Trends shaping the future of business intelligence.

1. Trends in Business Intelligence 2013
Studie en Advies Johan Blomme
Data Consulting Services
www.johanblomme.com

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Transformational changes that take place in the digital world
definitely change the nature of business intelligence and
represent a new normal.
The Internet is the societal operating system of the 21st
century and its underlying infrastructure – the cloud computing
model – represents a « disruptive » change.
A networked infrastructure, big data from disparate sources
and social media among other trends as predictive analytics,
the self-service model and collaboration are changing the way
BI systems are deployed and used.
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Trends in
BI
Introduction
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 In today’s marketplace, change is a constant.
 Products are increasingly commoditised, development cycles have shortened and
expectations of consumers are rising. To achieve a sustainable competitive position,
companies must react in an agile way to changing market conditions.
 The current business environment evolves from a transition towards globalization and a
restructuration of the economic order. The pace of technological changes that allow instant
connectivity and the current era of ubiquitous computing that resulted from it, represent
« the new normal in business intelligence».
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 As an industry, business intelligence has to adapt to environmental changes.
 The evolution of the Internet as a new societal operating system, reshapes the future of
business intelligence.
 The Internet evolves as a platform for the use of interoperable resources (storage, computing,
applications and services) and drives the development of information intensive services in the
21st century. Increasingly, the cloud becomes the vehicle for the Internet of Services.
 The business ecosystem generates a huge amount of data in terms of volume, variety and
velocity, and requires businesses to take on a data-driven approach to differentiate. It’s about
gaining actionable insights faster than the competition by reducing the data-to-decision gap.
 This highlights the integration of structured and unstructured data (esp. social media content) to
derive actionable insights from « big data » and the leverage of predictive analytics for agile
decision-making.
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 The exponential growth of data and the increased reliance on insights derived from data for
decision-making, causes a shift in the focus of business intelligence. BI is more than an IT-function
and is about people and business decisions.
 Therefore, the emphasis of next-generation BI should be on designing solutions that focus on
answering business questions of the end user. In the field of BI the finished product is not a
dashboard displaying metrics but actionable intelligence answering the business question at hand.
Users want seamless access to information to support decision-making in their day-to-day activities.
 The future direction of BI will thereby be shaped by the new age of computing. In both their
personal and professional lives, Web-savvy users have adopted the principles of interactive
computing and have come to demand customizable BI-tools with high responsiveness. Business
intelligence, and the insights it delivers, evolves towards an enterprise service that follows the lines
of a self-service model with business users producing their own reports in an interactive way and
performing analytics on demand.
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 Furthermore, Web 2.0 and social networks function as catalysts for highly intuitive user interfaces and
the collaborative features of computing allow users to share insights, which transforms BI from a
solitary to a collaborative activity.
 Companies are exploring the connection between analytical activity and knowledge sharing. Combined
with collaborative technologies that « crowdsource » intelligence from various partners of the
extended enterprise, this approach provides the context for better and faster decision-making.
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The factors that constitute the new normal in BI can be summarised as follows :
The Future Internet
Predictive Analytics Big Data
Trends in
Social Media Analytics Cloud Computing
BI
Collaborative BI Embedded BI
User Empowerment / Self-Service BI
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Trends in
BI
1. The Future Internet
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 The main objective of enterprise computing is to be adaptive to change.
 The new generation of enterprise computing must enable pervasive BI deployments :
 spreading BI to more users and more devices :
• consumerization of IT : enterprise computing aligns with consumer-class technologies ;
• BI-tools are more and more organized around the user’s experience to interactively
discover hidden relationships, trends and patterns and to create new information and
relate it with external data sources ;
 using multiple data sources : the use of structured as well as semi- and unstructured
data sources (e.g. social media content) extends the playing field of BI.
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 The new generation of enterprise computing needs to be developed within the perspective of
the future Internet :
 the Internet as data source :
• BI applications no longer limit their analysis to data inside the company and increasingly
source their data from the Internet to provide richer insights into the dynamics of today’s
business ;
 the Internet as software platform :
• BI applications are moving from company-internal systems to service-based platforms on
the Internet.
