BRAIN STORMING SESSIONS

During brain storm sessions, the identified lead may want to initiate the necessary tools for each participant to bring along including a piece of paper and pencils to take down notes.

  • Usually, these sessions should last an hour. The first 30 minutes will be open-ended questions and answers from the group.
  • The next 15 minutes should be elimination of similar suggested questions or identified attributes by multiple participants.
  • The last 12 minutes is to finalize the necessary needed questions and newly identified attributes to include into the existing systems.
  • The last 3 minutes is use for addressing questions if not mentioned during the entire session. These questions if any, will be documented and discuss during the consecutive sequential schedule meetings for this project.

Brain storming is one of the tools recommended by 6-Sigma on how to reduce cluttering in order to accelerate with the documentation of the necessary agreeable requirements after presentations to Stakeholders of this project.

1.1 Causes and Solutions

Brainstorming is a method for generating ideas in a group situation. Teams and departments should use brainstorming when:

  • Determining possible causes and / or solutions to problems.
  • Planning out the steps of a project.
  • Deciding which problem (or opportunity) to work on.
1.1.1 Running a Brainstorming Session

Provide a time limit for the session. Generally 30 minutes is sufficient. Identify one or more recorders to take down notes during the session. The recorders’ job is to write all ideas down (where everyone can see them, such as on a flipchart or overhead transparency) as they are voiced.

Choose either:

  • a freewheeling format (share ideas all at once, list all ideas as they are shouted out) or
  • a round robin format (everyone takes a turn offering an idea, anyone can pass on a turn, continue until there are no more ideas, all ideas are listed as they are offered).
  • Filling and sharing feedback / questionnaire forms.

1.2 Establish the ground rules

1.2.1 Ground Rules
  1. Don’t edit what is said and remember not to criticize ideas.
  2. Go for quantity of ideas at this point; narrow down the list later.
  3. Encourage wild or exaggerated ideas (creativity is the key).
  4. Build on the ideas of others (e.g. one member might say something that “sparks” another member’s idea.

End the session when – everyone has had a chance to participate, no more ideas are being offered, you have made a “last call” for ideas, remember to thank all the participants.

1.2.2 Next steps
  • Prioritize your ideas to help you decide where to start.
  • Sort large amounts of information according to common themes (use post-its, one idea on each, all generated by individuals in response to a goal statement, within a limited time frame, and sorted into groupings).
  • Remember brainstormed ideas may be based on opinion and data may need to be gathered to support or prove ideas.
  • Some of these issues discussed may be miniscule very microscopic  that the group would be able to finalize their analysis for those particular issues and ready to meet next to go through the expected results by comparing display values to what is produce by the application systems.
  • The design efforts in enhancing existing related reports, modifies the process to generate such requested reports. 

1.3 Identifying the Need of Applying Changes to COMMERCIAL ACCOUNT SYSTEMS

During brain storming sessions, it’s always prudent and necessary to capture suggestions and feedbacks which might be related to existing or identified problems from our users’ population including internal customers to these applications.

I recently ran across a tenacity, persistence, intransigence and inflexible condition which existed within COMMERCIAL ACCOUNT SYSTEMS that needed to be address immediately to reduce and avoid misunderstanding, misleading, miscommunication within the entire BANKING SYSTEMS and other partnership or business-to-business INVENTORY SYSTEMS where the naming of tables schemas within their RDBMS created abnormalities how the data, transaction threads were processed.

At first the problem appeared to be easy and solvable but the more I thought about the definition of the tables and operating sources where these data would be coming from, I realized it was going to involve multiple parties and experts from different subject areas of BANKING ACCOUNTING SYSTEMS.

Stepping through the process, the necessary requirements and data collections were the initial phase to identify where to start and also, how we could easily provide and satisfy our customer’s needs using JIT while the rest of the project is handled in a full lifecycle methodology when faced with issues like this.

In this situation a CHANGE MANAGEMENT (Maintenance Request) record and subsequence records were necessary, and meetings held with top management to scope, plan, dissect and forecast the project.

Within the IMS group, the following tables were identified for table names and definitions changes to reflect the actual nature of the data.

COMMERCIAL ACCOUNT SYSTEM consist of profitable, invested, credited, debited, assets and liabilities records including book entries and ledger transaction records.

The table names are misleading and have been interruptive, disturbing and a nuisance to business users who have been given the exposure of EIS nomenclature, categorization and architectural infrastructure without the complete understanding of why, where, and when the transactions actually occurred.

The reasoning behind this change is to eliminate misunderstanding both in their users’ communities and IMS groups. The architecture team should be aware of these changes and Management Initiatives should include, involve the planning, scoping, forecasting and implementation schedules of the project.

This project would need to use the normal full life cycle through implementation; and all necessary metadata changes should be reflective and maintained within the DATA DICTIONARY REPOSITORY accordingly following the DATA STANDARDS requirements.

Majority of the effort would occur in the Design phase of the project, coordinated by a dedicated Data Architect assigned to the project.

The COMMERCIAL_LOAN_TH tables needs change along with the ACCT_TYPE_CD value reflecting the nature of the data stored within these tables in the COMMERCIAL ACCOUNT SYSTEMS.

In the interim, JIT adhocs should be applied to apprehend the necessary changes.

OLDNEW
COMMERCIAL_LOANCOMMERCIAL
COMMERCIAL_LOAN_THCOMMERCIAL_TH
COMMERCIAL_LOAN_STMT_THCOMMERCIAL_STMT_TH
ACCT_TYPE_CD (CLA)ACCT_TYPE_CD (CMA)

In short, the word LOAN should be removed and drop out of the identified table names.

No record length data should be affected with this MR(Maintenance Request). But ALL of the systems where these table names and ACCT_TYPE_CD are stated as those under the OLD column should be changed as recommended to reflect the names and values under the NEW column.

