You can model and predict ... integrating data across call centers, ... Abstract

4th International Conference on Humanities, Geography and Economics (ICHGE'2013) Oct. 6-7, 2013 Dubai (UAE)
A New Data Management Challenge:
How to Handle Big Data
Zeljko Panian
You can model and predict customer behaviors by
integrating data across call centers, blogs, forums, and social
media platforms into deeper analytics. You can deploy better
call-center metrics for customer management or even create an
effective micro-targeting and micro-segmentation model for
new customer acquisition with better response rates of
acceptance.
If an enterprise deals with products and/or services, with
Big Data analytics you can create powerful models for trends,
behaviors, and markets, and you can solve research and
ideation issues by leveraging “crowd-sourcing” models and
embedding analytical results from your work [2]. If you work
in the utility industry, you can create predictive models of
consumer markets by deploying technologies such as a smart
grid. This would create revenue opportunities in advisory
services and provide better models for rate management.
Another popular area where Big Data analytics is being used
is health care. For example, service providers can leverage Big
Data to deploy Body Area Networks, helping lower patient
costs while providing "patient-centric" services. Lowering
costs and enabling efficiencies are critical goals for hospitals,
nursing homes, and caregivers. Another application of Big
Data is to optimize clinical trials to prevent errors, reduce
costs, and ensure compliance and ensure you're meeting
regulatory requirements consistently. Although these analytics
are partially fulfilled today, their expansion will enable
proactive approaches rather than reactive ones.
There is considerable focus these days on new technology and it’s possible to integrate survey, social media feedback,
and participation information into traditional platforms. These
data points can be represented in analytic and reporting
visualizations, helping hospitals and care providers (for
example) to improve their quality of service [3].
Abstract—Humans and their machines are cranking out digital
information at a mind-boggling rate. Therefore, scientists coined a
special term – “Big Data” – to convey the extraordinary scale of the
data collections now being amassed inside public and private
organizations and out on the Web. Big Data has been of concern to
organizations working in select fields for some time, such as the
physical sciences (meteorology, physics), life sciences (genomics,
biomedical research), government (defense, treasury), finance and
banking (transaction processing, trade analytics), communications
(call records, network traffic data), and, of course, the Internet
(search engine indexation, social networks). While some research
organizations may rely on supercomputers to meet their Big Data
needs, we propose a toolbox to be offered like Software-as-a-Service
(SaaS) with tools accessible to organizations of all sizes and types.
Keywords— Big Data, Business Intelligence, Data Management,
Software-as-a-Service.
I. INTRODUCTION
F
UELED by the pervasiveness of the Internet,
unprecedented computing power, ubiquitous sensors and
meters, addictive consumer gadgets, inexpensive storage and
(to-date) highly elastic network capacity, we humans and our
machines are cranking out digital information at a mindboggling rate. No wonder then scientists coined a special term
– “Big Data” – to convey the extraordinary scale of the data
collections now being amassed inside public and private
organizations and out on the Web [1].
Big Data provides access to data that was always available
but not consumable. The expansion of Big Data into Business
Intelligence (BI) platforms enables BI professionals to
leverage expanded analytics and create 360° perspectives. Let
us provide a few sample areas that can benefit from BI and Big
Data analytics.
For customer relationship management systems, you can
create powerful visualizations of customer sentiments, wish
lists, and actual customer response data from campaigns to
measure true campaign effectiveness.
II. BIG FATA CHARACTERISTICS
A. What Exactly Is Big Data
Big Data is more a concept than a precise term. Some apply
the ”Big Data” label only to petabyte-scale data collections (>
one million GB) [4]. For others, a Big Data collection may
house ‘only’ a few dozen terabytes of data [5]. More often,
however, Big Data is defined depending on situation rather
than by size. Specifically, a data collection is considered “Big
Data” when it is so large an organization cannot effectively or
affordably manage or exploit it using conventional data
Zeljko Panian, Ph.D. is professor at the Faculty of Economics and
Business, University of Zagreb, Croatia (phone: 385-1-238-3217; fax: 385-1233-5633; e-mail: zpanian@efzg.hr).
