2. Data Warehouses and Data Mart
Business intelligence (BI) is all about turning an organization’s
data into insights that can be used to inform business decisions.
BI analysts will use BI tools, software or services to access and
analyze datasets and translate their findings into reports,
summaries, dashboards, graphs, charts or maps.
In recent years, the advent of modern data visualization and
reporting tools has transformed the discipline, empowering
businesses to use big data insights to identify, develop and create
new business opportunities.
2
3. Business Intelligence
3
“a set of methodologies, processes, architectures and technologies
that transform raw data into meaningful and useful information”.
This can then be “used to enable more effective strategic, tactical
and operational insights and decision-making”.
This definition acknowledges that data cannot be effectively
analyzed or used to generate meaningful insights if it is poor quality.
BI should not be confused with‘business analytics’.
Business intelligence is descriptive and uses metrics to generate
clear snapshots of business performance.
Meanwhile, business analytics is predictive, and describes what
organizations should do in future to generate better outcomes.
4. Data Warehouse
4
A DataWarehouse (DW) is a relational database that is designed
for query and analysis rather than transaction processing.
It includes historical data derived from transaction data from
single and multiple sources.
A Data Warehouse provides integrated, enterprise-wide,
historical data and focuses on providing support for decision-
makers for data modeling and analysis.
A Data Warehouse is a group of data specific to the entire
organization, not only to a particular group of users.
It is not used for daily operations and transaction processing but
used for making decisions.
5. 5
A Data Warehouse can be viewed as a data system with the
following attributes:
It is a database designed for investigative tasks, using data
from various applications.
It supports a relatively small number of clients with relatively
long interactions.
6. 6
It includes current and historical data to provide a historical
perspective of information.
Its usage is read-intensive.
It contains a few large tables.
"Data Warehouse is a subject-oriented, integrated, and time-
variant store of information in support of management's
decisions."
8. Subject-Oriented
8
A data warehouse target on the modeling and analysis of data
for decision-makers.
Therefore, data warehouses typically provide a concise and
straightforward view around a particular subject, such as
customer, product, or sales, instead of the global
organization's ongoing operations.
This is done by excluding data that are not useful concerning
the subject and including all data needed by the users to
understand the subject.
9. Integrated
9
A data warehouse integrates various heterogeneous data
sources like RDBMS, flat files, and online transaction
records.
It requires performing data cleaning and integration during
data warehousing to ensure consistency in naming
conventions, attributes types, etc., among different data
sources.
11. Time-Variant
11
Historical information is kept in a data warehouse. For
example, one can retrieve files from 3 months, 6 months, 12
months, or even previous data from a data warehouse.
These variations with a transactions system, where often only
the most current file is kept.
12. Non-Volatile
12
The data warehouse is a physically separate data storage, which is
transformed from the source operational RDBMS.
The operational updates of data do not occur in the data warehouse,
i.e., update, insert, and delete operations are not performed.
It usually requires only two procedures in data accessing: Initial
loading of data and access to data.
Therefore, the DW does not require transaction processing,
recovery, and concurrency capabilities, which allows for substantial
speedup of data retrieval.
Non-Volatile defines that once entered into the warehouse, and data
should not change.
14. Goals of Data Warehousing
14
To help reporting as well as analysis
Maintain the organization's historical information
Be the foundation for decision making.
16. Data Mart
16
A Data Mart is a subset of a directorial information store,
generally oriented to a specific purpose or primary data
subject which may be distributed to provide business needs.
Data Marts are analytical record stores designed to focus on
particular business functions for a specific community within
an organization.
Data marts are derived from subsets of data in a data
warehouse, though in the bottom- up data warehouse design
methodology, the data warehouse is created from the union
of organizational data marts.
17. 17
The fundamental use of a data mart is Business
Intelligence (BI) applications.
BI is used to gather, store, access, and analyze record.
It can be used by smaller businesses to utilize the data they
have accumulated since it is less expensive than implementing
a data warehouse.
18. Reasons for creating a data mart
18
Creates collective data by a group of users
Easy access to frequently needed data
Ease of creation
Improves end-user response time
Lower cost than implementing a complete data warehouses
Potential clients are more clearly defined than in a
comprehensive data warehouse
It contains only essential business data and is less cluttered.
19. Types of Data Marts
19
There are mainly two approaches to designing data marts.These
approaches are
Dependent Data Marts
Independent Data Marts
21. 21
A dependent data mart is a logical subset of a physical subset of
a higher data warehouse.
According to this technique, the data marts are treated as the
subsets of a data warehouse.
In this technique, firstly a data warehouse is created from which
further various data marts can be created.
These data mart are dependent on the data warehouse and
extract the essential record from it.
In this technique, as the data warehouse creates the data mart;
therefore, there is no need for data mart integration. It is also
known as a top-down approach.
22. Independent Data Marts
22
The second approach is Independent data marts (IDM) Here,
firstly independent data marts are created, and then a data
warehouse is designed using these independent multiple data
marts.
In this approach, as all the data marts are designed
independently; therefore, the integration of data marts is
required.
It is also termed as a bottom-up approach as the data marts
are integrated to develop a data warehouse.
Other than these two categories, one more type exists that is
called "Hybrid Data Marts."
24. Hybrid Data Marts
24
It allows us to combine input from sources other than a data
warehouse.
This could be helpful for many situations; especially when
Adhoc integrations are needed, such as after a new group or
product is added to the organizations.
25. Steps in Implementing a Data Mart
25
The significant steps in implementing a data mart are to
design the schema, construct the physical storage, populate
the data mart with data from source systems, access it to
make informed decisions and manage it over time.
26. Designing
26
The design step is the first in the data mart process.This
phase covers all of the functions from initiating the
request for a data mart through gathering data about the
requirements and developing the logical and physical
design of the data mart.
It involves the following tasks:
Gathering the business and technical requirements
Identifying data sources
Selecting the appropriate subset of data
Designing the logical and physical architecture of the data mart.
27. Constructing
27
This step contains creating the physical database and
logical structures associated with the data mart to
provide fast and efficient access to the data.
It involves the following tasks:
Creating the physical database and logical structures such as
table spaces associated with the data mart.
Creating the schema objects such as tables and indexes describe
in the design step.
Determining how best to set up the tables and access
structures.
28. Populating
28
This step includes all of the tasks related to the getting data from
the source, cleaning it up, modifying it to the right format and
level of detail, and moving it into the data mart.