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Web-based technologies enable BI-applications are delivered
the implementation of user-configurable as a service on the Web or
BI applications connecting to a wide hosted in the cloud
arrangement of data
INTERNET-ENABLED
NE ING
X IT-INFRASTRUCTURE UT
T-G MP
EN E CO
ER RIS
AT RP
IO TE
N EN
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Business
Networks  The Internet of the future gives rise to a new business model
that allows enterprises to form business networks :
 in the knowledge economy economic activity is based on
The highly networked interactions ;
Future
 the amount of digital collaboration is increasing among
Internet
Int rvic
people, things and their interactions (through the
Se
Internet of People and the Internet of Things, networking
ern es
ta
is expanding not only in person-to-person interactions,
et
Da
but also in person-to-machine and machine-to-machine
interactions).
of
g
Bi
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Globalization T he
ing Con
and sum
s Exp for e
of IT rization
ba m nges
We yste a
The Ecos Exch
in ess
Bus
Device-Indepen
Information Acce
Demographic Shifts
Drivers of
Workforce
NETWORKED
INFRASTRUCTURE
dent
ss
Hyp e
e
Soc r Adop ativ s
tion bor e
ial N olla ologi
Tec etworki of C hn
c
hno
logy ng Te
Bandwidth
Cloud Computing
& Connectivity
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Business
 Business networks take on a data-driven approach to
Networks differentiate and apply fact-based decision-making enabled by
advanced analytics:
 economic interactions are based on the principle of
scarcity and in the knowledge economy the concept of
scarcity applies to information ;
The
Future  information in itself does not create competitive
advantage (access to lots of information has already
Internet
Int rvic
become ubiquitous) ; competitive advantage is defined as
access to information, the decisions based on that
Se
ern es
information and the actions taken on these decisions ;
ta
et
Da  business networks manage data in real-time, support
of
anywhere, anytime and any device connectivity and
g
provide the appropriate information to users across and
Bi
beyond the enterprise (business users, partners,
suppliers, customers).
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Business
 The Internet serves as a platform for a service-oriented
Networks approach that changes the way of enterprise computing. With BI-
applications moving to the web, the Internet emerges as a global
SOA that is referred to as an Internet of Services. The IoS serves
as the basis for business networks.
The  The new BI requires technologies that integrate multiple data
sources, address business needs in a dynamic way and have a
Future short time to deployment.
Internet
Int rvic
Se
 Contrary to large scale application development of traditional BI,
ern es
the new BI moves towards smaller and flexible applications that
ta
can adopt quickly and are supported by a service-oriented
et
Da architecture.
of
g
Bi
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 SOA is an architecture whereby business applications use a set of loosely coupled and reusable
services that can be accessed on a network.
 Often implemented by Web services, a SOA is a building block for flexible access to multiple data
sources and the very nature of services that can be reused and integrated with each other allows
business processes to be adopted in an agile way to adjust to changing market conditions and to meet
customer demands.
 With cloud computing, this service model is delivered on demand. The delivery model is no longer
installed software but services.
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Trends in
BI
2. Big Data
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Major sources of « big data »
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The evolution of the Internet and the proliferation of data
Data 3V
The Cloud
The Web
The Internet Semantic Web
Social Web
Desktop/PC era
Static Web
Internet of People Internet of People and Things
producer generated content user generated content. system generated content
time
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 As connectivity reaches more and more devices, the volume, variety and velocity of data from
clickstreams, social networks and the Internet of Things (through which the physical world itself
becomes an information system) creates a new economy of data.
 Traditionally, BI applications allow users to acquire knowledge from company-internal data through
various technologies (data warehousing, OLAP, data mining). However, the typical pattern of
cleaning and normalizing proprietary information through an ETL process into a data warehouse is
challenged by the transition to big data that is marked by greater accessibility, interoperability and
3rd party leverage of online data.
 For businesses to become responsive to market conditions, it is necessary to look at the whole
ecosystem by connecting internal business data with external information systems. BI-applications
must access data from disparate sources inside and outside the firewall, consider qualitative and
quantitative data and include structured as well as semi-structured and unstructured data.
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 Data from the Web is feeding BI applications :
 BI applications no longer limit their analysis to data inside the company, but also source
data from the outside, especially data from the Web. The Web is a data repository.
 An important challenge is the extraction, integration and analysis from hererogeneous data
sources.
