This presentation centres on how Businesses can take advantage of this era of information overload for enhancing their Business Intelligence and Data Analytic exploits to assure greater values with the available technology solutions. It is focused on demystifying the BIG DATA phenomenon of the information age, and also on motivating traditional business drivers to begin to take advantage of business decision support systems (DSS) for their business intelligence and data analytics needs. The objective is to help organizations discover what and what they can do with these ICT solutions in their business for greater value realization. These values are expressed in building agile business that are able to thrive, make profit, grow and remain sustainable in the midst of stiff competition, globalization, innovation and regulatory pressures, even with elastic customers’ demands.

1. Business Intelligence (BI) and Data Analytic
for Value Realization
Iyke Ezeugo, MSc IT, CEH, CSA, LPT, CCFI,
Email: iykye@yahoo.com
Conference, Abuja 2015
Presented at
CyberSecurity : Aligning Nigeria
with the rest of the World

2. Content list
1. Discussion Objective
2. Business Intelligence (BI) - Introduction
3. BI Key Driving factors
4. How BI impacts today's businesses
5. How the business drivers /owners think
6. Understanding the BI and Data Analytic
Concept
7. Business Intelligence Frameworks
8. How BI and Data Analytics can be
implemented
9. Who uses BI, and for what within the
organization
10. BI users’ goals
11. Data Analytic
12. Why Data Analytic
13. Data Analytic Driving factors
14. Data Analytic Framework
15. How Data Analytic fits into BI frameworks
16. Predictive Analytics
17. Predictive Analytics Applications
18. The Big Data paradigm
19. How is the Big Data a factor in data
analytics
20. Why Predictive Analytics
21. Challenges and Barriers to Adoption
22. Addressing the challenges
23. Between BI and Data Analytic
24. Some popular vendors of BI Tools
25. Value realization - conceptual
overview
26. What BI and data analytic offer to
business on the spot
27. Business Intelligence Value realization
28. Data Analytics Value realization
29. Predictive Analytic value realization
30. Rounded and sustainable growth
31. Values in Business Agility
32. Questions

3. We live in an era of globalization, technological innovations and information overload
• Today’s business decisions require automated scientific supports for smart and agile
solutions; such supports must have real-time access to all forms of data from the business
environment and also the capacity to derive meaning and value from the data.
• While the gaps in accessing real-time data, and making it available to users are speedily
closing up, most of these solutions have not been very successful in helping all users to
directly link its products (strategic information) to action, and also to its corresponding
value. How can we achieve that in our own case?
Discussion Objective
Big Data
Predictive Analytics + BI
ValueInformation Strategy & Action
BI
Closing these Gaps is the objective of this discussion
Analytics Smart + Agile Businesses

4. Business Intelligence (BI) - Introduction
Business intelligence (BI) is an ICT-
driven process for aggregating and
analyzing data to produce
information of intelligence
(strategic and actionable) values
for making more informed
business decisions for enhanced
operational efficiency.

5. BI Key driving factors
Operating pressures and the need for precise responses that support critical decisions
BI uses information to formulate more reliable strategies for narrowing the gaps between current
performance and preset goals.
Environmental Stimuli
Stiff Competition,
Global market forces,
unstable business frontiers,
Consumers elastic demands,
adverse operating environment,
government regulations
Operator's Reactions
Business Strategy
formulations,
real-time
responses,
partnership and
collaborations,
enhanced
productive , new
products and
business models
Business Decisions
support
Data,
Information,
Analytics,
Business
Intelligence
Predictions
Agile
Businesses

6. They need to deal effectively with the emerging BIG
DATA – transforming the raw data into meaningful and
useful information.
They require capacity for interpreting large amounts of
unstructured data to help identify, develop and
otherwise create new strategic business opportunities.
 With BI, a single view of historical, current and possible outcome of
business operations can be produced to support operational and strategic
business decisions.
 Used to identify new opportunities, potential issues and escape routes so as to
implement effective strategies based on insights to gain competitive market
advantage, AGILITY and long-term stability.
How BI impacts today's businesses
Businesses are under PRESSURE to cope with new business trends, challenges and
opportunities.

