1. technology and computing

How to embrace Big Data
A methodology to look at the new technology
Contents
2 Big Data in a nutshell
3 Big data in Italy
3 Data volume is not an issue
4 Italian firms embrace Big Data
4 Big Data strategies and operations need enhancements
5 The “Big” misunderstanding
5 How to approach Big Data effectively?
6The Reply value offering
7 The technological perspective
7
Big Data as a ‘Washing Machine’
8
Traditional architecture as a data source for Big Data analytics
8
Traditional and Big Data architectures working together
9 Business perspective
9 Can Big Data help in detecting insurance fraud?
11 Big Data to improve ‘churn’ analysis in the telecoms industry
12 New boundaries in customer profiling
13Conclusion
How to embrace Big Data
A methodology to look at the new technology
How to embrace Big Data. A methodology to look at the new technology
Big Data in a nutshell
Over the last twenty years, ideas of how to assemble a
decision support system have coalesced around the con-
In it’s short life Big Data assumed a wide range of mean-
cept of a data warehouse as a tool for navigating business
ings: on the one hand it refers to the global phenomenon
issues but today the real challenge for business intelli-
of information growth, resulting from the proliferation of
gence is to let emerge hidden value through intelligent
activities and data generated on the net via the social net-
filtering of low-density and high volumes of information,
work, smartphones or machine to machine interactions.
being them operational or unstructured data arising from
On the other hand, Big Data describes a new generation of
sensors, transactions or either the web. Unfortunately the
technologies and architectures, designed to extract value
unstructured data sources may not easily and cheaply fit
in a cost-effective manner from very large volumes of in-
in traditional data warehouses, which may not be able to
formation, by enabling high-speed data capture, discovery
handle the processing demand imposed by unstructured
and analysis.
data which for this reason remains largely untapped.
In the already well-established technical literature, ‘three
Vs’ are generally used to characterize Big Data:
To help connecting the dots of all the content that’s out
Volume: the total amount of data to be managed
there by analyzing a huge data set and returning results
Velocity: the pace at which the data can be processed
in seconds a new class of technology has emerged; it in-
Variety: the complexity and heterogeneity
cludes new tools as NoSQL databases, Hadoop and Map
of the data set
Reduce. These tools form the core of an open source software framework that supports the processing of very large
Please forget it all! Big Data solutions cannot be defined
data sets across clustered systems. Let we show you how
by how you can measure data in terms of Volume, Velocity
and why these technologies are gaining a leading position
and Variety. The three Vs are just measures of data related
in the interest of companies
issues. One firm’s “big data” is another firm’s peanut as
velocity appreciation strongly depends on any single context behavior.
So, what is Big Data? A nice definition says aloud: “The
frontier of a firm’s ability to store, process, and access all
the data it needs to operate effectively, make decisions,
reduce risks and serve customers”: that’s probably the real
essence of the paradigm change addressed by Big Data
technology.
2
Big data in Italy
Data volume is not an issue
In January 2013, in collaboration with Forrester Consult-
The results of the survey confirm that a high percentage
ing, Reply carried out a survey to evaluate interest in adop-
of respondents do not have to deal with ‘petabytes’, ‘zet-
tion of Big Data solutions in Italy. The aim was to explore
tabytes’ or ‘yottabytes’ of data. The most of the Italian
not just the acceptance of Big Data but also the stage of
companies manage volumes of data that are relatively in-
maturity reached by organisations in building their strat-
significant in comparison with the vast size of major enter-
egy towards Big Data implementation.
prises, being them social networks like Facebook, Twitter
or important retailers as Walmart and Target.
The study spanned vertical sectors across the country’s top
100 organisations, with concentrations in financial ser-
Anyway, Italian companies seem to have realised that vol-
vices, telecoms, energy, utilities and waste management,
ume is not the only or primary characteristic of Big Data.
retail and professional services. Key findings included the
The interest in Big Data technologies is then driven by
following, in such a way surprisingly, results:
the necessity to acquire and process heterogeneous data,
while fastening computational time at a greater level of
accuracy. This is pretty similar to the findings of a recent
study conducted in the U.S., where it became evident that
the amount of useful data generated inside and outside
the company is not raising the hugeness of the major social network.