 BI applications move to the Web :
 BI applications are increasingly accessible over the Web : BI is consumed as a service from
the cloud.
 The challenge here is the development of Web-based applications that access and analyze
both historical enterprise data and real-time data, especially from the world wide market
and making the information available on a variety of devices.
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The increasing volume and complexity of
The 3 V’s represent the common data has forced organizations to look at
dimensions of big data, but the real new data management and analytic tools
challenge lies in extracting to optimize performance, improve service
actionable insights from it. delivery and discover new opportunities.
Variety Database Technology
Velocity Analytics
Volume Services
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 Heterogenous datasets are no longer manageable by a traditional relational database approach.
 Requirements for next-generation BI-tools include :
 connect directly to the underlying data sources to capture distributed data ;
 schema-free : relationships between data are discovered dynamically ;
 anytime, anywhere access with multiple devices ;
 real-time visibility of what is happening now is needed and analytics must be used in the
stream of business operations.
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New approaches such as in-database analytics, massive parallel processing, columnar databases and « No
SQL » will increasingly be used for the analysis of structured as well as unstructured data.
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 Traditional RDBMS and SQL-based access languages are unfit to the new world of unstructured
information types.
 NoSQL (« Not only SQL ») is a database management system that is more versatile than
traditional database systems.
 Map Reduce and Hadoop, for example, are currently the most widely known NoSQL
approaches.
 Data is stored without a pre-defined schema and big data sets are analyzed in parallel by
assigning them to different servers.
 Results are then collected and aggregated and can be further used in conjunction with
relational database systems.
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 BI has evolved from historical reporting to the pervasive analysis of (real-time) data from multiple data
sources. Transactional data is analyzed in combination with new data types from social, machine to
machine and mobile sources (e.g. sentiment, RFID, geolocation data).
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 Organizations that embrace a « socialization of data »-approach by incorporating and converging
disparate data sources into their BI-platforms, acquire a holistic view that provides them with
the opportunity to derive actionable insights, e.g.
 analytics of real-time customer sentiment and behaviour yield indicators of product or
service issues ;
 geospacial information of customers can be combined with transactional data to make
targeted product or service offerings ;
 combining internally generated data with publicly available information can reveal
previously unknown correlations.
 In its focus on the user experience, BI embraces Web 2.0-technology that focusses on intuitive
user interfaces. Organizations must master visualization tools that let business users
interactively manipulate data to find tailored insights that can be shared with other stakeholders
(customers, partners, suppliers).
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Trends in
BI
3. Cloud Computing
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# apps / # users
ING
PUT
COM
UD
CLO
AG E
CO M
DOT  virtualized connected
R N ET / environment
INTE
 Internet-based data
ER access & exchange
S ERV  eCommerce
NT-
CLIE  « as a service »-
 service-oriented paradigm
architecture
 networking
PC  Web 2
 office automation
 data warehousing
I
/MIN
AM E
MA INFR  desktop computing
 centralized
automation
1970s 1980s 1990s 2000s 2010 & beyond
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 As the competitiveness of businesses increasingly depends on adapting to changing market conditions,
companies outsource tasks and processes to external providers.
 This trend can be linked to the creation of business ecosystems in The Future Internet with vendors
offering their services.
 Software-as-a-Service (Saas), for example, is a type of cloud offering for software delivery.
Applications are hosted by a provider and made available on demand.
 Cloud computing is the backbone for the Internet of Services and provides resources for on demand,
networked access to services.
Infrastructure as a service
Platform as a service
Software as a service
Data as a service
ERP Analytics as a service
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“Cloud computing is enabling the consumption of IT as a service. Couple this with the “big data” phenomenon,
and organizations increasingly will be motivated to consume IT as an external service versus internal
infrastructure investments”.
The 2011 Digital Universe Study : Extracting Value from Chaos, IDC, June 2011
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 Cloud computing alters the way computing, storage and networking
resources are allocated. Through virtualization, the traditional server-
centric architecture model in which applications are tied to the
underlying hardware is altered to a service-centered cloud architecture.
Applications are decoupled from the physical resource which implies
that services (computing resources, e.g. processing power, memory,
storage, network bandwidth) in a cloud computing environment are
dynamically allocated to on demand requests.