It involves the following tasks:
Mapping data sources to target data sources
Extracting data
Cleansing and transforming the information.
Loading data into the data mart
Creating and storing metadata
This step involves putting the data to use: querying the data,
analyzing it, creating reports, charts and graphs and
publishing them.
29. 29
It involves the following tasks:
Set up and intermediate layer (Meta Layer) for the front-
end tool to use.This layer translates database operations
and objects names into business conditions so that the
end-clients can interact with the data mart using words
which relates to the business functions.
Set up and manage database architectures like summarized
tables which help queries agree through the front-end tools
execute rapidly and efficiently.
30. 30
Managing
This step contains managing the data mart over its lifetime. In
this step, management functions are performed as:
Providing secure access to the data.
Managing the growth of the data.
Optimizing the system for better performance.
Ensuring the availability of data even with system failures.
33. Knowledge Management
33
The Connection Between Business Intelligence and
Knowledge Management
Information simply has to be accessible – gathering, managing and
utilizing information is an inevitable part of running any modern
business.
There are two information management technologies we
use: Business Intelligence (BI) and Knowledge
Management (KM).
However, terms information, data and knowledge are often
used interchangeably, thus, apart from being confused about
each phrase, there is oftentimes much confusion around the
definitions of BI and KM.
34. 34
Business Intelligence (BI): Generally, BI is considered to be a set of
tools and techniques applied to gather data and transform it into
information that can be used in business analysis for the purposes of
business development.
Every company gathers, collects, or to say more accurately, deals with
a large amounts of data, including various business documents,
emails, newspaper articles, web pages, reports, contracts, technical
journals and reviews, spreadsheets, graphs and charts and other relevant
sources of business data.
BI technologies usually deal with large amounts of unstructured data via
the use of data warehousing and online analytical processing (OLAP).
35. 35
All these data needs to be organized and validated – prepared
for business analytics.
Thus, “BI is about providing the right data at the
right time to the right people so that they can take
the right decisions.”
Knowledge Management can be defined in many ways as
it spans many multi- disciplinary approaches –
content management, collaboration, the science of
organizational behavior, analyses like observation of trends
and appearance of anomalies, clustering, classification,
summarization, taxonomy building and so on.
36. 36
“Knowledge management is the process of
capturing, distributing, and effectively using
knowledge.”
KM refers to a set of techniques used to capture, share, and
use the information available in order to achieve business
objectives and to aid in business decision making based on
business analytics.
There has been immense growth in the domain of knowledge
management in the last decade and new applications and
solutions that empower knowledge sharing and knowledge
management have appeared.
37. 37
Knowledge is a mix of contextual information, experiences,
rules, and values.
Richer, deeper, and more valuable.
Consider knowing –
What? - based upon assembling information and eventually
applying it.
How? – applying knowledge leads to learning how to do
something.
Why? – casual knowledge of why something occurs.
39. Tacit vs. Explicit Knowledge
39
Tacit knowledge is personal, context-specific and hard to
formalize and communicate.
A [knowledge] developed and internalized by the knower
over a long period of time . . . incorporates so much accrued
and embedded learning that its rules may be impossible to
separate from how an individual acts.
40. 40
Explicit knowledge can be easily collected, organized and
transferred through digital means.
A theory of the world, conceived of as a set of all of the
conceptual entities describing classes of objects,
relationships, processes, and behavioral norms.
Often referred to as knowing that’, or declarative
knowledge.
42. Why Manage Knowledge?
42
Information and knowledge have become the fields
in which businesses compete.
Several important factors include:
Sharing Best Practice
Globalization
Rapid Change
Downsizing
Managing Information and Communication Overload –
Knowledge Embedded in Products
Sustainable Competitive Advantage.
44. Sharing Best Practices
44
Sharing best practices means leveraging the knowledge
gained by a subset of the organization.
Increasingly important in organizations who depend on
applying their expertise such as accounting, consulting and
training firms.
KM systems capture best practices to disseminate their
experience within the firm.
Problems often arise from employees who may be reluctant
to share their knowledge (managers must encourage and
reward open sharing).
45. Globalization
45
Historically three factors, land, labor and capital were the
key to economic success.
Knowledge has become a fourth factor.
Knowledge-based businesses can grow without traditional
land, labor, and capital requirements.
Key competitive factor will be how well an organization
acquires and applies knowledge
46. Other factors
46
Rapid change: firms must be nimble and adaptive to compete.
Downsizing: sometimes the wrong people get fired when creating a
leaner organization.
Managing Info. and Comm. Overload: data must be categorized
in some manner if it is to be useful rather than overwhelming.
Knowledge Embedded in Products: the intangibles that add the
most value to goods and services are becoming increasingly
knowledge-based.
Sustainable Competitive Advantage: KM is the way to do this.
Shorter innovation life cycles keep companies ahead of the
competition.
47. Categorizing knowledge
47
Process knowledge – best practices, useful for increasing
efficiency.
Factual knowledge – easy to document; basic information
about people/things.
Catalog knowledge – know where things are; like directories
of expertise.
Cultural knowledge – knowing how things get done
politically and culturally.
48. 48
Process Knowledge
Definition:
Process knowledge refers to understanding the best practices,
workflows, and methodologies used to complete tasks
efficiently. It involves knowing how things should be done to
optimize productivity and quality.
Characteristics:
It includes step-by-step procedures and workflows.
Often developed through experience, training, and
observation.
Can be standardized through documentation and
automation.
Helps in continuous improvement and efficiency.
49. 49
Examples:
A software company follows an Agile methodology for
software development.
Manufacturing industries use Six Sigma to improve quality
control.
Hospitals have standard operating procedures (SOPs) for
patient care.
Importance:
Reduces errors and inefficiencies in operations.
Improves consistency and quality of outcomes.
Enables new employees to learn processes quickly.
Helps organizations adapt to changing environments through
refined workflows.
50. 50
Factual Knowledge
Definition:
Factual knowledge consists of basic, objective, and easily verifiable information about
people, objects, events, and concepts. It is often recorded and shared in structured formats like
databases, manuals, or books.
Characteristics:
It is static and does not change frequently.
Can be easily documented and shared.
Used for decision-making and reference.
Examples:
A customer service representative needs customer details (name, address, contact information).
Engineers refer to mathematical formulas and physical laws.
An AI model needs a dataset containing facts about weather conditions to make predictions.
