Transformational changes that take place in the digital world definitely change the nature of business intelligence and represent an 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.

1. Trends in
in Business Intelligence
Studie en Advies Johan Blomme
Data Consulting Services
www.the-new-bi.be

2. 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.
2

3. Trends in
BI
Introduction
3

4. • 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».
4

5. • 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.
5

6. • 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.
6

7. • 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.
7

8. 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
8

9. Trends in
BI
1. The Future Internet
9

10. • 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.
10

11. • 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.
11

12. 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
XT IT-INFRASTRUCTURE UT
-G MP
E CO
NE
RA ISE
TIO R PR
TE
N EN
12

13. • The Internet of the future gives rise to a new
Business business model that allows enterprises to form
business networks :
Networks
– in the knowledge economy economic activity is
based on highly networked interactions ;
The
Future – the amount of digital collaboration is increasing
Internet among people, things and their interactions
Int rvic
(through the Internet of People and the Internet
Se
ern es
of Things, networking is expanding not only in
ta
et
Da
person-to-person interactions, but also in
of
person-to-machine and machine-to-machine
g
Bi
interactions).
13

14. Globalization T he
ng
ndi Con
sum
xpa r e
E
as m fo es of IT rization
eb e g
e W osyst chan
Th Ec x
s sE
ine
Bus
Device-Indepen
Information Acce
Demographic Shifts
Drivers of
Workforce
NETWORKED
INFRASTRUCTURE
dent
ss
Hyp
e
Soc r Adop tive
ora
ial N tion lab logies
Tec etworki of Col hno
hno
logy ng Tec
Bandwidth
Cloud Computing
& Connectivity
14

15. • Business networks take on a data-driven
approach to differentiate and apply fact-
Business based decision-making enabled by advanced
Networks 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
Internet advantage (access to lots of information has
Int rvic
already become ubiquitous) ; competitive
Se
ern es
advantage is defined as access to information,
ta
the decisions based on that information and the
et
Da
actions taken on these decisions ;
of
g
Bi
– business networks manage data in real-time,
support anywhere, anytime and any device
connectivity and provide the appropriate
information to users across and beyond the
enterprise (business users, partners, suppliers,
customers).
15

16. • The Internet serves as a platform for a
Business service-oriented approach that changes the
Networks 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
The basis for business networks.
Future
Internet • The new BI requires technologies that integrate
Int rvic
multiple data sources, address business needs
Se
ern es
in a dynamic way and have a short time to
ta
et
Da
deployment.
of
g
Bi
• Contrary to large scale application
development of traditional BI, the new BI
moves towards smaller and flexible
applications that can adopt quickly and are
supported by a service-oriented architecture.
16

17. • 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.
17

18. Internet of Services and BI
User empowerment / Self-service Cloud computing
Embedded BI
Cloud computing emerges as a new
Users expect to have access to deployment model of BI by the
business information in the same way BI moves into the context of business adoption of a service-oriented
as they use the Internet and search processes and transforms from a architecture and drives a
the Web. Self-service BI is the reactive to a proactive decision- transformation in application
implementation of this service- making tool by monitoring architectures through using “the Web
orientation at the end-user level. performance and the prediction of as a platform” for interoperable
future events. This change in the use applications and services.
and delivery of software is guided by
the adoption of a service-oriented
approach.
18

19. Trends in
BI
2. Big Data
19

20. 20

21. VARIETY
VELOCITY
VOLUME
21

22. Major sources of « big data »
22

23. 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
23

24. • 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.
24

25. • 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.
25

26. The increasing volume and complexity of data
The 3 V’s represent the common has forced organizations to look at new data
dimensions of big data, but the real management and analytic tools to optimize
challenge lies in extracting actionable performance, improve service delivery and
insights from it. discover new opportunities.
Variety Database Technology
Velocity
Analytics
Volume Services
26

27. • 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.
27

28. • 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.
28

29. • 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.
29

30. • 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).
30

31. • 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).
31

32. Trends in
BI
3. Cloud Computing
32

33. # apps / # users
ING
PUT
COM
UD
CLO
GE
O MA
OTC  virtualized connected
ET/D
RN environment
INTE
 Internet-based data
VER access & exchange
SER  eCommerce
NT-
CLIE  « as a service »-
 service-oriented paradigm
architecture
 networking
PC  Web 2
 office automation
 data warehousing
INI
E/M
INFRA M  desktop computing
MA
 centralized
automation
1970s 1980s 1990s 2000s 2010 & beyond
33