7. How BI impacts today's businesses cont’d
• Always above board in solving business challenges and exploiting opportunities
• Ready to tackling today’s turbulent business environment and thrive in such an environment.
• Resources and readiness to make complex decision using DSS for reliable outcome – no gambling.
Business Agility
Ensuring that the business thrives in a constantly changing business environment
Agile
Operations
Agile
Finance
Agile
Innovation
Agile
Systems
Agile
Leadership
Agile
Decisions
Precision
Leverage Big-data & predictive analytics
Value
Innovation & Growth
Speed

8. For a core business person, the whole essence of any innovation like BI should
be to improve decision qualities for profitability, growth and sustainability
How the business drivers think
Enablement Profitability Growth Sustainability
 Challenges must be leaped over and Business opportunities must be
maximized at times
 Managers must act prudently, predict trends of events and rightly respond
to pressures, make profit, grow the business and sustain the growth

9. Understanding the BI and Data Analytic Concept
The concept of BI & big data in today’s business operations centre on deriving
values from open source information in conjunction with operations’
generated high volume, high speed & varied complex data through analytics.
Agility through DSS
BI Users
Executives
Managers
Operators
The Big Data
Volume, Velocity & Variety
Complex Business
Environments
Decisions
Data
Data Analytics
BI Applications:
- Data warehousing
-Data mining
- BPM
- OLAP
- etc.

10. Structured to pull together the entire business architecture, databases, analytical tools
and applications to provide enhanced business decision support systems and dynamic
view of the past, present and simulated possible moves and outcome
Business Intelligence Frameworks
DASHBOARD
Consolidated,
dynamic &
interactive
Agile
business
Analytics

11. How BI and data analytic can be implemented
• Intelligence Gathering
Ethically and legally assembling and processing information
from:
• Business environment
• Business processes
• Customers
• Stakeholders
• Competitors
• Other sources of potentially valuable information
Business Intelligence

12. • Data Analytics
In order to make effect use of the data, the
derived information must be:
 Dynamically Accessed
 Rightly Collected
 Properly Cataloged
 Tagged
 Sorted
 Analyzed
 Usable in different ways
How BI and data analytic can be implemented cont’d

13. Who uses BI, and for what within the organization
13
– Executives – Those who focus on the overall business
development
– Business Managers/Decision Makers – Usually focused on
single areas of the business (finance, HR, manufacturing,
etc.)
– Opertors/Information Workers – Typically managers or
staff working in the back office
– Line Workers – Employees who might use BI in delivering
their tasks without knowing it
– Analysts & Consultants – Contractors and Employees who
will perform extensive data analysis and business process
modeling

14. BI users’ goals
• To save time and improve business decision processes
• To achieve faster, more accurate reporting and a unified
view of the key parameters, status, cause and effects.
• To better realize short, mid and log term goals through
improved strategies, implementation and benchmarking
techniques
• Improved customer service and build more efficient
processes
• To Save Cost and increased revenue
• To protect the business
What do businesses implementing BI hope to achieve

15. BI users’ goals cont’d
Supporting Competitive Intelligence (CI) with BI
 Understanding and Tracking competitors’ moves by
gathering and analyzing information on their
recent and in-process activities for decisions in
matching or countering competitor’s advantage and
building a market niche.
 Identifying and testing critical components for
building brand and customer loyalty for
sustaining any achieved competitive
advantage.

16. Data Analytic
Analytic applications are designed to utilize all features of the BI
solution to provides RICH DYNAMIC VISUALIZATIONS that allow
much easier understanding of trends and relationships to create
scenario simulations and predict outcome with precision.

17. Why Data Analytic
 The need to understand changes in customer behavior to
enable actions that will lead to desired business outcomes.
 The need to be agile in marketing and sales, in operations
management, in finance, and risk management.
 Organizations today need to be proactive and consequently
predictive - using information and insight from data
to detect patterns and trends, anticipate events, spot
anomalies, forecast and simulate the future.
The need for deeper insights to help corporations realize a 360-
degree view of their business operations

18. Data Analytic Driving factors
– Computing power is constantly increasing, and everyone is struggling
with data – Volume, velocity, variety, availability, timeliness, integrity,
etc.
 People are gaining better understanding of technology value and emergency
of the big data phenomenon.
 Corporate data architecture and metadata strategy are either lacking in some
places or getting more complex
 Getting data out of the ERPs is difficult and requires considerable processing
– The rise of certain economic forces and the fact that critical business dots
aren’t always connected
– Management systems are generally not aligned and frameworks does not
easily flow seamlessly.
– The need for integrated financial and non-financial data with unified
dynamic view showing capabilities to make predictions as well.

19. Data sources for Data Analytic
Data Analytic
Archives
Transactional
data
Social
Media
Activity
Generated
data
Enterprise
data
Public
Trends &
Competition
Structured
Warehoused data
(Tables and
records)
Demographic &
Geospatial Data
Demographic
Data, Geospatial
Data and public
discussions/
observations
Websites click
stream data &
Web Logs
Time Series &
Machine
generated/ Meta
data (Sensors, RFID,
etc.)
Internal text data
(emails, memos,
customers
suggestions, call
logs, claims, etc.)
Real-time
events, news,
surveys and
reports

20. Data Analytic frameworks
Though this implies a significant increase in power to support critical business
decisions in fast paced environment, complexity is by no means part of it.
Starts with resource deployment,
monitoring for understanding of the
business environment to understanding
the data surrounding it.
Then data is captures, filtered and
prepared for processing by way of
analytics. Thereafter, modeled and
evaluated to provide strategic and
actionable information.
Output will be interactive dashboards
providing consolidated view of desired
parameters, including KPI, benchmarks
or more basic inputs and measures for
simulation.

