Vol.1 Business Analytics Utilizing Big Data(PDF: 4Pages ... - NTT Data

NTT DATA Technology Foresights 2012
Business Analytics Utilizing Big Data
Every two years, the amount of data in the world doubles, and by 2015, it is estimated that the total data on Earth will
amount to 7.9 zettabytes. Unstructured data, such as text and images accounts for 90% of this amount. From here on, it is
highly anticipated that this massive amount of data will be used in business analytics to improve operations and offer
innovative services. This document will introduce business analytics that use big data and the technological trends surrounding
it, as well as NTT DATA’s approach.
Technological trends of business analytics
Until now, businesses were limited to utilizing customer
and business information contained within an
extremely small units of time for acquisition, such as
every second, or several hundred milliseconds. Management
or processing of data which has these characteristics
in-house system.
will inevitably present problems for current database and
However, performance improvements
in hardware and the emergence of new services in
recent years have shown an explosive growth in the
types of information available for use. Typical examples
are sensing data and lifelog data (Figure 1). Use of sensors
and smart devices are expanding, and more detailed
data about people and things are becoming easier
to acquire. We are also seeing a rapid increase of individuals
disseminating information via social networking
services and blogs.
With big data, it is necessary to keep an eye on not only
the volume, but also the variety and velocity of the data.
Rather than just single source numerical data, it is necessary
to process unstructured data such as text and image
acquired from multiple sources. Data that was previously
acquired within a number of minutes or hours now use
processing technology.
How should we utilize such a big data? Business intelligence
(BI) has developed along with visualization in
the business environment; however, to utilize big data,
visualization is not enough. Incorporating business analytics
(BA) which includes prediction and optimization
is the key to success.
There are three major types of business analytics.
Type 1 is to find the relationship and regularity between
data sets. For example, good customers can be determined
based on an analysis of the causal relationship
between their attributes and purchasing history. Type 2
is to find an optimal solution under a specified set of
constraints. This type is valid for problems where limited
resources are used effectively, for instance, when
optimizing order quantity or scheduling shift workers.
Type 3 is to anticipate future trends by understanding
customer behaviors. Attentive services and functions
that are ahead of the curve can be offered by this type.
Examples include financial services detecting fraud or
anomaly, and offering recommendations.
In order to realize BA, many IT service providers already
offer solutions for large-scale distributed processing
(Hadoop), and streaming data processing (CEP:
Figure 1: Expectations for big data use
Complex Event Processing). Some providers have even
established specialized teams for analytics in-company,
1

and continue to advance in these types of activities.
assist with identifying revolutionary changes and other
important social incidents. Although it is currently in
Advancements in technology
How far is big data and business analytics related
the research stage, it may help prevent terrorism and
large-scale crimes in the near future.
technology actually advancing? We believe that the advancement
in the axis of growth and diversity of information
(big data aspects), and axis of analytic sophistication
(analytic aspects), are each progressing (Figure 2).
Use and analysis of large amounts of numerical data
from sensors continue to progress. From here on, in aspects
of big data, progression of diversified data such as
unstructured data will lead toward data fusion where
data is fused from multiple sources. For example, in the
field of transportation, there is currently an effort to
integrate traffic information as text expressions, and
weather information as graphical expressions to analyze
traffic congestion. The important technical point is how
to supplement and overlay data which differ in spatial
granularity and acquired timing.
From an analytical aspect, analytical technology for
diverse data continues to progress, leading toward even
more accurate future predictions and control of the real
world. For example, a retailer anticipates demand based
on sales, and by automatically calculating the appropriate
order for number of products, the retailer determines
the actual order amount. For retailers, the need for optimization
of order amount is large because opportunity
loss resulting from inventory shortages and excess inventory
is a risk too large to be ignored.
Five to ten years down the line, with an integration of
these technologies, comprehensive decision making,
which currently only possible for humans, will partially
be done by machines. A predecessor to this is the Open
Source Indicators project at Intelligence Advanced Research
Projects Activity (IARPA) of the U.S. Department
of Defense. This initiative uses Twitter posts, search engine
queries, and street corner surveillance webcams,
integrating all kinds of data for automatic analysis to
Figure 2: Steps in advancement
NTT DATA’s approach
At NTT DATA, we perceive the extensive use of big
data as an extension of business intelligence.
Traditional business intelligence was based on aggregate
analysis, and stopped at the point of visualization.
However, visualization alone has limits to how high a
degree of knowledge can be derived from data. NTT
DATA perceives a wider application of business intelligence
including visualization and business analytics,
and categorizes BI into four categories based on our data
analysis consulting experience in various fields of business.
Aggregate Analysis BI immediately aggregates and
analyzes all data. Discovery BI analyzes variations to
match data granularity and discovers rules. WHAT-IF BI
uses simulations and searches for optimal solutions to
optimize business operations. And Proactive BI analyzes
data in real time to offer future services that are ahead of
the curve. Of these abovementioned categories, Discovery
BI, WHAT-IF BI, and Proactive BI are analogs of
business analytics, and these are equivalent to the previously
described Type 1, Type2, and Type 3, respectively.
At NTT DATA, the data analysis methodology
2

