Business Intelligence (BI) and Data Management Basics

By
A.Morshedsolouk
Business Intelligence
(BI)
& Data Management
Basics
By Ali Morshedsolouk
Sep 2022
1
BI & Data Management

By
A.Morshedsolouk
Topics
• New Digital Age
• DX requirements
DX: Motives and
Enablers to BI
• Data Strategy
• DM Culture
• Data Literacy
• DM Governance
• DM technology
Data-Driven
Company
• Why BI?
• Common Myths on
BI
• How BI works?
What is BI?
• DAMA Framework
• Data Management
basics
• DAMA Wheel
Introduction to
DMBOK2
• BI Tools features
• Market leaders
• MS Power BI
• Power Automate
An overview to
BI Tools
• Principles
• Guide lines
• Scenarios
BI
Implementation
• Pillars
• Plans
• BI challenges
• BI outcomes
• Demo
ICASAT case
study
• Summary
• Any Question?
Q&A
BI & Data Management Page 2

By
A.Morshedsolouk
Digital Transformation
BI & Data Management 3

By
A.Morshedsolouk
What is DX (Digital Transformation)?
Digital transformation
is the incorporation of
new computer-based
technologies into an
organization's
products, processes
and strategies.
We are at the age of DX (Next Digital Universe); Digital Game Players:
•Cloud solutions: DC-> IaaS/PaaS/SaaS/(Desktop-> Enterprise (private cloud)-> Edge ->
Public Cloud)/Hybrid/Multi-Cloud/
• ST Engineering: Idirect + NewTech & Comtec + UHP Romantis  New VSAT Hub is cloud-
based!/Multi-Orbit (GEO/MEO/LEO)
•Connectivity: 5G/6G/Mobile standards/WiFi6/WiFi6E/WiFi7/LoRaWAN
•Block chain: DeFi/Crypto/NFT/(VARA in Dubai, Virtual Assets Regulatory Authority)
•AI/ML/DL/Intuitive AI/Conversational AI/NLP/Vision/Robots/Robotics/AIoT
•Data Science/Big Data/Data Analytics
•AR/VR (augmented reality/virtual reality)
•X-verse: Metaverse/Gaming/Web 3/UAE vision: top 10 cities in Metaverse economy/attract
5000 metaverse companies in 5Y (Healthcare, manufacturing, education, retail, future of
work, gaming)
•Remote Working/WFH/Emirates: 42000 virtual Jobs by 2030 for $4Billion
•New IT/IS management best practices: Agile/DevOps/DevSec/CI-CD/DataOps/MLOps/New
Job functions/Kubernetes/100,000 Golden Visa by UAE for top coders!/
•Low-code, No-code/focus on customer and employee experiences
•SDN (software defined networks)/A case on VSAT last year in CABSAT
•Adopting API framework (delivering data securely)
•IoT/IIoT/AIoT/Smart Home/Smart City/Industry 4.0/Digital Twins/Drones/UAV/
•Digital economy/Digital smart cities/Autonomous vehicles/Future Mobility(flying
taxis)/Health Care/Fintec/Energy/Education/Data economy/CX
•BI/RPA/cybersecurity/edge computing
•A Changing Demand Environment
for Digital Innovation

By
A.Morshedsolouk
Job Function Growth
5

By
A.Morshedsolouk
Benefits of DX
increased efficiency and productivity
better resource management
more resiliency
greater agility
improved customer engagement
increased responsiveness
greater innovation
faster time to market
increased revenue
continued relevancy

By
A.Morshedsolouk
What DX bring us?
Should sync with this
global fast track journey
Customers are asking
and chasing DX
innovations
Should not lag the
competitors
Should decide on any
changes needed in the
organization
•New business opportunities
•New Strategies
•Digital Culture
•Improved Processes
•Digital-literated peoples and
hires
•Considering New
technologies
•Planning New IT/IS solutions
Digital Age and DX
relies totally on
“DATA”
So big impact::
DX is a key driver guiding
broad Data Strategy and
Data Management goals
and activities
Shall think “Data-driven”
on all these levels
•Should Setup a Data-
Driven Organization

By
A.Morshedsolouk
Data-Driven
Organization
“Without Data you’re jut another person with an opinion”
W. Edwards Deming, Data Scientist

By
A.Morshedsolouk
Data-Driven companies
What does a manager do?
• Controlling and exploiting the best out of its assets
Assets
• Physical assets
• Hub, modems, routers, BW, .. Inventory, staff, time, …
• Virtual assets
• Licenses, reputation, time, .. AND Data
Work flows ->generates Data flows
Data Hierarchy (data/information/knowledge->Wisdom)
• Informed decisions via High quality data
Poor or low quality data
• Inhibits integration
• misguides analyze
• Failure to decide
• Blockage to action
So What does a manager do?
• Deciding (by deriving value from data) and Acting
• Work flow-> Data flow -> (data -> insight -> decide -> action) -> (improve) work flow (Cycle)
So there is need to do Data Management (BI is part of DM)

By
A.Morshedsolouk
Data-Driven companies
At which DATA (Management) level our organization resides?
• Data Strategy?
• Data Culture?
• Data Governance?
• Authority, control, decision-making on managing data assets
• Data governance is the process of organizing, securing, managing, and presenting data using methods and technologies that ensure it remains correct, consistent, and accessible to
verified users
• Data Architecture
• A pillar of digital transformation, connects business strategy and technical execution
• Data Modeling
• Documenting the core business rules and relations around data
• Data technology? Data warehouse?
• Data quality?
• Data quality is the degree to which data is accurate, complete, timely, and consistent with your business’s requirements
• Data literacy?
• Data literacy ensures all data users within an organization are educated to a level that enables them to consume data with confidence within a specific business context
• Data Security? Privacy?
• BI & dashboards?
• Data Access? (visibility)
Data Usage? Do we Derive value? (informed Decisions or Daily operations?)
What is our “Data Maturity level” in organization/enterprise?

By
A.Morshedsolouk
Data Maturity Model

By
A.Morshedsolouk
Data Maturity Assessment

By
A.Morshedsolouk
Data Governance Maturity Model
Level 1: Unaware
• Unaware of the importance of data
• No action taken
• Processes are reactive and generally chaotic
Level 2: Aware
• There is an awareness of the importance of data
• Existing data practices are understood and well documented
• An inventory of data sources is available
Level 3: Defined
• Data governance rules and policies are defined
• Data owners and data stewards are identified
• A governance committee is set up
• A data catalog is installed
Level 4: Implemented
• Data governance policies and implementing rules are enforced
• There is training conducted
• Data is collected and measured
• Alerts are set up to monitor data quality issues raised by users
Level 5: Optimized
• Rules and policies for better efficiency are optimized
• Redundancies are reduced with redesigned workflows
• Data is tagged by users to increase discoverability

By
A.Morshedsolouk
Reports (BI) Maturity Levels

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A.Morshedsolouk
Analytics Maturity Model

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A.Morshedsolouk
Analytics Complexity (and Cost)

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A.Morshedsolouk
Roadblocks to achieve data value (IDG survey)

By
A.Morshedsolouk
Main Business Goals and Drivers for DM (A survey)

By
A.Morshedsolouk
Data value
Data does not make decisions – people do.
Having all the relevant data in the world makes little impact if it is not properly leveraged by decision-makers.
Conversely, having dedicated, data-driven leaders in every key position of your organization will hardly matter unless the right data is made available
data is an incredibly valuable resource
Work flow creates data flow
Data flow CAN lead to information flow
Information flow improves work flow
So the question is rarely whether or not your organization has data.
The question is: What does your organization do with it?
do decision-makers trust its purity, quality and authenticity?

By
A.Morshedsolouk
Why a data-driven organization?
• Gaining a competitive edge through better decision-making and increased efficiency
• increase revenue and reduce costs
• a data-driven organization can trust that it always makes informed decisions upon a foundation that is always reliable and up to date.
• Reducing cost through more efficient, data-driven processes – both administrative and operational – such as overtime or
inventory management
• As such, you remove whim and guesswork from the equation, while simultaneously negating the
garbage-in-garbage-out problem
• Bolstering the quality of products, reputation and organizational processes
• Decision support for operational systems and processes
• which can range from sales, production and marketing, to maintenance, logistics, service delivery, HR and other industry specific needs
• More nimbly (agilely and smartly) adjust to market changes
• Paves the way for being more innovative, proactive and agile
• letting the data reveal new business opportunities for which to adapt
• On top of this, the organization frees up human capital that can be allocated towards efforts
of creating additional value
• Empower your employees, equipping them with the tools to increase their autonomy and
strengthen their decision-making foundation
• a leaner, more efficient organization – and reduced dependency on external assistance
• Reducing cost through more efficient, data-driven processes – both administrative and
operational – such as overtime or inventory management

By
A.Morshedsolouk
Why a data-driven organization?
• Increased quality
• Quality in this context is highly connected to accuracy in decision making, sustainability and reputation.
• More data-driven, and hence more qualified decisions, run all the way through your organization, ensuring:
• Increased trust
• improved environment, health & safety (HSE) procedures
• Fewer decisions and reduced loss during production
• Increased product quality
• Increased customer satisfaction
• As for corporate reputation, having precise, actionable data available – and
the know-how to apply them – allows you to: (called BI)
• Make better business decisions
• more precisely communicate with target audiences, where market data are available, strengthening
organization-stakeholder relationship
• being at the bleeding-edge of what is often referred to as the fourth industrial
revolution
• Increases brand awareness
• augments market sentiment
• Attracts tech-savvy, aspiring young talent

By
A.Morshedsolouk
What exactly constitutes a data-driven organization?
• A data-driven organization manages data in such a way that it creates a
single version of the truth
• This means and requires that the data is both relevant, reliable and available
• Furthermore, this data is then used as a foundation for making business
decisions

By
A.Morshedsolouk
Business strategy
• Building a data-driven organization must be rooted in your organization’s
business strategy
• Both clear budgetary allocations and leadership involvement
• It is crucial at this stage to start with your business needs, not technology
• The main challenge is almost never a lack of tools or technology – it is
knowing how to leverage what you have at hand to create additional value
or a competitive advantage.
• You need to start by asking the following key questions:
• What are the most pressing business needs to which more and better data could
be the
answer?
• What is our actual level of ambition when it comes to gathering, storing and
sharing
data?
• What KPIs are relevant going forward?
• These are questions that need to be answered at a strategic level
• Again: At this stage you should be technology agnostic, and remain laser-
focused on your actual business needs.
• Furthermore, corporations tend to narrowly target their budgetary resources
into procuring and implementing technology – neglecting needed allocation
to the other two pillars.
• It is not about technology alone. Technology is primarily a foundation – an
enabler.
• Modern tools and technology will not by themselves automatically result in a
data-driven organization if the users cannot or will not use them.