21. Predictive Analytics
Meaning: Advanced analytical
methods that complement
business intelligence to enable
organizations to go beyond
regular data analytic in exposing
hidden patterns, motivations,
human behavior. This is primarily
used to predict trends, possible
reactions and understanding for
better business performance.
This is applied across a range of
disparate data types (the Big
Data) to realize greater value.
Why?
What is
happening?
Business Intelligence
Analytics
Predictive Analytics
What if
the input
changes
What is the
implication?
What will
be the
outcome?
Predictive analytic tools are highly useful in alerting enterprises to new opportunities,
creating different scenarios and simulating possible outcome to advise business owners on
the best course of action to take to exploit this. (http://kognitio.com/predictive-analytics-
boosts-firms-productivity)

22. Why Predictive Analytics
Key Drivers: The need to rightly predict human behavior—and even
hidden human motivations from the understood patterns .
Normally, analytics can yield literally hundreds of millions
of data points—far too many for human intuition to make
any sense of the data.
The need to extend the capabilities of regular analytics
tools to deal with emerging BIG DATA from all forms of
transactions and online behavior while incorporating data
mining, statistically evaluating correlations between many
types and sources of data to expose hidden patterns and
connections.

23. The Big Data paradigm
The Big Data paradigm has remained the key factor for today’s analytics
Data with the 3Vs - High Volume, High Velocity and Varied data sets.
Often beyond the capacity of the of traditional data processing.

24. How is the Big Data a factor in data analytics
Deriving value from
the Big Data
evolution requires
special capabilities
for capturing, data
curation,
searching, sharing,
storing, transferring,
integration,
processing,
visualization,
and manipulation.

25. Predictive Analytics Applications
 Marketing analysis and scenario simulation
 Discovering cross-sell/Up-sell/ propensity to
spend
 Portfolio analysis and projections
 Online Presence and social media analysis
 Product development and lifecycle
management and predictions
 System optimization and reaction predictions
 Sells, economic forecasting and risk Analysis
 Quality Assurance
 Fraud detection, Criminal activities
reconstruction, and Scientific Investigation
 Loan default prediction, etc.
Predictive analytics can be used from predicting consumer behavior to predicting
machine behaviors and to finding patterns, and more:

26. Challenges and Barriers to Adoption
o Predictive analytics customization can be quite
complex and require specialized skill sets (technical,
analytical and critical thinking skills).
o Understanding of technology and its value
realization roadmap.
o Lack of a strong business case for investment
justification

27. Addressing the challenges
• Committed Leadership (at all levels)
– Active involvement (not just verbal support)
– Focal points (if everyone owns it… no one owns it)
– Set clear goals and ways of measuring performance
– Accountability contracts aligned with performance targets
• Trained Staff (especially Program Managers)
– Understand concept of performance/results-based management
– Primary “users” are actively engaged and understand where they fit
within the framework
• Incentives to Use Performance Information
– From top leadership down to front line employee
– Measures make sense to staff throughout the organization linking
what employee does day-to-day with organizational goals

28. Between BI and Data Analytic
BI discovers facts
from a particular
business and its
environment to avail
the business drivers
information of
intelligence value to
support their
decisions
Analytics processes
available data to provide
a consolidate dashboards
that avail business
operators 360degree
view of the business with
useful possible pointers
to the direction of flow
of things
Originally, BI started more like reading the newspaper for information of intelligence
values, BUT now BI is focusing more on real-time events and predictive analytics