ESTIMATE THE SIZE/VOLUME OF DATA WITHIN YOUR COMPANY
>1000TB
100-1000TB
14%
10-100TB
1-10TB
24%
<1TB
26%
None
25%
Don’t know
7%
2% 2%
6%
3% 1%
UNSTRUCTURED
DATA
13%
STRUCTURED
DATA FROM
TRANSACTIONAL
SYSTEMS
11%
15%
25%
44%
18%
24%
26%
7%
2% 3%
SEMI-STRUCTURED
DATA
0
20
40
60
80
100
3
How to embrace Big Data. A methodology to look at the new technology
Italian firms embrace Big Data
or drivers organisation cares while overall orchestrating
its business intelligence strategy, 34% of respondents
Reply identified a significant amount of interest in Big
pointed to improving data quality and consistency. But
Data technologies and solutions. It looks as the wish to
data quality is not the end goal. The whole idea of Big
take a competitive advantage from the analysis and inte-
Data is to improve business success, through factors as
gration of unstructured data is driving companies to adopt
customer insights, operational efficiencies and cost con-
Big Data technologies.
trol. Business targets must come first and data quality is
Notwithstanding only around a quarter of respondents
a prerequisite for these.
have already implemented a solution, 40% were planning
to implement in the next 12 months and a further 28%
Only 11% of respondents claimed to have a business case
planned stretching out to a slightly longer time horizon.
for Big Data with concrete KPIs and proven ROI. They
These companies are struggling with the data coming into
represent the highest level of maturity in the Big Data
their organisations and are looking for new methods to
initiatives. A further 19% reported having a business case
better leverage data to improve their businesses.
with KPIs but no proven ROI. The majority (47%) have a
business case with intangible benefits.
“BASED ON FORRESTER’S DEFINITION OF BIG DATA, WHAT BEST
DESCRIBES YOUR FIRM’S CURRENT USAGE/PLANS TO ADOPT
BIG DATA TECHNOLOGIES AND SOLUTIONS?” (SELECT ONE)
As shown by the results, 70% of respondents are not yet
able to translate the advantages of Big Data initiatives into
3%
tangible business benefits. This would indicate that fur-
EXPANDING/UPGRADING
IMPLEMENTATION
ther expertise is needed to lead Italian companies into the
19%
PLANNING TO IMPLEMENT
IN MORE THAN 1 YEAR
Big Data world. Starting small and demonstrating tangible
benefits will enable organisations to prove the ROI on a
28%
PLANNING TO IMPLEMENT IN
THE NEXT 12 MONTHS
40%
INTERESTED BUT NO PLANS
10%
NOT INTERESTED
0%
DON’T KNOW
0%
Big Data strategies and operations
need enhancements
small scale before ‘going big’.
“DO YOU HAVE A BUSINESS CASE FOR YOUR BIG DATA
INITIATIVE IN PLACE?"
WE HAVE A BUSINESS CASE
FOR BIG DATA WITH MEASURABLE
KPIS AND ALREADY PROVEN ROI
11%
WE HAVE A BUSINESS CASE
FOR BIG DATA WITH MEASURABLE
KPIS AND A PROJECTED BUT NOT
YET PROVEN ROI
19%
WE HAVE A BUSINESS CASE
FOR BIG DATA BUT WITH
INTANGIBLE BENEFITS ONLY
47%
Key goals focus firstly on data quality, followed by business objectives. The business cases that companies have
developed do not measure concrete key performance indi-
CURRENTLY WE HAVE NO
BUSINESS CASE, BUT WE ARE
CURRENTLY WORKING ON ONE
19%
cators. Moreover, Italian organisations aren’t ‘pushing the
envelope’ when exploring the potential of Big Data.