By
A.Morshedsolouk
Data Strategy
How to gain support for your data strategy
• Transparency: share data across the business
• Readability: present data that anyone can
understand
• Trackability: track data that monitors business
performance
• Actionability: source data that pinpoints where to
take action
Page 24
What is the problem I need to solve?
What kind of data would help?
Where will I source it from?
How will I store and safeguard it?
How will I analyze it?
Who will be responsible?
How will it be shared across the team?
How will it be implemented into the team’s working
processes?

By
A.Morshedsolouk
Data Strategy

By
A.Morshedsolouk
Data: Holistic and Relevant
• Data: internally sourced, others external
• Structured, unstructured, and somewhere in between
• Varying Volume and velocity
• Holistic – or Relevant
• Holistic data:
• refer to data that concerns the whole organization – across all organizational silos and levels
• Remember the single version of the truth we talked about?
• This shared insight is constructed first once you are able to paint a complete picture across silos like ERP, CRM and operational
data.
• Holistic data spans the entire organizational value chain, from operational data such as orders and deliveries, to controlling,
financials and other administrative data.
• As for relevance, we consider this a key data quality aspect.
• Essentially, it means the right data, at the right time – for the right purpose
• And consequently: how well the data can support a specific need or decision
• It is once again important to start by mapping out your data and business needs and priorities
• Keep in mind that most data use cases fall into three broad categories, with the timeframe as a key separator:
• Operational decisions (seconds, hours, days)
• Pure operational decisions could be everything from monitoring critical processes for a production facility to speeding up the application processes for a
municipality
• Tactical decisions (days, weeks)
• Tactical decisions are typically about where to focus resource spending and how to detect new opportunities ahead of competitors
• Strategic decisions (months, quarters, years)
• Strategic decisions typically address high-level goals related to growth, profitability, HR & employees, regulatory compliance and sustainability
• Examples of needs driving innovation measures could be related to a company’s deliveries, like product development, price
elasticity or supply and demand matching – or customer relationship management.

By
A.Morshedsolouk
Culture and Competency
• The graveyard of data and analytics projects is filled with great ideas.
• We have seen the proof of concepts, the pilot projects and the headstrong initiatives. Many of them are superb –
on paper.
• The payoffs seem bright as day, the ROI is a no-brainer and the technology is sound. Even the implementation
can seem smooth as butter. And yet, months later, the tools are collecting dust – before the project gets shelved
altogether.
• What on earth happened? More often than not, the problem is that the organization simply was not culturally
prepared
• Yes, becoming a data-driven organization is partly a data, strategy and technology project, but it is just as much a
change management process and cultural project.
• If you want user adoption, you cannot forget the user.
• In our experience, it is crucial to remember the importance of leading by example.
• Leaders should, whenever possible, actively use data to substantiate (prove) claims, decisions, ideas and
business cases.
• Becoming a data-driven organization also requires a certain level of digital maturity or competency. However, you
cannot simply give every user a crash course and expect lasting change.
• This shows how culture and competence are closely linked.
• Knowledge expires, and change in people’s behavior takes motivation and time.
• If you want to not only get ahead, but stay ahead, you should aim for a curious, data-driven and data-seeking
culture – driving competence forward continuously.

By
A.Morshedsolouk
Schedule regular data-competency training sessions across the entire organization
• Where are we right now in terms of culture and competency?
• How does this map onto our level of ambition as decided in the business strategy?
Map out organization’s digital maturity
• Training
• Inspirational workshops
• Motivational storytelling
Set Educational or Culture-building measures to implement
Use tools to be quick, reusable, simple to understand – and providing a clear answer to the
business challenges
IT might expect users to not only be able to procure data and analyses themselves – but use
these without needing guidance
Building a culture where mutual learning and understanding is a core principle

By
A.Morshedsolouk
Use self service BI, tools that make it easy for every user to access and draw key insights
from data – without having to consult the IT department
• What use cases do we need these tools for?
• What types of data can give us the insights to make better decisions – or operate more efficiently?
• What available tools can actually give us the relevant data?
• Who in our organization has the right skills and motivation to use this data?
• How can we set realistic expectations for both people and software?
Need to be answered:
Keep eyes on gaps across organizational departments and levels
Remind that these gaps are never filled by the click of a button, but through gradual
changes over time

By
A.Morshedsolouk
Data-Driven thinking cycle
• data-driven thinking is a learned organizational behavior

By
A.Morshedsolouk
Summary
• strategic
• tactical and
• operational
A truly data-driven organization manages to use data both as:
• decision support
• and to create more efficient business processes
For
• Clear data strategy
• Data governance
• Having holistic and relevant data available
• Fostering the right culture & competency
This is made possible through:
All of this needs to be rooted in the organization’s business strategy
With all of this in hand, we wish you good luck on your path to becoming more
data-driven!

By
A.Morshedsolouk
DMBOK2.0
Framework

By
A.Morshedsolouk
Data Management
Data
• Is currency, life blood, new oil
• Is an (virtual) asset like any other (physical) asset
• Is a meta-asset that describes other assets
• Key to competitive advantage
• Enabler for decision-making
• Failure to manage data is like failure to manage capital
• Is the means by which an organization knows itself
• So it is a strategic goal: to get (derive) value from data (assets)
• Not only assets but also vital to the day-to-day operations
• When it is exchanged (internally or externally); it can provide information about how an
organization functions -> shows department or company’s data maturity level
• Assumption is that data simply exists. But data does not simply exist. Has to be created or
Obtained
• Data is a form of information and information is a form of data
• Both data and info should be managed

By
A.Morshedsolouk
Data Management
Data
Management
• “Data Management is the development, execution, and supervision of plans,
policies, programs, and practices that deliver, control, protect, and enhance
the value of data and information assets throughout their lifecycles.”
• “Data Management provides quality and time-sensitive information to all
business units to make decisions.”
• “Data Management treats our data as a corporate asset so that it is reliable,
protected, and the highest quality, enabling informed, accurate, and confident
decisions.”
• Will help improve operational efficiencies and reduce risks and generate more
revenues

By
A.Morshedsolouk
Data Management Goals
Understanding and supporting the information needs of the enterprise and its stakeholders, including
customers, employees, and business partners
Capturing, storing, protecting, and ensuring the integrity of data assets
Ensuring the quality of data and information
Ensuring the privacy and confidentiality of stakeholder data
Preventing unauthorized or inappropriate access, manipulation, or use of data and information
Ensuring data can be used effectively to add value to the enterprise

By
A.Morshedsolouk
Data Management Activities
Data management activities
• wide-ranging (both technical and non-technical (i.e., ‘business’) skills)
• everything from the ability to make consistent decisions about how to get strategic value from data
• technical deployment and performance of databases
• Responsibility for managing data must be shared between business and information technology roles, and
people in both areas must be able to collaborate to ensure an organization has high quality data that meets
its strategic needs.
• Both will be of higher quality if they are managed together with uses and customer requirements in mind
DMP
• A Data Management Professional is any person who works in any facet of data management (from technical
management of data throughout its lifecycle to ensuring that data is properly utilized and leveraged) to
meet strategic organizational goals.
• Data management professionals fill numerous roles, from the highly technical (e.g., database
administrators, network administrators, programmers) to strategic business (e.g., Data Stewards, Data
Strategists, Chief Data Officers).