Importance:
Forms the foundation for knowledge-based decision-making.
Essential for training AI models and automation.
Helps organizations store and retrieve critical information efficiently.
51. 51
Catalog Knowledge
Definition:
Catalog knowledge refers to knowing where to find information, resources, or
expertise when needed. It acts as a directory that helps individuals or teams navigate
complex systems.
Characteristics:
Helps in locating resources quickly.
Often stored in knowledge management systems, directories, or databases.
Used for networking and collaboration.
Examples:
A university maintains a faculty directory with specializations and contact details.
IT support teams have a knowledge base for troubleshooting guides.
A hospital maintains a medical inventory catalog to track the availability of drugs and
equipment.
Importance:
Reduces time spent on searching for information.
Improves collaboration and communication.
Enhances decision-making by connecting people with the right expertise.
52. 52
Cultural Knowledge
Definition:
Cultural knowledge refers to understanding how things get done within an
organization, community, or society based on norms, traditions, and political
structures. It includes unwritten rules, beliefs, and power dynamics.
Characteristics:
Often implicit and learned through experience.
Can vary significantly between organizations and regions.
Helps in navigating workplace relationships and decision-making.
Examples:
In a multinational company, employees need to understand cultural differences in
communication styles.
A new employee in a government agency learns about bureaucratic procedures
and how to get approvals efficiently.
In academia, researchers need to understand funding priorities and grant
application processes.
Importance:
Helps employees adapt to the workplace environment.
Avoids conflicts due to cultural misunderstandings.
Enhances leadership and teamwork by understanding organizational dynamics.
53. KM involves four main processes
53
Generation – all activities that discover “new” knowledge.
Capture – all continuous processes of scanning, organizing,
and packaging knowledge after it has been generated.
Codification – the representation of knowledge in a manner
that can be easily accessed and transferred.
Transfer – transmitting knowledge from one person or group
to another, and the absorption of that knowledge.
54. Knowledge Generation
54
Concerns the intentional activities of an organization to
acquire/create new knowledge.
Two primary ways are knowledge creation and knowledge
sharing.
Methods include: – Research and Development – Adaptation
– Buy or Rent – Shared Problem Solving – Communities of
Practice.
55. 55
Knowledge capture takes into account the media to
be used in the codification process.
The 3 main knowledge capture activities are:
Scanning (gather “raw” information) – can be
electronic or human.
Organizing (move it into an acceptable form) – must
be easy for all types of users to access.
Designing knowledge maps (providing a
guide for navigating the knowledge base)
56. Designing Knowledge Maps
56
A knowledge map serves as both a guide to where knowledge
exists in an organization and an inventory of the knowledge
assets available.
A knowledge map can consist of nothing more than a list of
people, documents, and databases telling employees where to
go when they need help.
Provides access to resources that would otherwise be difficult
or impossible to find.
58. Knowledge Codification
58
Knowledge must be used or shared to be of value.
Codification puts the knowledge into a form that makes it
easy to find and use.
It is difficult to measure knowledge in discreet units (since it
changes over time).
Knowledge has a shelf life.
59. Knowledge Transfer
59
Knowledge Transfer describe four different modes of
knowledge conversion (transfer):
Socialization: from tacit knowledge to tacit knowledge
Externalization: from tacit knowledge to explicit knowledge
Combination: from explicit knowledge to explicit knowledge
Internalization: from explicit knowledge to tacit knowledge
61. Similarities and Differences between BI and KM
61
Confusion between these two technologies comes from the fact that they deal
with many similar processes.
Both business intelligence and knowledge management capture, collect,
organize, analyze and aggregate data in order to find the best solutions
regarding business decision making processes.
Business intelligence goes as far back as the 19th century and the beginnings of
entrepreneurship and it has been developing steadily over many years.
BI enables organizations to integrate data across the enterprise, unlock the
information and empower knowledge worker to make better (and faster)
decisions – it focuses on explicit knowledge.
However, KM deals with the creation of new knowledge and the dispersion of
existing knowledge throughout an organization – it encompasses both tacit and
explicit knowledge, thus, we can say that KM can influence the very nature of
business intelligence.
62. Types of Decisions
62
Modern cloud-native organizations have constantly growing streams of
raw data flowing from every corner of the enterprise.
Determining the impact this data has on business performance can be
an overwhelming task requiring teams of analysts.
That’s where employing business intelligence (BI) can help.
By presenting current and historical data within a business context, the
data insights supplied by BI tools enable organizations to make smarter,
more confident decisions that provide strategic direction for years to
come.
Instead of relying on intuition and “gut feel,” companies can use BI to
find new ways to increase revenue, track performance, boost
operational efficiency, identify market trends, expose problems, and
much, much more.
63. Decision-making
63
Simply put, decision-making is the process of deciding something, especially
with a group of people.
From a business decision perspective, the aim is to achieve business objectives
to satisfy stakeholder requirements, needs, and expectations.
For the decision to be effective, however, decision makers must forecast the
outcome of each option and determine which is best for a particular situation.
That makes decision support systems (DSS) like decision intelligence and
business intelligence absolute essentials.
Business intelligence refers to the technology tools and processes that enable
businesses to organize, analyse, and contextualize business data from around
the company.
Business intelligence tools and decision-making transform raw data into
meaningful and actionable information.
64. The role of business intelligence
64
Companies make big mistakes when they base business
decisions on what they think will happen instead of relying on
facts.
Using BI and advanced analytics, organizations can extract
crucial facts from the mountain of data, transforming it into
information companies can act on to make informed strategic
decisions.
The result: improved business processes, operational efficiency,
and business productivity.
65. Business intelligence decisions
65
Business intelligence decisions typically fall into three
categories: strategic, tactical, and operational.
An organization needs to gain a complete understanding of
these types of decisions in business intelligence to make
better-informed decisions that lead to increased customer
retention, stakeholder satisfaction, operational efficiency, and
revenue.
66. The relationship between business
intelligence and business analytics
66
Business intelligence tells you what is currently happening
and what happened in the past to bring you to that state.
On the other hand, business analytics is an umbrella term for
predictive data analysis techniques and prescriptive .
Using business intelligence and analytics efficiently is the
difference between companies that succeed and those that fail
in the modern environment.
