Lec 1 Data Viz.pdf

Data visualization and performance
• Lec 1
Dr. Mohammad Mhawish 1

Introduction to Business Intelligence
and Power BI
• Power BI is a powerful Business Intelligence tool Microsoft.
• Business intelligence is all about leveraging data in order to make better
decisions.
• This can take many forms and is not necessarily restricted to just business. We
use data in our personal lives to make better decisions as well.
• For example, if we are remodeling a kitchen, we get multiple approaches
from different firms.
• The prices and details in these approaches are pieces of data that allow us to
make an informed decision in terms of which company to choose. We may
also research these firms online. This is more data that ultimately supports our
decision.
2
Dr. Mohammad Mhawish

Key concepts of business intelligence
• Business intelligence, in the context of organizations, revolves around making better
decisions about your business
• Unlike the example in the previous slide, organizations are not generally concerned with
kitchens, but rather with what can make their business more effective, efficient, and
profitable.
• The businesses that provided those approaches on kitchen remodeling need to answer
questions such as the following:
• How can the business attract new customers?
• How can the business retain more customers?
• Who are the competitors and how do they compare?
• What is driving profitability?
• Where can expenses be decreased?
3

Concepts of B.I
• The key concepts of business intelligence can be broken down into five
areas:
• Domain
• Data
• Model
• Analysis
• Visualization
4

1. Domain
• A domain is simply the context within which business intelligence is applied.
• Most businesses are comprised of relatively standard business functions or
departments, such as the following:
• Sales
• Marketing
• Manufacturing/production
• Logistics
• Research and development
• Purchasing
• Human resources
• Accounting/finance
5

Cont..
• Each of these business functions or departments represents a domain within
which business intelligence can be used to answer questions that can assist us
in making better decisions.
• The domain helps in narrowing down the focus regarding which questions can
be answered and what decisions need to be made.
• For example, within the context of sales, a business might want to know which
sales personnel are performing better and which sales personnel are
performing worse.
6

Cont..
• Business intelligence can provide this insight as well as help determine
which activities enable certain sales professionals to outperform
others.
• This information can then be used to train and mentor sales personnel
who are performing more poorly.
7

2. Data
• Once a domain has been decided upon, the next step is identifying the
data that’s related to that domain. This means identifying the sources
of relevant data.
• These sources may be internal or external to an organization and may
be structured, unstructured, or semi-structured in nature.
8

Internal and External data
• Internal data is data that's generated within an organization by its business processes and
operations. These business processes can generate large volumes of data that is specific to that
organization's operations.
• This data can take the form of net revenues, sales to customers, new customer acquisitions,
employee turnover, units produced, cost of raw materials, and much more time series or
transactional information.
• This historical and current data is valuable to organizations if they wish to identify patterns and
trends, as well as for forecasting and future planning
• In addition to internal data, business intelligence is most effective when internal data is combined
with external data. Crucially, external data is data that is generated outside of the boundaries of
an organization's operations. Such external data includes things such as the business's overall global
economic performance, and competitor prices.
9

Structured, Unstructured, And Semi-structured
Data
• Structured data is data that conforms to a rather formal specification of tables
with rows and columns. Think of a spreadsheet where you might have columns for
the transaction ID, customer, units purchased, and price per unit. Each row
represents a sales transaction.
• Structured data sources are the easiest sources for business intelligence tools to
consume and analyze. In addition, this category of data sources includes relational
database standards and APIs such as Open Database Connectivity (ODBC) and
Object Linking and Embedding Database (OLE DB).
10

Data
• Unstructured data is effectively the opposite of structured data. Unstructured data
cannot be organized into simple tables with rows and columns. Such data includes
things such as videos, audio, images, and text. Word processing documents, emails,
social media posts, and web pages are also examples of largely unstructured data.
• Unstructured data sources are the most difficult types of sources for business
intelligence tools to consume and analyze. This type of data is either stored as binary
large objects (BLOBS) or as a file in a filesystem such as the New Technology File
System (NTFS) or the Hadoop Distributed File System (HDFS).
11