By
A.Morshedsolouk
Data Management Principles
• Even small organizations can have complex technical and business process landscapes. Data is created in many places and
is moved between places for use. To coordinate work and keep the end results aligned requires planning from an
architectural and process perspective.
It takes planning to manage data
• A single team cannot manage all of an organization’s data. Data management requires both technical and non-technical
skills and the ability to collaborate.
Data management is cross-functional; it
requires a range of skills and expertise
• Data management has local applications, but it must be applied across the enterprise to be as effective as possible. This is
one reason why data management and data governance are intertwined.
Data management requires an
enterprise perspective
• Data is fluid. Data management must constantly evolve to keep up with the ways data is created and used and the data
consumers who use it.
Data management must account for a
range of perspectives
• Data has a lifecycle and managing data requires managing its lifecycle. Because data begets more data, the data lifecycle
itself can be very complex. Data management practices need to account for the data lifecycle.
Data management is lifecycle
management
• And for this reason, they have different management requirements. Data management practices have to recognize these
differences and be flexible enough to meet different kinds of data lifecycle requirements.
Different types of data have different
lifecycle characteristics
• In addition to being an asset, data also represents risk to an organization. Data can be lost, stolen, or misused.
Organizations must consider the ethical implications of their uses of data. Data-related risks must be managed as part of
the data lifecycle.
Managing data includes managing the
risks associated with data
• Data and data management are deeply intertwined with information technology and information technology
management. Managing data requires an approach that ensures technology serves, rather than drives, an organization’s
strategic data needs.
Data management requirements must
drive Information Technology decisions
• Data management involves a complex set of processes that, to be effective, require coordination, collaboration, and
commitment. Getting there requires not only management skills, but also the vision and purpose that come from
committed leadership.
Effective data management requires
leadership commitment

By
A.Morshedsolouk
Data Management Challenges
Data Differs from Other Assets
• Is not tangible
• Is durable (does not wear out, although its value ages)
• Is easy to copy and transport
• Not easy to reproduce if it is lost or destroyed (impossible or extremely costly)
• Not consumed when used (can be stolen without being gone)
• Is dynamic
• Is multiple purposes
• Can be used at the same time with multiple people
• Many uses of data beget (generate) more data
• Is challenging to put a monetary value on data (it is difficult to measure how data
contributes to organizational success)
• Data ownership? Inventory? Data Protection? Data Quality?

By
A.Morshedsolouk
Data Valuation
Value: Difference between Cost and the Benefit
• Cost of obtaining and storing data
• Cost of replacing data if it were lost
• Impact to the organization if data were missing
• Cost of risk mitigation and potential cost of risks associated with data
• Cost of improving data
• Benefits of higher quality data
• What competitors would pay for data
• What the data could be sold for
• Expected revenue from innovative uses of data
Data valuation: Articulating general cost and benefit categories for an organization
• Is contextual (Per Organization or per Department)
• Is temporal (what was valuable yesterday may not be valuable today)
Value of data

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A.Morshedsolouk
The DAMA-DMBOK Framework
• defines the 11 Data Management Knowledge Areas
The DAMA Wheel
• provides direction and oversight for data management by establishing a system of decision rights over
data that accounts for the needs of the enterprise.
1.Data Governance
• defines the blueprint for managing data assets by aligning with organizational strategy to establish
strategic data requirements and designs to meet these requirements.
2.Data Architecture
• is the process of discovering, analyzing, representing, and communicating data requirements in a
precise form called the data model.
3.Data Modeling and Design
• includes the design, implementation, and support of stored data to maximize its value. Operations
provide support throughout the data lifecycle from planning for to disposal of data.
4.Data Storage and Operations
• ensures that data privacy and confidentiality are maintained, that data is not breached, and that data
is accessed appropriately.
5.Data Security

By
A.Morshedsolouk
The DAMA-DMBOK Framework
•includes processes related to the movement and consolidation of data within and between data stores,
applications, and organizations.
6.Data Integration and Interoperability
•includes planning, implementation, and control activities used to manage the lifecycle of data and
information found in a range of unstructured media, especially documents needed to support legal and
regulatory compliance requirements.
7.Document and Content Management
•includes ongoing reconciliation and maintenance of core critical shared data to enable consistent use across
systems of the most accurate, timely, and relevant version of truth about essential business entities.
8. Reference and Master Data
•includes the planning, implementation, and control processes to manage decision support data and to
enable knowledge workers to get value from data via analysis and reporting.
9.Data Warehousing and Business Intelligence
•includes planning, implementation, and control activities to enable access to high quality, integrated
Metadata, including definitions, models, data flows, and other information critical to understanding data and
the systems through which it is created, maintained, and accessed.
10.Metadata
•includes the planning and implementation of quality management techniques to measure, assess, and
improve the fitness of data for use within an organization.
11.Data Quality

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A.Morshedsolouk
DAMA Wheel Evolved
• Providing a functional framework for
the implementation of enterprise data
management practices; including
guiding principles, widely adopted
practices, methods and techniques,
functions, roles, deliverables and
metrics.
• Establishing a common vocabulary for
data management concepts and
serving as the basis for best practices
for data management professionals.
• Serving as the fundamental reference
guide for the CDMP (Certified Data
Management Professional)and other
certification exams
DAMA’s mission by:

By
A.Morshedsolouk
Dimensional Modeling
Fact Tables
• the rows of a fact table correspond to particular
measurements and are numeric, such as amounts,
quantities, or counts.
• Some measurements are the results of algorithms, in
which case Metadata is critical to proper understanding
and usage.
• Fact tables take up the most space in the database (90% is
a reasonable rule of thumb), and tend to have large
numbers of rows.
Dim Tables
• Dimension tables represent the important objects of the
business and contain mostly textual descriptions.
• Dimensions serve as the primary source for ‘query by’ or
‘report by’ constraints, by acting as the entry points or
links into the fact tables.
• Dimensions are typically highly denormalized and typically
account for about 10% of the total data.

By
A.Morshedsolouk
Data Security- Sources of Requirements
•For general audiences: Information available to
anyone, including the public.
•Internal use only: Information limited to employees or
members, but with minimal risk if shared. For internal
use only; may be shown or discussed, but not copied,
outside the organization.
•Confidential: Information that cannot be shared
outside the organization without a properly executed
non-disclosure agreement or similar in place. Client
confidential information may not be shared with
other clients.
•Restricted confidential: Information limited to
individuals performing certain roles with the ‘need to
know.’ Restricted confidential may require individuals
to qualify through clearance.
•Registered confidential: Information so confidential
that anyone accessing the information must sign a
legal agreement to access the data and assume
responsibility for its secrecy.
Confidential Data

By
A.Morshedsolouk
Data Integration and Interoperability
• Format changes
• Structure changes
• Semantic conversion
• De-duping
• Re-ordering
ETL
(Extract,
Transform,
Load)
ELT
(Extract,
Load,
Transform)

By
A.Morshedsolouk
Data Warehousing and Business Intelligence
• First, it refers to a type of data analysis aimed at
understanding organizational activities and
opportunities. Results of such analysis are used to
improve organizational success. When people say
that data holds the key to competitive advantage,
they are articulating the promise inherent in Business
Intelligence activity: that if an organization asks the
right questions of its own data, it can gain insights
about its products, services, and customers that
enable it to make better decisions about how to fulfill
its strategic objectives.
• Secondly, Business Intelligence refers to a set of
technologies that support this kind of data analysis.
An evolution of decisions support tools, BI tools
enable querying, data mining, statistical analysis,
reporting, scenario modeling, data visualization, and
dashboarding. They are used for everything from
budgeting to advanced analytics.
BI has two meanings:

By
A.Morshedsolouk
• is a combination of two primary
components: An integrated decision
support database and the related
software programs used to collect,
cleanse, transform, and store data from a
variety of operational and external
sources.
• To support historical, analytical, and BI
requirements, a data warehouse may
also include dependent data marts,
which are subset copies of data from the
warehouse.
• In its broadest context, a data warehouse
includes any data stores or extracts used
to support the delivery of data for BI
purposes.
Data
Warehouse
(DW)

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A.Morshedsolouk
• Conceptual DW/BI and Big Data Architecture

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A.Morshedsolouk
Data Flow Design
• AM

By
A.Morshedsolouk
Data Quality
Ensuring that data
meet business needs
Without it the effort
to collect, store,
secure, and enable
access to it is wasted
Data should be
reliable and
trustworthy else
people lose trust to
it
Poor quality data will
have a negative
impact on decisions
Poor quality data is
simply costly to any
organization
organizations spend
between 10-30% of
revenue handling
data quality issues
Many of the costs of
poor quality data are
hidden, indirect, and
therefore hard to
measure. Others,
like fines, are direct
and easy to
calculate.
Costs of poor data quality
• Scrap and rework
• Work-arounds and hidden correction processes
• Organizational inefficiencies or low productivity
• Organizational conflict
• Low job satisfaction
• Customer dissatisfaction
• Opportunity costs, including inability to innovate
• Compliance costs or fines
• Reputational costs

By
A.Morshedsolouk
Data Quality – Benefits and Must Do’s
benefits of high quality data
• Improved customer experience
• Higher productivity
• Reduced risk
• Ability to act on opportunities
• Increased revenue
• Competitive advantage gained from insights on
customers, products, processes, and opportunities
• More job satisfaction
• Robust, sustainable organization
Managing Data Quality is
not a one-time job
Producing high quality data requires
planning, commitment, and a mindset that
builds quality into processes and systems
Requires a strategic approach to
architecture, modeling, and other
design functions
Depends on strategic
collaboration between business
and BI team
Balance for:
• organizational pressures <-> eternal
pressures (time & money)
• Long-term and short-term goals

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A.Morshedsolouk
Characteristics of Data for Good Decision Making

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A.Morshedsolouk
Data Quality vs. Human Effort

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A.Morshedsolouk
Data Literacy
Data literacy means making
data usable and accessible to
everyone in your organization
Enables new insights and new
innovations by unleashing
people’s curiosity and giving
them the knowledge, tools,
and confidence to follow
wherever it leads
It’s not about hiring or
training more analysts. It’s
about giving regular people
the ability and confidence to
leverage data in their day-to-
day work
When people see how easy it is
to access data, pose queries,
and gain insights, they start
wondering what other questions
they can ask, what other ideas
they can explore
It’s a major mindset
change that leads to new
innovations
Training and work on data
culture is needed for effective
understanding, use and
exploit of data
With BI you can expand data
literacy to partners,
vendors, and customers
outside your company

By
A.Morshedsolouk
Data Literacy
Data literacy is about making users that are not part of an organization’s data team more data literate.
It’s about educating regular business users about the information available to them and organizing this information
in a way that makes it easy to identify and consume.
When a data governance team acknowledges the importance of data literacy in an organization’s data governance
strategy, the result is a well-defined data catalog that any member of staff can access.
When they don’t, many users are left without access to important data impeding their ability to perform
professionally and contribute to the overall growth of a data-driven company.
Without widespread data literacy and clearly defined data terms and frameworks, communication channels can
break down—and the results can be catastrophic.
Before implementing a data literacy program your data team needs to ask these key questions:
•How can we organize our data so people can find it easily?
•How do we find and determine which terms are necessary for our company?
•How do we achieve consensus on, define, and present these terms?
•How do we provide universal access when confidential user data is included in the data catalog?