 In addition to a better utlization of IT resources, hardware cost
reduction and greener computing, cloud computing provides an agile
infrastructure to respond to business needs in a flexible way.
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The commoditization of analytics
The trend towards the hosting of services, leads to the commoditization of analytics.
As a result, the creation of a competitive advantage depends on 2 factors .
Analytics in itself don’t
guarantee a competitive
The management of large advantage. The insights,
data volumes (data integration, communications and decisions
data quality). As data fuels that follow analysis become
analytic processes, big data more important. This stresses the
becomes increasingly important.. role of self-service and
collaboration.
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In the pre-cloud world, the implementation of data warehouses needed serious
upfront costs and designing database schemas was time consuming. Moreover,
database schemas have their limitations because some data types (e.g.
unstructured) don’t fit the schema. Combined with the need to manage big
data volumes new database technologies (e.g. NoSQL) are used. For example,
in the case of a Hadoop cluster that runs in parallel on smaller data sets,
multiple servers are needed. Making use of cloud computing services in a pay-
for-use formula is appealing. Furthermore, a service-oriented cloud
Cloud computing and architecture is ideally suited to integrate data from various sources (e.g. « mash
big data up » enterprise data with public data).
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Cloud computing gives a new meaning to the consumerization of IT. The
convergence of cloud computing and connectivity is changing the way
technology is delivered and information is consumed. Cloud applications are
available on demand and developed to meet the immediate needs of users.
Cloud computing is an important catalyst for self-service BI. Users do not need
to be concerned with the technical details of software and hardware when
using services. User-friendly interfaces and visualization capabilities make the
generation, sharing and acting on information in real-time easier. This permits
faster and better decision-making as well as greater collaboration internally
Cloud computing and and outside the firewall.
self-service BI
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Trends in
BI
4. Embedded BI
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 The consumerization of IT and the need of business decisions to be made on relevant information are
drivers for placing reporting and analytics in the hands of more decision-makers and to apply analytics in
real-time to production data.
 A broader user adoption of BI results from :
 faster and easier executive access to information ;
 self-service access to data sources ;
 right-time data for users’ roles in operations ;
 more frequently updated information for all users.
 The business benefits are :
 improved customer sales, service and support ;
 more efficiency and coordination in operations and business processes ;
 faster deployment of analytical applications and services ;
 customer self-service benefits.
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BI delivery framework
(adapted from Eckerson, 2011)
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to
from service-oriented architecture
monolithic applications
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SOA
Companies move away from large-scale monolithic
application development and turn to service-oriented
architectures that represent the technological foundation of
the Internet of Services.
Web Services
SOA’s are based on the principle that
applications can be created as a composition
of loosely coupled and reusable services. Open
standards and the implementation of SOA’s
through Internet-based technologies as Web
services represent a new way of computing.
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Mashups and customer service
An obvious implementation area for enterprise mashups applies to customer service.
CRM implies multiple processes (customer contact, sales, billing, support). Very often
the delivery of a process like that of customer service relies on end-users accessing
multiple applications. A major drawback is that customer-facing personnel (e.g. call
center agents, sales representatives) lack a unified customer view which causes a poor
quality of the customer experience. On the other hand, applications require a high
involvement of IT in the lifecycle of each application.
Therefore, enterprise mashups can provide a solution by the integration of disparate
data sources into a composite application. End users can use and reuse application
building blocks as “mashable” components to construct user-centric solutions. This not
only reduces the cost and time to build and maintain applications, but also allows
business users to create applications that are mapped with processes. Customer service
processes are optimized because employees are able to service customers more
efficiently.
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Mashups and social media analytics
Social media is empowering customers to reveal their thoughts and preferences through
the Internet. This also enables businesses to look for competitive advantage by
monitoring and managing the many conversations that take place in the social media
world. Social media content can be tagged to look for pieces of information that can be
further structured to provide aggregate customer data revealing customer service issues,
consumer attitudes and brand-related topics. Furthermore, sentiment analysis that
extracts the semantics of user-generated content allows for the creation of mashups that
identify trends in unstructured data.
For example, dashboards can use sentiment measures as key performance indicators to
monitor product performance. Consumer sentiment can serve as an indicator of the
performance of a new product that is introduced in the market. Sentiment measures can
reveal the importance of product features and key customer needs. Retailers can
estimate demand for products based on expressed satisfaction of discontent with
products.