Initial research work is needed to take place to identify ALL the affected tables while team meetings are scheduled, organized and led by team leaders to make sure all necessary requirements are met, identified and captured by developers performing these search. The preliminary data investigation should include and involve database analysts and Database Administrators to go through all the connectivity systems and platforms, making sure all necessary tables, databases, extracts, ETLs are listed on the requirement documentation.

Possible extension of this project may result in cases where certain commercial threads (transaction records) as listed on the table definitions are not past into the COMMERCIAL ACCOUNT SYSTEMS. Extracts should be sources from those operating systems into the COMMERCIAL ACCOUNT SYSTEMS using the new ACCT_TYPE_CD as CMA.    

Figure 1.3.1 Related IO of Commercial Banking Systems

Definitions for Aggregating Online Transactions as related to Commercial Account Systems (CMA).

By collecting input from online transactions systems such as SUNGUARD, FINTECH, COINBASE and other PAYMENT Systems.

Summarizing transaction records:

Amount values should reflect POSITIVE or NEGATIVE as reported on the INPUT datafiles. These may generate NEW COLUMNS and an EXPANSION of RECORD LENGTH resulting to CHANGE on TABLESPACE sizing and STORAGE capabilities.

Both Design and Production teams were notified, alert and scheduled recurring meetings to mitigate any such changes coming down the pipe (Waterfall methodology) during R&D (Research and Development). 

Apply scheduled RUNS on summarized records to Extract – Transform – Load and ADD to CUSTOMER ACCOUNTS depending on the ACCT_TYPE_CD. ETL input transaction records from SUNGUARD, FINTECH, COINBASE depending on the CUSTOMER. Input data may be received DAILY; WEEKLY; BIWEEKLY; MONTHLY.

Both AMOUNT and CHARGES should be summarized before scheduled RUNS.

Deduct CHARGES from summarize AMOUNTS. SEND INPUT files INTO ACH DEPARTMENT depending on the ACCT_TYPE_CD. Records are then stored on the following tables – PI, DDA, TDA, CMA, INV based on the specific account type.

Keep in mind that as we walked through the different phases of the project, we all realized, it was very close to home as had originally imagined. Another condition to the puzzle was, with ANY BALANCE to the client, customer, entity or institution record, a MONTHLY STATEMENT should be sent to the rightful OWNERS.

These processes would help to limit the number of accounts per USER. Again, ACCOUNT NUMBER REUSE is to allow previous OWNERS to have their same ACCOUNT NUMBERS.

1.3.1 Distribution of Securities

 As we drill down to the dying issue, I discovered some of their systems did not have either these columns SECURITY_TYPE_CD, SECURITY_CODE_NM nor the following code and name values for the above corresponding columns.

These classification and categorization of investment products were very important to derive with the mapping of these accounts as depicted in figure 1.3.1.

 SECURITY_TYPE_CDSECURITY_CODE_NM  
 ACHAutomated Clearing House
 ICSInvestment Credit Securities
 WMDWinning Money Distribution
 SPGStructured Product Group
 CCOCollateralized Credit Obligations
 IPNIndex Profitability Notes
 PPPProfitability Pass-thru Payments
 MBSOMonetary Bypass Systems to Owners
 CDOCredit Deductible Obligations

Table 1.3.1.1 Security Type Code valid values.

1.3.2 Decision Making During Corporation Share Split

Most corporations rely, depend and thrust subtle resolutions to their corporate financial statements without actually taken the burden to reflect on certain factors such as dividends when their company stocks (shares) splits.

Splits do not occur in most cases on regulated timing / scheduled but base on their corporation, company or incorporation’s performance / merger procedures such as convergence, absorption, mergence or combination of multiple corporations to restructure overheads by the owner(s), reducing the number of officers hence busting the overall performance by identifying newly improve methodology to accumulate profitability per investors / owners of either or companies or shares.

Resolutions generally indicated by Financial Adaptive Finite Quantitative (FAFQ) platforms may not indicate the In-House Financial Statements for such corporations.

  1. Many as often, the most debated, disputed column value amongst Economists, savvy IT specialists with an incline to actuate / accrued [of sums of money or benefits) be received by someone in regular or increasing amounts over time] logics or Financial Mathematicians have always been the DEBT amount.

The DEBT amount on total number of available shares multiplied by the price per share (P/S) should NOT be regarded when considering performance of any corporation. Neither should the company’s bottom line performance be evaluated by considering the outstanding shares.

When considering the total number of shares including outstanding shares, the figure associated with or display on the “DEBT” column should be regarded as positive. In this case, trailing issues regarding the distribution of profitability’s should not be apparent or questionable to its holders.

During corporations, companies, or incorporated stock (shares) splits, the dividend is usually the inverse (vice versa) of the split ratio. In case the value is very high compare to the value per share, an additional amount of share could be distributed per holders / employees.

Let’s go through a simple example for instance GOOGLE, Inc with a share price of $2260.00 and had to split 20:1; the resulting table is a typical example on how the dividends would be reflected and further distributed.

EQUITYCURRENT SHARE PRICESPLIT RATIORESULTING SHARE PRICE
GOOG$2260.0020:1$113.00
FOR DIVIDEND
 CURRENT DIVIDEND VALUERATIO        (VICE VERSA)RESULTING DIVIDEND VALUE
IF$2.0020:1$0.10

The shareability, distribution and usability of the resulting dividend value would eventually drop down the final reporting value. At that instance, the board of directors is deemed to decide what value to agree on as dividend which should not be ≤ $2.00/20 at a minimum.

It’s always interesting when such minimal arithmetic consumes our precious time, while socializing over the latest pastries, traveling from unnecessary distance just to decide when to publicize such crucial information as pertinent to stock holders as well as employees.                    

