Basics of BI and Data Management (Summary).pdf

By
A.Morshedsolouk
Business Intelligence and Data Management Page: 1
Basics of
Business Intelligence (BI)
and Data Management
(Summary)
By Ali Morshedsolouk
Oct 2022
Free download the Complete version at:
https://www.slideshare.net/amorshed/amkbidmv10publishpdf

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
Organization
• Data Management
basics
• DMBOK Framework
• DAMA Wheel
A glance to
DMBOK2
• Why BI?
• Common Myths on
BI
• How BI works?
What is BI?
• BI Tools features
• Market leaders
• MS Power BI
An overview to
BI Tools
• Steps to
implement BI
BI
Implementation
• Plans
• Challenges
• Outcomes
• Demo
ICASAT BI case
study
• Summary
• Any Question?
Q&A

By
A.Morshedsolouk
DX: Enabler for BI

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/SDSN: SpaceBridge
•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

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A.Morshedsolouk
Cloud Service Model

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-literate 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)
There is a strong need to do Data Management (BI is part of DM)

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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?

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A.Morshedsolouk
Data Maturity Model

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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

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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
• 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

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
Building a data-driven organization must be rooted in your
organization’s business strategy
Both clear budgetary allocations and leadership involvement on
Data Strategy and Data Culture
It is crucial at this stage to start with your business needs, not
technology
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?

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A.Morshedsolouk
A Glance at DMBOK2.0
Framework

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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

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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
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

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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
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?

By
A.Morshedsolouk
Data Risk and Data Quality
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
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
Benefits of BI
speed up and improve
decision-making
(Faster Analysis by
Visualization)
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, Why changed?
What changed?
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
Transparency,
Efficiency, Profitability,
Sustainability
It can help optimize
processes and Govern
Data
Centralized Intuitive
KPI dashboards and
Easy to access and
share info, No Silo
Clear Accountability
through efficient
Governance and
Reporting
Time Efficiency by
shortening decision-
making process
Allows manpower to
focus on skillful tasks
rather than
monotonous tasks

By
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
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?

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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.

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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
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
Common Myths on BI

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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.

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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.

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A.Morshedsolouk
How does BI work?

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A.Morshedsolouk
BI Architecture

By
A.Morshedsolouk
How BI Works?
Transactional Analytical(Slicing, Diced, Roll-up, Drill-down)

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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

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A.Morshedsolouk
Data visualization
ETL, Integration, Data warehouses
Interactive dashboards, Modeling, Query, metrics, KPIs definitions, Languages (DAX, R, Python, …)
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
Desktop, Cloud, PaaS, SaaS, Enterprise , Self-service
Data Mining, Big Data (Hadoop)
•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

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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
Gartner magic Quadrant (2022)

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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.

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A.Morshedsolouk
Power BI Components

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A.Morshedsolouk
How Power BI works?

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A.Morshedsolouk
Power BI Architecture

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A.Morshedsolouk
Power BI Products and Pricing

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

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A.Morshedsolouk
BI implementation

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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
Thank You
for taking your time!

By
A.Morshedsolouk
DX Journey

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A.Morshedsolouk
Chief “Something” Officer -- CDO?!

By
A.Morshedsolouk
BI Funnel

By
A.Morshedsolouk
Data Quality

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A.Morshedsolouk
Data Strategy!

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A.Morshedsolouk
BI Budget

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A.Morshedsolouk
Data-Driven Organization

By
A.Morshedsolouk
And BI Journey
Does not
End!
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Basics of BI and Data Management (Summary).pdf

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Basics of BI and Data Management (Summary).pdf