WE HAVE NO EXPLICIT
BUSINESS CASE FOR BIG DATA
4%
Although the demonstrated significant interest for the new
technology, Italian organisations need definitely to invest
more attention in improving their Big Data strategies and
operations. When asked about the most important goals
4
DON’T KNOW
MATURITY
IMPLEMENTED,
NOT EXPANDING
0%
The “Big” misunderstanding
We can segment company’s behavior into 4 blocks:
Inactive: Companies deal with Big Data issues as a
A cause of frustration for the customer trying to tap into
storage problem and essentially deny that there is a
the ability to design and embrace a strategy about Big
problem. When issues come up, they just try to fix the
Data is the fundamentally misleading view of Big Data as
problem using standard techniques. This approach
a social phenomenon on the net, generated by millions or
results from a lack of business awareness and has
even billions of pieces of information and backed by tech-
several failings: it is expensive and unpredictable.
nologies that have been developed to extract the hidden
Proactive: Companies have the technologies and the
value of that data.
infrastructures to deal with Big Data but they still
Too often technology key users (marketing or sales depart-
don’t have business cases with measurable KPIs and
ment, product development team, security and fraud of-
proven ROI.
fices, to mention just a few), are asking for solutions that
Reactive: Companies have business cases and the
will never come because echoing the traditional approach.
maturity to start a Big Data project but lack of ability
It is not simply a matter of technology. Within the func-
and expertise to address technological issues.
tional organisation Big Data demands new processes, a
Active: Companies view Big Data as an asset and
different way of interacting with the end customer, even
own the necessary human resources, processes and
new skills to leverage the increased power of the analysis.
technologies to gain insight into their data. These
Simply Big Data requires a shift, in the corporate analyst
companies looks at Big Data as an opportunity to dif-
behaviors, to leverage the potentiality of new information
ferentiate and gain competiveness, while well under-
made available and in the IT departments, to deploy a
stand it is not the last technological hype. The final
new array of IT architectures that will enable companies to
goal is then putting in place a comprehensive strategy
handle both the data storage requirements and the heavy
to maximize the data value to business purposes.
computational processes needed to handle cost-effectively large volumes of data.
How to approach Big Data
effectively?
The survey, in line with our overall understanding of company’s behaviours, suggests that the strategy to deal with
Big Data challenges will strongly differ depending on the
maturity of the organisation towards this topic. Reply has
developed a ‘Big Data maturity model’ to measure the organisation’s aptitude in approaching Big Data. The real
aim of this model is to help CxOs in better understanding
the company behavior alongside the new technology and
then properly identify the correct strategy for implementing a coherent and profitable Big Data project.
5
How to embrace Big Data. A methodology to look at the new technology
The Reply value offering
Reactive: Organisation has established a business
case with measurable KPIs but it lacks the technical
As the model demonstrates there are several challenges on
experience necessary to develop a Big Data archi-
both technical and organisational side that must be care-
tecture. Reply’s technologists can help customers in
fully addressed to achieve the full potential of Big Data,
finding the best architectural solution. The first step
while finding the right solution involves more than a simple
is to analyse the organisation’s data sources and IT
evaluation of price/performance of any specific tool.
infrastructure. The overall overview of the technical
Reply has built an integrated a consistent methodology
environment enables technologists to develop a Big
to support clients in the development of suitable strat-
Data infrastructure tailored to the customer’s goals.
egies to let them able to benefit of best of breed solu-
Active: Organisation has here reached a high maturity
tions. A multidisciplinary team of business analysts and
level in both the technology and business issues. Fine-
technologists has been established to address the main
tuning job can still be done, however, to make it easier
issues related to a Big Data project implementation within
to better develop business opportunities: this is the
a comprehensive approach. Additionally, to help business
typical environment where data scientists can, use their
people in challenging value from data, has been founded
expertise to refine the logical approach to data discove-
a data scientists team. The goal is to help customers by
ry and modeling to deliver more detailed insights.
proposing the most appropriate business and technology
model fittings to their needs.
In summary, Big Data may be approached by two different
roadmaps: starting from the business issues, using Big
This heterogeneous team can help companies at any stage
Data as a very powerful tool to redesign and improve data
of the Reply’s maturity model:
analysis processes and from a technological perspective,
Inactive: The first stage, where the organisation has no
looking at Big Data solutions to reshuffle best practices
expertise in Big Data. Technologists provide the archi-
and infrastructure in order to provide faster and cheaper
tectural solution, while business analysts and data
results.
scientists collaborate with business in discovering new
patterns from the data, to create a business case with
a proven ROI.