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 Another implementation area of mashups is data visualization that integrates location intelligence in
a composite application.
 Data streams within the enterprise can be joined with virtually any data source that can be accessed
from the Web. Web-based visualizations spacially represent the inherent relationships between the
underlying data.
 An example is Visual Fusion, data visualization software of IDV Solutions (www.idvsolutions.com) that
unites data sources in a web-based, visual context for better insight and understanding. Commercial
applications include the monitoring of inventory through RFID systems, field service management,
sales and marketing analysis, supply chain management, and more.
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http://www.idvsolutions.com/Products/VisualFusion/Gallery.aspx?view=8
To view all suppliers for several auto assembly plants, a manufacturer developed an application
that visualizes suppliers on a map. Supply lines show which suppliers support which plants and
can be color-coded based on key information such as deliveries in progress and KPI data. Views
can be analyzed, sorted, filtered and collaborated upon to show how a selected supplier performs
compared to others via KPI-based charts and graphs.
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reach the long tail of
the application spectrum
user-driven
cloud adoption real-time data view
incorporate social & collaborative
agility computing features
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Trends in
BI
5. User-Empowerment / Self-Service
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 A confluence of factors (including ubiquitous broadband, a growing technology-native workforce, the
adoption of social networking tools tools, mobile apps) is driving a trend called the consumerization
of IT.
 Enterprise application development is driven by the need for interactive access to disparate data,
self-service capabilities that offer a flexibility for personalization and end-user customization. BI
shifts towards the self-service delivery model that accomodates knowledge workers to search,
access and analyze data from a variety of sources and available on a range of devices.
 Empowerment of users is an important trend in BI. Business users generate their own reports and
analysis and are no longer dependent on IT to deliver them. The ownership of BI shifts from IT to
the business.
 By incorporating collaborative features, BI environments are getting social. These enhancements
facilitate the creation of user-generated content that can be shared with stakeholders across and
beyond corporate boundaries, enabling the networked enterprise and optimized decision-making.
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Traditional BI The New BI
based on open standards and loosely
client server, closed, coupled services that can be
proprietary architecture reconfigured easily
structured data (data gathering data of any source is used
depends on data warehousing (structured, semi- and unstructured
methodology) data data)
analytics and presentation no separation between analytics and
are separated ; data-centric analytics presentation ; decision-centric
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Traditional BI The New BI
deliver relevant data, ensure
create data models, control security and scalability, enable
of data and applications IT role self-service
focused on standard reports ; focused on interactive analysis
predefinied reports to answer by end-users ; used to derive new
predefined questions BI-delivery insights (“business discovery”)
on premise, desktop and on premise and on demand
server deployment type (cloud, SaaS)
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traditional report-centric approach data discovery approach
monolithic applications intuitive applications
close coupled enterprise loose coupled services
architecture « app-ification »
IT-driven user-driven
data warehousing infrastructure Web-based (cloud-)infrastructure
STRUCTURED DATA (RDBMS) STRUCTURED & SEMI-/UNSTRUCTURED DATA
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technological innovations Consumerization
are user-driven and increasingly of IT
outside central IT-control
self-directed analytics
business discovery
long tail solutions
reusability
infrastructure Traditional IT
data governance
security
Adapted from Hinchcliffe, 2011.
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Trends in
BI
6. Collaborative BI
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 The idea of collaborative BI is to extend the processes of data organization, analysis and
decision-making beyond company borders.
 While Web 2.0-technologies are migrating into the enterprise, consumer-oriented social
media tools do not provide the necessary components for collaborative BI. Collaborative BI
requires the principle of information sharing to be incorporated into day-to-day workflows.
 A difference also exists between analyzing social media on the one hand and collaborative
BI on the other hand.
 Social media provide a new source of data that complements traditional data analysis to
help organizations capture market trends, better understand customer attitudes and
behaviour and uncover product sentiments.
 Collaborative BI uses web-based standards to connect people (enterprise users, partners,
suppliers, customers) to build dynamic networks that share information and analysis results
to enable timely decisions that drive actions.
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 Collaborative BI correlates with the analysis of big data and self-service BI.
 Big data involves the analysis of ever-increasing volumes of structured and semi- or
unstructured data. In the context of always changing business requirements, organizations
need to act quickly and decisively on business and consumer trends derived from petabytes
of data.