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A.Morshedsolouk
What is a Data Catalog?
A data catalog is a software application that creates an inventory of an organization's data assets to help data professionals
and business users find relevant data for analytics uses
Is a metadata repository that helps companies organize and find data that’s stored in their many systems
It works like a library catalog about tables, files, and databases.
This information comes from a company’s ERP, HR, Finance, and E-commerce systems (as well as social media feeds)
The catalog also shows where all the data entities are located
A data catalog contains a number of critical information about each piece of data, such as the data’s profile (statistics or
informative summaries about the data), lineage (how the data is generated), and what others say about it.
A catalog is the go-to spot for data scientists, business analysts, data engineers, and others who are trying to find data to
build insights, discover trends, and identify new products for the company.
A data catalog works differently than a data lake.
A data catalog contains the metadata and its whereabouts, which enables the user to move to the appropriate place.

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A.Morshedsolouk
Data and Risk
Information gaps – the difference between what we know and what we need to know to make an effective decision; and so profound impacts on
operational effectiveness and profitability.
Organizations get the most value from the highest quality data –
available relevant complete accurate consistent timely usable meaningful understood
But data is also risky because it can be misunderstood and misused
Low quality data (inaccurate, incomplete, or out-of-date) obviously represents risk because its information is not right
Data not only represents value, it also represents risk

By
A.Morshedsolouk
What is BI?

By
A.Morshedsolouk
Traditional Reporting

By
A.Morshedsolouk

By
A.Morshedsolouk
Definitions of BI
BI is a technology-driven process for analyzing data and delivering actionable
information that helps executives, managers and workers make informed
business decisions.
BI is a set of practices of collecting, structuring, analyzing, and turning raw
data into actionable business insights.
BI considers methods and tools that transform unstructured data sets,
compiling them into easy-to-grasp reports or information dashboards.
BI comprises the strategies and technologies used by enterprises for the data
analysis of business information.
BI is the process of turning raw data into actionable information that can
improve business decisions.
It is an umbrella term that stands for both processes and solutions — the
process of transforming data into actionable insights and the tools that access
and analyze data and present those findings in an accessible way.

By
A.Morshedsolouk
What is BI Goal?
• As part of the BI process, organizations collect data from internal IT systems and
external sources, prepare it for analysis, run queries against the data and create data
visualizations, BI dashboards and reports to make the analytics results available to
business users for operational decision-making and strategic planning.
• BI’s purpose is to help organizations understand themselves better so they can rise
above their competitors.
• Business intelligence is more than useful charts, graphs, and data summaries.
• It’s also understanding when data changes, why it changed, and what your
organization needs to do about it.
• The ultimate goal of BI initiatives is to drive better business decisions that enable
organizations to increase revenue, improve operational efficiency and gain
competitive advantages over business rivals.
• The main purpose of BI is to provide Action

By
A.Morshedsolouk
The Data Perspectives in BI
PAST: What Happened?
• Reactive reporting
• Common among most companies
PRESENT: What is Happening?
• KPI’s and CPM Concepts
• Streaming analytics
FUTURE: What will Happen?
• Predict based on trends and external
data
• Understand impact and what-if
analysis

By
A.Morshedsolouk
Types of Analytics
• Three main types of data management:
1.Descriptive analytics (BI)
• Basically, BI is a data analytical approach, answering the questions what
was happening? and what is happening?
• Businesses can study the market conditions of their industry, as well as
their internal processes.
• Historical data overview helps to find a business’s pain-points and
opportunities.
2.Predictive analytics (BA) (Analytics)
• Is concerned with forecasting based on data processing of past events.
• Instead of producing overviews of historical events, predictive analysis
makes forecasts about future business trends.
• Those predictions are based on past events analysis. So, both BI and
predictive analysis can use the same techniques to process data.
• Is the next stage of BI
3.Prescriptive analytics (AI)
• Prescriptive analytics is the third type that aims at finding solutions to
business problems and suggests the actions to solve them.
• Currently, prescriptive analytics is available via advanced BI tools, but
the whole area hasn’t developed to a reliable level yet.

By
A.Morshedsolouk
BI impact on organization Strategies
Transparency
• Most business intelligence tools
feature a dashboard interface that
makes insights available to any
stakeholder, not just those with
analytics backgrounds.
• This increased transparency and data
access keeps teams on the same page
and improves decision making from
the ground up.
Efficiency
• BI tools help organizations run more
efficiently by identifying areas for
optimization where outcomes are
lagging behind competitors and
discovering issues or problems that
have been overlooked.
Profitability
• With insights gained from business
intelligence, stakeholders can identify
ways to increase profits by better
analyzing customer behaviors and
spotting market trends. Sustainability

By
A.Morshedsolouk
Benefits of BI
speed up and improve
decision-making
(Faster Analysis)
Cost cutting by
optimize internal
business processes and
Single Truth
increase operational
and organizational
efficiency and
productivity
spot business problems
that need to be
addressed
identify emerging
business and market
trends
develop stronger
business strategies
(Data-Driven Business)
drive higher sales and
new revenues by Trend
Awareness
gain a competitive
advantage over rival
companies
It can monitor
customer behavior and
Improve CX
It could provide solid
information to
determine the most
effective option for you
It can help optimize
processes and Govern
Data
Centralized Intuitive
KPI dashboards and
Easy to access and
share info
Clear Accountability
through efficient
Reporting
Time Efficiency by
shortening decision-
making process
Allows manpower to
focus on skillful tasks
rather than
monotonous tasks

By
A.Morshedsolouk
How BI Help?

By
A.Morshedsolouk
BI Adoption Stats

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A.Morshedsolouk
How the BI Process works?

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A.Morshedsolouk
BI Functions
Common functions of BI
•Reporting
•Online analytical processing
•Descriptive Analytics
•Data mining
•Process mining
•Complex event processing
•Business performance management
•Benchmarking
•Text mining
•Predictive analytics
•Prescriptive analytics
BI solutions provide historical, current and
predictive views of business operations
By Business Intelligence,
Transform data into successful decisions

By
A.Morshedsolouk
8 Reasons to Use BI to Improve Customer Experience (CX)
Creating a Data-Driven Culture
Smash Sales and Marketing Goals
Get a Deeper Understanding of Your Customers
Easier and Faster Reporting
Break Down Silos
Call Center Efficiency
Improving Employee Performance
Visualization
•Customer satisfaction scores
•Customer surveys
•Social listening
•Monitoring customer service in real-time or over time

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A.Morshedsolouk
BI Features
Mobile BI
• Mobile business intelligence refers to
accessing data and performing analysis
on mobile devices and tablets. Mobile
BI provides on-the-go access to KPIs,
metrics and dashboards to make
intelligent business decisions.
• Add comments and share annotated
mobile screens to facilitate
collaboration between team members.
It offers mobile-optimized dashboards
and interactive reports.
Self-Service Analytics
• A robust BI platform should enable
everyone in your organization to
interact with data and derive
meaningful insights regardless of their
skills.
• Self-service analytics capabilities help
you foster data culture by making
information accessible to everyone.
• The right BI solution establishes a
secured and governed environment to
protect data and ensure integrity
without compromising agility and
innovation.

By
A.Morshedsolouk
BI features

By
A.Morshedsolouk
Top BI features requested

By
A.Morshedsolouk
BI outcomes
Insight
•Reports which tell us the current or former status of our data. This insight answers questions such as:
•How is the organization performing?
•How much revenue did we incur?
•Where do our constituents live?
•How much funds did we raise?
•What is the average patient discharge rate during weekends?
•Reports providing insight are valuable, but they mostly offer an operational perspective. Some are used to inform strategic decisions,
but they don’t always provide the full picture as to why the numbers and outcomes are the way they are.
Hindsight
•This second outcome of the Analytics & Business Intelligence umbrella provides the analysis needed to understand why we have the
current numbers we do –what were the factors, the environment, and decisions which impacted the outcome of these numbers. It
answers questions such as:
•Why are we performing this way?
•Which investments proved to be successful?
•What are we learning from the results of A/B testing?
•What customer factors affected the sales outcomes?
•Hindsight also determines and provides knowledge and understanding of the context.
Foresight
•The third outcome is about foresight. This showcases the true value of analytics, depending how you define it, because through the
exploration of historical and live data and application of different statistical, data mining, predictive, and other analytics’ methods, it
provides us with a better understanding of the future and the potential paths to follow. It answers questions such as:
•How will the organization perform in the future?
•How can we gain a competitive advantage?
•What effect might certain changes have on our bottom line?
•Where will most alumni move to one year after graduation?
•Which customers are more likely to purchase?
•What impact will the next flu season have on the respiratory clinic?
So:
•Each one of the three outcomes brings in different benefits, but they are also interconnected. In the end, if you don’t have insights,
you have nothing to base a hindsight or foresight on. Though, from a strategic perspective, the value which foresight provides is larger
than the one gained from hindsight and insight.