Proactive: Organisation has already gained experience
in the technology but do not know how to apply it to a
real business case. Reply’s business analysts can help
supporting the development of a Big Data roadmap,
to transform customer’s needs into a real business
scenario. Data scientists work with business analysts
in finding new insight and perspectives, helping the
company to improve data value.
6
The technological perspective
Big Data as a ‘Washing Machine’
The most important value offered by the Reply’s approach
A major problem when approaching a data warehouse solu-
to Big Data implementation lies in the development and
tion is represented by extracting data from outside sources,
delivery of solutions which strongly fits with the custom-
transforming and loading it into the data warehouse. ETL
er’s technological architecture.
processes (extract, transform, load) can involve considerable complexity while significant operational problems can
The goal is addressing the resolution of specific business
occur with improperly designed ETL systems, whereas – for
problems while maximizing the safeguard of the current
the business purposes – they represents a null-value, ex-
investments in technology, through a gradual integration
pensive and time consuming set of activities.
of the Big Data architecture into existing legacy systems.
Big Data can solve this problem by substituting the traFounding on the distinctive competencies and wide op-
ditional ETL process with a new kind of storage architec-
erational expertise of the Group companies, Reply devel-
ture and, on top of this, a processing layer able to quickly
oped a framework to tailor Big Data implementation in
transform data and load it into a data warehouse.
three different scenarios.
This approach can appreciably lower the overall time
needed to satisfy the necessity of building the base of
reporting/analytics value chain, at a fraction of the cost
incurred by the traditional approach. It will also introduce
a faster management of the quality and coherence of the
data ‘ignited’ into the systems.
CALCULATION ENGINE
DATA MART
DWH
DATA
ANALYTICS &
VISUALIZATION
PRESENTATION
CALCULATION ENGINE
DATA
DATA STORAGE
DATA
INGESTION
& STORAGE
STAGING AREA
Unstructured
Structured
7
How to embrace Big Data. A methodology to look at the new technology
Traditional architecture as a data source for
Traditional and Big Data architectures working together
Big Data analytics
In some cases, thinking of replacing or supplementing IT
Traditional data warehouses can handle many situations
architectures can be valued as a disruptive approach, so
but they do have limits. The volume of data imported into
that the Companies prefer to keep their incumbent sys-
a data warehouse is a critical issue in terms of costs to up-
tems despite the loss of the information that - if properly
grade the system and in data elaboration time. The higher
used - could dramatically upgrade their competiveness or
the volume, the greater the impact on processing perfor-
the revenue streams.
mance. The usual solution for this problem is to back up
Big Data architecture, more than others solutions, allows
the data but in most cases this is tantamount to losing
companies to implement a parallel infrastructure to ex-
the information.
ploit new data sources in counterpart with the traditional
Big Data architectures can load data from existing data
B.I. ones. The cost-effective hardware jointly with the
warehouse systems and process it along with data from
open source software, which represents the foundations of
sources such as data streams or unstructured data that
the Big Data solutions, enable a company to manage both
are not easily managed by the traditional data warehouse.
scenarios at a very marginal differential cost. Moreover,
There are many benefits from using this approach; it is
some of the tools that belong to the Big Data ecosystem
possible, for example, to combine classical structured data
(e.g. analytics, presentation and data integration layers)
with other sources, enabling new insights and achieving a
can be substituted by or integrated with resources already
better granularity in the data analysis. Furthermore, hav-
present in the traditional architectural stack.
ing a Big Data storage structure means that data coming
from the data warehouse will never be lost; it will always
DWH
STAGING AREA
DWH
CALC. ENGINE
STAGING AREA
DATA STORAGE
ETL - ELT
DATA
ETL - ELT
Unstructured
8
Structured
Unstructured
Structured
DATA
ANALYTICS &
VISUALIZATION
ANALYTICS
DATA
MANAGEMENT
& STORAGE
DATA STORAGE
PRESENTATION
DATA
CALCULATION ENGINE
DATA
INGESTION
& STORAGE
PRESENTATION
DATA
ANALYTICS &
VISUALIZATION
be possible to use historical data and analyse it.
Business perspective
Can Big Data help in detecting
insurance fraud?