 Closely related to the expectations of users to access applications anaywhere, at any time
on any device are self-service features that allow them to interact with data in a flexible
way. Accordingly, technologies as advanced data visualization, embedded BI and in-
memory analysis rank high in preference lists.
 The pervasive use of BI that is stimulated through these technologies is a necessity to
enable analytic agility and responsiveness.
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Contrary to the traditional linear nature of data processing, collaborative BI
incorporates various feedback loops at different places in the analysis cycle.
Applied to BI, collaboration frameworks can be built that enable teams
to interact and socialize on data analysis-related topics.
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« The world is rapidly turning into a network society. … The need to quickly adapt to this
changing environment is evident. The new paradigm in innovation is joining forces in an
online environment and activily working together. If we collaborate, we can co-create and
grow our ideas together, which ultimately leads to better, faster and higher value
www.innovationfactory.eu/vision Innovation ».
A McKinsey study gives evidence that the application of Web 2.0-technologies to
increase collaboration fosters the creation of networked organizations. Enterprises that
connect employees to forge close networks with customers, business partners and
suppliers become more competitive and show improved performance in the areas of
market share gains, market leadership and margins. Through the use of collaborative
tools, information flows become less hierarchical and access to expert knowledge is
facilitated. Operational costs and time to market for new products/services are
reduced.
The rise of the networked enterprise : Web 2.0 finds its payday, McKinsey Quarterly, spring 2011.
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The business value of Web 2.0 for collaborative BI can be situated from the eight core patterns of Web 2.0.
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Web 2.0-features focus on the user experience. The
customer-centric focus of Web 2.0 has created a demand
for applications that move from the traditional
transaction platform to a model that is more accessible
and personal for the user.
Web 2.0-applications represent an opportunity for BI to
build Web-based collaboration. Reports can be published
in blogs and wikis, which help construct a knowledge base
to share interpretations. Users will learn to use
information more dynamically which allows the
generation of « crowd-sourced wisdom ». Besides
reporting and analysis, decisions are part of the BI
delivery mechanism.
Gaining insights from data to drive better decisions is no
longer constrained by the limits of internal data. The
open access to information in the Web 2.0-space allows
users to combine existing information with consumer-
generated content from the social networking spectrum
like blogs and wikis.
Social media analytics presents a unique opportunity to
threat the market as a « conversation » between
consumers and businesses. Companies that harness the
knowledge of social networks compile enterprise data
with streams of real-time data from Web 2.0-sources to
better access marketplace trends and customer needs.
The adoption of Web 2.0-technologies and applications
can help businesses to expand the reach of BI and improve
its effectiveness.
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Trends in
BI
7. Social Media Analytics
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 An important BI trend is the incorporation of the growing streams of data
generated by social media networks in BI-applications.
 Social BI is a type of intelligence that focuses on data that is generated in
real-time through Internet-powered connections between businesses and the
public.
 Social media analytics give companies insights into the mindset of their
(prospective) customers, help them improve media campaigns and offerings
and accelerate responses to shifts in the marketplace.
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Drivers for social media analytics
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The spectrum of available data has been enlarged with new soures, esp. social media data streams.
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The explosion of social media drives the need to analyze and get insights from
customer conversations.
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The mobile and social media explosion empowers customers and through the
rapid growth of digital channels, the customer experience takes on a new
meaning. The objective of social media analytics is to analyze social media
data in context and generate unique customer experiences across channels.
attitudinal
data
descriptive interaction
data data
behavioral
data
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Examples of the use of social media analytics in day-to-day operations :
 Baynote (www.baynote.com) provides recommendation • Wise Window (www.wisewindow.com) distills
services for websites. Websites using Baynote social media content automatically and in real-
recommendations deliver relevant products and
personalized content that create an intuitive user time into industry-specific taxonomies. The
experience. approach that Wise Window calls « Mass
Opinion Business Intelligence » (MOBI) does not
 Baynote applies « interest mining ». It attempts to cluster focus on individual behavior but the type of
consumers to provide product or content syndicated research that Wise Window
recommendations that are based on a broader
understanding of consumer behaviour. Baynote goes performs is aimed at giving a broader
beyond the clickstream by examining the words associated understanding of consumer sentiments and
with the clicks the user makes. Combining the behavior in the market at large.
clickstream and the semantic stream reveals the
communality of cluster members above a pure statistical
or demographic cluster approach. The resulting • MOBI discovers leading indicators with data
« integrest graph » is used to personalize product and
content recommendations that lead to maximum derived from social media to make
engagement, conversion and lifetime value. organizations more agile and responsive.