Data
• Semi-structured data has a structure but does not conform to the formal
definition of structured data, that is, tables with rows and columns. Examples of
semi-structured include XML, other markup languages such as HTML and XSL,
JavaScript Object Notation (JSON), and electronic data interchange (EDI).
• Semi-structured data sources have a self defining structure that makes them easier
to consume and analyze than unstructured data sources, but require more work
than true, structured data sources
12

3. Model
• A model, or data model, refers to the way in which one or more data sources are
organized in order to support analysis and visualization.
• Models are built by transforming and cleansing data, helping to define the types
of data within those sources, as well as the definition of data categories for
specific data types.
13

4. Organizing
• Models can be extremely simple, such as a single table with columns and rows.
• However, business intelligence almost always involves multiple tables of data, and most often
involves multiple tables of data coming from multiple sources. Thus, the model becomes more
complex as the various sources and tables of data must be combined into a cohesive whole.
• This is done by defining how each of the disparate sources of data relates to one another. As an
example, let's say you have one data source that represents a customer's name, contact
information, and perhaps size in revenue and/or the number of employees.
• This information might come from an organization's customer relationship management
(CRM) system.
• The second source of data might be order information, which includes the customer's name, units
purchased, and the price that was paid. This second source of data comes from the organization's
enterprise resource planning (ERP) system.
• These two sources of data can be related to one another based on the name of the customer.
14

Transforming and Cleansing
• When building a data model, it is often necessary to clean and transform the
source data. Data is never clean; it must always be edited for bad data to be
removed or resolved.
• For example, when dealing with customer data from a CRM system, it is not
uncommon to have the same customer entered the system with multiple
spellings. In addition, data may have errors, missing data, inconsistent
formatting, or even have something as seemingly simple as trailing spaces. All
these types of situations can cause problems when performing business
intelligence analysis. Luckily, business intelligence tools such as Power BI
provide mechanisms for cleansing and reshaping the data to support analysis.
15

Transforming and Cleansing
• Transforming and cleansing technologies are often referred to as
extract, transform, load (ETL) tools and include products such as
Microsoft's SQL Server Integration Services (SSIS)
16

Defining and Categorizing
• Data models also formally define the types of data within each table.
• Data types generally include formats such as text, decimal number, whole number,
percentage, date, time, date and time, duration, true/false, and binary.
• The definition of these data types is important as this defines what kind of analysis can
be performed on the data.
For example, it does not make sense to create a sum or average of text data types;
instead, you would use operations such as count, first, or last.
17

4. Analysis
• Once a domain has been selected and data sources have been combined into a model, the
next step is to perform an analysis of the data.
• This is a key process within business intelligence as this is when you attempt to answer
questions that are relevant to the business using internal and external data.
• Simply having data about sales is not immediately useful to a business. In order to predict
future sales revenue, it is important that such data is collected and analyzed in some form.
• For example, analysis can determine the average sale for a product, the frequency of
purchases, and which customers purchase more frequently than others.
• This is the information that allows for better decision-making by an organization.
18

Cont..
• Data analysis can take many forms, such as grouping data, creating simple aggregations
such as sums, counts, and averages, as well as creating more complex calculations,
identifying trends, correlations, and forecasting.
• Many times, organizations have, or wish to have, key performance indicators (KPIs),
that are tracked by the business in order to help determine the organization’s health or
performance.
19

Cont..
• KPIs might include such things as employee retention rate, net promoter score,
new customer acquisitions per month, gross margin, and Earnings Before
Interest, Tax, Depreciation, and Amortization (EBITDA).
• Such KPIs generally require that the data be aggregated, calculations performed
on it, or both.
• These aggregations and calculations are called metrics or measures and are used
to identify trends or patterns that can inform business decision-making.
• In some cases, advanced analysis tools such as programming languages, machine
learning and artificial intelligence, data mining, streaming analytics, and
unstructured analytics are necessary in order to gain the proper insights.
20

5. Visualization
• The final key concept in business intelligence is visualization, or the actual presentation
of the analysis being performed.
• Humans are visually oriented and thus must be able to see the results of the analysis
being performed in the form of charts, reports, and dashboards.
• This may take the form of tables, matrices, pie charts, bar graphs, and other visual
displays that help provide context and meaning to the analysis.
• In the same way that a picture is worth a thousand words, visualizations allow
thousands, millions, or even trillions of individual data points to be presented in a concise
manner that is easily consumed and understandable.
• Visualization allows the analyst or report author to let the data tell a story. This story
answers the questions that are originally posed by the business and thus delivers the
insights that allow organizations to make better decisions.
21