Using Google Data Studio for Data Visualization and Exploration

Data Studio is use for data visualization and as a reporting tool. It was created by Google in 2016. And it has gained a lot of traction from Data Scientists, Analysts, and Sales and Marketing Experts.

Data Studio is completely free. There’s no paid version of it. You can use it as an alternative to paid reporting tools such as Tableau and Power BI.

Data Studio is cloud-based:

It’s accessible through any browser and an internet connection. The reports you create are saved automatically into Google Data Studio framework, so they’re available anytime and anywhere. No worries about losing the files.

There are many pre-built templates in Data Studio, allowing you to create beautiful dashboards full of charts quickly and easily. It’s very easy to share reports and dashboards with your internal / external teams if they have a Google account. It enables collaboration within business groups.

With Data Studio, you can connect, analyze, and present data from different sources. You don’t even need to be tech-savvy or know programming languages to get started with Data Studio.

Google Data Studio: Data sources and connectors:

Every time you want to create a report, first, you’ll need to create a data source. It’s important to note that data sources are not your original data. To clarify and avoid confusion, see the explanation below:

  • The original data, such as data in a Google spreadsheet, MySQL database, LinkedIn, YouTube, or data stored in other platforms and services, is called a dataset.
  • To link a report to the dataset, you need a data connector to create a data source.
  • The data source maintains the information of the connection credential. And it keeps track of all the fields that are part of that connection.  
  • You can have multiple data sources connected to a dataset, and this may come in handy when collaborating with different team members. For example, you may want to share data sources with different connection capabilities for different team members.

When Data Studio was first released, there were only six Google-based data sources you could connect to. But a lot has changed since then! 

As of this writing, there are 400+ connectors to access your data from 800+ datasets. Besides Google Connectors, there are also Partner Connectors (third-party connectors). 

In the example below we’ll go through US Office Equipment Sample Dataset to visualize different charts representing the data.

  • Open Google Data Studio from your browser by using this link.
  • Click Create button on the left
  • Open a connection to the data source of interest. In our case, we’ll use this link to the CSV file Dataset.

File Upload / Locate File:

  • Upload CSV file
  • On the next screen, you will be presented with a data file schema for the uploaded CSV file.
  • The data types can be changed on existing fields within the data file schema and new calculated fields added if needed.

CSV files are called Unmapped data because their contents are unknown in advance.

Analyze and Visualize the Data:

  • Add the data source and you will end up in the report canvas.
  • Use the appropriate charts from the Add Charts tool bar menu above to select the desire charts as shown below to create data visualization reports.

Quick Steps to Set Up Data Visualization on Google Data Studio:

  1. Open Data Studio.
  2. Familiarize yourself with the dashboard.
  3. Connect your first data source.
  4. Create your first report.
  5. Add some charts.
  6. Customize the formatting and add a title and captions.
  7. Share the report.

Conclusion:

Congratulations! We just went through how to create a Business Intelligence BI dashboard using Google Data Studio for visualizing and exploring a sample Office Equipment dataset.

Data Studio allows you to create beautiful dashboards full of charts quickly and easily. It’s very easy to use for sharing reports and dashboards with your internal/external teams if they have a Google account. It enables collaboration within business groups.

With Data Studio, you can connect, analyze, and present data from different sources. You don’t even need to be tech-savvy or know programming languages to get started with Data Studio.

Differences between Power BI, Tableau and Python Dash

Data visualization has gained massive popularity in recent years owing to the demand for data. In a business setup, these business intelligence tools can help in analyzing all the data and monitor performance to enhance growth for the firm, and productivity for the employees. With the world switching to digital means all together in the year that went by, data is now considered fuel for every small, medium, or big firm.

In such a scenario, what sounds better- a spreadsheet that mentions the date, time, sales, and profit OR a colorful, descriptive bar chart interactively explaining all the details? Our vote goes to the latter.

What is a data visualization tool?

An essential part of any business strategy, data visualization is the process of collecting data and transforming it into a meaningful visualization to support decision-making. These visualizations could be in the form of bar charts, maps, or anything that is visually appealing and interactive. They convey the information to the viewer by simply looking at them, whereas normally one needs to read spreadsheets or text reports to understand the data.

Talking of the best data visualization tools used by analysts in various industries according to their specifications and applications, they comprise Power BI, Tableau and Python Dash. All these software programs help businesses make decisions questions faster.

Power BI is a Data Visualization and Business Intelligence tool provided by Microsoft. It can collect data from different data sources like Excel spreadsheets, on-premise database, cloud database and convert them into meaningful reports and dashboards. Its features such as creating quick insights, Q&A, Embedded Report, and Self Service BI made it top among all BI tools. It is also robust and always ready for extensive modeling and real-time analytics, as well as custom visual development.

Tableau offers business analysts to take business decisions by its feature, data visualization available to all business users of any background. It can establish a connection with any data source (Excel, local/on-premise database, cloud database).

Tableau is the fastest growing Data Visualization Tool among all visualization tools. Its visualizations are created as worksheets and dashboards. The beauty of tableau is that it does not require any technical or programming knowledge to create or develop reports and dashboards.

Python Dash

Dash is a python framework created by plotly for creating interactive web applications. With Dash, you don’t have to learn HTML, CSS and Javascript in order to create interactive dashboards, you only need python. Dash is open source and the application build using this framework are viewed on the web browser.

Dash is Downloaded 600,000 times per month, it’s the original low-code framework for rapidly building data apps in Python, R, Julia and F#(experimental).

It’s written on top of Plotly.js and React.js. Dash is ideal for building and deploying data apps with customized user interfaces. It’s particularly suited for anyone who works with data.