29. Common Big Data, Business Intelligence and Data Analytic tools
Data analysis has become a do-or-die requirement for real 21st century businesses. Data analysis tools and
software are used to sort through enterprise data in order to identify patterns and establish relationships.
This is helps businesses gain greater insight into organizational, industry, and customer trends; it allows
users to quickly slice and dice data to conduct in-depth analysis. Below are some popular vendors:
1. SAP Hana, SAP IQ - http://go.sap.com/solution/sthash.t6JYqlxY.PnoGspjW.dpbs
2. Oracle Database, Oracle MySQL, Oracle Essbase - https://www.oracle.com/big-data/index.html
3. 12- Microsoft SQL Server 2012 Parallel Data Warehouse (PDW) - http://www.microsoft.com/en-us/server-
cloud/solutions/data-warehouse-big-data.aspx
4. The WebFOCUS business intelligence (BI) platform -
http://www.informationbuilders.com/products/webfocus
5. 1010data columnar analytical database - https://www.1010data.com/company
3. Actian Matrix (formerly ParAccel), Actian Vector (formerly Vectorwise) http://www.actian.com/
4. Amazon Redshift service (based on ParAccel engine); Amazon Relational Database Service -
http://aws.amazon.com/
5. Cloudera Impala - HBase, (not really DBMS), but supports SQL querying on top of Hadoop. -
http://www.cloudera.com/content/www/en-us.html
6. HP Vertica Analytics Platform - http://www8.hp.com/us/en/software-solutions/big-data-platform-
haven/index.html
7. Hortonworks Data Platform (HDP)freely available opensource - http://hortonworks.com/
7. IBM DB2, Netezza.- http://www.ibmbigdatahub.com/ibm-watson-foundations
9. Infobright - https://www.infobright.com/
10.Kognitio Analytical Platform - http://www.kognitio.com/
11. MapR - https://www.mapr.com/
13. Pivotal Greenplum Database - http://pivotal.io/
16.Teradata, Teradata Aster. - http://www.teradata.com/?LangType=1033

30. This focuses on how organizations can and
are using BI and data analytic to derive
business value.

31. What BI and data analytic offer to business on the spot
• Reliable Dashboard & Portals for specific subjects
– Total Business performance overview
– Deep and Drilldown insights
• Executive performance metrics
• Financials, Sales performance, customer/partner & Competitors
performance outlook
• Focus and detailed reporting of specific operational segments and
selected user groups.
• Easy plan simulations and forecasting
– Scenario simulation
– Prediction of events and outcome
• Event & Schedule based reporting
– Reveals issues without sifting through reports to find the
problem – costing example
31

32. • Rapid business implementation cycle with minimal hitches
• Strategic Business Decision Support Systems
• Sure way to business agility
• Standards-based approach that still allows customization & path
for business changes
• Ends re-invention of the wheel
32
Clearly, data integration is a key component of any predictive
analytics effort focusing on operationalizing and ease of use
What BI and data analytic offer to business on the spot

33. • What key values should a Business expect from BI:
– Investments are no more done blindly - BI is Critical for assessing the
business readiness for meeting the challenges posed by emerging
business realities.
– Provides deep and realistic overview - Enable a holistic view of the
business dynamic and informed approach to business functionality.
– Helps Business drivers to act like gods - Leverage deep terrain
knowledge, privileged information of (intelligence and strategic values,
and imbibe best practices).
– Makes implementation of balanced scorecard methodology easy –
helps in defining, implementing, and managing an enterprise’s
business strategy by linking objectives with factual measures.
Business Intelligence Value realization

34. Business Intelligence Value realization cont’d
– Helps investors to overcome unfounded fear and the terror of
myths - Barriers to entry of new business terrain and tackling
challenges are significantly diminished
– Competitive advantage - Measuring up in stiff competitive
landscapes and adaptation to the globalization and innovation
demands.
– Preempt the market and influencing factors - Understanding
how consumers are finding better or less expensive suppliers all
over the globe so as to build reliable strategies

35. • The Strategic Imperative of Data Analytic
• Going beyond the static, historical reporting
of traditional business intelligence
• More sophisticated and interactive than
descriptive techniques (such as reporting or
dashboards).
Data Analytics Value realization

36. Predictive Analytic value realization
Provides business with more advanced techniques
that move from reactive to proactive, from historical
to future, and from Informative to preempted action
and reaction simulations.
Business drivers gain business AGILITY - greater accuracy in
the big data analytics for a more confident decision making
to achieve enhanced operational efficiency, dynamism,
cost and risk reduction.

37. Rounded and sustainable growth
Superior Decisions and business Solutions
Enhanced adaptation, change and
performance management capabilities
Rounded &
sustainable
GROWTH

38. Values in Business Agility
38
Creating significant business value from technology and the big data evolution
Predictive Data Analytics

39. Professional
QUESTIONS
1. How can we protect collected confidential data from
the espionage of malicious insiders and competing
counterparts?
2. What is the implication of BI in an era of heightened
cybersecurity challenges - will your organization require
to take strategic steps in protecting its trade secretes,
intellectual property and confidential information to
remain safe from the prying eyes of competitors?

40. ethical
QUESTIONS
1. How can we effectively collect data about our rivals and
customers without the ethical and professional issues
spying and privacy invasion ?

41. QUESTIONS

42. Iyke Ezeugo, MSc IT, CEH, CSA, LPT, CCFI,
Email: iykye@yahoo.com