Our daily confrontation with CxOs make clear that many
organisations start in claiming how business intelligence
The technology that most insurers have currently in place
solutions are failing to meet their current business needs;
to help to fight frauds is a blend of business rules and
this is the major push to accept looking at Big Data as
database searches, where the results rely heavily on the
the ultimate instrument to design a new, more effective
sensitivity of the claims auditor. While these techniques
information strategy. From being the sort of tool that was
have proved being successful in detecting known fraud
only needed for meteorology or mathematical simulations,
patterns, insurers today need to invest in new analytical
Big Data has pretty recently moved into the industry main-
capabilities to help them to spot unknown and complex
stream as the easiest and cheapest way to overcome tra-
fraud activities. These analytical capabilities include in-
ditional costs and implementation times of complex data
congruity detection, predictive modelling, unstructured
management systems, essential to encompass and manage
data mining and social network analysis.
heterogeneous and multi source data sets.
Anomaly detection aims at discovering fraud by identify-
Not all industries are likely to benefit from Big Data projects
ing those elements that vary from the norm. Key perfor-
equally and not surprisingly, the first movers were internet
mance indicators associated with tasks or events are base-
companies; in fact, the most popular Big Data platforms
lined and thresholds set. When a threshold for a particular
has been built on top of software originally used to batch
measure is exceeded, then the event is reported. Outliers
process data for search analysis but now retail, telecom,
or anomalies could indicate a new or previously unknown
financial services and media sectors are quickly recovering
fraud pattern.
while manufacturing and process industry are definitely approaching.
But just having the Big Data tools isn’t enough: enterprises
need to know what questions to ask, actually ask them and
then translate that into strategy or tactics. It will be important for enterprises to develop new policies around privacy,
security and intellectual property. Big Data isn’t just about
technology and employees with the right skill sets, it will
also require businesses to align work flows, processes and
organization to get the most out of it. It is important to note
that enterprises should not concentrate on destructured
data at the expense of “current data” or business information as normal. There is still a lot of value to be extracted
from the information inside their traditional databases!
Reply can help customers in designing and addressing the
right path to define an appropriate strategy, by identifying business cases where a Big Data approach can create
a true difference to meet unsolved organisation’s needs.
Below are summarized some of the most common usage
patterns explored by Reply; while the explanation of the
usage patterns may be industry-specific, the rational basis
can be applied across industries to bring new sparks that
ignite the change.
9
How to embrace Big Data. A methodology to look at the new technology
Predictive models use past fraud events to produce fraud-
Reply has established a proven methodology to apply a
propensity scores. Adjusters simply enter data and claims
Bayesian model in fraud recognition combined with Big
are automatically scored against the likelihood of them
Data analysis techniques. This is a comprehensive ap-
being fraudulent. These scores are then made available
proach, which includes data discovery through all the
for review. Use of predictive modelling makes it possible
available internal and external structured and unstruc-
to understand new fraud trends.
tured data sources, combined with the powerful computa-
Since around 80 percent of claims data is unstructured,
tional capabilities of a Big Data infrastructure to support
the use of tools able to mine unstructured data enables
the claims manager in every phase of the investigation.
insurers to analyse information arising from medical
First of all, a network analysis will identify any histori-
chronicles, police records, external and internal database
cal relationship between the actors in a specific claim,
sources or even e-mails.
revealing any connection in the past that could suggest a
Social network visualisation tools allow investigators to ac-
propensity to commit a fraud. Then a clusterization of the
tually see network connections so they can uncover previ-
actors and related behaviors based on a self-learning sta-
ously unknown relationships and conduct more effective
tistical model let emerge similarities in the data model, to
and efficient investigations.
better represent relations and attitudes to plausible fraud
By using Big Data technologies companies are able to
existence.
manage all of these issues and to ‘learn’ from experience
While this technology is still in its early stages, the bottom
to improve their fraud detection and pattern identification
line is that new Big Data analytics can be used to explore
capability. This learning characteristic enables the soft-
large volumes of networked data, using high-speed pro-
ware to adapt and increase in sophistication as more and
cessing with configurable data entry from multiple internal
more intelligence is gathered over time. The more analyti-
and external sources, to reveal fraudulent behaviour. Can
cal the tools, the higher the chance of detecting fraud in
you imagine how far you could go using a so strong para-
the early stages and predicting potential areas of abuse
digm change in tracking frauds?
before fraudsters discover the opportunity themselves. Automation also means less reliance on the human element,
and provides greater accuracy and homogeneity in fraud
discovery activity.