Through a couple of simple patterns, Dash abstracts away all of the technologies and protocols that are required to build a full-stack web app with interactive data visualization.

Dash is simple enough that you can bind a user interface to your code in less than 10 minutes.

Dash apps are rendered in the web browser. You can deploy the apps to VMs or kubermetes clusters and then share them through URLs. Since Dash apps are viewed in the web browser, Dash is inherently cross-platform and mobile ready.

There is a lot behind the framework. To learn more about how it’s built and what motivated Dash, read announcement letter or Dash is React for Python post.

Dash is an open source library released under the permissive MIT license. Plotly develops Dash and also offers a platform for writing and deploying Dash apps in an enterprise environment.

Python Dash is mostly suited for the quick and easy representation of big data which helps in analyzing and resolving issues. Power BI, on the other hand, has its data models focused on ingestion and building relatively complex models. Python is the best when it comes to handling streaming data.

Power BI vs. Tableau vs, Python Dash:

Power BITableauPython Dash
It is provided by MicrosoftIt is provided by TableauIt is a Python library, provided by the Python Software Foundation
It is available at a moderate priceIt is expensive than Power BIIt is an open-source programming language that is freely available for everyone to use.
Need a business/private email to open an accountNeed a business/private email to open an accountAny email address is acceptable
Uses DAX for Measures and Calculated columnsUses MDX for Measures and DimensionsUses dynamic, interpretive script programming language
Connect limited Data Sources but increases it Data Source connections in Monthly updatesIt can connect to numerous Data SourcesPython has an ecosystem of modules and tools to collect data from multiple sources.
Can handle large Datasets using Premium capacityCan handle large DatasetsCan handle large Datasets
It provides Account base subscriptionIt provides Key base subscriptionNo subscription necessary
Embedding report is easyEmbedding report is a Real time challengeDash is simple enough that you can bind a user interface to your code in less than 10 minutes
It is integrated with Microsoft Azure, which helps in analyzing the data and understanding the patterns of the productTableau has built-in machine learning capabilities which makes it suitable for doing ML operations on datasetsDash is integrated with Python which offers multiple libraries in graphics that are packed with different features. Python is preferred for data analysis of the highest levels
It supports R and Python language-based visualizationsIt provides full integrated support for R and PythonDash is ideal for building and deploying data apps with customized user interfaces.

Which one to choose, Power BI, Tableau or Python Dash?

Data Analytics field has been changed over time from traditional bi practice, embedded bi and collaborative bi. Initially, data analytics was led by companies like IBM, Oracle, SAP but now this is not the situation. Now, this led by companies like Microsoft, Tableau and Python because of their features like Embedded BI Collaborative BI, Data Blending, Data Binding and Multi Data Source Connection.

Power BI, Tableau and Python Dash have their own Pros and Cons. The right product can be chosen based on touchstones & priority.

TouchstonesPower BITableauPython Dash
DescriptionA cloud-based business intelligence platform which offers an overview of critical dataA collection of intuitive business intelligence tools used for data discoveryA python framework created by Plotly for creating interactive web applications. It is best when it comes to handling Streaming Data
VisualizationProvides various visualizationsProvides a larger set of visualizations than Power BIProvides numerous set of visualizations
OS SupportOnly WindowsWindows and Macintosh OSMac OS, Windows, Linux, AWS, and others
Graphical featuresRegular charts, graphs, and mapsAny category of charts, bars, and graphsDash is ideal for building and deploying data apps with customized user interfaces. Any category of charts, bars and graphs
CostCheaperCostlyFree
OrganizationSuitable for Small, Medium & Large type of OrganizationSuitable for Medium & Large type of OrganizationSuitable for Small, Medium & Large type of Organization

Data Analysis Expressions (DAX) is a programming language that is used throughout Microsoft Power BI for creating calculated columns, measures, and custom tables. It is a collection of functions, operators, and constants that can be used in a formula, or expression, to calculate and return one or more values.

In Multidimensional Expressions (MDX), a measure is a named DAX expression that is resolved by calculating the expression to return a value in a Tabular Model. This innocuous definition covers an incredible amount of ground.

 


Differences between Power BI and Tableau

Power BI is a Data Visualization and Business Intelligence tool provided by Microsoft. It can collect data from different data sources like Excel spreadsheets, on-premise database, cloud database and convert them into meaningful reports and dashboards. Its features such as creating quick insights, Q&A, Embedded Report, and Self Service BI made it top among all BI tools. It is also robust and always ready for extensive modeling and real-time analytics, as well as custom visual development.

Tableau offers business analysts to take business decisions by its feature, data visualization available to all business users of any background. It can establish a connection with any data source (Excel, local/on-premise database, cloud database).

Tableau is the fastest growing Data Visualization Tool among all visualization tools. Its visualizations are created as worksheets and dashboards. The beauty of tableau is that it does not require any technical or programming knowledge to create or develop reports and dashboards.

Power BI vs. Tableau:

Power BITableau
It is provided by MicrosoftIt is provided by Tableau
It is available at a moderate priceIt is expensive than Power BI
Need a business/private email to open an accountNeed a business/private email to open an account
Uses DAX for Measures and Calculated columnsUses MDX for Measures and Dimensions
Connect limited Data Sources but increases it Data Source connections in Monthly updatesIt can connect to numerous Data Sources
Can handle large Datasets using Premium capacityCan handle large Datasets
It provides Account base subscriptionIt provides Key base subscription
Embedding report is easyEmbedding report is a Real time challenge
It is integrated with Microsoft Azure, which helps in analyzing the data and understanding the patterns of the productTableau has built-in machine learning capabilities which makes it suitable for doing ML operations on datasets
It supports R and Python language-based visualizationsIt provides full integrated support for R and Python

Which one to choose, Power BI or Tableau?