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4th International Conference on Humanities, Geography and Economics (ICHGE'2013) Oct. 6-7, 2013 Dubai (UAE)
management tools.
Relationship Management (CRM) system.
Or, you can extend a digital product catalog with Web
content (like, product descriptions, photos, specifications, and
supplier information). You can even use such content to
improve the quality of your organization’s master data
management, using the Web to verify details or fill in missing
attributes.
B. Who Is Affected By Big Data?
Big Data has been of concern to organizations working in
select fields for some time, such as the physical sciences
(meteorology, physics), life sciences (genomics, biomedical
research), government (defense, treasury), finance and banking
(transaction processing, trade analytics), communications (call
records, network traffic data), and, of course, the Internet
(search engine indexation, social networks).
Now, however, due to our digital fecundity, Big Data is
becoming an issue for organizations of all sizes and types. In
fact, in 2008 businesses were already managing on average
100TB or more of digital content. Big Data has even become a
concern of individuals as awareness grows of the depth and
breadth of personal information being amassed in Big Data
collections (in contrast, some, like LifeLoggers, broadcast
their day-to-day lives in a Big Data stream of their own
making) [6].
V. DIFFICULTIES WITH BIG DATA ADOPTION
Adoption of Big Data is slower than expected in traditional
enterprises. There are several reasons for this. At the top of the
list is that current business models and goals do not require
Big Data integration. Furthermore, there is no perceived
additional value offered by Big Data as to the organization -there is no clear business case articulated, and thus no business
value calculated.
There are other inhibiting factors. Executives lack an
understanding of (and thus sponsorship of) Big Data, which
also brings processing complexities that create additional
stress on IT teams (in terms of maintenance) and business
teams (in terms of adoption and usage). In these times of tight
budgets, IT teams simply do not have the necessary bandwidth
to implement yet another new system or technology.
C. Bringing All Components Together
For some, Big Data simply means Big Headaches, raising
difficult issues of information system cost, scaling and
performance, as well as data security, privacy and ownership.
But Big Data also carries the potential for breakthrough
insights and innovation in business, science, medicine and
government—if we can bring humans, machines and data
together to reveal the natural information intelligence locked
inside our mountains of Big Data.
VI. RECOMMENDATIONS FOR BIG DATA ADOPTION
IMPROVEMENT
In order to implement a Big Data project, here are a few
recommendations.
First, it would be helpful to create a powerful team that can
set up a platform to explore Big Data. This team will work
with business and data analysts to create the road map for
further execution. Critical success factors include [8]:
 Availability of IT resources to build and configure the
selected Big Data platform;
 Availability of business SMEs with data and domain
expertise;
 Availability of resources with BI expertise and deep
statistical knowledge;
 Implementation of a technology center of excellence to
provide Big Data infrastructure support;
 Extension of other BI best practices, including data
governance, Master Data Management, metadata
management, analytics definition, and visualization, to
include Big Data;
 Adequate training for users to understand the new data
and its integration into the analytical and reporting
platforms.
When it comes to people, having a combination of
individuals mentioned above will create a team that can
leverage each other's skills and create a unified vision for
exploring Big Data.
While some research organizations may rely on
supercomputers to meet their Big Data needs, we propose a
toolbox to be offered like Software-as-a-Service (SaaS) with
tools accessible to organizations of all sizes and types. These
III. BIG DATA OPPORTUNITIES
Innovative public and private organizations are already
demonstrating that transforming raw Big Data collections into
actionable wisdom is possible. They are showing in particular
that tremendous value can be extracted from the ”grey” data
that makes up the bulk of Big Data, that is to say data that is
unused (or under-used) because it has historically been [7]:
 Too voluminous, unstructured and/or raw (i.e., minimally
structured) to be exploited by conventional information
systems, or
 In the case of highly structured data, too costly or
complex to integrate and exploit (e.g., trying to gather
and align data from dozens of databases worldwide).
These organizations are also opening new frontiers in
operational and exploratory analytics using structured data
(like database content), semi-structured data (such as log files
or XML files) and unstructured content (like text documents or
Web pages).
IV. DATABASE ENRICHMENT
Once you can collect, analyze and organize unstructured Big
Data, you can use it to enhance and contextualize existing
structured data resources like databases and data warehouses.