67. Three primary types of business
intelligence decisions
67
Business intelligence supports the three types of decision-
making mentioned above:
strategic
tactical
operational
68. Strategic decisions
68
Strategic decisions comprise the highest level of organizational
business decisions and are usually less frequent and made by the
organization’s executives. Yet, their impact is enormous and far-
reaching.
Some types of strategic decisions include selecting a particular
market to penetrate, a company to acquire, or whether to hire
additional staff.
Decisions made at this level usually involve significant
expenditure.
However, they are generally non-repetitive in nature and are taken
only after careful analysis and evaluation of many alternatives.
69. Tactical decisions
69
Tactical decisions (or semi structured decisions) occur with
greater frequency (e.g., weekly or monthly) and fall into the
mid-management level. Often, they relate to the
implementation of strategic decisions.
Examples of tactical decisions include product price changes,
work schedules,departmental reorganization, and similar
activities.
The impact of these types of decisions is medium regarding
risk to the organization and impact on profitability.
70. Operational decisions
70
Operational decisions (or structured decisions) usually
happen frequently (e.g., daily or hourly), relate to day-to-day
operations of the enterprise, and have a lesser impact on the
organization.
Operational decisions determine the day-to-day profitability
of the business, how effectively it retains customers, or how
well it manages risk.
Answering a sales inquiry, approving a quotation, or
calculating employee bonuses may be examples of this
decision type.
71. 71
Strategic: Long-term, complex, made by senior
managers
Tactical: Medium-term, less complex, made by mid-
level managers
Operational: Day-to-day, simple, routine, made by
junior managers
72. How to make the best decisions for your
business
72
How do you make the best business decisions? Some people trust
intuition or gut feeling. Others reach out to constituents and experts
for advice.
Still, others decision-making to information systems and automation.
However, the smartest business decisions are made by those who look
at the numbers.
In a competitive business landscape, where agility, flexibility, and a
real-time decision-making process are critical and timely, accurate data
analysis is more important than ever.
In that respect, relying on the types of decisions in business intelligence
is non-negotiable.
It is required for long-standing success and market dominance.
73. Decision Making Process
73
As a business owner, you need to make decisions that have a large
impact on the success of your business.
You need to make decisions ranging from the small, such as what
color your company logo should be, to the larger, such as whether
to expand your business.
No matter what your decision, it should be based on facts, not
emotions.
Decision-making is a crucial aspect of running a business.
Whenever you decide what to do next, a chain of events is
triggered, which may eventually lead to the outcome you desire.
These events constitute the Decision-Making Process using
predictive analytics consulting.
74. 74
The decision-making process is a term used to describe how a
company gets to a point where it can make the best decision for its
company.
This process is a way of thinking, a way of seeing the world, and a
method of arriving at what you believe are the best possible
decisions.
75. 75
From the day a company is founded, it is believed that each
executive should also be a manager in the right sense.
It is believed that the executives should be able to take the right
action with the help of the appropriate decision-making
process.
And the best decision-making process is one that is not only
efficient but also cost- effective.
76. 76
In order to make the best decisions, companies need to use tools to decipher
their data and better understand their customers.
Business Intelligence (BI) is a set of tools used by companies to analyze data
using data analytics tools, gain valuable insights, and make better and more
informed decisions using Business Intelligence reports.
Since its early days, business intelligence has been a tool for decision-making. It’s
a way to ensure that managers and executives make better decisions so that
businesses can run more effectively.
These decisions can range from basic operations like deciding how much to
produce to broader strategic considerations like deciding what products to sell.
However, BI tools vary considerably in how they are designed, how they interact with
other applications, and how they are used. Ultimately, BI is about having access to the
right information at the right time.
77. THE EFFECTIVE STEPS IN THE DECISION-MAKING
PROCESS THAT USES BI
77
Gathering information
If you’re like most of us, you’re using spreadsheets and
dashboards to visualize and present data on theWeb.
But some businesses are turning to dashboards to make smart
decisions that improve operations, enhance employee
satisfaction, process more data, and have greater visibility into
the bottom line.
78. Design and analyze
78
Routinely encountering data requires a person to make
decisions on how to process the data.
It can be viewed as a few steps involved in this process.
First, it involves the analysis of data, followed by the
identification of the most appropriate decision- making model.
The implementation comes after the analysis of data.
The data analysis process is then repeated, and the model is
altered based on the latest information or discoveries, if
necessary.
79. Select and implement using ad hoc query,
what-if, and forecasting
79
The importance of data in business is beyond dispute. But, the
problem is that most companies are not making the most of
the information available to them.
What they have is not necessarily the right data and is not
being put to its best use.
Ad hoc queries, what-if scenarios, and forecasting are
particularly effective tools for uncovering hidden information.
It can influence risk and make decisions. Ad-hoc query,What-
If, and Forecasting are three terms used in business
intelligence to help make decisions.
80. Do evaluations using the vital tools
80
Effective decision-making is critical to business success. The
good news is that business intelligence tools can help you make
better decisions.
Using dashboards and reports can help you identify key
performance indicators, make comparisons between data sets.
Also, identify opportunities to improve your business. If you
have any experience managing a business, you know that these
vital tools are a necessity, providing you with the information
you need to make business decisions.
These tools can be used for assessing your performance,
motivating staff, and improving the customer experience.
81. Separate the components that are not
related
81
At one point in time, every decision we make appears to have
little connection to anything else.
This is because we are able to separate decisions into distinctly
different components, such as the sales forecast, the profit, the
hiring policy, and so on.
The trick is to recognize that these components are inseparable.
Moreover, a decision made without the sales forecast is like an
engine without the air.
A decision made without a profit is like an engine without fuel.
A decision made without a hiring policy is like an engine
without the air.
82. Increase the coherence
82
Business intelligence is a technology that extracts and
analyzes historical data to help organizations make better
decisions.
It is a key asset for businesses that use it to gain value from all
their data and to make better decisions on how to spend their
resources.
83. 83
There is a trend among different organizations in today’s world
to adopt the use of Business Intelligence.
The use of business intelligence in these organizations helps in
making decisions faster and more effectively.
With the help of business intelligence, organizations can keep
track of their processes.
Moreover, the performance of their business, and the
consumption of resources and get insights into the
organization’s current situation.
Business intelligence solutions help in making more effective
decisions.
84. Decision Support Systems
84
Broadly speaking, a decision support system (DSS) is an
analytics software program used to gather and analyze data
to inform decision making.