34. • 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
34

35. “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
35

36. • 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.
36

37. 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.
The management of large Analytics in itself don’t
data volumes (data integration, guarantee a competitive
data quality). As data fuels advantage. The insights,
analytic processes, big data communications and decisions
becomes increasingly important.. that follow analysis become
more important. This stresses the
role of self-service and
collaboration.
37

38. 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 architecture is ideally suited to integrate
Cloud computing data from various sources (e.g. « mash up » enterprise data with
and big data public data).
38

39. 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 and
Cloud computing outside the firewall.
and self-service BI
39

40. Trends in
BI
4. Embedded BI
40

41. As the market changes faster and faster, BI has to adopt to support decisions in day-to-day
operations. The role of BI has changed beyond its original purpose of supporting ad hoc
queries and analysis of historical information. With changing market dynamics there is a
The Need for Agile BI growing need to monitor performance using the latest data available and to predict
future events.
The new BI delivers information to users within the context of operational activities.
Rather than reporting on the business, BI moves into the context of business processes.
Data is analyzed in the flow of transactions to produce real-time metrics, alerts,
recommendations and predictions for action. BI transforms from a reactive to a
Process Orientation proactive decision-making tool.
Operational BI is related to the subject of real-time processing. Through the Internet
of people (e.g. social media) and the Internet of Things (e.g. RFID and other sensored
data), information becomes available that helps enterprises to improve business
EMBEDDED BI processes.
41

42. • 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.
42

43. Next-generation business applications will be more people- and process-oriented and have the computing power to
proactively generate information that supports operational decisions.
PEOPLE PROCESS
Next-generation applications are
Self-directed analytics give users the
ability to navigate through and not static but interactive,
visualize business data, allowing allowing users to couple the right
them to generate views and reports actions based on the insights that
relevant to their job function. are delivered.
For example :
Business - analytics on browser-based BI
applications allow the mobile
Analytics
workforce to take actions ;
- in an inventory application, proactive
decision-making is supported through
real-time information about which
items are running low in inventory.
TECHNOLOGY
New approaches such as in-memory processing, in-database analytics, CEP,
etc. contribute to the broader adoption of BI.
43

44. BI delivery framework
(adapted from Eckerson, 2011)
44

45. to
from
service-oriented architecture
monolithic applications
45

46. 1 changes in the nature of BI : from
1 2 3 stand-alone applications to
embedded applications
2 changes in the function of
applications : from dedicated
applications to composite
applications
3 changes in the way data is
accessed : from data as an isolated
resource to data as a service
Source : SINGH KHALSA, R.H., REASON, A., BIERE, M., MEYERS, C., GREGGO, A & DEVINE, M., 2009.
46

47. 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.
47

48. The Internet of Services allows for the personalisation of services, tailored to the user’s needs.
Example : mashups (combining data from different sources into an integrated application)
Web services are an important tool for
data integration from multiple sources
and provide access to real-time
information that can be fed into Open access makes BI-functionality
operational applications. accessible across and beyond the
enterprise.
Web services are user-centric because
information is provided in the context
of day-to-day activities.
48

49. 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.
49

50. 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.
50

51. • 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.
http://www.idvsolutions.com 51

52. 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.
52

53. reach the long tail of
the application spectrum
user-driven
cloud adoption real-time data view
incorporate social & collaborative
agility
computing features
53

54. Trends in
BI
5. User-Empowerment / Self-Service
54

55. • 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.
55

56. 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
56

57. 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)
57

58. 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
58

59. 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.
59

60. Drivers of the consumerization of IT
CoIT
UBIQUITOUS CONNECTIVITY
60

61. User-generated content
Power shift from expert-generated to
user-generated content. Because
markets are more volatile, businesses
seek greater agility to respond faster
to market requirements. The
democratizaton of BI is driven bottom-
up and top-down. Users want
customized tools, while the ability to
mine data is critical for business
competitiveness, which causes
informed decision-making to be
CoIT Crowdsourcing.
Architecture of participation.
extended across more roles.
UBIQUITOUS CONNECTIVITY
Big data.
The googlization of BI.
Data and desktop
BI as a service virtualization
The cloud as a delivery Accessing data and applications
mechanism for self-service BI. from any location, on any
device, at any time. 61