For instance, you can use information extracted from highvolume sources like email, chat, website logs and social
networks to enrich customer profiles in a Customer
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4th International Conference on Humanities, Geography and Economics (ICHGE'2013) Oct. 6-7, 2013 Dubai (UAE)
tools include [9]:
 Data Capture & Preprocessing:
 ETL (Extract, Transform and Load) Tools;
 APIs (Application Programming Interfaces)/Connectors;
 Crawlers;
 Messaging System.
 Data Processing & Interaction:
 NoSQL Systems;
 NewSQL Systems;
 Search Engine.
 Auxiliary Tools:
 Cloud Services;
 Visualization tools.
Each has a different role to play in capturing, processing,
accessing or analyzing Big Data. Let’s look first at data
capture and preprocessing tools.
With Big Data loads, APIs can cause bottlenecks due to
poor design or insufficient computing or network resources,
but they have generally proven to be flexible and capable tools
for exchanging large-volume data and services. In fact, one
could argue the proliferation of public and private APIs has
played an important role in creating today’s Big Data world.
Nonetheless, you can still sometimes achieve better
performance with an embedded ETL tool than an API, or, in
the case of streaming data, with a messaging architecture (see
Messaging Systems below).
Moreover, APIs are generally not the best choice for
collecting data from the Web. A crawler is a better tool for that
task. There are three main drawbacks to APIs in the Web
context:
 In spite of their proliferation, only a tiny percentage of
online data sources are currently accessible via an API.
 APIs usually offer access to only a limited portion of a
site’s data.
 Formats and access methods are at the owner’s discretion,
and may change at any time. Because of this variability
and changeability, it can take a significant amount of time
to establish and maintain individual API links, an effort
that can become completely unmanageable in Big Data
environments.
Crawlers are software programs that connect to a data
source, methodically extract the metadata and content it
contains, and send the extracted content back to a host system
for indexation [11].
One type of crawler is a file system crawler. This kind of
crawler works its way recursively through computer
directories, subdirectories and files to gather file content and
metadata (like file path, name, size, and last modified date).
File system crawlers are used to collect unstructured content
like text documents, semi-structured content like logs, and
structured content like XML files.
Another type of crawler is a Web (HTTP/HTTPS) crawler.
This type of crawler accesses a Web site, captures and
transmits the page content it contains along with available
metadata (page titles, content labels, etc.), then follows links
(or a set visitation list) to proceed to the next site.
Typically a search engine is used to process, store and
access the content captured by crawlers, but crawlers can be
used with other types of data management systems (DMS).
As with other data collection tools, one needs to configure
crawls so as not to place an undue load on the source system –
or the crawler. The quality of the crawler determines the extent
to which loads can be properly managed. It should also be kept
in mind that crawlers recognize only a limited number of
document formats (e.g., HTML, XML, text, PDF, etc.).If you
want to use a crawler to gather non-supported document
formats, data need to be converted into an ingestible format
using tools like API connectors (standard with most
commercial search engines), source-system export tools, ETL
platforms or messaging systems.
MOM (Message-Oriented Middleware) systems provide an
A. Data Capture & Preprocessing
ETL (Extract, Transform and Load) tools are used to map
and move large volumes of data from one system to another.
They are most frequently used as data integration aids. More
specifically, they are commonly used to consolidate data from
multiple databases into a central data warehouse through bulk
data transfers. ETL platforms usually include mechanisms for
”normalizing” source data before transferring it, that is to say,
for performing at least the minimal processing needed to align
incoming data with the target system’s data model and
specifications, and removing duplicate or anomalous data [10].
In Big Data environments, the Extract process can
sometimes place an unacceptable burden on source systems,
and the Transform stage can be a bottleneck if the data is
minimally structured or very raw (most ETL platforms require
an external or add-on module to handle unstructured data).
The Load process can also be quite slow even when the code is
optimized for large volumes. This is why ETL transfers, which
are widely used to feed data warehouses, tend to be executed
during off-hours – usually overnight – resulting in
unacceptable data latency in some situations. It should be
noted, however, that many ETL vendors are developing – or
have already developed – special editions of their software to
address these limitations.