There are many different types of decision support systems,
from modern business intelligence which uses AI and machine
learning to suggest insights and analyses for humans to perform,
to model-based DSS systems which use predefined criteria to
perform automated calculations and deliver best-case decisions.
For all types, DSS is used in timely problem solving to improve
efficiency and streamline operations, planning and company
management.
85. Traditional vs Modern DSS
85
Traditional DSS: Historically, DSS and BI tools relied on
preconfigured, historical data with no ability to drive real-time
decisions and action. With this approach, decisions are made
based on the past.
Modern DSS: New tools and processes allow for “active
intelligence”, a state of continuous intelligence with an end-to-
end analytics data pipeline delivering real- time, up-to-date
information designed to trigger immediate insights and actions.
86. DSS Characteristics
86
Prior to decision support systems, organizational leaders relied
heavily on a combination of their experience and
professional training, and applied those to thoughtful use of
the advanced insights generated by a data analytics platform.
Decision support systems systematize that by taking
organizational data, analyzing it, and presenting it for use in
company decision making.
87. Categories of decision support systems
87
Data-driven DSS
A data-driven DSS gives users access to a large amount of
internal and external data.
This DSS will query a database using the web, an external
server or a company's mainframe.
It relies on data mining to provide patterns and information
about the data being assessed.
88. 88
Users rely on data-driven decision support systems to make
decisions about businesses, inventories and products.
Managers might find data-driven decision support systems most
helpful when analyzing current and historical data to report on the
conditions of a department or the business.
CEOs, managers and staff might use a data-driven DSS.
Software examples of a data-driven DSS include:
Geographic Information Systems (GIS)
File drawer systems
Executive information systems
Computer-based databases with query systems
89. Model-driven DSS
89
A model-driven DSS allows a user to analyze and manipulate specific
models of data, such as statistics, finances or scheduling.
These decision support systems are specific to the type of model
the user wants to interact with and typically offer less data than
other DSS types.
They analyze scenarios and data to allow the user to manipulate a
model, such as creating a work schedule.
They might use simple analysis tools or complex statistics,
depending on the model's purpose and the user's needs.
Managers, staff and third parties who interact with a business might
use a model-driven DSS.
91. Knowledge-driven DSS
91
With a knowledge-driven DSS, a knowledge-management system
monitors continually updated data about an organization to support decisions.
The DSS uses diagnosis, prediction, interpretation and classification to
recommend actions consistent with the business.
A knowledge-driven DSS can be helpful to managers because it performs tasks
faster than a human might.They can also help consumers decide which products
and services to buy.This kind of DSS often relies on a data-mining component.
Managers, staff and external users, such as customers, might use a knowledge-
driven DSS.
Software examples of a knowledge-driven DSS include:
Software that identifies new or current customers who might be interested in
products
Product selection software
92. Document-driven DSS
92
A document-driven DSS retrieves unstructured information
from a variety of electronic sources. It searches web pages,
documents in databases and other information based on a user's
search terms to gather relevant information.
A document-driven DSS might be specific to a business' private files
or as broad as a common internet search engine.
Anyone using a database's search function or an internet search
engine is using a document-driven DSS.
Software examples of a document-driven DSS include:
Search engines
Database search software
Article databases with search functions
93. Communication-driven DSS
93
A communication-driven DSS uses tools to support
communication and collaboration.
Email is an example of a communication-driven DSS.
This type of DSS includes share tools that allow multiple
people to work on a project at once and software that allows
for digital communication between people.
94. 94
It improves a shared project's efficiency and effectiveness and can help
facilitate meetings and conversations.
Internal team members, virtual business meeting hosts and online chat and
video meeting software users can benefit from a communication-driven DSS.
A communication-driven DSS might also be called a group DSS.
A communication- driven DSS focuses on communication and collaboration,
while a group DSS helps groups streamline the decision-making process.
A communication-driven DSS, for example, might help two people who work
for the same company on different shifts share documents. It might also allow
employees on opposite sides of the country to meet virtually to view a shared
file.
Software examples of a communication-driven DSS include:
Chat and instant messaging services
Collaboration software, such as document sharing and editing software
95. Intelligent DSS
95
Any DSS with artificial intelligence in its design is an example of intelligent
DSS (IDSS).
Within an IDSS, AI does data mining and processing to filter through large
datasets.
An IDSS is designed to offer similar services to a human consultant.
They're programmed to identify patterns and trends to guide
decision-making.
They can also resolve problems and analyze solutions. AI components add
advantages, such as fuzzy logic and machine learning, to a DSS.
Managers, diagnosticians and other decision-makers might use an IDSS.
Software examples of an intelligent DSS include:
Smart manufacturing systems
Medical diagnostic systems
96. Manual DSS
96
A manual DSS relies on individuals instead of computers to
support decision- making.
A group of experts analyses the strengths, weaknesses,
opportunities and threats of their organization or project.
A manual DSS is much slower than a computer-based DSS, but
certain types of analysis still need a human eye at every step.
Economists, executives and managers might use a manual DSS.
Examples of manual DSS include:
Cost-benefit analyses
Decision matrixes
97. Hybrid DSS
97
A hybrid DSS combines parts of multiple DSS types to create a complex outcome.
Large issues in industries such as finance and health care might require the tools of
multiple decision support systems, such as a knowledge-driven DSS and a data-driven
DSS.
A hybrid DSS might use additional software to help these components work together.
Sometimes a human analyses and combines the results of each DSS.
A hybrid DSS might also describe a system in which a human works with a DSS to
extract and manipulate data.
Medical professionals, financial decision-makers and researchers might use a hybrid
DSS.
Software examples of a hybrid DSS include:
Risk assessment
Clinical DSS
Web-based DSS
99. Business Intelligence
99
Business intelligence combines business analytics, data
mining, data visualization, data tools and infrastructure,
and best practices to help organizations make more data-driven
decisions.
100. Techniques Used In Business Intelligence
100
1. DataVisualization
When data is stored as a set or matrix of numbers, it is
precise but difficult to interpret.When looking at more than
one dimension of the data, this becomes even harder.
Creating charts, graphics or dashboards from the data makes
it much easier for people to understand and interpret.
2. Data Mining
Data mining is a computer supported method to reveal
previously unknown or unnoticed relations among data
entities.
101. Example
101
In retail: shopping basket analysis can examine products
consumers buy together in order to better promote other
products.
In banking: using an automated risk assessment based on
historical data to evaluate whether a customer is likely to pay
back a loan.