Data Analytics field has been changed over time from traditional bi practice embedded bi and collaborative bi. Initially, data analytics led by companies like IBM, Oracle, SAP but now this is not a situation. Now, this led by companies like Microsoft & Tableau because of their features like Embedded BI Collaborative BI, Data Blending, Multi Data Source Connection.

Both Power BI and Tableau have their own Pros and Cons. The right product can be chosen based on touchstones & priority.

TouchstonesPower BITableau
DescriptionA cloud-based business intelligence platform which offers an overview of critical dataA collection of intuitive business intelligence tools used for data discovery
VisualizationProvides various visualizationsProvides a larger set of visualizations than Power BI
OS SupportOnly WindowsWindows and Macintosh OS
Graphical featuresRegular charts, graphs, and mapsAny category of charts, bars, and graphs
CostCheaperCostly
OrganizationSuitable for Small, Medium & Large type of OrganizationSuitable for Medium & Large type of Organization

What is Power BI? 

Power BI is a Data Visualization & Business Intelligence tools that offer us to connect to single or multiple data sources and convert that connected raw data into impressive visual and share insights across an organization. It also offers us to embed the report into our application or website.

Product Suite of Power BI:

  • Power BI Desktop 
    • Free to download and install.
    • Connect and access various of on-prem and cloud sources like Excel, CSV/Text files, Azure, SharePoint, Dynamics CRM, etc.
    • Prepare by mashing up the data and create a data model using power query which uses M Query Language
    • After loading data to Power BI Desktop can establish the relationship between tables.
    • Create calculated measures, columns, and tables using Data Analysis eXpression(DAX).
    • Drag & drop interactive visuals on to pages using calculated measures and columns.
    • Publish to Power BI Web Service.
  • Power BI Service
    • This is one of the ways to embed the reports within the Website under an organization.
    • In the Power BI service forum, there are a collection of sections like Workspace, Dashboards, Reports, and Datasets.
    • Can create our own workspace as My-Workspace which helps to maintain personal work in Power BI Service.
    • Can pin number of Reports to a Dashboard to get together a number of meaningful Datasets for clear insight.
    • In this we can interact with our data with the help of Q&A {natural language query.}
  • Power BI Report Server
    • This is one of the products to allow businesses to host Power BI reports on an on-premise report server.
    • Can use the server to host paginated reports, KPI’s, Mobile reports and Excel workbook.
    • Shared data sets and shared data sources are in their own folders, to use as building blocks for the reports.

  • Power BI Mobile
    • Over Power BI provides mobile app services for IOS, Android and Windows 10 mobile devices.
    • In the mobile app, you can connect to and interact with your cloud and on-premise data.
    • It is very convenient to manage dashboard and reports on the go with your mobile app to stay connected and being on the same page with the organization.
  • On-Premise Gateway
    • This is a bridge to connect your on-premise data to online services like Power BI, Microsoft flow, Logic App’s and Power App’s services, we can use a single gateway with different services at the same time.

e.g.: – If you are using Power BI as well as Power App’s, a single gateway can be used for both which is dependent on the account you signed with it.

  • The on-premises data gateway implements data compression and transport encryption in all modes.
  • On-premises data gateway is supported only on 64-bit Windows operating system.
  • Multiple users can be share and reuse a gateway in this mode.
  • For Power BI, this includes support for schedule refresh and Direct Query.

What is Tableau?

Tableau is a Business Intelligence & Data Visualization Tool that used to analyze our data visually. Users can create and share interactive reports & dashboards using it. It offers Data Blending to users to Connect multiple data sources.

Product Suite of Tableau:

  • Tableau Server
    • Tableau Server is an enterprise-wide visuals analytics platform for creating interactive dashboards.
    • It is essentially an online hosting platform to hold your entire tableau Workbooks, Data sources and more.
    • Being the product of tableau, you can use the functionality of tableau without needing to always be downloading and opening workbooks with tableau desktop.
    • Can give security level permission to different work in an organization to determine who can access and interact with what.
    • As a tableau server user, you will be able to access UpToDate content and gain quick insight without relying on static distributed content.   
  • Tableau Desktop
    • This is a downloadable on-premise application for Computers and it is used for developing visualization in the form of sheets, Dashboards, and Stories.
    • There are some useful functionalities of tableau desktop are: Data transformation, Creating Data Sources, Creating Extracts and Publishing Visualizations on tableau server.
    • Tableau desktop produces files with extensions twb and twbx.
    • It is a licensed product but comes with two weeks of the trial period.
    • Starting from creating reports and charts to combining them to form a dashboard, all this work is done in tableau desktop.
  • Tableau Prep
    • Tableau Prep is a personal data preparation tool that empowers the user with the ability to cleanse aggregate, merge or otherwise prepare their data for analysis in tableau.
    • Tableau Prep has a simple and clean user interface that looks and feels like a final form of tableau desktop data sources screen.
    • In Tableau Prep the data is stored in flow pane manner with has universal unique identifier [UUID] which can store big data sets in a secure way.
  • Tableau Reader
    • Tableau Reader is a free desktop application that you can use to open with data visualizations built in tableau desktop.
    • It required reading and interacting with tableau packaged workbooks.
    • Tableau reader has the ability to retain interaction with visualization created in tableau desktop but will not allow connections to data which can be refreshed.
    • It only supports to read tableau data files; without the reader, you may need to share it publicly or convert the workbook into a pdf format.
  • Tableau Online
    • Tableau online is an analytics platform which is fully hosted in the cloud.
    • It can publish Dashboards and share your discoveries with anyone.
    • It has a facility to empower your organization to ask any question from any published data source using natural language.
    • It can connect to any cloud databases at any time anywhere and it can automatically refresh the data from Web-App like Google analytics and salesforce.
    • It empowers site admins to easily manage authentication and permissions for users, content, and data.
  • Tableau Public
    • This is a free service that lets anyone public interactive data visualizations to the web.
    • Visualizations are created in the accompanying app Tableau Desktop Public edition which required no programming skills.
    • It is for anyone who’s interested in understanding data and sharing those discoveries as a data visualization with the world.
    • It has some features highlights those are: – Heat Maps, Transparent sheets, Automatic Mobile Layouts, and Google Sheets.
    • As visualization are public so anyone can access the data and make a change by downloading the workbook so it is totally unsecured.
    • It has limitations of 15,000,000 rows of data per workbook.
    • It has 10GB of storage space for your workbook which is kind of limitation towards storage.
    • It supports Python with Tableau public called ‘Tabpy’, A new API that enables evaluation of python code within a tableau workbook  

