Decision Optimization
Predictive Analytics
Forecasting
Statistical models
Alerts
Query/drill down
Ad hoc reports
Standard reports
http://bauer.uh.edu/uhisrc/ppt/ISRC_CompetingonAnalytics_T.Davenport.ppt

8. 8 | 2007 © All Rights
Reserved.
What Should Organizations Do with
Analytics?
• Using analytics is good
o Finding the best customers, and
charging them the right price
o Minimizing inventory in supply chains
o Allocating costs accurately and
understanding how financial
performance is driven
• Competing on analytics is better
o Making analytics and fact-based
decisions a key element of strategy and
competition
 Subset of BI
http://bauer.uh.edu/uhisrc/ppt/ISRC_CompetingonAnalytics_T.Davenport.ppt

9. Gartner : The source
• What is BI? (Gartner)
o Integration
• BI Infrastructure
• Metadata management
• Development tools
• Collaboration
o Information Delivery
• Reporting
• Dashboards
• Ad hoc query
• Microsoft Office Integration
• Search Based BI
• Mobile BI
o Analysis
• OLAP
• Interactive Visualization
• Predictive modeling and data mining
• Scorecards
• Prescriptive modeling, simulation and optimization
 Our own tool @ 4S

10. © 2011 FOUR SOFT LIMITED. All rights reserved. “This presentation is for informational purposes only and may not be incorporated into a contract or agreement.”
for Informed Decision Making

11. The Product
4S Infotips is a futuristic SCM BI tool which helps the company
CXOs, Managers, Supervisors & executives across all departments;
 Visualize the Business Performance across various parameters
through Dashboards
 *Analyze huge volume of Data to understand relationships, trends
in business through a very simple, powerful and user-friendly GUI
 Identify exceptional events, analyse the causes and make informed
decisions by studying the required information represented on the
dashboard.
* Some Features limited for Enterprise Edition User Licenses

12. What’s Infotips?
DATA
INFORMATION
KNOWLEDGE
DECISION
So what are the Critical Success Factors?
(2) *The Ability to study the info
from any angle
(1) The Ability to combine Data
from multiple sources
(3) The Speed of Analysis
* Feature limited for Enterprise Edition User Licenses

13. 4S PRE DEFINED KPIs
4S PREDESIGNED DASHBOARDS
Customer
Views with 4S Predefined KPIs
* Defines New KPIs with available data fields
Model
eTrans
eCustoms
Visilog
Visilog Plus
eLog
4SeProductsDB
ExternalproductsDB
4S Data Modeling & Loading
eTrans-FF
eCustoms-
Customs
Visilog -
Visibility
eLog-
Warehousing
VisiLogPlus-
Shipper
4S Creates new KPIs & related changes in
Data Modeling as per Customer Request
* Feature limited for Enterprise Edition User Licenses

14. Standard KPIs for Shipper Logistics (Visilog Plus)
1. Purchase Order Response Time
2. Purchase order Quantity Fulfillment
3. Purchase Order Lead time
4. Carrier delivery efficiency
5. Item profitability Against Storage
6. Inventory Ageing Analysis

15. Sample Dashboard
 What is the current state
in the field?

16. Current State
• Descriptive to Diagnostics
• Emphasis on DD (what is it)
• Big Data: The ability to find patterns, correlations and
insights across multistructured data will become a
mainstream requirement as companies try to better
innovate and find operational efficiencies across
business processes that leverage data. These include
capabilities that enable the
collection, storage, management, correlation, organizati
on, exploration and analysis of multistructured data.
(Gartner 2013)
(JasperSoft with native interfaces to MongoDB, / HBase
, Oracle Big Data Appliance, Tibco Spotfire for Big Data
Analytics, SAP Data Integration with Hadoop / Hive etc).

17. The source

18. Key points
• Term origin
• Definition
• What it is not?
• Three major mindset shifts
o N = all
o Loosen up our desire for exactitude
o Correlation over causality
• Datafication
 Let’s talk about what’s happening in the enterprise.

19. 8 Business Functions TCS Explored for Big Data Practices
In addition to surveying IT and analytics executives, TCS
also wanted to collect the experiences of senior managers
in eight core business functions:
• Marketing and Sales
• Customer service (post-sale)
• Manufacturing (or production in services companies)
• R&D/product development/product engineering
• Logistics/distribution
• Human resources
• Finance/accounting
These managers accounted for 62% of the total survey
population.
http://sites.tcs.com/big-data-study/big-data-pie-business-
function/

20. Cutting the pie
How Companies Cut the Big Data Pie by Functional Area

21. Departmental Impact
Highlights:
• Sales and marketing get the biggest shares of the Big Data pie
• However, finance and logistics expect the highest ROI on Big Data
• Eight business functions vary significantly in where they see the benefits
from Big Data – and the biggest challenges they face in gaining those
benefits
http://sites.tcs.com/big-data-study/findings-business-functions/

22. Logistics : The source

23. The Story
• Gripping story of globalization
• McLean’s total involvement
• Coastal route – container ships

24. Cargo cost of the past
Cash Outlay Percent of Cost
Freight to U.S. port city $341 14.3%
Local freight in port vicinity $95 4.0%
Total port cost $1,163 48.7%
Ocean shipping $581 24.4%
European inland freight $206 8.6%
Total $2,386
Cost of Shipping One Truckload of Medicine from Chicago to
Nancy, France (estimate ca. 1960)

25. The SS Warrior : Cost and Time
Number of Pieces Percent of weight
Case 74,903 27.9%
Carton 71,726 27.6%
Bag 24,036 12.9%
Box 10,671 12.8%
Bundle 2,880 1.0%
Package 2,877 1.9%
Piece 2,634 1.8%
Drum 1,538 3.5%
Can 888 0.3%
Barrel 815 0.3%
Wheeled vehicles 53 6.7%
Crate 21 0.3%
Transporter 10 0.5%
Reel 5 0.1%
Undetermined 1,525 0.8%
Total 194,582 98.4%
5,015 tons, 194582 individual items, 95 days, $237577, 36.8%

26. Impact of the box
• Cut costs
• Cut time
• Destroyed old economy
• Helped build a new economy
• Massive global trade
• Combined with the computer, it lead to JIT

27. Pondering : Out of the box
The Box Big Data
A self – made ruthless business
magnate
???
Excessive focus on cost cutting ???
Consolidating items into the container ???
End to end innovations ???
Standardizations ???
 A couple of Box inspired innovations mentioned in the book.
 And I want to explore more.

28. Things I want to explore
• Hazy
• Big Data and Philosophy
• Brain : The ultimate domain

29. Questions ???
Thanks