The document discusses big data, highlighting its complexity and the challenges enterprises face in managing it, encapsulated in Gartner's '3Vs' model: volume, velocity, and variety. It emphasizes the importance of effective data strategies and analytics in enhancing decision-making and driving business growth, alongside the necessary executive support for successful implementation. The document also outlines the role of data-driven insights in transforming operations and creating competitive advantages across various sectors.

1. Big Data and Predictive Analytics
By: Prof. Lili Saghafi
Montreal , January 2014

2. Big data
• Big data is the term for a collection of data sets
so large and complex that it becomes difficult to
process using on-hand database management
tools or traditional data processing applications.

3. Big Data, what is the use?

4. Data warehouses feed with Big
Data

5. data growth challenges and
opportunities
• In a 2001 research report and related lectures,
META Group (now Gartner) analyst Doug Laney
defined data growth challenges and opportunities
as being three-dimensional,
• increasing volume (amount of data),
• velocity (speed of data in and out), and
• variety (range of data types and sources).

6. The challenges
• The challenges include capture, creation,
storage, search, sharing, transfer, analysis, and
visualization.

7. 3Vs
• Gartner, and now much of the industry, continue
to use this "3Vs" model for describing big data.
• In 2012, Gartner updated its definition as follows:
"Big data is high volume, high velocity, and/or
high variety information assets that require new
forms of processing to enable enhanced decision
making, insight discovery and process
optimization." Additionally, a new V "Veracity" is
added by some organizations to describe it.

8. challenges
• Big data causes three key strategic and
operational challenges:
1. Information Strategy:
You need to harness the power of information
assets. Big data is causing enterprises to find
new ways to leverage information sources to
drive growth.

9. key strategic and operational
challenges: 1-Information
Strategy:

10. key strategic and operational
challenges:
2 -Data Analytics:
You need to draw more insight from your big data
analytics or large and complex datasets. You
need to predict future customer behaviors, trends
and outcomes.

11. key strategic and operational
challenges: 2-Data Analytics

12. key strategic and operational
challenges:
3- Enterprise Information Management:
Information is everywhere – volume, variety,
velocity – and it keeps growing. You need to
manage access to growing extreme information
management requirements and drive innovation
in rapid information processing.

13. key strategic and operational
challenges: 3-Enterprise
Information Management:

14. Turning Data Into A Customer
Experience
• George-Edouard Dias, head of Digital Business
for L‘Oréal gave a great example of digital
business transformation during his interview at
SAPPHIRENOW Madrid at the end of last year.
• By combining analytics, social, mobile, and
the cloud, the company aims to create new
customer experiences.

16. Difference between big data
and Business Intelligence
• Difference between big data and Business
Intelligence, regarding data and their use:
– Business Intelligence uses descriptive statistics with
data with high information density to measure things,
detect trends etc.;
– Big data uses inductive statistics and concepts from
nonlinear system identification to infer laws
(regressions, nonlinear relationships, and causal
effects) from large data sets to reveal relationships,
dependencies, and to perform predictions of outcomes
and behaviors.

17. Big Data Processing

18. Cycle for Big Data

20. Issues regarding Big Data
• issues regarding Big Data was storage,
especially with respect to the exponential growth
and size of unstructured data that did not fit into
databases

21. Storage

22. issues regarding Big Data

23. SO, Why larger data sets?
• The trend to larger data sets is due to the
additional information derivable from analysis of
a single large set of related data, as compared
to separate smaller sets with the same total
amount of data,
• It allows correlations to be found to "spot
business trends, determine quality of research,
prevent diseases, link legal citations, combat
crime, and determine real-time roadway traffic
conditions.

24. larger data sets

25. When your carpet calls your
doctor
The Human Face of Big Data
• ―magic carpet‖ ―My mother is 90 years old, and my father passed
away six years ago. Six months ago, my mother fell, and then she fell
another time, and the third time, nobody found her for five hours. We
asked her to move in with us, but she didn‘t want to. We hired people
to live with her in shifts, and she hated it. Now GE and Intel are
introducing products aimed at aging at home, and one of the
prototypes is a carpet filled with sensors. Over time, it creates a
baseline knowledge of ‗normal behavior‘ – she walks on the carpet at
9:30 am, and here‘s her gait – over time it can predict muscle
weakness, and changes to normal patterns and tweet me to ―call
mom!‖ Rick Smolan
• http://www.economist.com/node/15868133
• https://sites.google.com/site/professorlilisaghafi/newtechnologies/magiccarpet

