PTF20 Power BI Data Analytics
A. Getting started with Power BI

Platform where you can share your
reports.
1. Microsoft Power BI

and connections (excel, csv, etc)

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immersive, and interactive insights.
For on-the-go access to Power BI
service content, designed for mobile.
The above work together to turn
unrelated data into coherent,
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3. Mobile Apps (mobile Power BI apps)
Collection of software service, apps,

Where coworkers or clients can
consume the reports.
Comprehensive set of software
tools, apps, and connectors that
transform data into interactive
insights.
II. Flow of Work in Power BI
Common flow of work in Power BI is:
1. Get Dataset and bring to Power BI
2. Business Intelligence

Desktop
Comprises the strategies and
2. Use Power BI Desktop to create a
technologies used by enterprises
Report/Visualization
for data analysis of business info.
3. The Report is published to Power
BI Service (where you can create new
I. Parts/Elements of Power BI
visualizations then share it)
4. Through Power BI Mobile Apps,

These 3 elements- Desktop, Service,
users can read, interact, and
and Mobile apps- are designed to let
consume the information.
people create, share, and consume
III. Building Blocks of Power BI
business insights.
1. Power BI Desktop
1. Visualizations – A visual representation of

Creates reports/visualizations.

Authoring reports made of datasets and
visualizations.
2. Power BI Service

For creating dashboards from published
reports & distributing content w/ apps.

Online Saas (Software as a Service).
data (like chart or map).
2. Datasets – unique collection of data, used to
B. Getting started with Data Analytics
create visualizations.
3. Reports – a collection of visualizations that
1. Data Analysis

appear together in 1 or more pages.
Process of identifying, cleaning,
transforming, and modeling data to

A collection of items that are related to
discover meaningful and useful info.
each other.

The data is then crafted into a story,
4. Dashboards – collection of
through reports, for analysis to
visuals/visualizations from a single page.
support critical decision-making.

Must fit on a single page, often called a
Canvas (the blank backdrop).
5. Tiles – a single visualization/square on a
report or dashboard.
I. Core Components of Analytics
1. Descriptive Analytics
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Help answer question about WHAT has
happened based on historical data.
IV. Power BI Service

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summarize large datasets to describe
Power BI Service permits creation of
Apps for easy distribution &
consumption.
1. App – a collection of preset, ready-made
visuals and reports that are shared with an
Descriptive Analytics Techniques
outcomes.

Develops Key Performance Indicators
(KPI), KPIs track the success or failure
of key objectives.
o
on Investment/Profits/Sales,
entire organization.

are developed to track
Way to group or collection of related
reports and dashboards and distribute to
audiences.
Examples: Metrics like Return
performance.
o
Example: Generating reports to
provide a view of an
organization’s sales and
financial data.
2. Diagnostic Analytics
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Help answer questions about WHY
dataset. By analyzing past decisions and
events.
events happened.

Diagnostic Analytics Techniques
supplement basic descriptive analytics
used the findings from descriptive
analytics to discover the cause of

 Attempt to draw inferences from
these events.
existing data and patterns, derive
Therefore, performance indicators are
conclusions from existing knowledge
further investigated to discover why
bases, then add these findings into the
these events improved/became worse.