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A.Morshedsolouk
The evolving landscape of BI

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A.Morshedsolouk
How do different industries use business intelligence?
Manufacturing
• Identifying where delays happen most frequently and where glitches exist in the shipping process
• Discovering which products often face delays or which form of transportation is delayed the most
Sales and Marketing
• Tracking new client acquisition and retention
• Generating sales and delivery reports from CRMs
• Illustrating where prospective clients are on the sales pipeline
Education
• Examining data on attendance rates and pass rates
• Analyzing and tracking student performance
Finance
• Viewing data from multiple branches or offices together in one place
• Predicting trends and how they will affect clients
Healthcare
• Improving operational efficiency to reduce overspending
• Streamlining the insurance claims process to optimize patient billing

By
A.Morshedsolouk
Business Analytics, BI, Big Data, Data Mining -
What’s the difference?
Business Analytics – Tools to explore past data to gain insight into future business
decisions
BI – Tools and techniques to turn data into meaningful information
Big Data –data sets that are so large or complex that traditional data processing
applications are inadequate
Data Mining - Tools for discovering patterns in large data sets

By
A.Morshedsolouk
BI Functional level Applications
Customer Analytics
Human Capital Productivity Analysis
Business Productivity Analytics
Sales Channel Analytics
Supply Chain Analytics
Behavior Analytics

By
A.Morshedsolouk
Traditional BI vs. Modern BI
Traditional BI
• Traditional business intelligence tools were driven by IT departments and
relied on static reporting
• If a stakeholder had a follow-up question about the report they received
from IT, they would have to initiate an entirely new data request. This
process was slow and frustrating
Modern BI
• Modern BI tools are designed for interactivity and accessibility
• Stakeholders can customize dashboards and create automated reports that
give them the insights they need without having to go through IT
• Modern BI puts the power of data into more people’s hands throughout
your organization, taking just minutes or even seconds to get the
information they need.
• It also helps unlock dark data — data that has been inaccessible in the past.
• And, data governance is simple as administrators control who can access
what data.
• Remember, modern business intelligence modernizes your business and
helps you rise above your competition.

By
A.Morshedsolouk
How will BI evolve in the future?
Business
intelligence
platforms will
continue to
offer more and
more
customization
for
organizations.
Instead of opening up a
company-wide
dashboard,
stakeholders will be
able to access a custom
app built specifically for
their department. This
approach will help
streamline and
automate workflows.
Also, the move
toward self-
service BI will
give more
individuals the
ability to access
information
without
involving the IT
department.
Modern BI will enable
organizations to go beyond
accessing data and move to
actually take action on the
data insights they see,
whether that’s through
custom apps where users
can access and act in the
same interface or through
automating actions in other
business systems.
We’ll also see
better ways to
share BI
beyond its
original
organization.
BI users can share BI
within their organization
as well as with customers
or partners by embedding
visualizations in their own
app or website or by
letting customers upload
and combine data.

By
A.Morshedsolouk
Self-service BI
• The emerging trend of self-service
business intelligence, made possible
by the advancement of mobility,
analytics, and other relevant
technology, empower end-users
with the ability to undertake
business intelligence tasks by
themselves, without necessarily
having advanced technical skills.
• Google Data Studio reporting tool
and Google BigQuery
• Microsoft Power BI
• Low-code, No-Code
• Easy and fast setup
• Least IT and DMP involvement

By
A.Morshedsolouk
Self-service BI vs. Traditional BI (Static Reports)
Self-service BI
•Today, modern companies and solution providers utilize self-service BI. This approach
allows business users as well as executives to get the reports that are automatically
generated by the system.
•Automated reporting doesn’t need power users (admins) from your IT to process
each request to your data warehouse; however, technical staff is still required to set
up the system.
•Automation may lower the quality of the end reports and their flexibility as it will be
limited by the way the reporting is designed. But, as a benefit, the self-service
approach doesn’t require actual technical staff to operate in the system all the time.
Users that are not tech-savvy will be able to serve a report for themselves or access a
dedicated section of the data storage.
Traditional BI
•Traditionally, BI was designed for executives only. Since the number of users and
types of data is limited, there’s no need for full automation. So, a traditional BI flow
type requires technical staff as an intermediary between the reporting tool and the
end user.
•If an end user wants to extract some data, he or she has to make a request and tech
staff will generate a report from the required data. In this case, your IT department
acts as a power user, a user that can access data and influence its transformation.
•The traditional approach offers a more secure and controlled data flow. But, relying
on the IT department may introduce a lag in flexibility and speed in case of
processing big amounts of data (especially for big data). If you strive for more report
control and precision of reports, form a dedicated IT team to take care of queries and
report formation.

By
A.Morshedsolouk
Types of BI Tools
• Ad hoc analysis. Also known as ad hoc querying, this is one of the foundational elements of modern BI applications and a key feature of self-service BI tools. It's the
process of writing and running queries to analyze specific business issues. While ad hoc queries are typically created on the fly, they often end up being run regularly, with
the analytics results incorporated into dashboards and reports.
• Online analytical processing (OLAP). One of the early BI technologies, OLAP tools enable users to analyze data along multiple dimensions, which is particularly suited to
complex queries and calculations. In the past, the data had to be extracted from a data warehouse and stored in multidimensional OLAP cubes, but it's increasingly
possible to run OLAP analyses directly against columnar databases.
• Mobile BI. Mobile business intelligence makes BI applications and dashboards available on smartphones and tablets. Often used more to view data than to analyze it,
mobile BI tools typically are designed with an emphasis on ease of use. For example, mobile dashboards may only display two or three data visualizations and KPIs so they
can easily be viewed on a device's screen.
• Real-time BI. In real-time BI applications, data is analyzed as it's created, collected and processed to give users an up-to-date view of business operations, customer
behavior, financial markets and other areas of interest. The real-time analytics process often involves streaming data and supports decision analytics uses, such as credit
scoring, stock trading and targeted promotional offers.
• Operational intelligence (OI). Also called operational BI, this is a form of real-time analytics that delivers information to managers and frontline workers in business
operations. OI applications are designed to aid in operational decision-making and enable faster action on issues -- for example, helping call center agents to resolve
problems for customers and logistics managers to ease distribution bottlenecks.
• Software-as-a-service BI. SaaS BI tools use cloud computing systems hosted by vendors to deliver data analysis capabilities to users in the form of a service that's typically
priced on a subscription basis. Also known as cloud BI, the SaaS option increasingly offers multi-cloud support, which enables organizations to deploy BI applications on
different cloud platforms to meet user needs and avoid vendor lock-in.
• Open source BI (OSBI). Business intelligence software that is open source typically includes two versions: a community edition that can be used free of charge and a
subscription-based commercial release with technical support by the vendor. BI teams can also access the source code for development uses. In addition, some vendors of
proprietary BI tools offer free editions, primarily for individual users.
• Embedded BI. Embedded business intelligence tools put BI and data visualization functionality directly into business applications. That enables business users to analyze
data within the applications they use to do their job. Embedded analytics features are most commonly incorporated by application software vendors, but corporate
software developers can also include them in homegrown applications.
• Collaborative BI. This is more of a process than a specific technology. It involves the combination of BI applications and collaboration tools to enable different users to
work together on data analysis and share information with one another. For example, users can annotate BI data and analytics results with comments, questions and
highlighting via the use of online chat and discussion tools.
• Location intelligence (LI). This is a specialized form of BI that enables users to analyze location and geospatial data, with map-based data visualization functionality
incorporated. Location intelligence offers insights on geographic elements in business data and operations. Potential uses include site selection for retail stores and
corporate facilities, location-based marketing and logistics management.

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A.Morshedsolouk
BI team Roles
• BI managers
• BI architects
• BI developers
• BI analysts
• BI specialists
who work closely with:
• Data architects
• Data engineers
• Other data management
professionals (DMP)
And Also with:
• Business analysts
• End users

By
A.Morshedsolouk
BI Developer

By
A.Morshedsolouk
BI technologies stats

By
A.Morshedsolouk
BI Technologies by Department
• Whether employees
within the same
organizations have access
to the same BI
technologies, survey
results demonstrated that
different solutions suit
different departments
depending on the needs
and requirements. Again,
this highlights the fact
that there is not one
perfect solution for all
users.