26. • Rick believes one of the biggest opportunities is making better use of
previously ignored ―dark data‖. ―For years, meteorologists have had to
filter out ‗bioclutter‘ from Doppler radar weather systems – the ―noise‖
generated by flocks of birds or bats. But when bird researchers
realized they had 15 years of invaluable data on migration patterns
they were delighted!‖
• But Rick also cautioned that there will be new challenges. For
example, today, data is typically owned by governments and
businesses, not individuals, and there needs to be more thinking
about how to make sure that powerful data is not misused.
• For more fantastic examples and stories about Big Data is affecting
people‘s lives, visit http://bigdata.saphana.com/

27. So, Where is Big Data?
• Petabytes and exabytes of data exist in science,
technology, commerce, national defense,
telecommunications, and other fields.
• Proper storage is merely a pre-condition to
finding the real jewels in Big Data—turning data
from massive streams into knowledge, and
thereby actionable intelligence in real time as
events unfold.

28. Need of Data
What’s the ROI of What You
Don’t Know?

29. Making Strategic Decisions With
Data

30. Reliability of Data
The Bad Data Breakup

31. • Through 2015, 85% of Fortune 500
organizations will be unable to exploit big
data for competitive advantage. (Gartner)

32. Understand
Empower a
Executive
Data
and Predictive
Analytics
Champion
Measure And
Modify Your
Supply Chain In
A
Multidimensional
Global
framework
Give Your
Data TimeCritical
Situational
Awareness
Rely On A
Core
Platform
That Creates
Derivative
Intelligence
And
Knowledge
In Real Time

33. How To Deal With Big Data And
Drive Business Growth?
1. Understand it
2. Empower a Executive Data and Predictive
Analytics Champion
3. Measure And Modify Your Supply Chain In A
Multidimensional Global framework
4. Give Your Data Time-Critical Situational
Awareness
5. Rely On A Core Platform That Creates
Derivative Intelligence And Knowledge In Real
Time

35. Understand
• That in an era of data-centric science, we now
have advanced analytics that permit inferences
from granular data.
• Inferences transform data into knowledge, which
results in greater process transparency and
improvements.

36. Understand
• That when evaluating the need to institute
analytics as part of your data strategy, it is
important to remember that actionable
knowledge is not inherent in data per se;
rather, it must be extracted based upon
established rules and algorithms.

37. Understand
• That ―sophisticated analytics solutions . . . must
be embedded in frontline tools so simple and
engaging that managers and frontline employees
will be eager to use them daily.‖ Mobilizing Your
C-Suite For Big Data Analytics (McKinsey &
Company 2013).

38. These Analytics Folks Think They
Know Everything!

39. Understand
• That even ―naive users‖ should be able to ―carry
out massive data analysis without a full
understanding of systems and statistical uses.‖
National Research Council of the National Academies
• Data scientists play an indispensable role in
today‘s corporation, but business line executives
should not have to rely on them to run analytics
and make the inferences that are the basis for
decisions

40. Some Middle Managers Don‘t Like
Analytics

41. Empower an Executive Data
and Predictive Analytics Champion
• With big data analytics changing rapidly and
straining information structures, corporations and
governments need ―executive horsepower‖ or
―top-management muscle‖ behind its data
initiatives. McKinsey & Company 2013

42. Empower an Executive Data
and Predictive
Analytics Champion
• Accordingly, a C-level officer (e.g., Chief Data
Officer, CTO, or Chief Analytics Officer) who
comes from both a supply chain and analytics
background must have the mandate to lead
model analytic centers.
• In order to succeed, analysts with deep data
experience must have a clear strategy with
defined initiatives to achieve business results.

43. What You Get If You Don‘t Have
Analytics. A HIPPO.

44. Empower a C-Level Data
and Predictive
Analytics Champion
• A forward-thinking analytics strategy thus needs
to take place at the business unit level. Why?
– First, priorities will differ by business unit; the
treatment of data in one business unit may have little
utility in another.
– Second, management priorities have to reinforce
functional level goals with targets and metrics.
• A C-level executive who can work with business
line managers and still champion analytics in the
C-suite is a must.

45. Search for Data
Opinions Are Good. Data is
Better

46. Measure And Modify Your Supply
Chain In A Multidimensional Global
framework
• Do not examine your supply chain without first
considering logistics at a macro level.
• ―ratios of trade to GDP for the world as a whole
have increased from 39% in 1990 to 59% in
2012.‖ the World Economic Forum’s Outlook on the
Logistics & Supply Chain Industry (2013)
• This change is in large part the result of a
―targeted and concerted effort by industry and
governments to increase economic growth and
jobs.‖

47. Measure And Modify Your Supply Chain In
A Multidimensional Global framework
• How does supply chain fit into this larger context?
– a corporation‘s failure to maximize the knowledge in its
data and thereby to contribute to unnecessary logistics
costs imposes upon itself and others (as well as
international trade) what amounts to an inefficiencybased tax.
• How are corporations faring on other fronts?
– They spend an astonishing average of 8%of net sales
on transportation, warehousing, customer service,
administration, and inventory carrying costs. Yet many
do not have a comprehensive view of their data, let
alone their upstream or downstream logistics
functions. Supply Chain Logistics As A Driver of Business Strategy
and Profitability (C.H. Robinson 2013).