Cont..
• Individual charts or visualizations typically display aggregations, KPIs, and/or other calculations of
underlying data that's been summarized by some form of grouping.
• These charts are designed to present a specific facet or metric of the data within a specific context.
• For example, one chart may display the number of visitors to a website by country while another chart
may display the number of website page visits per browser.
• Business intelligence tools allow multiple individual tables and charts to be combined on a single page or
report.
• Modern business intelligence tools such as Power BI support interactivity between individual
visualizations in order to further aid in the discovery and analysis process.
22

Power BI Terms
• Power Query is the data connectivity and data preparation technology that enables end
users to seamlessly import and reshape data from within a wide range of Microsoft
products, including Excel, Power BI and more.
• Data Analysis Expressions (DAX) is a programming language that consists of a
collection of functions, operators, and constants that can be used to write formulas, or
expressions, that return calculated values. Like how the Excel Functions or SSAS MDX
help you create new information from data already in your model, DAX is the Power BI
equivalent.
• In addition to simply sharing Power BI files (.pbix), which are the files that are created by
the Power BI Desktop program, Microsoft provides a free method of using Power BI
Service so that you can publish and share reports via a featured called Publish to web.
23

Getting data
• The first step in working with Power BI Desktop is to connect to data
sources.
• There are currently over 100 connectors that can be used to connect to
different data sources, including many general-purpose connectors such
as the web connector, OData feed, and JSON connector, which enable
connections to hundreds, if not thousands, of different sources of data.
24

Creating a Data Model
• Connecting to a data source creates a query within a tool called the Query
Editor.
• The Query Editor utilizes Power Query technology and provides a
graphical interface that allows the user to create a series of steps that are
recorded and then replayed every time data is loaded or refreshed from the
source. This means your data always ends up in your desired form.
• Queries load data into structured data tables within Power BI Desktop.
Once these tables of data have been loaded, a data model can be constructed
by relating these tables to one another.
25

Analyzing data
• The data that's used within the model does not have to come solely from
data sources.
• Power BI uses a technology called DAX, which allows users to create
calculations in the form of calculated columns, measures, and even entire
tables.
• This allows analysts to create simple measures such as gross margins and
percentage of totals, as well as more complex measures such as year over
year revenue.
26

Creating and Publishing reports
• Once a data model has been built and analyzed, visuals can be created on report
pages by dragging and dropping fields onto the report canvas.
• Visuals are graphical representations of the data within the model. There are 32
default visuals within Power BI Desktop, but hundreds more can be imported
from Microsoft AppSource and used within Power BI Desktop.
• Multiple visuals can be combined on one or more pages to create a report. These
visuals and pages can interact with one another as users click within the report.
• Once the reports have been finalized, the reports can be published to the Power
BI Service. These reports can then be shared with other users.
27

Touring the Desktop
• The following screenshot depicts the nine major interfaces of Power BI.
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The Software
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Cont..
• Quick Access Toolbar: This toolbar can be displayed above or below the ribbon and commands within the
ribbon can be added to this toolbar by right-clicking an icon in the ribbon and selecting Add to Quick
Access Toolbar.
• To the right of the Quick Access Toolbar is the name of the currently opened file and next to that is the
name of the application. For Power BI Desktop, this will be Power BI Desktop.
Title Bar: Where you can find the name of the file that you are using now in the software.
Formula Bar: Is the place to write down any functions or codes related to the targeted procedure.
Ribbon: Are like the ribbons that are used in the Microsoft Office Software, which includes mainly all the
desired functions and selections that allows the user to process the included data.
Views Bar: Includes three main areas which are the (Report, Data, Model).
Panes: Contains the different types of visuals for the edited data.
Footer: Contains the number of pages + No. of rows when data is included.
Page Tabs: Contains number of pages that you're working on.
Canvas: Is the place where the data is viewed and considered the working area.
30

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