In insurance: mining behavioural and historical data to
detect fraud.
In health: analysis of complications and common diseases
may help to reduce risk.
102. Reporting
102
One area where BI tools commonly help business users is by
designing, scheduling and generating reports, for example
regular performance, sales or marketing reports.
Reports output by BI tools efficiently gather and present
information to support the management, planning and
decision making process.
Once the report is designed it can be automatically run at set
intervals and sent to a predefined distribution list so key
people can see regularly updated numbers.
103. Time-Series Analysis And Predictive
Techniques
103
Nearly all data warehouses and all enterprise data have a time
dimension.
For example, product sales, phone calls, patient
hospitalizations, etc.
Time-series analysis can reveal changes in user behaviour
over time, relationships between sales of different products,
or changes in sales figures based on marketing promotions.
Historic data can also be used to extrapolate and try to
predict future trends, outcomes or financial results.
104. Online Analytical Processing (OLAP)
104
OLAP is best known for the OLAP-cubes which provide a visualization of
multidimensional data.
OLAP cubes display dimensions on the cube edges (e.g. time, product, customer
type, customer age etc.).
The values in the cube represent the measured facts (e.g. value of contracts, number
of sold products etc.).
The user can navigate through OLAP cubes using drill-up, drill-down and drill-
across features.
The drill-up functionality enables the user to easily zoom out to more coarse-grained
details.
Conversely, drill-down displays the information with more details.
Finally, drilling-across means that the user can navigate to another OLAP cube to see
the relations on another dimension(s).
All the functionality is provided in real-time.
105. Statistical Analysis
105
Statistical analysis uses mathematic foundations to qualify the
significance and reliability of the observed relations.
The most interesting features are distribution analysis,
confidence intervals (for example for changes in user
behaviours, etc.)
Statistical analysis is used for devising and analysing the
results from data mining.
106. The Necessary BI Skills
106
Data Analysis: Most BI skills and intelligence analyst-related skills are about
using data to make better decisions.You need to be good at examining many
different sources of data and then making accurate conclusions about them.
Problem-solving: BI isn’t just about analyzing data; it’s also about creating
business strategies and solving real-world business problems with that data.
For example, you could be the one to extract actionable insights from specific
retail KPIs that need to be visualized and presented during a meeting.
Specific industry knowledge: While some of this can and will be learned
on the job, you need to have a solid grasp of the industry’s dynamics,
particularly the areas of the field that you’re looking to work in. Over time,
you’ll want to become an expert in your industry as this will increase your
ability to connect data with business problem-solving.
107. 107
Communication skills: In addition to acquiring intelligence analyst-related
skills, you’ll need to be able to communicate your findings effectively to the
other professionals you’ll be working with. To some extent, if you work in
back-end BI, you won’t need to communicate quite as much. However, if you
work in the front- end, you’ll be responsible for communicating technical
concepts to non-technical people. This kind of role requires excellent
communication skills.
Data visualization: Expanding on the point above, in order to ensure good
communication you will also need to have data visualization skills.
Visualizations are the best tools to make trends and general insights
understandable. Being able to clearly see how the data changes in time is what
makes it possible to extract relevant conclusions from it. For this purpose, you
should be able to differentiate between various charts and report types as well
as understand when and how to use them to benefit the BI process.
108. 108
Advanced vision and attention to detail: By its very nature,
business intelligence is incredibly detail-oriented.As a BI analyst or
developer, you'll often work with the smallest fragment of
information with the objective of turning it into actionable insight.
You will need a great deal of forward-thinking vision and the
ability to pay very close attention to detail to succeed in the fast-
paced world of BI.
Statistical analysis: Statistical knowledge is another important
skill especially if you want to become a BI analyst. Understanding
various statistical components such as mean, median, range,
variance, and others, can enable you to go deeper into the data and
extract relevant conclusions from it.
109. 109
Programming knowledge: On a more technical side of things,
having programming language knowledge can also be a very valuable
skill when it comes to pursuing a career in BI. Many solutions require
the use of different programming languages to perform advanced
analysis such as R, Python, Javascript, just to name a few, and knowing
them can significantly enhance your skillset.
Technical notion: Our next BI skill is not fundamental, but it can
certainly make you a more complete and prepared professional. Business
intelligence is an industry that highly relies on technology and having a
technical notion of how to manage these technologies can be a plus.
With this, we do not mean that you need to know how to use every tool
in the market, but understanding how these technologies can work to
your advantage.
110. 110
Business acumen: To thrive in a business intelligence
career, you will need to possess a swift ability to understand
your company’s business model and how to tailor your
efforts to not only gain maximum value from your key
performance indicators (and the KPI management process)
but also make strategic decisions that will help your
organization succeed on a continual basis.
111. 111
Benefits of business intelligence
Data clarity
Increased efficiency
Better customer experience
Improved employee satisfaction
112. 112
How to develop a business intelligence strategy
A BI strategy is your blueprint for success. You’ll need to
decide how data is used, gather key roles, and define
responsibilities in the initial phases. It may sound simple at a
high level; however, starting with business goals is your key
to success.
113. 113
How to create a BI strategy from the ground up:
Know your business strategy and goals.
Identify key stakeholders.
Choose a sponsor from your key stakeholders.
Choose your BI platform and tools.
Create a BI team.
Define your scope.
Prepare your data infrastructure.
Define your goals and roadmap.
114. 114
Advantages of BI include:
Data visibility
Accurate reports
Streamlined processes
Disadvantages of BI include:
Initial cost
User resistance
Data skills gap
115. OLAP
115
OLAP is an acronym for Online Analytical Processing.
OLAP performs multidimensional analysis of business data
and provides the capability for complex calculations, trend
analysis, and sophisticated data modeling.
116. 116
OLAP offers five key benefits:
Business-focused multidimensional data
Business-focused calculations
Trustworthy data and calculations
Speed-of-thought analysis
Flexible, self-service reporting
117. Characteristics of OLAP
117
It defines which the system targeted to deliver the most
feedback to the client within about five seconds, with the
elementary analysis taking no more than one second and very
few taking more than 20 seconds.
118. Analysis
118
It defines which the method can cope with any business logic
and statistical analysis that is relevant for the function and the
user, keep it easy enough for the target client.Although some
pre programming may be needed we do not think it
acceptable if all Share.