Application fields include simple mindshare
analysis, discovering new products and niches,
spotting fast movers, performing constituent
analysis and predicting demand.
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Trends in
BI
8. Predictive Analytics
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Potential growth vs. commitment for analytics options
advanced analytics (e.g. mining, predictive)
data marts for analytics
advanced data visualization
predictive analytics
commitment
enterprise data warehouse (EDW) analytics processed
within EDW
statistical analysis
data mining
OLAP tools real- time reports or dashboards
analytic database scoring
outside the EDW in- database analytics accelerator (hardware or software based)
hand- coded SQL
data warehouse appliance text mining
DBMS for data warehousing in- memory database
sandboxes for analytics
column oriented storage engine visual discovery
private cloud
DBMS for transaction processing closed- loop processing
mixed workloads in a DW MapReduce, Hadoop, Complex Event Processing
extreme SQL
in- line analytics
public cloud
Software as a Service
-30 -15 0 15 30 45
potential growth
Graphic based on survey results reported in Big Data Analytics, TDW Best Practices Report, Q4 2011, pp. 23.
Potential growth is an indicator for the growth or decline of usage for big data analytics over the next three years.
Commitment is a cumulative measure representing the percentage of respondens (N= 325) who selected using today and/or using in three years.
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Current trends affecting predictive analytics :
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Standards for data mining and model deployment : CRISP-DM
 A systematic approach to guide the data mining process has been developed
by a consortium of vendor and users of data mining, known as Cross Industry
Standard for Data Mining (CRISP-DM).
 In the CRISP-DM model, data mining is described as an interative process that
is depicted in several phases (business and data understanding, data
preparation, modeling, evaluation and deployment) and their respective
tasks. Leading vendors of analytical software offer workbenches that make
the CRISP-DM process explicit.
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Standards for data mining and model deployment : PMML
 To deliver a measurable ROI, predictive analytics requires a focus on decision optimization to achieve
business objectives. A key element to make predictive analytics pervasive is the integration with
commercial lines operations. Without disrupting these operations, business users should be able to
take advantage of the guidance of predictive models.
 For example, in operational environments with frequent customer interactions, high-speed scoring of
real-time data is needed to refine recommendations in agent-customer interactions that address
specific goals, e.g. improve retention offers. A model deployed for these goals acts as a decision
engine by routing the results of predictive analytics to users in the form of recommendations or
action messages.
 A major development for the integration of predictive models in business applications is the PMML-
standard (Predictive Model Markup Language) that separates the results of data mining from the
tools that are used for knowledge discovery.
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PMML represents an open standard for interoperability of
predictive models. Most development environments can
export models in PMML. As analytics increasingly drive
business decisions, open standards like PMML facilitate
the integration of predictive models into operational
systems. The deployment of predictive models in an
existing IT-infrastructure no longer depends on custom
code or the processing of a proprietary language.
Besides the flexible integration of predictive models into business
applications, continuous analysis is key to enable business process
optimization. The broad acceptance of the PMML-standard further
stimulates the exchange of predictive models. Open standards like
PMML contribute to the wider adoption of predictive analytics and
stimulate collaboration between stakeholders of a business
process. In a similar vein, the increased use of open-source
software can profit from PMML. Open-source environments can
visualize and further refine predictive models that were produced
in a different environment.
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Structured and unstructured data types
 The field of advanced analytics is moving towards providing a number of solutions for the handling of big
data. Characteristic for the new marketing data is its text-formatted content in unstructured data sources
which covers « the consumer’s sphere of influence » : analytics must be able to capture and analyze
consumer-initiated communication.
 By analyzing growing streams of social media content and sifting through sentiment and behavioral data
that emanates from online communities, it is possible to acquire powerful insights into consumer attitudes
and behaviour. Social media content gives an instant view of what is taking place in the ecosystem of the
organization. Enterprises can leverage insights from social media content to adapt marketing, sales and
product strategies in an agile way.