62. interactive data
visualization
(business discovery)
in-memory Web-based
data management delivery
(processing large amounts (delivery to a variety
of data) of devices)
self-service, fact-based decisions, agile BI
62

63. The BI-landscape is reshaped by the model of the consumer Web.
user-driven analysis,
open standards,
intuitive user
loosely coupled services
interfaces, easy to use,
work from browser,
culture of sharing
real-time,
and collaboration
zero wait,
app-driven,
multiple devices
63

64. Collaboration is more than
distributing and sharing of
Business users are empowered to documents ; it implies bringing
gain insights into data (through context to analytics : different
exploration, visualization) people track the relevancy of
analytics and the decisions that
will be based on it
The result is faster
and better decision-making
Value created from data can be
shared internally within the
company and externally with
customers and partners
64

65. Trends in
BI
6. Collaborative BI
65

66. • 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.
66

67. • 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.
67

68. 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.
68

69. « 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
www.innovationfactory.eu/vision and higher value 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.
69

70. • The business value of Web 2.0 for collaborative BI can be situated from the eight core patterns
of Web 2.0.
70

71. 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.
71

72. Trends in
BI
7. Social Media Analytics
72

73. • 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.
73

74. Drivers for social media analytics
74

75. The spectrum of available data has been enlarged with new soures, esp. social media
data streams.
75

76. The explosion of social media drives the need to analyze and
get insights from customer conversations.
76

77. 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.
interaction
data
descriptive attitudinal
data data
behavioral
data
77

78. Examples of the use of social media analytics in day-to-day operations :
• Baynote (www.baynote.com) provides • Wise Window (www.wisewindow.com) distills
recommendation services for websites. social media content automatically and in real-
Websites using Baynote recommendations time into industry-specific taxonomies. The
deliver relevant products and personalized approach that Wise Window calls « Mass
content that create an intuitive user Opinion Business Intelligence » (MOBI) does not
experience. focus on individual behavior but the type of
syndicated research that Wise Window
performs is aimed at giving a broader
• Baynote applies « interest mining ». It
understanding of consumer sentiments and
attempts to cluster consumers to provide
behavior in the market at large.
product or content recommendations that
are based on a broader understanding of
consumer behaviour. Baynote goes beyond • MOBI discovers leading indicators with data
the clickstream by examining the words derived from social media to make
associated with the clicks the user makes. organizations more agile and responsive.
Combining the clickstream and the semantic Application fields include simple mindshare
stream reveals the communality of cluster analysis, discovering new products and niches,
members above a pure statistical or spotting fast movers, performing constituent
demographic cluster approach. The resulting analysis and predicting demand.
« integrest graph » is used to personalize
product and content recommendations that
lead to maximum engagement, conversion 78
and lifetime value.

79. Trends in
BI
8. Predictive Analytics
79

80. Traditionally, BI systems provided a retrospective view of the
business by querying data warehouses containing historical data.
Contrary to this, contemporary BI-systems analyze real-time event
streams in memory.
Analysis
In today’s rapidly changing business environment, organizational
(Why did it happen ?) agility not only depends on operational monitoring of how the
business is performing but also on the prediction of future
Reporting outcomes which is critical for a sustainable competitive position.
(What happened ?) Predictive analytics leverages actionable intelligence that can be
integrated in operational processes.
HISTORY
FUTURE
PRESENT
Monitoring Predictive Analytics
(What is happening now ?) (What might happen ?)
80

81. 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.
81

82. Current trends affecting predictive analytics :
82

83. 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.
83

84. 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.
84

85. 85

86. 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.
86

87. 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.
87

88. Predictive
modeling
Advanced
visualization
multidimensional
Self-service view of data
business discovery in
an interactive way
Data-as-a-service
making multiple
data sources Social media
available for analysis analytics
analyze customer
Text mining sentiment
pattern detection in
unstructured data
Collaboration
adding context
to decision
making
Real-time
dashboards
monitor KPI’s
88

89. 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.
89

90. 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
parallel environment.
90

91. 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.
91

92. 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
predictive modeling are available almost simultaneously.
92

93. • 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.
93

94. • 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.
94

95. 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.
95

96. References
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Press, New York, 2011.
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