By
A.Morshedsolouk
BI Adoption Rate By Company Size

By
A.Morshedsolouk
Business Intelligence Pyramid

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A.Morshedsolouk
Business Intelligence or Business Analytics?
BI & BA
• Are not the same
Business
intelligence
• relies on real-time and historical data. In essence, it tells organizations what’s happening and how
things got to this point
Business
analytics
• focuses on predicting what is going to happen in an organization in the future based on past
trends and offering suggestions for things that could be done differently for improved outcomes
B. Analytics
• can be a part of the business intelligence process
Question:
• What is Business Analysis? Answer: BA -> BABOK; BI -> DMBOK; DA -> DSBOK

By
A.Morshedsolouk
BI vs. Advanced Analytics

By
A.Morshedsolouk
Data analytics and business intelligence: Differences

By
A.Morshedsolouk
Business Analytics Pyramid

By
A.Morshedsolouk
Examples of business intelligence
•Analyzing data to find out what is happening right now
Descriptive analytics
•Using results from descriptive analytics to determine why trends happen
Statistical analysis
•Using a BI tool to ask your data-specific questions and pull the answers
Querying
•Gathering data from multiple sources, identifying its characteristics, and preparing it for
analysis
Data preparation
•Using tools like machine learning to track trends in large data sets
Data mining
•Comparing performance data to historical data to determine whether or not teams or
individuals are meeting goals
Benchmarking
•Sharing analysis with stakeholders
Reporting
•Using visuals like charts and graphs to make data analysis results easier to understand
Data visualization

By
A.Morshedsolouk
Why is BI important?
Organizations sit on huge amounts of data
Business intelligence helps organizations stay competitive by putting that
data to good use
With BI, anyone can pull data insights and use them to inform decisions
Businesses across industries that prioritize business intelligence experience
significant return on investment (ROI) and improved performance
Those that don’t invest in business intelligence leave data unused and the
decision-making process incomplete

By
A.Morshedsolouk
Common Myths on BI

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A.Morshedsolouk
Common myths about BI tools
• It never fails that when BI is mentioned in a meeting, at least one person will
pipe up with an objection based on some common misconceptions about BI
tools.
• “This is going to be too expensive for our small business.”
• “We don’t have enough data to make BI worthwhile.”
• “Only huge enterprises need BI tools.”
• These objections might sound valid at first, but they’re actually based on myths
that can easily be debunked with a bit of knowledge.
• So let’s bust some of these myths about BI tools, shall we?

By
A.Morshedsolouk
What are BI tools, and what do they do?
• Our world revolves around data. Data on:
• who you are, what you do, and who you are can be used to entice you to purchase more
stuff.
• Businesses are no different than individuals when it comes to data, except businesses
have even more of it.
• A business’s data can be
• internal
• Internal data is generated by the business itself through things like financial transactions, employee
information, customer records, and so on.
• external.
• External data comes from sources outside of the business, such as market trends, weather patterns,
and competitor analysis.
• Business intelligence (BI) tools help businesses to make sense of all this data so
that they can make better decisions about their operations.
• BI tools can include a variety of capabilities, but some common features
include data visualization, reporting, and dashboards.

By
A.Morshedsolouk
BI tools common features
• Data visualization
• Data visualization is a way of representing data in a graphical format, such as a chart or
graph.
• This makes it easier to spot patterns and trends in the data, which can be helpful for
things like identifying customer behavior or forecasting future sales.
• Reporting
• Reporting is another common feature of BI tools.
• Reports provide insights into what’s happening within the business, and they can be
generated on-demand or on a schedule.
• For example, a business might generate a report every week that shows how the sales
team has been performing.
• Dashboards
• Dashboards are another popular feature of BI tools.
• Dashboards provide a quick, at-a-glance view of key metrics and performance indicators.
This can be helpful for quickly identifying areas that need attention or for getting an
overview of how the business is doing.

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A.Morshedsolouk
The benefits of using BI tools
• One of the main benefits of using BI tools is that they can help businesses save
time.
• If a business wants to know how its sales have been performing over the past
year, it can use a BI tool to generate a report in minutes.
• This is much faster than manually sorting through all of the data to find the
information that’s needed.
• Another benefit of using BI tools is that they can help businesses make better
decisions.
• BI tools can provide insights that businesses might not be able to get from
looking at their raw data.
• For example, a BI tool might be able to identify a trend in customer behavior
that the business wasn’t aware of. This information can then be used to make
decisions about things like marketing campaigns or product development.

By
A.Morshedsolouk
7 common myths about BI tools
• Myth 1: “This is too expensive for my business.”
• One of the most common myths about BI tools is that they’re only for huge enterprises with deep pockets.
• This simply isn’t true.
• There are BI tools available for businesses of all sizes, and many of them are very affordable.
• In fact, some BI tools are even free.
• BI tools can save businesses a lot of time and money. So, even if you’re on a tight budget, a BI tool may be
an investment you can’t afford to pass up.
• Myth 2: “I don’t need a BI tool because I already have a reporting system.”
• Another common myth about BI tools is that they’re unnecessary if you already have a reporting system in
place.
• But the truth is that BI tools can do much more than just generate reports.
• As we’ve seen, they can also provide insights through data visualizations and dashboards that businesses
might not be able to get from their reporting system alone.
• This can help businesses save time and make better decisions.
• Myth 3: “I don’t need a BI tool because I have a data analyst.”
• Another common myth about BI tools is that they’re only needed if you don’t have a data analyst on staff.
• But the truth is that BI tools can be helpful even if you do have a data analyst.
• Data analysts can use BI tools to save time and make their jobs easier. And, as we’ve seen, BI tools can also
provide insights that data analysts might miss on their own.
• So even if you have a data analyst on staff, it’s still worth considering whether a BI tool could be beneficial
for your business.

By
A.Morshedsolouk
7 common myths about BI tools
• Myth 4: “I don’t need a BI tool, I can just use Excel.”
• Another common myth about BI tools is that they’re not necessary if you’re already using Excel.
• While it’s true that Excel can be used for some basic data analysis, it’s not designed for complex tasks like data visualization or reporting.
• BI tools are much better suited for these tasks, and they can save you a lot of time.
• So even if you’re already using Excel, you don’t necessarily have to completely reject the idea of using a BI tool.
• In fact, you might find that using both Excel and a BI tool can be beneficial for your business.
• Myth 5: “I don’t need a BI tool because I have a CRM.”
• Another common myth about BI tools is that they’re not needed if you have a customer relationship management (CRM) system.
• But the truth is that BI tools can be very helpful for businesses that have CRMs.
• CRMs can be complex, and it can be difficult to get the information that you need from them.
• But BI tools can make it much easier to access and analyze your CRM data.
• CRMs are great for managing customer data, but they’re not always the best tool for analyzing that data.
• With BI tools, you can easily access your CRM data and generate reports that will help you make better business decisions.
• Myth 6: “BI tools are only for big companies.”
• This is another myth that simply isn’t true. As we’ve seen, BI tools can be beneficial for businesses of all sizes.
• They can help small businesses save time and money, and they can provide insights that businesses might not be able to get from their data
alone.
• This myth is likely based on the fact that BI tools have traditionally been expensive and difficult to use. But as we’ve seen, there are now many
BI tools that are affordable and easy to use.
• Myth 7: “BI tools are a passing tech fad.”
• BI tools have been around for many years, and they’re only getting more popular.
• In fact, Gartner predicts that BI and analytics will be one of the most important trends in business in the coming years.

By
A.Morshedsolouk
How does BI work?

By
A.Morshedsolouk
How does business intelligence work?
As organizations operate, they create raw data.
That data is collected, processed, and stored as part of data integration.
It could be stored in a data warehouse or directly within a BI tool.
With the data consolidated in one repository, stakeholders can now initiate analysis to answer their business
questions.
Simply put, every organization has goals, and every organization has questions — most about how to reach
the goals.
Business intelligence answers these questions by gathering data, analyzing it, and presenting the finding in
an understandable way so that stakeholders can decide what steps they should take to meet their goals.

By
A.Morshedsolouk
BI Process
Business Intelligence
(BI) turns data into
information.
It is the sum of
applications,
technologies, and
methods adopted to
glean strategically
relevant information
for the business.
The task involves
collecting, integrating,
analyzing, and
applying historical,
current and predictive
information related to
the business.
The objective is to gain
strategic insights,
evaluate risk, and
improve decision-
making capabilities for
top management,
business owners, and
other stakeholders.

By
A.Morshedsolouk
BI Architecture

By
A.Morshedsolouk
BI Framework

By
A.Morshedsolouk
OLTP vs. OLAP

By
A.Morshedsolouk
How BI Works?

By
A.Morshedsolouk
Data Source vs. Data Warehouse

By
A.Morshedsolouk
Data sources or platforms

By
A.Morshedsolouk
Data Source Types

By
A.Morshedsolouk
Measures vs. Dimensions

By
A.Morshedsolouk
Report Actions on OLAP Cubic files

By
A.Morshedsolouk
How does BI work?

By
A.Morshedsolouk
Data repository types

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A.Morshedsolouk
Data mart (Datamart)
A data mart is a repository of data that is designed to
serve a particular community of knowledge workers. Data
marts enable users to retrieve information for single
departments or subjects, improving the user response
time. Because data marts catalog specific data, they often
require less space than enterprise data warehouses,
making them easier to search and cheaper to run.
Data lakes consist of raw, undefined data; often, the
purpose of this data has not yet been determined. data
lakes serve as a repository for unstructured, unrefined
data.

By
A.Morshedsolouk
Top BI tools in 2022
According to Mordor Intelligence, the business intelligence industry is so
popular that it is predicted to reach a value of USD 40.50 billion by 2026

By
A.Morshedsolouk
Evolution of BI

By
A.Morshedsolouk
Best of Breed BI tools
These business intelligence tools all vary in robustness, integration capabilities,
ease-of-use (from a technical perspective) and of course, pricing.