 The convergence between social media feeds and analytics also goes beyond the aggregate level. Social
network analytics enhance the value of predictive modeling tools and business processes will benefit from
new inputs that are deployed. For example, the accuracy and effectiveness of predictive churn analytics
can be increased by adding social network information that identifies influential users and the effects of
their actions on other group members.
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Advances in database technology : big data and predictive analytics
 As companies gather larger volumes of data, the need for the execution of predictive models becomes more
prevalent.
 A known practice is to build and test predictive models in a development environment that consists of
operational data and warehousing data. In many cases analysts work with a subset of data through sampling.
Once developed, a model is copied to a runtime environment where it can be deployed with PMML. A user of an
operational application can invoke a stored predictive model by including user defined functions in SQL-
statements. This causes the RDBMS to mine the data iself without transferring the data into a separate file.
The criteria expressed in a predictive model can be used to score, segment, rank or classify records.
 An emerging practice to work with all data and directly deploy predictive models is in-database analytics. For
example, Zementis (www.zementis.com) and Greenplum (www.greenplum.com) have joined forces to score
huge amounts of data in-parallel. The Universal PMLL Plug-in developed by Zementis is an in-database scoring
engine that fully supports the PMML-standard to execute predictive models from commerial and open source
data mining tools within the database.
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Data is partitioned across multiple
segment servers and each segment
manages a distinct portion of the
overall data.
The Universal PMML Plug-in enables
predictive analytics directly within
the Greenplum Database for high-
performance scoring in a massively
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Predictive analytics in the cloud
 While vendors implement predictive analytics capabilities into their databases, a similar development is
taking place in the cloud. This has an impact on how the cloud can assist businesses to manage business
processes more efficiently and effectively. Of particular importance is how cloud computing and SaaS
provide an infrastructure for the rapid development of predictive models in combination with open
standards. The PMML standard has yet received considerable adoption and combined with a service-oriented
archirtecture for the design of loosely coupled systems, the cloud computing/SaaS model offers a cost-
effective way to implement predictive models.
 As an illustration of how predictive models can be hosted in the cloud, we refer to the ADAPA scoring engine
(Adaptive Decision and Predictive Analytics, www.zementis.com). ADAPA is an on demand predictive
analytics solution that combines open standarfds and deployment capabilities. The data infrastructure to
launch ADAPA in the cloud is provided by Amazon Web Services (www.amazonwebservices.com). Models
developed with PMML-compliant software tools (e.g. SAS, Knime, R, ..) can be easily uploaded in the ADAPA
environment.
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Since models are developed outside the ADAPA environment, a first
step of model deployment consists of a verification step to ensure
that both the scoring engine and the model development environment
produce the same results. Once verified, models are executed either
in batch or in real-tile. Batch processing implies that records are run
against a loaded model. After processing, a file with the input and
predicted values is available for download. Real-time execution of
models in enterprise systems is performed through Web services
that are the base for interoperability. As new events occur, a request
is submitted to the ADAPA engine for processing and the results of
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 The on-demand paradigm allows businesses to use sophisticated software applications over the
Internet, resulting in a faster time to production with a reduction of total cost of ownership.
 Moving predictive analytics into the cloud also accelerates the trend towards self-service BI. The so-
called democratization of data implies that data access and analytics should be available across the
enterprise. The fact that data volumes are increasing as well as the need for insights from data,
reinforce the trend for self-guided analysis. The focus on the latter also stems from the often long
development backlogs that users experience in the enterprise context. Contrary to this, cloud
computing and Saas enable organizations to make use of solutions that are tailored to specific
business problems and complement existing systems.
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 PMML represents a common standard for the representation of predictive models.
 PMML eliminates the barriers between model development and model deployment.
 Through PMML predictive models can be embedded directly in a database.
 PMML-models can score data on a massive scale through parallel processing or in the cloud.
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BI has evolved from performance reporting on historical data to the
pervasive use of real-time data from disparate sources.
To respond faster to market conditions, a much broader user base
needs data access to interactively explore and visualize information
sources and share insights to make faster and better
informed decisions.
In the era of big data, a Web-based platform enables business
discovery and data as well as analytics are consumed as services
in the cloud.
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