Application Programming Interfaces (APIs) are softwareto-software interfaces for exchanging almost every type of
service or data you can conceive, though we focus here on the
use of APIs as tools for data exchange or consolidation. In this
context, an API may enable a host system to receive (ingest)
data from other systems (a “push” API), or enable others to
extract data from it (a publishing or a ”pull” API).
APIs typically employ standard programming languages and
protocols to facilitate exchanges (e.g., HTTP/REST, Java,
XML). Specific instances of packaged APIs on a system are
often referred to as ”connectors,“ and may be general in
nature, like the Java Database Connectivity (JDBC) API for
connecting to most common RDBMSs, or vendor/platform
specific, like a connector for IBM Lotus Notes.
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4th International Conference on Humanities, Geography and Economics (ICHGE'2013) Oct. 6-7, 2013 Dubai (UAE)
enabling backbone for enterprise application integration. Often
deployed within service-oriented architectures (SOA), MOM
solutions loosely couple systems and applications through a
bridge known as a message bus. Messages (data packets)
managed by the bus may be configured for point-to-point
delivery (message queue messaging) or broadcast to multiple
subscribers (publish-subscribe messaging). They vary in their
level of support for message security, integrity and durability.
Exchanges between disparate systems are possible because
all connected systems (“peers”) share a common message
schema, set of command messages and infrastructure (often
dedicated). Data from source systems is transformed to the
degree necessary to enable other systems to consume it, for
example, binary values may need to be converted to their
textual (ASCII) equivalents, or session IDs and IP addresses
may be extracted from log files and encoded as XML records.
APIs for managing this data processing may be embedded in
individual systems connected to the bus, or they may be
embedded in the MOM platform.
Messaging systems were specifically designed to meet the
high-volume, high-velocity data needs of industries like
finance, banking and telecommunications. Big Data volumes
can nonetheless overload some MOM systems, particularly if
the MOM is performing extensive data processing—filtering,
aggregation, transformation, etc. – at the message bus level. In
such situations, performance can be improved by offloading
processing tasks to either source or destination systems.
integrate NoSQL or Search components to address the 4V
challenges of Big Data). Their primary drawback is cost and
rigidity (most are integrated hardware/software appliances).
Unlike NoSQL solutions, NewSQL systems tend to be
commercial rather than open source (though they may
incorporate open source components), with their MPP capacity
usually achieved through symmetric processing across a large
number of processors embedded within a single high-end
computer (usually proprietary), or a small cluster of such
computers.
Search platforms as a complete search engine system that
can serve as a multi-purpose, information aggregation, access
and analysis platform in addition to meeting classic enterprise
or Web search needs. Such a search platform, also referred to
as a “unified information access” (UIA) platform,
encompasses all core data management functions, though with
an NLP (Natural Language Processing) or indexing twist.
These functions include:
 Data capture (crawlers, connectors & APIs);
 Data storage (cached copies of source content and the
index itself);
 Data processing (NLP and index construction and
maintenance);
 Data access (human and machine information retrieval,
faceted navigation and dashboard analytics).
A search system is therefore a DMS like its NoSQL and
NewSQL counterparts, and it achieves massive scalability in
much the same way, i.e., through distributed architectures,
parallel processing, column-oriented data models, etc. [13].
However, it is the semantic capabilities and high usability of
search-based DMS that make them ideal complements to (and
in some cases, alternatives to) NoSQL and NewSQL systems.
Tools like natural language search, faceted navigation and
data visualization provide users of all skill levels with an
instantly familiar way of exploring and analyzing Big Data.
That is to say, they allow users to launch any search or
analytical task the same way they launch a search on the Web
– by entering a phrase or a few keywords in a text box. They
also enable users to conduct iterative exploratory analytics
simply by clicking on (traversing) dynamic data clusters
(represented as text menus or in visual forms like charts or
graphs).
Search platforms are responsive because they are optimized
for fast query processing against large volumes of data (e.g.
read operations), and because most of the calculations they use
to produce dashboard analytics and ad hoc drilling are
automatically executed as part of routine indexing processes:
the results are there waiting to be exploited with no processing
overhead.