It defines which the system tools all the security requirements
for understanding and, if multiple write connection is needed,
concurrent update location at an appropriated level, not all
functions need customer to write data back, but for the
increasing number which does, the system should be able to
manage multiple updates in a timely, secure manner.
119. Multidimensional
119
This is the basic requirement. OLAP system must provide a
multidimensional conceptual view of the data, including full
support for hierarchies, as this is certainly the most logical
method to analyse business and organizations.
Information
The system should be able to hold all the data needed by the
applications.
Data sparsity should be handled in an efficient manner.
OLAP Operations in the Multidimensional Data Model
120. Roll-Up
120
The roll-up operation (also known as drill-up or aggregation
operation) performs aggregation on a data cube, by climbing
down concept hierarchies, i.e., dimension reduction. Roll-up
is like zooming-out on the data cubes.
When a roll-up is performed by dimensions reduction, one
or more dimensions are removed from the cube.
122. Drill-Down
122
The drill-down operation (also called roll-down) is the reverse
operation of roll-up.
Drill- down is like zooming-in on the data cube. It navigates
from less detailed record to more detailed data.
Drill-down can be performed by either stepping down a concept
hierarchy for a dimension or adding additional dimensions.
Figure shows a drill-down operation performed on the
dimension time by stepping down a concept hierarchy which is
defined as day, month, quarter, and year.
Drill-down appears by descending the time hierarchy from the
level of the quarter to a more detailed level of the month.
124. Slice
124
A slice is a subset of the cubes corresponding to a single value
for one or more members of the dimension.
For example, a slice operation is executed when the
customer wants a selection on one dimension of a three-
dimensional cube resulting in a two-dimensional site.
So, the Slice operations perform a selection on one
dimension of the given cube, thus resulting in a sub cube.
126. Dice
126
The dice operation describes a sub cube by operating a
selection on two or more dimension.
127. Pivot
127
The pivot operation is also called a rotation. Pivot is a
visualization operations which rotates the data axes in view to
provide an alternative presentation of the data.
It may contain swapping the rows and columns or moving
one of the row-dimensions into the column dimensions.
130. Difference between OLTP and OLAP
130
OLTP (On-LineTransaction Processing) is featured by a large
number of short on-line transactions (INSERT, UPDATE, and
DELETE).
The primary significance of OLTP operations is put on very
rapid query processing, maintaining record integrity in
multi-access environments, and effectiveness consistent by
the number of transactions per second.
131. 131
OLAP (On-line Analytical Processing) is represented
by a relatively low volume of transactions.
Queries are very difficult and involve aggregations. For
OLAP operations, response time is an effectiveness measure.
OLAP applications are generally used by Data Mining
techniques.
In OLAP database there is aggregated, historical information,
134. Relational OLAP (ROLAP) Server
134
These are intermediate servers which stand in between a relational back-end
server and user frontend tools.
They use a relational or extended-relational DBMS to save and handle warehouse
data, and OLAP middleware to provide missing pieces.
ROLAP servers contain optimization for each DBMS back end, implementation
of aggregation navigation logic, and additional tools and services.
ROLAP technology tends to have higher scalability than MOLAP technology.
ROLAP systems work primarily from the data that resides in a relational
database, where the base data and dimension tables are stored as relational tables.
This model permits the multidimensional analysis of data.
This technique relies on manipulating the data stored in the relational database to
give the presence of traditional OLAP's slicing and dicing functionality. In
essence, each method of slicing and dicing is equivalent to adding a "WHERE"
clause in the SQL statement.
136. Multidimensional OLAP (MOLAP) Server
136
A MOLAP system is based on a native logical model that
directly supports multidimensional data and operations.
Data are stored physically into multidimensional arrays, and
positional techniques are used to access them.
One of the significant distinctions of MOLAP against a
ROLAP is that data are summarized and are stored in an
optimized format in a multidimensional cube, instead of in a
relational database.
In MOLAP model, data are structured into proprietary
formats by client's reporting.
138. Hybrid OLAP (HOLAP) Server
138
HOLAP incorporates the best features of MOLAP and
ROLAP into a single architecture.
HOLAP systems save more substantial quantities of detailed
data in the relational tables while the aggregations are stored
in the pre-calculated cubes.
HOLAP also can drill through from the cube down to the
relational tables for delineated data. The Microsoft SQL
Server 2000 provides a hybrid OLAP server.
140. Relational Online Analytical Processing
(ROLAP)
140
ROLAP servers are placed between relational backend server
and client front-end tools.
It uses relational or extended DBMS to store and manage
warehouse data. ROLAP has basically 3 main components:
Database Server, ROLAP server, and Front-end tool.
141. 141
Advantages of ROLAP –
ROLAP is used for handle the large amount of data. ROLAP
tools don’t use pre-calculated data cubes.
Data can be stored efficiently.
ROLAP can leverage functionalities inherent in the relational
database.
142. 142
Disadvantages of ROLAP –
Performance of ROLAP can be slow.
In ROALP, difficult to maintain aggregate tables. Limited by
SQL functionalities.
143. Multidimensional Online Analytical
Processing (MOLAP)
143
MOLAP does not uses relational database to storage.
It stores in optimized multidimensional array storage. The
storage utilization may be low With multidimensional data
stores.
Many MOLAP server handle dense and sparse data sets by
using two levels of data storage representation. MOLAP has
3 components : Database Server, MOLAP server, and Front-
end tool.
144. 144
Advantages of MOLAP –
MOLAP is basically used for complex calculations.
MOLAP is optimal for operation such as slice and dice.
MOLAP allows fastest indexing to the pre-computed
summarized data.
Disadvantages of MOLAP –
MOLAP can’t handle large amount of data.
In MOLAP, Requires additional investment.
Without re-aggregation, difficult to change dimension.
145. Hybrid Online Analytical Processing
(HOLAP)
145
Hybrid is a combination of both ROLAP and MOLAP. It
offers functionalities of both ROLAP and as well as MOLAP
like faster computation of MOLAP and higher scalability of
ROLAP.
The aggregations are stored separately in MOLAP store. Its
server allows storing the large data volumes of detailed
information.
146. 146
Advantages of HOLAP –
HOLAP provides the functionalities of both MOLAP and
ROLAP.
HOLAP provides fast access at all levels of aggregation.
Disadvantages of HOLAP –
HOLAP architecture is very complex to understand because
it supports both MOLAP and ROLAP.
150. 150
Disadvantages
Pre-demonstrating is an absolute necessity.