USERS
Internet
data base
ACTUARIAL
Risk/Tarif
Evaluation
Real time
evaluation
Contracts
Customers
Claims
BRMS
CLAIM
MANAGER
Claims
Managements
Fraud
Monitoring
Case Manager
Dashboard
Case
Analysis
SOGEI
Case
Assignment
Fraud reporting
MCTC
Data Matching
ANIA /
ISVAP
Data Certification
Others
Scoring
Clustering
10
External
data base
Workflow Mgmt
Big Data Analytics
Frauds
black list
RISK
MANAGER
Network
analysis
Big Data to improve ‘churn’ analysis
in the telecoms industry
from reactive churn management to proactive customer
retention is to use predictive churn modelling based on social analytics to identify potential ‘churners’, thereby ena-
Today’s customers want competitive pricing, value for mon-
bling the operator to act on such predictions, rather than
ey and, above all, a high quality service. They won’t hesitate
waiting for explicit trigger points (e.g. credit on prepaid
to switch providers if they don’t find what they’re looking
card running down), by which time the churn is most prob-
for. So particularly in mature markets or where regulations
ably inevitable, irrespective of any act or offer on the part
and service dematerialisation makes ‘churn’ easier, it is ab-
of the operator. Big Data analytics offer the opportunity
solutely crucial to put in place a sustainable and robust
to process and correlate new data sources and types with
strategy for customer retention to preserve customer life-
traditional ones, to achieve better results more efficiently
time value. The telecoms market provides a good example
and receive insights that will set alarm bells ringing before
of why the high acquisition costs and slim profit margins for
any damage has been done, so giving companies the op-
each customer make churn analysis vital to help companies
portunity to take preventive measures.
identify and retain the most profitable among them.
Pricing analytics and ‘next best offer’ recommendations
In this context, the paradigm change ‘more is more’ is in
in particular are classic examples of how, by analysing
tune with the main aim of Big Data analytics. The uncov-
structured data (such as CDRs) and unstructured or semi-
ering of hidden value, through the intelligent filtering of
structured data types (such as log files, IVR tracked calls
low-density and high volumes of data, can become a real
to call centres, clickstreams and, ultimately, text from
differentiating factor. The more data you have, and the
e-mails), telecoms operators can provide more accurate,
more recent and accurate it is, the faster you can learn
personalised offer recommendations.
from it and the more predictive you can be.
Last but not least is the issue of timing. It is true that
The value of Big Data can then be exploited in two dif-
traditional business intelligence solutions have allowed
ferent directions: to decrease the capital expenditure
enterprises to move forward by consolidating data sources
(CAPEX) or operational expenditure (OPEX) associated
into centralised data centres. However, this data is used
with the computational infrastructure needed to address
‘simply’ for reporting. We are now moving into a new era
the huge amount of data used to feed predictive analyti-
where information can and must be converted into real-
cal models; and/or to increase the data sources used for
time actionable insight, to enable the company to respond
the integration and leverage of new kinds of unstructured
in real-time to behavioural changes in the customer mind-
information, enabling companies to better describe and
set or to react quickly to threats on the competitive hori-
understand customer behaviour.
zon. This is exactly why and where Big Data analytics can
One method now emerging to enable an operator to move
win the battle against ‘old’ BI tools.
Feedback
VOICE
NETWORK
DATA
MOBILE WEB
NAVIGATION
DATA
CUSTOMER
INTERACTION
DATA
CRM
TOOLS
HDFS &
MAP
REDUCE
REAL TIME
ANALYTICS
AD SERVER
INSIGHTS
CAMPAIGN
MNGT
CALL
CENTER
CELL TOWERS
DATA
BIG DATA PLATFORM
OPERATIONAL
STACK
11
How to embrace Big Data. A methodology to look at the new technology
New boundaries in customer
profiling
ing more dynamic to deal with the geo-spatial and temporal dimensions, acknowledging the fact that location and
time events impact people’s propensity to react to external
Customer analytics start with data. To get better customer
stimulation; in this case, the ability to react in real-time or
insight, most companies begin by analysing their struc-
near real-time becomes a ‘must have’ feature.