Here is a link to Tableau

Strengths & Weakness of Power BI:

Strengths:

  • Free Power BI Desktop application for authors to develop reports
  • Uses DAX expressions for data calculations
  • Free Training Modules available for users
  • Composite Model (Direct Query, Dual, and Import) to connect dispersed multiple data sources and create a model
  • Multiple visuals in a single page
  • Also has Drill Down-Drill Up in visuals, Drill through pages, Toggle page or visual using Bookmarks, selection pane & buttons
  • Ability to connect multiple data sources
  • It is affordable desktop – free and pro (Power BI Service to share and collaborate with other users in the organization) – $9.99
  • Can integrate with Cortana – Windows Personal Voice Assistant
  • Power BI has integrated with all Microsoft products (Azure, SharePoint, Office 365, Microsoft Dynamics, Power Apps, Microsoft Flow)
  • Dataflow in power BI Service to connect to Azure Data lake storage 2 and other online services.

Weakness:

  • It is difficult for users who do not have knowledge of Excel
  • Clients who use large data sets must opt for Premium Capacity services to avoid unpleasant experience with datasets and its users with performance and timeouts issues
  • Power BI service compatible with few database drivers
  • Power BI has got a large set of product options which make it complex to understand, which option is best suited for a Business.


Strengths & Weakness of Tableau:

Strengths:

  • Tableau provides much beautiful visualization for which it stood top in the market among all BI tools.
  • Quickly combine shape, & clean the data for analysis.
  • It provides Data Blending.
  • Capable of Drill Down-Drill Up in visuals, Drill through pages and filters.
  • It can handle a large amount of data.
  • Uses Scripting languages such as R & Python to avoid performance and for complex table calculations.
  • Can build reports, dashboards, and story using Tableau Desktop.

Weakness:

  • Tableau is expensive when compared to other tools.
  • Scheduling or Notification of reports & dashboards.
  • Importing Custom Visualization is a bit difficult.
  • Complexity in embedding report to other applications.
  • Tableau is suitable for Huge organization which can pay for licensing cost.


Benefits of Power BI

  • Microsoft is a Brand. I hope everyone remembers the school or college days, the time when we started learning and using Microsoft products as they are very simple to understand and user-friendly. Hence, obvious that our eyes and brain are trained on all Microsoft products.
  • One who has working experience excel can easily cope up with Power BI Desktop & Mobile in no time.
  • Pin the visual available in Excel to Power BI Service Using Excel Add-on.
  • Once can build swift & reliable reports by simply drag and drop both inbuilt/custom visuals and this URL for Best practices to make an optimum performance for the report.
  • Accessibility of Colossal Learning Assets available Guided Learning in this URL.
  • As Power BI belongs to Microsoft family, hence it has privileged with Single Sign-On (SSO) and also tight integration with Microsoft products like Dynamics 365, Office 365, SharePoint Online, Power Apps, MS Flow, Azure SQL Database, Azure SQL Data warehouse, Azure Analysis server database… etc.
  • Power Query Many options related to wrangling and clean the data bring it as a perfect data model.
  • Post publishing the data into Power BI web service can schedule refresh without manual intervention.
  • Power BI backed superpower of with Artificial intelligence and Machine learning
  • Microsoft introduced Power Platform (Power BI to Measure, Power Apps to Act & Microsoft Flow to automate) and you can find more details in this URL.
  • Forthcoming Road Map provided for Power BI by Microsoft available in this URL.
  • Power BI is integrated with both Python and R coding to use visualizations.
  • Power BI Desktop Free – $0.00 & Power BI Web Service (Azure) Pro – $9.99 Monthly

Disadvantages of Power BI

Power BI desktop is the best tool to analyze your data while you connect using Direct query (or) Live connections and might struggle handle huge if you import data into the application and at times it might get hung or simply crashes. However, in future monthly updates, Microsoft Product team will surely resolve this problem.

Benefits of Tableau

  • Tableau can connect various sources, can effortlessly handle huge data and is a very good tool for Data visualization and create dashboards by simply drag and drop.
  • Tableau supports Python and R languages for creating visuals.
  • Tableau has spent its term as Leader in Gartner’s report URL from 2012 – 2018 and now moved to second place.

Disadvantages of Tableau

Tableau Creator – $70.00 & Tableau Online – $35 Monthly

  • Tableau product team has not concentrated advanced technologies missed integrated with Artificial intelligence and Machine learning.
  • Once pushed the reports to tableau online, it does not support scheduled refresh and one must refresh the data manually.
  • Analyst must use only inbuilt visual available in Tableau and no option to import custom visuals from the portal. Instead, according to the requirement developers need to create custom visuals by themselves.
  • To create a data model, data preparation options in Tableau is limited. For advance data wrangling and cleaning one must take the help of other tools like Excel, Python, R, or Tableau Prep.
  • There is integration with other Microsoft products like Dynamics 365, Office 365, Power Apps, Microsoft Flow which uses Single Sign-On (SSO).