By
A.Morshedsolouk
Data visualization
Data warehouses
Interactive dashboards (metrics, KPIs)
Alerts and notifications (set thresholds for high or low numbers)/can be outside of BI tools
Embedded Analytics (visualization in the company web page, cloud app, or for customer access, …)
BI reporting tools
Big Data (Hadoop)
Data Mining
•Also known as “data discovery,” data mining involves automated and semi-automated data analysis to uncover patterns and inconsistencies. Common correlations drawn from data mining include grouping specific sets
of data, finding outliers in data, and drawing connections or dependencies from disparate data sets.
Predictive analytics
•forecast future events based on current and historical data. By drawing connections between data sets, these software applications predict the likelihood of future events, which can lead to a huge competitive advantage
for businesses.
Descriptive modeling
•seeks to reduce data into manageable sizes and groupings. Descriptive analytics works well for summarizing information such as unique page views or social media mentions.
Decision analytics
•Take into account all the factors related to a discrete decision. Decision analytics predict the cascading effect an action will have across all the variables involved in making that decision.
NLP (Natural Language Processing)
•Data comes in three main forms: structured, semi-structured, and unstructured. Unstructured data is the most common, and includes text documents and other types of files that exist in formats that computers can’t
read easily.
•also known as text analytics software, combs large sets of unstructured data to find hidden patterns.
BI Tools features

By
A.Morshedsolouk
BI Tools
Self-Service Data Visualization Data Warehousing BI Platforms
SAP Crystal Reports iDashboards Sisense Tableau
Chartio Dundas Oracle BI InsightSquared
Alteryx Segment SAS Domo
Jaspersoft Geckoboard Birst
SAP Lumira (formerly
BusinessObjects)
Business
intelligence
tools and
platforms come
in several forms
for varying
business needs.
Self Service:
Companies looking to provide data services to
business users will find self service BI software will
meet the needs of most of their users.
Data visualization
tools are helpful for teams that are dipping their toes
into data analytics but may not have lots of extra
development resources available.
Data warehousing
tools provide the underlying infrastructure that can
house and cleanse data before serving it up through
visualizations.
Platforms
And BI tools provide end-to-end dashboard tools to
store, cleanse, visualize, and publish data.

By
A.Morshedsolouk
25 BI Tools
• Top Business Intelligence Tools
• Enterprise Business Intelligence Platforms
• #1) Oracle NetSuite
• #2) Integrate.io
• #3) Zoho Analytics
• #4) HubSpot
• #5) Query.me
• #6) SAS
• #7) Birst
• #8) WebFOCUS
• #9) BusinessObject
• #10) IBM Cognos
• #11) MicroStrategy
• #12) Pentaho
• Database Integrated Products
• #13) Microsoft BI and Power BI
• #14) Oracle BI (OBIEE+ and Endeca)
• #15) SAP BW + HANA
• #16) Oracle Hyperion
• Data Discovery And Visualization
• #17) Qlik and QlikSense
• #18) Tableau
• #19) Board
• #20) Sisense
• #21) Adaptive Discovery
• Niche And Innovative
• #22) Yellowfin BI
• #23) Style Intelligence
• #24) Bizzscore
• #25) Jaspersoft

By
A.Morshedsolouk
BI vendors: MicroStrategy
• MicroStrategy is an enterprise business intelligence (BI) application software vendor.
• Its flagship platform contains multiple features designed to help enterprises make data-driven decisions and optimize business processes. These
tools and capabilities include interactive dashboards, scorecards, ad hoc queries, automated report distribution and highly formatted reports,
thresholds and alerts.
• MicroStrategy's architecture can be deployed on premises with Windows or Linux servers or as a service in the AWS or Microsoft Azure clouds. Its
client interfaces enable users to access MicroStrategy applications and services via the web, Windows, Mac and mobile devices. In addition to its
suite of development and administrative tools, MicroStrategy provides a software developer kit (SDK) to customize an application and integrate it
with other applications. The platform contains APIs and gateways that allow users to integrate MicroStrategy functionality with third-party analytics
tools, including Tableau and Power BI, as well as a Microsoft Office integration.
• What is in the MicroStrategy product suite?
• MicroStrategy's product suite includes HyperIntelligence, Embedded Intelligence, Cloud, Consulting, Education, and BI and Analytics tools. Its tools
and services are designed to be user-friendly and intuitive for both end users and BI professionals to perform such tasks as data discovery, data
wrangling, data visualization and advanced big data analytics.
• Because MicroStrategy's advanced BI tools are meant for business users, organizations can enable end users to build transaction-enabled analytics
apps that optimize operations and boost productivity with code-free development tools. For example, tools like HyperIntelligence are always
available everywhere in real time.
• Unlike a traditional multidimensional online analytical processing (MOLAP) architecture, which supports summary-level reporting, MicroStrategy's
relational online analytical processing (ROLAP) architecture allows users to "drill anywhere" in the entire relational database -- all the way down to
the transactional level of detail. This enables the use of a virtual multidimensional cube structure to express complex relational databases, making it
easier for business users to understand and navigate through the data.
• MicroStrategy has optimizations for all major relational databases and data warehouse vendors and enables access to multidimensional
databases and flat files. While many BI vendors offer full-featured BI products, MicroStrategy's ROLAP architecture and integrated metadata are key
differentiators.
• The MicroStrategy platform uses a single common metadata for consistency and streamlined maintenance. Its 64-bit architecture supports in-
memory analytics with "Intelligent Cubes" (or OLAP reports cached in memory as data sets). Metrics and attributes are created once and used
across different types of reports. Changes are made in one place, and all related reports are automatically updated. Similarly, security permissions
are granted in one place, reducing administrative costs.

By
A.Morshedsolouk
BI Players: Gartner magic Quadrant (2022)
• Solutions
• Desktop level
• Enterprise level
• Cloud level
• Features
• Platforms
• Visuals richness
• Data Scalability
• Complexity to develop
• How easy to learn and use
• Develop costs
• Changes costs
• Maintain costs
• Microsoft is a Top leader

By
A.Morshedsolouk
BI Tools comparison

By
A.Morshedsolouk
Microsoft Power BI
Bridge the gap between data and decision making
Microsoft is named a Leader in the March 2022 Gartner®
Magic Quadrant™ for Analytics and Business Intelligence Platforms.

By
A.Morshedsolouk
Microsoft Power BI
• Power BI is an interactive data visualization software product developed by Microsoft with a primary focus on business intelligence. It is part of the
Microsoft Power Platform.
• Easily connect to, model, and visualize your data, creating memorable reports personalized with your KPIs and brand.
• Get fast, AI-powered answers to your business questions—even when asking with conversational language.
• Make the most of your big data investments by connecting to all your data sources with the scale to analyze, share, and promote insights across
your organization while maintaining data accuracy, consistency, and security.
• Extend data loss prevention and governance to Power BI users—including report sharing—with Microsoft Cloud App Security.
• Create a data-driven culture across your organization by offering everyone BI and analytics capabilities with Power BI Desktop (free to use) and
Power BI Pro (available for a low monthly price per user). https://powerbi.microsoft.com/en-us/pricing/
• Receive weekly and monthly updates that improve Power BI features and capabilities—based on thousands of ideas submitted annually from a
community of more than half a million members worldwide.
• Meet both your self-service and enterprise data analytics needs on a single platform. Access powerful semantic models, an application lifecycle
management (ALM) toolkit, an open connectivity framework, and fixed-layout, pixel-perfect paginated reports.

By
A.Morshedsolouk
Microsoft Power BI
• Turn your data into a competitive advantage by using Power BI and Azure
together to connect, combine, and analyze your entire data estate.
• Take advantage of the latest advances in Microsoft AI to help non-data scientists
prepare data, build machine learning models, and find insights quickly from both
structured and unstructured data, including text and images.
• Anyone who’s familiar with Microsoft 365 can easily connect Excel queries, data
models, and reports to Power BI Dashboards—helping to quickly gather, analyze,
publish, and share Excel business data in new ways.
• Go from data to insights and insights to action with Microsoft Power Platform—
combining Power BI with Power Apps and Power Automate to easily build
business applications and automate workflows.
• Know what’s happening now, not just in the past. From factory sensors to social
media sources, get access to real-time analytics so you’re always ready to make
timely decisions.
• Start visualizing your data in seconds with an extensive library of data
visualizations. Browse hundreds more in AppSource. Each visual has been tested
and approved by Microsoft to integrate seamlessly with Power BI and provide
valuable insights.
• Explore how data visualization transforms complicated information into
something that’s easy to share and understand with this interactive airplane
engine diagnostics visualization. https://powerbi.microsoft.com/en-us/power-bi-
visuals/
• Guided Tour: https://dynamics.microsoft.com/en-us/guidedtour/power-
platform/power-bi
• Success Stories: https://powerbi.microsoft.com/en-us/customer-showcase/

By
A.Morshedsolouk
What does Power BI do?
Power BI is a business analytics service that delivers insights to enable fast, informed
decisions.
Transform data into stunning
visuals and share them with
colleagues on any device.
Collaborate on and share
customized dashboards and
interactive reports.
Visually explore and analyze
data—on-premises and in
the cloud—all in one view.
Scale across your
organization with built-in
governance and security.

By
A.Morshedsolouk
Visualizations of Data
Visualizations allow data to be represented in different
ways, leading to insights into data relationships that may
not be easily seen.
How does visualizing data help users?
Power BI allows users to create and adjust
visualizations based on their own needs as they look
at data.
Users will be able to look at data from different
perspectives and find insights into data relationships
that help them make better informed decisions.