tured transactional data, which typically includes information such as demographics, purchase history, com-
As demonstrated by a recent Reply project, Big Data
plaints and retention information. Statistical algorithms
technologies provide a very powerful tool-set to address
can help companies to create meaningful segments and
all of these issues. The ability to digest and elaborate in
gain insight into buying patterns. These insights and ten-
real-time huge amounts of data as single cash lines in
dencies are then encapsulated in models which are used
till receipts, and compare them with the purchase his-
as a basis for future predictions; basically, an extrapola-
tory of each customer in order to generate promotions in
tion of past history. Is this enough in today’s markets?
real-time is without any doubt a capability that would be
Probably not!
extremely hard to achieve using traditional analytics solu-
In recent years every one of us has become a powerful
tions - which would in any event be prohibitively expen-
‘walking data generator’, delivering personal information
sive. The more data and information to be analysed, the
(that reflects daily changes in our habits) through many
longer the process required (days); while Big Data solu-
different channels. Information sources include call cen-
tions allow retail companies to analyse huge volumes of
tre records, email communications and transactional data
data, with more granularity, in a shorter period (hours vs.
as well as usage patterns on company websites. Very few
days). Retailers can now get insight into customers’ sea-
enterprises, however, are in a position to probe this ‘gold
sonal trends and use it to improve the management of
mine’ of information.
stock or create tailored pricing and promotions.
In their quest to make these models more accurate, com-
While embracing this new customer approach companies
panies are starting to embrace new sources of data; but
must be aware there is a very fine line between using cus-
most of this data is unstructured and it is quite expensive
tomer analytics to create value by serving customers with
to have it integrated into traditional data warehouse and
customised precision, and destroying value by surprising
data-mart infrastructures, both in terms of cost and time.
customers with actions that erode trust. Privacy policies
Moreover, analytical algorithms are continuing to evolve to
and a consistent execution across the enterprise are es-
deal with the changing landscape brought about by new
sential and must be properly performed to understand the
trends (such as mobility, social media and e-Commerce),
ever-narrower segmentation of customers and so deliver
while the need for a very fast computational time is in-
much more precisely tailored products or services. It is
creasingly becoming a necessity to help companies to seg-
worth it, however, and the reward will surely overcome
ment their customer base more effectively, attract more
best expectations.
profitable customers, improve campaign handling or reduce customer churn. Propensity models are also becom-
12
Conclusion
As organizations will definitely understand this pattern
and invest to become more dependent on information,
While other business metrics come and go, growth con-
the processes of gathering, managing, and utilizing data
tinues to be the most important criterion used to meas-
will become more central to operational success, because
ure companies value, the measure by which the market
data is only as valuable as our ability to access and extract
assesses companies and managers evaluate their perfor-
meaning from it. This is probably the main reason why Big
mance compared to competitors. Daily we appreciate as
Data solutions have definitely left their primordial field of
competiveness passes more and more through a better
application, entering to its own right the industrial world.
understanding of the huge amount of data organizations
Also if there could be reasons to be skeptical about the
collect and store about employees, customers, finances,
Big Data expansion we can say without risk of contradic-
vendors, inventory, competitors and markets, to name only
tion that a disciplined, targeted approach to Big Data de-
a few. The amount of data needed is important because
serves a very focused attention; when organizations will
people generally make better decisions if they have more
recognize that Big Data’s ultimate value lies in generat-
data available to them.
ing higher quality insights looking in a different way to
available data to enable better decision making, interest
In parallel, even more in the coming years we will ap-
and related revenues will accelerate sharply. Albeit in this
preciate the increasing volume and detail of information
field Big Data is still in its infancy, the rapid and constant
captured by enterprises as the rise of multimedia, social
growth of attention to this technology suggests that indus-
media and the Internet of Things will fuel exponential
try begin to embrace the challenge and is ready to take on
growth in data for the foreseeable future. The real issue is
transformative measures, using the next generation of Big
data have swept into every industry and business function
Data industrial solutions.
and are now an important factor of production, alongside
Then, the final and most important question is: are you
labor and capital.
ready to harness the power of Big Data?
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