As to business information, the traditional OLAP tools don't take
into consideration quick investigation without pre-demonstrating.
Extraordinary reliance on IT.
Helpless calculation capacity.
Shy Interactive examination capacity.
Slow in responding.
Theoretical model.
Extraordinary, expected danger.
151. Analytic functions
151
Analytical functions are one of the most popular tools among
BI/Data analysts for performing complex data analysis.
These functions perform computations over multiple rows
and return the multiple rows as well.
152. 152
analytic_function_name([argument_list]) OVER (
[PARTITION BY partition_expression,…]
[ORDER BY sort_expression, … [ASC|DESC]])
There are three parts to this syntax,
namely function, partition by and order by.
153. 153
analytic_function_name: name of the function — like
RANK(), SUM(), FIRST(), etc
partition_expression: column/expression on the basis of
which the partition or window frames have to be created
sort_expression: column/expression on the basis of which
the rows in the partition will be sorted
155. 155
Trendline definition is a function that fits a linear or
exponential model and returns the fitted values or model.
The numeric_expr represents the Y value for the trend.The
series (time columns) represent the X value.
156. 156
Syntax:
TRENDLINE(numeric_expr, ([series]) BY ([partitionBy]), model_type,
result_type)
numeric_expr represents the data to the trend.This is theY-axis, usually a
measure column.
series is the X-axis is a list of numerics or time dimension attribute columns.
partitionBY is a list of dimension attribute columns that are in the view but
not on the X-axis.
model_type is one of the following (‘LINEAR’,‘EXPONENTIAL’).
result_type is one of the following (‘VALUE’,‘MODEL’).
‘VALUE‘ will return all the regressionY values given X in the fit. ‘MODEL’
will return all the parameters in a JSON format string.
157. 157
TRENDLINE((Sales),(Order Date) BY (Product Sub
Category), 'LINEAR', 'VALUE')
numeric_expr = Sales
series = Order Date (Time dimension)
partitionBy = Product Sub Category
model_type = LINEAR
result_type =VALUE
158. 158
CLUSTER((dimension_expr1 , ... dimension_exprN),
(expr1, ... exprN),
output_column_name, options, [runtime_binded_options])
dimension_expr represents the list of dimensions, e.g.,
(productID, companyID), to be clustered.
expr represents the list of dimension attributes or measures
to be used to
cluster the dimension_expr.
159. 159
output_column_name is the output column.The valid values
are ‘clusterId’, ‘clusterName’, ‘clusterDescription’,
‘clusterSize’,‘distanceFromCenter’,‘centers’.
options mean the string list of name=value pairs separated by
‘;’. The value can include %1 … %N, specified using
runtime_binded_options.
runtime_binded_options is an optional comma-separated list
of run-time binded columns or literal expressions.
160. 160
OUTLIER((dimension_expr1 , ... dimension_exprN), (expr1, .. exprN),
output_column_name, options, [runtime_binded_options]))])
dimension_expr indicates a list of dimensions.
expr represents a list of dimension attributes or measures to find outlier.
output_column_name indicates the output column name. Valid values are
'isOutlier' and 'distance'.
options indicates a string list of name/value pairs separated by a semi-colon (;).
The value can include %1 ... %N, which can be specified using
runtime_binded_options.
runtime_binded_options is an option comma separated list (,) of run-time
binded columns and or literal expressions.
REGR(y_axis_measure_expr, (x_axis_expr), (category_expr1, ...,
category_exprN), output_column_name, options, [runtime_binded_options])
![Tacit vs. Explicit Knowledge
39
Tacit knowledge is personal, context-specific and hard to
formalize and communicate.
A [knowledge] developed and internalized by the knower
over a long period of time . . . incorporates so much accrued
and embedded learning that its rules may be impossible to
separate from how an individual acts.](https://image.slidesharecdn.com/unit-2-250617151033-d986f04d/85/Business-analysis-of-business-of-current-39-320.jpg)
![152
analytic_function_name([argument_list]) OVER (
[PARTITION BY partition_expression,…]
[ORDER BY sort_expression, … [ASC|DESC]])
There are three parts to this syntax,
namely function, partition by and order by.](https://image.slidesharecdn.com/unit-2-250617151033-d986f04d/85/Business-analysis-of-business-of-current-152-320.jpg)
![156
Syntax:
TRENDLINE(numeric_expr, ([series]) BY ([partitionBy]), model_type,
result_type)
numeric_expr represents the data to the trend.This is theY-axis, usually a
measure column.
series is the X-axis is a list of numerics or time dimension attribute columns.
partitionBY is a list of dimension attribute columns that are in the view but
not on the X-axis.
model_type is one of the following (‘LINEAR’,‘EXPONENTIAL’).
result_type is one of the following (‘VALUE’,‘MODEL’).
‘VALUE‘ will return all the regressionY values given X in the fit. ‘MODEL’
will return all the parameters in a JSON format string.](https://image.slidesharecdn.com/unit-2-250617151033-d986f04d/85/Business-analysis-of-business-of-current-156-320.jpg)
![158
CLUSTER((dimension_expr1 , ... dimension_exprN),
(expr1, ... exprN),
output_column_name, options, [runtime_binded_options])
dimension_expr represents the list of dimensions, e.g.,
(productID, companyID), to be clustered.
expr represents the list of dimension attributes or measures
to be used to
cluster the dimension_expr.](https://image.slidesharecdn.com/unit-2-250617151033-d986f04d/85/Business-analysis-of-business-of-current-158-320.jpg)
![160
OUTLIER((dimension_expr1 , ... dimension_exprN), (expr1, .. exprN),
output_column_name, options, [runtime_binded_options]))])
dimension_expr indicates a list of dimensions.
expr represents a list of dimension attributes or measures to find outlier.
output_column_name indicates the output column name. Valid values are
'isOutlier' and 'distance'.
options indicates a string list of name/value pairs separated by a semi-colon (;).
The value can include %1 ... %N, which can be specified using
runtime_binded_options.
runtime_binded_options is an option comma separated list (,) of run-time
binded columns and or literal expressions.
REGR(y_axis_measure_expr, (x_axis_expr), (category_expr1, ...,
category_exprN), output_column_name, options, [runtime_binded_options])](https://image.slidesharecdn.com/unit-2-250617151033-d986f04d/85/Business-analysis-of-business-of-current-160-320.jpg)