Power BI & Tableau are most happening BI tools among all tools in business intelligence because of their features and capabilities like Embedded BI, Data Blending, Multi Data Source connection like Cloud databases and on-premise databases. They make sharing of reports and dashboards for the users, easy. Business Analyst without even having to access these tools can access reports & dashboards and take critical business decisions.

These two tools stood top in the BI market because of the attractive visualizations available. Power BI offers a feature of import of custom visual and creation of custom visual which is its beauty. These facts have made these BI tools most happening BI tools in the market till the date.

According to Gartner Magic Quadrant for Analytics and Business Intelligence Platforms report, the 1st choice is Power BI and  2nd top choice is Tableau in BI Tool in the present market.

Data Analysis Expressions (DAX) is a programming language that is used throughout Microsoft Power BI for creating calculated columns, measures, and custom tables. It is a collection of functions, operators, and constants that can be used in a formula, or expression, to calculate and return one or more values.

In Multidimensional Expressions (MDX), a measure is a named DAX expression that is resolved by calculating the expression to return a value in a Tabular Model. This innocuous definition covers an incredible amount of ground.

How to Create a Python Dash Plotly Dashboard App

In this tutorial, I will discuss and go through a practical example on how to create a Python Dash Plotly App. I will create multiple charts for Data Visualization using Dynamic Callbacks which is also known as Pattern Matching Callbacks from Plotly.com. I will use data of The World Population to create the Dashboard App.

Introduction:

Pattern Matching Callbacks – Creating different charts for Data Visualization with callbacks. The users get much more power and control over the App. It gives the users much more flexibility to create callbacks for every set of inputs and outputs that doesn’t yet exist in the App.

MATCH will fire the callback when any of the component’s properties change. However, instead of passing all of the values into the callback, MATCH will pass just a single value into the callback. Instead of updating a single output, it will update the dynamic output that is “matched” with.

Install / Import Python necessary Libraries:

Let’s get started. Import the following libraries as listed below: I’m using Anaconda Jupyter Notebook, launch the CMD Prompt and install the following libraries if you don’t currently have them installed on your computer.

import dash     #pip install dash
from dash import dcc
from dash import html
from dash.dependencies import Input, Output, ALL, State, MATCH, ALLSMALLER
import plotly.express as px   #pip install plotly==5.2.2
import pandas as pd     #pip install pandas 
import numpy as np      #pip install numpy

Get Data:

We then read in the Panda data frame file. I have download the file to my computer but you can get it from my Github repository link.

df = pd.read_csv("Documents/Data Science/population.csv")     #https://github.com/Valnjee/datascience/blob/master/population.csv
print(df)
           country    year    population
0             China  2020.0  1.439324e+09
1             China  2019.0  1.433784e+09
2             China  2018.0  1.427648e+09
3             China  2017.0  1.421022e+09
4             China  2016.0  1.414049e+09
...             ...     ...           ...
4180  United States  1965.0  1.997337e+08
4181  United States  1960.0  1.867206e+08
4182  United States  1955.0  1.716853e+08
4183          India  1960.0  4.505477e+08
4184          India  1955.0  4.098806e+08

[4185 rows x 3 columns]

Cleanse Data:

Make sure to clean the data by dropping all the null values.

# dropping null values
df = df.dropna()
print(df.head(10))
  country    year    population
0   China  2020.0  1.439324e+09
1   China  2019.0  1.433784e+09
2   China  2018.0  1.427648e+09
3   China  2017.0  1.421022e+09
4   China  2016.0  1.414049e+09
5   China  2015.0  1.406848e+09
6   China  2010.0  1.368811e+09
7   China  2005.0  1.330776e+09
8   China  2000.0  1.290551e+09
9   China  1995.0  1.240921e+09

Form and App Layout Design:

Here we design the layout in HTML with the button. Every option will go into the children.

app = dash.Dash(__name__)
app.layout = html.Div([
    html.H1("The World Population Dashboard with Dynamic Callbacks", style={"textAlign":"center"}),
    html.Hr(),
    html.P("Add as many charts for Data Visualization:"),
    html.Div(children=[
        html.Button('Add Chart', id='add-chart', n_clicks=0),
    ]),
    html.Div(id='container', children=[])
])

First Callback:

The new child is append to the div_children. Every click triggers the callback, then you get another child to append to the div_children with everything created in it. The dcc.RadioItems have options of 4 charts.

Output – displays the chart.

State – saves the input of the children.

@app.callback(
    Output('container', 'children'),
    [Input('add-chart', 'n_clicks')],
    [State('container', 'children')]
)
def display_graphs(n_clicks, div_children):
    new_child = html.Div(
        style={'width': '45%', 'display': 'inline-block', 'outline': 'thin lightgrey solid', 'padding': 10},
        children=[
            dcc.Graph(
                id={
                    'type': 'dynamic-graph',
                    'index': n_clicks
                },
                figure={}
            ),
            dcc.RadioItems(
                id={
                    'type': 'dynamic-choice',
                    'index': n_clicks
                },
                options=[{'label': 'Bar Chart', 'value': 'bar'},
                         {'label': 'Line Chart', 'value': 'line'},
                         {'label': 'Scatter Chart', 'value': 'scatter'},
                         {'label': 'Pie Chart', 'value': 'pie'}],
                value='bar',
            ),
            dcc.Dropdown(
                id={
                    'type': 'dynamic-dpn-s',
                    'index': n_clicks
                },
                options=[{'label': s, 'value': s} for s in np.sort(df['country'].unique())],
                multi=True,
                value=["United States", "China"],
            ),
            dcc.Dropdown(
                id={
                    'type': 'dynamic-dpn-ctg',
                    'index': n_clicks
                },
                options=[{'label': c, 'value': c} for c in ['country']],
                value='country',
                clearable=False
            ),
            dcc.Dropdown(
                id={
                    'type': 'dynamic-dpn-num',
                    'index': n_clicks
                },
                options=[{'label': n, 'value': n} for n in ['population']],
                value='population',
                clearable=False
            )
            