For all these reasons, search platforms serve as perfect
complements to NoSQL and NewSQL systems, and, in some
contexts, provide a pragmatic alternative to them.
B. Data Processing & Interaction
Today, classic Relational Database Management Systems
(RDBMSs) are complemented by a rich set of alternative
DMSs (Database Management Systems) specifically designed
to handle the volume, variety, velocity and variability of Big
Data collections (the so-called “4Vs” of Big Data). These
DMS include NoSQL, NewSQL and Search-based systems.
All can ingest data supplied by any of the capture and
preprocessing tools discussed in the last section (ETLs, APIs,
crawlers or messaging systems).
NoSQL systems are distributed, non-relational databases
designed for large-scale data storage and for massively-parallel
data crunching across a large number of commodity servers
[12]. They can support multiple activities, including
exploratory and predictive analytics, ETL-style data
transformation, and non-mission-critical OLTP (Online
Transaction Processing), like, e.g., managing long-duration or
inter-organization transactions. Their primary drawbacks are
their unfamiliarity, and, for the youngest of these largely opensource solutions, their instability.
NewSQL systems are relational databases designed to
provide ACID (Analysis Console for Intrusion Databases)compliant, real-time OLTP and conventional SQL-based
OLAP in Big Data environments. These systems break through
conventional RDBMS performance limits by employing
NoSQL-style features such as column-oriented data storage
and distributed architectures, or by employing technologies
like in-memory processing, SMP or MPP (some go further and
C. Auxiliary Tools
To meet data management needs, Cloud services should be
used, including:
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4th International Conference on Humanities, Geography and Economics (ICHGE'2013) Oct. 6-7, 2013 Dubai (UAE)
 Data acquisition services;
 Data processing (computational) services;
 Data storage services.
In terms of data acquisition, commercial firms are offering
a growing range of data sets that companies can crossreference with their own Big Data, or with each other, to yield
new views and insights. These largely commercial offerings
are complemented by an ever-expanding number of public data
sets being published on the Internet by government,
educational and scientific organizations.
On the processing front, Amazon, Google, Microsoft and
others are further enabling companies to use their massive
MapReduce-based computing infrastructures to process or
analyze these collections.
In the case of data storage, many specialty providers of
storage/back-up/recovery solutions as well as general Web
services companies like Amazon and Microsoft now offer
NoSQL-based solutions to help companies affordably store
massive collections of semi-structured or unstructured data for
archiving or analysis – data companies might otherwise not
even have retained.
All these diverse Cloud services are helping organizations
of all sizes and types work around the technical and financial
barriers to exploiting Big Data.
Visualization implies representing Big Data in visual form
helps make it comprehensible to human beings. It is such an
effective aid that most science, engineering and Business
Intelligence (BI) software features embedded 2D and 3D data
visualization and navigational tools.
On the part of major BI vendors – including SAP Business
Objects, IBM Cognos, MicroStrategy, SAS Institute, and
Information Builders – visualization capabilities include, for
example, interactive bar charts, dashboard gauges, pie charts
and geographic mapping [14]. SBA engines like CloudView
offer this capability as well, generating navigable
representations like heat maps, scatter plots, charts, bullet
graphs, relationship graphs, tag clouds, sliders, wheels and
geospatial maps.
Visualization is a terrific tool for summarizing large sets,
and for discovering and exploring unexpected relationships
and trends, but it is important to select the right type of
representation for a given data set and analytical end.
Otherwise, you might wind up with a representation that is
misleading, confusing or just plain unreadable. To mitigate
this risk, and to make the best use of visualization in general,
make sure the tool you use produces fully interactive
representations.
actionable wisdom is possible. They are showing in particular
that tremendous value can be extracted from the “grey” data
that makes up the bulk of Big Data. It can be used for many
valuable purposes, but it also creates some new challenges as
far as data management methodology is concerned.
In the paper, we have explained a proposition of a new type
of toolbox for Big Data management with tools accessible to
organizations of all sizes and types.
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VII. CONCLUSION
Big Data, as the extraordinary scale of the data collections
now being amassed inside public and private organizations and
out on the Web, occupies attention of professionals, managers,
and businessmen.
Innovative public and private organizations are already
demonstrating that transforming raw Big Data collections into
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