4. Cognitive Analytics
Generally, this process occurs in 3
steps:
1. Identify anomalies in data (can be
knowledge base for future inferences,
a self-learning feedback loop.
 Cognitive analytics help us learn what
unexpected changed in the metric or
might happen if circumstances change
market).
and determine how to handle these.
2. Collect data that’s related to the
anomalies.
3. Use statistical techniques to
discover relationships and trends that
explain these anomalies.
 Inferences aren’t structured queries,
these are unstructured hypotheses,
gathered from several sources, and
expressed with varying degrees of
confidence.
 Effective cognitive analytics depend on
3. Predictive Analytics
machine learning algorithms, and
utilizes several natural language
 Help answer question about WHICH
actions should be taken to achieve a
goal/target.
 Orgs use insights from prescriptive
analytics to make data-driven
decisions.
 Prescriptive analytics techniques rely
on machine learning as one of
strategies to find patterns in large
processing concepts to make sense of
previously untapped data sources (like
call center conversation logs and
product reviews).
II. Roles in Data
c) Data Engineer – Provisions and set up data
a) Business Analyst – closer to business and is a
specialist in interpreting the data that comes
from visualization.
 Fact: Roles of data analyst and business
analyst could be a task of a single
person.
platform techs that are on-premises and cloud.
 Manages and secure the flow of
structured and unstructured data from
multiple sources.
 Data Platforms Used:
1. Relational Databases
b) Data Analyst – Enables businesses to
2. Nonrelational Databases
maximize the value of data assets through
3. Data Streams
Visualization and Reporting tools (Microsoft
4. File Stores
Power BI).
 Data Engineers ensure data services
securely and seamlessly integrate
 Data Analysts are responsible for
profiling, cleaning, and transforming
data.
 Responsibilities also include designing
and building scalable and effective data
models;
 Enabling and implementing advanced
data analytics capabilities into reports
for analysis.
 Tasked with identifying appropriate and
necessary data & reporting
requirements, then turning raw data
into relevant & meaningful insights.
 Responsible for Power BI Assets
(reports, dashboards, workspaces, and
underlying datasets).
 Tasked with implementing and
configuring proper security procedures.
across data platforms.
 Primary Responsibilities:
1. Use of on-premises and cloud data
services and tools to ingest, egress, and
transform data from multiple sources.
2. Design and implement solutions.
3. Add tremendous value to business
intelligence and data science projects.
4. Brings data together, called Data
Wrangling.
d) Data Scientist – Performs advanced analytics
e) Database Administrator – Implements and
to extract value from data.
manages operational aspects of cloud-native
 Their work varies from descriptive 
predictive analytics.
 Descriptive analytics: Evaluate data
through process known as exploratory
data analysis (EDA).
 Predictive Analytics: Used in machine
learning to apply modeling techniques
that detect anomalies/patterns.
 These two analytics are essential parts
of forecast models.
 Fact: Descriptive & Predictive Analytics
and hybrid data platform solutions built on
Microsoft Azure data services and MS SQL
server.
 Responsibility:
1. Overall availability and consistent
performance and optimizations of
database solutions.
2. Works with stakeholders to
identify and implement
policies, tools, and processes
for data backup and recovery
are only partial aspects of their work.
plans.
Data Scientists also work in realm of
3. Monitors and manages
deep learning, performing iterative
overall health of database and
experiments to solve complex data
the hardware it resides.
problem by using customized
algorithms.
 Anecdotal evidence: Data Science

In contrast, Data
Engineers is involved in
process of Data
project is mostly spent on Data
Wrangling (ingesting,
Wrangling and Feature Engineering.
transforming, validating
 Data Scientist looks at data to
& cleaning data)
determine questions that needs
4. Responsible for overall
answers and devise hypothesis, then
security of data, granting and
Data Analysts will assist with data
restricting user access and
visualizations and reporting.
privileges to data.
o
III. Tasks of Data Analysts
This process is done by defining
and creating relationships
between tables.
A. Prepare
o
 Data Preparation – process of profiling,
enhance the model by defining
cleaning, and transforming data to
metrics & adding custom
ready for modelling & visualization.
o
Also means process of taking
raw data and turning it into
information.
o
It involves ensuring data
integrity, correcting inaccurate
data, identifying missing data,
converting data from 1
structure to another, making
data readable.
 Data preparation involves
From this point, you can
calculations.
 Effective and Proper Data Model in
understanding and gaining valuable
insights.
 Effective Data Model makes reports
ACCURATE…
 Model is a critical component that has
direct effect on the performance of
your report and overall data analysis.
 Process of preparing and modeling data
is an Iterative Process.
understanding HOW you’re going to get
and connect to the data & performance
implications of decisions.
 Privacy and security assurances. These
C. Visualize
 Brings data to life.
 Ultimate goal of visualized task is to
include anonymizing data to prevent
solve business problems. A well-
seeing personal info.
designed report should tell a
 Data Preparation is a length process.
B. Model
compelling story about the data.
 Reports drive the overall actions,
decisions, and behaviors of an
 When data is in a proper state, it’s
ready to be modeled.
 Data Modeling – process of
determining HOW your tables are
related to each other.
organization based on the information
from the data.
 Built-in AI Capabilities in Power BI:
o
Built-in AI visuals
o
Quick Insights Feature (enable
discovering of data by asking
questions)
o
Creating machine learning
models in Power BI
D. Analyze
 Important step of understanding and
interpreting information displayed on
the report.
 Gain insights thru visuals and metrics.
E. Manage
 Data Analysts are responsible for
management of Power BI assets
(reports/dashboards/workspaces/datas
ets), overseeing the sharing and
distribution of items (such as reports
and dashboards) and ensuring security
of these assets.
 Apps can be a valuable distribution
method for content and management
for audiences.
 Management of Power BI assets helps
reduce duplication of efforts and
ensure security of data.