Table 1: Types of analysis scenarios
related to business analytics
tailed in a separate document.)
(2) Optimized supply chain management for CPFR
[Prediction and control]
CPFR (Collaborative Planning, Forecasting, and Replenishment)
is a cooperative initiative between manufacturers
and retailers to create sales plans, prevent defects,
and reduce inventory. This effort attempts to
predict demand for products over the short term, and
mid/long term, and then create an ordering model based
BICLAVIS ® has been developed around the axis of the
previously mentioned four classes. At the core of this
lies analysis scenarios classified into analysis patterns by
objective (Table 1). Based on these scenarios, NTT DATA
uses a template for efficient analysis.
At the Data Warehouse/Business Intelligence Lab ® ,
initial process assistance is offered in the form of support
in proof-of-concept design and selecting tools for
core products, and development of demo systems tailored
to customer requirements.
Global initiatives are also gaining speed. In addition
to efforts to provide high-level ITS in Asia, and efforts
toward a smart community in Europe, a large amount of
knowledge is concentrated in the BI Global One Team
consisting of the Europe/Americas and Asian NTT DA-
TA group companies. In this way, we are able to offer
full support business analytics services to customers
with locations around the globe.
Examples of initiatives
This section will introduce some examples of NTT
DATA initiatives.
(1) Bridge monitoring [Anomaly Detection]
Bridge deterioration detection involves large maintenance
costs. We have built a demonstration system to
detect anomalies denoting distortion in the sent data
from installed sensors on a bridge. (This test case is de-
on that information (how much to order at what timing).
Additionally, to evaluate the effectiveness of this effort,
simulations are used that include realistic constraints
such as defective items and delivery dates. Usually, real
changes in inventory ordering formats involve great risk,
and pre-evaluation through simulations is of great value.
NTT DATA already offers business services based on
this method.
(3) Shift scheduling for PO [Prediction and control]
BPO (Business Process Outsourcing) involves outsourcing
all but core aspects of a business, radically revising
business processes and resources. For this initiative,
we execute shift scheduling for offices that process
multiple types of duties, along with estimating work
volume of each task, considering the necessary time
limit for completing a task, as well as the personnel skill
needed, and other real constraints in the workplace.
With this system, we can automatically generate an optimum
schedule, maximizing BPO effectiveness.
(4) Medical cost reduction policy for health insurance
organizations [Targeting]
The increase in insured persons who become seriously
ill due to lifestyle diseases has caused a problem in increased
costs for health insurance organizations. From
insured persons’ current state of health, we identified
who are at high risk for serious diseases and gave health
3

counseling at an early stage to prevent such increase
cost. By looking at past insurance claims, data mining
can help identify patterns that lead toward lifestyle diseases
or complications, allowing organizations to make
a list of high-risk patients and to provide health counseling.
The result can lead to prevention of health risks
in insured persons, and curtailing extra costs for the
health insurance organization overall.
Future vision
At the beginning of the paper, we pointed out the
trend for business analytics advancement. These advancements
will be made possible by progression of the
previously described four types of business intelligence.
NTT DATA is already undertaking efforts to realize
comprehensive decision making in 5 to 10 years from
now.
Implementation architecture
As architecture for business analytics utilizing big data,
NTT DATA defines three layers (Figure 3). The first is
big data infrastructure layer, which is an engine for
processing ultra high amounts of data in diverse formats.
Hadoop and in-memory databases serve as such platform
solutions. The second layer is business analytics
infrastructure, which is equal to libraries equipped with
high-level data analysis functions and data mining
packages. The third layer consists of analysis templates
and applications to offer analysis templates for each task
or field, and analysis scenarios tailored to each type.
In addition to total support for each layer, NTT DATA
puts extra effort into added value services particularly
for the third layer. Using the previously-described BI-
CLAVIS ® , we will accumulate knowledge in a wide variety
of fields in order to offer even more sophisticated
analytics services.
For example, in the field of traffic control, by executing
a large-scale, multi-agent simulation with data input
from beacons, probe sensor data, and traffic information,
we are researching ways to forecast traffic conditions,
and control signals and lane usage.
Additionally, in the field of marine transportation logistics,
there are needs of anticipating specific marine
areas that are unable to pass through due to incidents
occurring throughout the world using predictive incident
detection, and securing port slots for ships traveling
numerous countries (Figure 4). Quickly identifying
trends which could affect economic activity is becoming
extremely important in order to avoid risks.
Business analytics that utilize big data is the key
technology that will be used to optimize business and
society adjusting to the environmental change. NTT
DATA has a wealth of experience in analytics, and with
its neutral stand toward IT infrastructure and tools,
along with a strong global business intelligence solu-
Analysis template, analysis AP
NTT DATA’s Data’s own data analysis technique
Business Business Analytics Analytics Infrastructure foundation
Mathematical Systems Inc’s own analysis PKG group
Big Big Data Data Infrastructure foundationIn-memory DB
Hadoop
DWH appliance
CEP
Data Warehouse/Business Intelligence Lab ®
Services offered
Big Data analysis
consulting
Xrosscloud
(M2M (M2M PF PFservice)
BizXaaS Biz XaaS
(Cloud solutions)
tions’ development and deployment capability, we are in
an excellent position to provide a new type of reform
through strategic use of information for our customers.
Total support from the foundation to analysis
Figure: 3 Implementation architecture
Figure 4: Marine transportation logistics
example
4
2012.10.18