        ]
    )
    div_children.append(new_child)
    return div_children
html.Br()

Second Callback and create Graphs:

  • The display_dropdowns callback returns two elements with the same index: a dropdown and a div.
  • The second callback uses the MATCH selector. With this selector, we’re asking Dash to:
    1. Fire the callback whenever the value property of any component with the id 'type': 'dynamic-dropdown' changes: Input({'type': 'dynamic-dropdown', 'index': MATCH}, 'value')
    2. Update the component with the id 'type': 'dynamic-output' and the index that matches the same index of the input: Output({'type': 'dynamic-output', 'index': MATCH}, 'children')
    3. Pass along the id of the dropdown into the callback: State({'type': 'dynamic-dropdown', 'index': MATCH}, 'id')
  • With the MATCH selector, only a single value is passed into the callback for each Input or State.
  • Notice how it’s important to design IDs dictionaries that “line up” the inputs with outputs. The MATCH contract is that Dash will update whichever output has the same dynamic ID as the id. In this case, the “dynamic ID” is the value of the index and we’ve designed our layout to return dropdowns & divs with identical values of index.
  • In some cases, it may be important to know which dynamic component changed. As above, you can access this by setting id as State in the callback.
  • You can also use dash.callback_context to access the inputs and state and to know which input changed. outputs_list is particularly useful with MATCH because it can tell you which dynamic component this particular invocation of the callback is responsible for updating. Here is what that data might look like with two dropdowns rendered on the page after we change the first dropdown.

The second callback renders the chart interactively. It uses a dictionary of ‘type and ‘index’. The dynamic part of the callback is the input – component_id and the component_property which is the value. Input will trigger when the value of the component_id is changed which refers to the dynamic-dpn-s. The index is going to be matched with the ‘index’ : MATCH = 1.

dff – Always make a copy of the data frame.

Sometimes the user wants to see the data in different charts. With the multiple charts and dropdown options, the user gets to select the different countries he/she is interested in.

@app.callback(
    Output({'type': 'dynamic-graph', 'index': MATCH}, 'figure'),
    [Input(component_id={'type': 'dynamic-dpn-s', 'index': MATCH}, component_property='value'),
     Input(component_id={'type': 'dynamic-dpn-ctg', 'index': MATCH}, component_property='value'),
     Input(component_id={'type': 'dynamic-dpn-num', 'index': MATCH}, component_property='value'),
     Input({'type': 'dynamic-choice', 'index': MATCH}, 'value')]
)
def update_graph(s_value, ctg_value, num_value, chart_choice):
    print(s_value)
    dff = df[df['country'].isin(s_value)]

    if chart_choice == 'bar':
        dff = dff.groupby([ctg_value], as_index=False)[['population']].sum()
        fig = px.bar(dff, x='country', y=num_value)
        return fig
    elif chart_choice == 'line':
        if len(s_value) == 0:
            return {}
        else:
            dff = dff.groupby([ctg_value, 'year'], as_index=False)[['population']].sum()
            fig = px.line(dff, x='year', y=num_value, color=ctg_value)
            return fig
    elif chart_choice == 'scatter':
        if len(s_value) == 1:
            return {}
        else:
            dff = dff.groupby([ctg_value, 'year'], as_index=False)[['population']].sum()
            fig = px.scatter(dff, x='year', y=num_value, color=ctg_value)
            return fig    
    elif chart_choice == 'pie':
        fig = px.pie(dff, names=ctg_value, values=num_value)
        return fig

Here is the link on how to setup a development server.

if __name__ == '__main__':
    app.run_server(debug=False)
Dash is running on http://127.0.0.1:8050/

 * Serving Flask app "__main__" (lazy loading)
 * Environment: production
   WARNING: This is a development server. Do not use it in a production deployment.
   Use a production WSGI server instead.
 * Debug mode: off

Conclusion:


CONGRATULATIONS! You have just learnt how to develop Web apps. Dash Plotly gives data scientists the power to build web apps to interact with data, deep learning, artificial intelligence and machine learning models.

In this introductory article, we’ve explored how to develop dashboard apps using Dash Plotly. Although it’s a trivial application, it illustrates the core concepts of this technology. Besides development, we’ve also seen how effortless it is to code in Plotly.

Dash is the original low-code framework for rapidly building data apps in Python, R, Julia, and F# (experimental).

Written on top of Plotly.js and React.js, Dash is ideal for building and deploying data apps with customized user interfaces. It’s particularly suited for anyone who works with data.

Through a couple of simple patterns, Dash abstracts away all of the technologies and protocols that are required to build a full-stack web app with interactive data visualization.

Dash is simple enough that you can bind a user interface to your code in less than 10 minutes.

Dash apps are rendered in the web browser. You can deploy your apps to VMs or Kubernetes clusters and then share them through URLs. Since Dash apps are viewed in the web browser, Dash is inherently cross-platform and mobile ready.

There is a lot behind the framework. To learn more about how it is built and what motivated Dash, read their announcement letter or their post Dash is React for Python.

Dash is an open source library released under the permissive MIT license. Plotly develops Dash and also offers a platform for writing and deploying Dash apps in an enterprise environment. If you’re interested, please get in touch.

Web Apps are great for Data Visualization and gives the clients more flexibilities to navigate and maneuver the data. It’s very user friendly and aid in simplifying the understanding of the DATA.

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