48. Measure And Modify Your Supply
Chain In A Multidimensional Global
framework
• Ignorance has impact.
• What effect is logistics framework having on
company? On customer loyalty? How can you
use most effectively, leverage your data both past
and present, and what technology do you need to
do so?
• And while an analysis of your supply chain will
ultimately include your relationships with parties
such as customers, manufacturers, providers and
retailers, it should begin with an inward-facing
assessment of key assets.

49. What‘s The ROI of Business
Intelligence? Higher than the ROI
of Ignorance.

50. Give Your Data Time-Critical
Situational Awareness
• In most organizations, data must be pulled from
disparate and distributed sources and then
processed to yield actionable intelligence.
• Analytics help a business line identify
potential points of improvement.
• ―Corporations need to make changes not only in
real time as events unfold, but also within the
constraints posed by the increasingly distributed
nature of modern data sets‖.

51. Big Data Boasts

52. Give Your Data Time-Critical
Situational Awareness
• Current supply chain management must be
concerned with multi-dimensional data that
includes temporal (handling data involving time ) and
geospatial elements (Data that have an explicit or implicit
geographic extent ).
• Examples of temporal data are the acquisition of
data from sources such as the Internet, speech
and video data, real-time imaging from satellites,
and ground-based sensors.
• Such inputs can be difficult to analyze
because the different sources that comprise
the data stream have different latencies.

53. Don‘t Trust Gut Feel

54. Give Your Data Time-Critical
Situational Awareness
• Temporal data is growing exponentially.
• Geospatial data, on the other hand, tracks location,
whether that of a storm, a car, or a tornado that may
render impossible to your trucks certain highways, thus
demanding quick redirection to avoid time lost and
equipment damage.
• For shippers, for example, both elements come into play:
it is useful to know the location of ships, containers, and
even packages in real time and/or two days prior in order
to see if interim movement in is unusual and requires
action.
• Coupled with temporal data, a logistics analyst can
make informed decisions as events in his supply chain
unfold.

55. Making Strategic Decisions With
Data

56. Rely On A Core Platform That
Creates Derivative Intelligence And
Knowledge In Real Time
• Building a robust supply chain management
platform from scratch or by combining point
solutions is nearly impossible.
• From the perspective of cost alone, it is much
more effective to partner with a third-party cloudbased solution provider.
• Your criteria when you choose a platform should
be stringent.

57. Rely On A Core Platform That
Creates Derivative Intelligence
And Knowledge In Real Time
• According to Tim Fleischer, CEO of TransVoyant,
a technology and services company that
enables sub-second operational decisions
and support, criteria should include:

58. Rely On A Core Platform That
Creates Derivative Intelligence
And Knowledge In Real Time
• “First, no data latency—you need to see your assets in real time, in
motion, as they unfold. This is critical to actionable intelligence.
• Second, the platform must be multi-dimensional. Failure to capture
temporal and geospatial data will leave even the savviest company
flat-footed.
• Third, your core platform must allow you to visualize your data assets
in multi-dimensions so that you can see what is happening in real
time.
• Fourth, it must be flexible enough to accommodate different supply
chains in your organization.
• Finally, the platform must yield derivative intelligence that will
become your company‘s intellectual property and thus comparative
advantage‖.

59. Rely On A Core Platform That
Creates Derivative Intelligence
And Knowledge In Real Time
• Supply chain management should take place on
a platform that resides in a cloud such as
Amazon Web Services (AWS), which recently
was predicted to be worth $50 billion by
2015. Tiernan Ray, Amazon’s AWS A $50 Billion Value (Barron’s
Nov. 18, 2003).
• Cloud computing is not going anywhere, and the
security of such massive vendors is appropriately
robust.
• The power of the cloud allows for the
extraordinary processing power made possible by
distributed computing

60. Cloud Computing

61. Rely On A Core Platform That
Creates Derivative Intelligence
And Knowledge In Real Time
• Now, statistical inferences can turn data into
actionable intelligence that supports reasoned
decisions.
• Moreover, companies can scale as they wish and
absorb only the marginal costs of their expansion.

62. WIFM
What‘s in it for me?

63. THANK YOU