By
A.Morshedsolouk
Examples of User Roles for BI
While not definitive, Power BI users may fall into one of many roles.
• Consumer of operational reports in BI tool
• MAY want to interact with reports beyond what is available
• Labeled as a “consumer” in Microsoft Power BI training manuals
General
User
• High-level executives who use delivered reports or dashboards to answer
complex questions with data.
• Mainly looking at reports in the Power BI service
• Labeled as a “consumer” in Microsoft Power BI training manuals
Managerial
User
• Utilizes queries, formulas, transformations, and visualizations to analyze data
• Develops own reports and dashboards to be shared with others
• Labeled as a “report designer” in Microsoft Power BI training manuals
Advanced
User

By
A.Morshedsolouk
Sections of a POWER BI Report
A Power BI Report is a collection of
visualizations that appear together on
one or more pages.
Visualizations present data in a simple
way that provides context and insight.
Slicers let users select and manipulate
the way data is visualized within a
report. (Apply and Clear slicer)

By
A.Morshedsolouk
How Power BI works?

By
A.Morshedsolouk
Power BI
Microsoft BI is a proprietary platform provides Integration Services and uses some applications o work with analytical data
This platform is well known for Analysis Services (SSAS) and Reporting Services (SSRS) and Master Data Services
Some of the BI features are available only in SharePoint that includes PowerPivot and Power View
Reporting Services provides interactive reports that run on PowerPivot
Power BI is a business intelligence platform used to transform collected data into an organized visual format for better
understanding
• Dataset to bring all data at a place,
• Dashboard that is created to represent visual data analytics and
• Report that contains several pages of visualization with organized data analytics in the form of charts and graphs
This platform is based on 3 pillar components such as:

By
A.Morshedsolouk
Microsoft Power BI
Consistently recognized as one of the best BI solutions around, Power BI offers flexible plans for businesses of all
sizes and integrates with your Microsoft office tools such as Excel.
Power BI is a collection of software services, apps, and connectors that work together to turn your unrelated sources
of data into coherent, visually immersive, and interactive insights.
Your data may be an Excel spreadsheet, or a collection of cloud-based and on-premises hybrid data warehouses.
Power BI lets you easily connect to your data sources, visualize and discover what's important, and share that with
anyone or everyone you want.
Standout feature: Leave no stone unturned with the ability to source from hundreds of data preparation and
connection sources, including IoT devices.

By
A.Morshedsolouk
Power BI Visuals
Interactive
dashboarding
• Presenting a
comprehensive visual
snapshot of business-
critical information in a
single location.
Drill-down
functionality
• Drilling up and down on
data points to explore
in-depth details about
company’s data.
Pre-built visuals
• Hundreds of out-of-the-
box visuals, including
charts, cards, KPIs,
maps, matrix, etc.
provided by Microsoft
and the community.
Custom visuals
• Creating propriety
visuals using the Power
BI open source custom
visuals framework.
Natural language
user interface
• Asking natural language
questions and getting
answers in the form of
automatically created
visuals and reports.

By
A.Morshedsolouk
Power BI Components (Modules)

By
A.Morshedsolouk
Power BI Components

By
A.Morshedsolouk
Power BI Products and Pricing

By
A.Morshedsolouk
Power BI (Online) Service
• Cloud based service (Part of Office 365)
• Access your data, wherever it may exist
• Ask questions, integrate with cortana
analytics and more
• Create curated content based on your
organizational needs
• Share insights across web, mobile and
embedded within your own applications
https://app.powerbi.com/groups/fcf53522-3df1-4510-8817-11a8cc96172f/reports/97163a2e-a60d-425a-95e8-
57bf8efe61ca/ReportSection03154589dc3ed4f6f5e2?ctid=dc53dc9d-7a57-417f-85b5-c2b3c05d2b9e

By
A.Morshedsolouk
Power BI Desktop - Introduction
• Free download that starts your Power BI
experience
• Not an end user tool, but a power user and
designer tool
• Can be used to mash, model and design
engaging experiences
• Transform and clean data
• Design once and view anywhere
• Power BI Desktop RS

By
A.Morshedsolouk
Power BI - Reporting
• Enterprise-wide reporting
• Distributing reports preconfigured by analysts across
the company.
• Scheduled reporting
• Periodically delivering reports with the preset values
defined by data analysts, BA, etc. at scheduling.
• Ad hoc reporting
• Self-service reporting on the as-needed basis to answer
specific questions.
• Paginated reporting
• Presenting all the required data in a table format.
• Mobile reporting
• Optimizing Power BI content for mobile users.
• Embedded reporting
• Incorporating Power BI content into other applications.

By
A.Morshedsolouk
Data Visualizations in Power BI

By
A.Morshedsolouk
Power Query
• ETL part of Power BI
• Also exists in Excel

By
A.Morshedsolouk
Power Query
• 100+ native data source connectors
• File data sources, databases, Azure
data source ecosystem, online
services.
• Data ingestion
• Ingesting various data structures,
including relational data, big data,
IoT data, streaming data.
• Integration with Azure Data Lake Storage
Gen2
• To unify and enable collaboration
on data across Power BI and Azure
data services.

By
A.Morshedsolouk
Power BI languages
• M-Language
• Query language
• Used by Power Query
• ETL functions
• DAX language
• Data Analysis Expressions
• A general calculation language to create columns and measures
• Rich functions
• R language
• The statistical language R support Using R for preparing data
models, reports, data cleansing, advanced data shaping, dataset
analytics, etc.
• Python language
• Optimized with Azure and SQL
• Predictive analytics
• Through ML models created in Azure Machine Learning Studio.
• Data modeling
• AI-powered data modeling with AutoML, Cognitive Services, Azure
ML (Power BI Premium).
• Real-time data analytics
• Real-time streaming with Power BI REST APIs, Streaming Dataset
UI, Azure Stream Analytics to display and update real-time data.

By
A.Morshedsolouk
Power BI Components and pricing
Power BI
Desktop
• Desktop App
• Power Query
• Report
• Free self-
service
Power BI Service
• A cloud service
(SaaS)
• Workspace
• Report
• Dashboard
• Application
• Admin portal
• Price $9 per
user per Month
for Pro And
price > $5000
for Premium
licenses
Data gateway
• Gateway for
transport data
from internal
net to PBI
service
Power BI Mobile
• Mobile app for
viewing
Reports
• Mobile View or
Desktop View
• Windows, iOS,
and Android
devices
Power BI Report
Server
• On-premises
version of
Power BI
service
• An on-premises
server where
you can publish
your Power BI
reports, after
creating them
in Power BI
Desktop RS
• Limited
capabilities
• SSRS, SSAS
• Pricing in Pro
license or SQL
Server
Enterprise
license
Power BI Report
Builder
• Tool for
creating
Paginated
reports
Sep2022 @ ICASAT Page 152

By
A.Morshedsolouk
Power BI pricing

By
A.Morshedsolouk
What is Power BI desktop?
Power BI Desktop is a free application you
install on your local computer that lets you
connect to, transform, and visualize your
data.
With Power BI Desktop, you can connect to
multiple different sources of data, and
combine them (often called modeling) into
a data model.
This data model lets you build visuals, and
collections of visuals you can share as
reports, with other people inside your
organization.
Most users who work on business
intelligence projects use Power BI Desktop
to create reports, and then use the Power
BI service to share their reports with
others.

By
A.Morshedsolouk
What is Power BI service?
Power BI is a collection of software services, apps, and connectors that
work together to help you create, share, and consume business insights in
the way that serves you and your business most effectively.
The Microsoft Power BI service (app.powerbi.com), sometimes referred to
as Power BI online, is the SaaS (Software as a Service) part of Power BI.
In the Power BI service, dashboards help you keep a finger on the pulse of
your business. Dashboards display tiles, which you can select to
open reports for exploring further. Dashboards and reports connect
to datasets that bring all of the relevant data together in one place.
The other main components of Power BI are the Windows desktop
application Power BI Desktop and the Power BI mobile apps for Windows,
iOS, and Android devices.
You and your colleagues can use these three elements—Power BI Desktop,
the service, and the mobile apps—to create, share, and consume business
insights.

By
A.Morshedsolouk
Power BI RS
On-premises
reporting with
Power BI Report
Server
What if you need to
keep your reports on
premises, say,
behind a firewall?
You can create,
deploy, and manage
Power BI reports in
Power BI Desktop,
and paginated
reports in Report
Builder, with the
ready-to-use tools
and services that
Power BI Report
Server provides.
Power BI Report
Server is a solution
that you deploy
behind your firewall
and then deliver
your reports to the
right users in
different ways,
whether that's
viewing them in a
web browser, on a
mobile device, or as
an email.
And because Power
BI Report Server is
compatible with
Power BI in the
cloud, you can move
to the cloud when
you're ready.

By
A.Morshedsolouk
Power BI Mobile

By
A.Morshedsolouk
Power BI Architecture

By
A.Morshedsolouk
BI implementation

By
A.Morshedsolouk
Steps for BI Implementation
Establish a BI
Vision, Mission
and Strategy Assess current
situation
Develop a BI
roadmap and
prioritize
initiatives
Establish BI
Governance and
funding process
Establish a BI
Competency
Center (BICC)
Align Business and
IT and BI teams
Deploy a Data
Dictionary/Master
Data
Measure and track
ROI/Benefits from
BI
Identify KPIs,
Metrics, Measures
Choose your BI
Tools, Technology,
Infra, DWH
Identify Data
Sources, start ETL
and Modeling
Design and
Implement BI
Reports
Onboard
Stakeholders and
End-users
Build Trust in the
system, Govern
your Data
Close the Cycle by
Continuous
Improvement

By
A.Morshedsolouk
BI Implementation; Steps and Roles

Business Intelligence (BI) and Data Management Basics

Business Intelligence (BI) and Data Management Basics

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