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Introduction to Power BI

Business Intelligence

Welcome to the Power BI tutorial . Before we begin this journey of learning a new BI technology called Microsoft Power BI, we must learn some basics about it. In this introductory tutorial of Microsoft Power BI, we will give you some interesting information and insights on this BI technology. We request you to take some time out and explore this thoroughly as it will act as a building block for learning Power BI technology.

We can confidently presume that not many of you here are not aware of business intelligence and data visualization. Especially in today’s world, where data is the Robin of every business and organization. And, without this Robin, there is no Batman! Data is a treasure of knowledge and valuable information which is used by the ones helming a business to make lucrative and effective decisions at the right time.

“BI is about providing the right data at the right time to the right people so that they can take the right decisions”

Power BI Tutorial

Before diving into the Power BI introduction, let’s have a quick look at Business intelligence (BI). It refers to taking raw data from a data source, transforming it into usable data and utilizing it to make reports and informative graphics for data analysis.

Graphically representing tabular data is known as data visualization. It enables a user to visualize important information through charts, graphs, KPIs, maps, etc. to attain valuable insights just by looking at them. Well Microsoft Power BI is a tool having business intelligence and data visualization capabilities.

Before we begin with this journey of learning a new BI technology called Microsoft Power BI, we must learn some basics about it. We request you to take some time out and explore this Power BI tutorial thoroughly as it will act as a building block for learning Power BI technology.

Power BI tutorial - Introduction to Power BI

Wait! Do you know about Power BI KPI 

What is Power BI?

Power BI is a cloud-based business analysis and intelligence service by Microsoft. It is a collection of business intelligence and data visualization tools such as software services, apps and data connectors.

We can use the datasets imported in Power BI for data visualization and analysis by making sharable reports, dashboards, and apps. Power BI is a user-friendly tool offering impressive drag-and-drop features and self-service capabilities.

Microsoft offers three types of Power BI platforms:

  • Power BI Desktop (A desktop application)
  • Power BI Service (SaaS i.e., Software as a Service)
  • Power BI Mobile (For iOS and Android devices)

Also, we can deploy Power BI on both on-premise and on-cloud platforms.

In the image given below, have a look at the process flow in Power BI.

Process Flow of Power BI - Power BI Tutorial

Why Power BI?

As we learned in the previous section of Power BI tutorial that, Power BI is an umbrella term having several different kinds of services under its tutelage.

  1. There is a cloud-based BI service called Power BI Services used to view and share dashboards.
  2. A desktop-based reporting interface known as Power BI Desktop.
  3. Another useful service is Power BI Embedded that runs on an Azure cloud platform and we can use it for report creation, ETL and data analysis.

Further, let us discuss a few points regarding why Power BI is an important tool in today’s time and why do we need it.

  • Real-time analysis in Power BI can be done by establishing direct connections to the data sources. Also, it keeps data updated to the latest second by data refreshing.
  • You can use custom visualizations from a custom visuals gallery. Custom visuals are divided into many options and categories.
  • You can quickly search for important insights and datasets within your data by using the Quick Insights option.
  • Establish a live or non-live connection to on-premises data sources like SQL Server, and use a secure channel to access data through data gateways. This makes Power BI enterprise-ready as on-premises connections make data transfer secure and the technology scalable and reliable.
  • You can connect to other services through Power BI such as SQL Server Analysis Services (SSAS), Microsoft Excel, etc.
  • Power BI is a new age software using the latest technologies such as HTML 5.0, column store databasescloud computing, mobile apps, etc. This helps in keeping Power BI on the top and popular as it is constantly getting updated with the latest features.

Take a deep insight into Custom Visuals in Power BI

History of Power BI

Power BI is a Microsoft’s product initially released on 11th July 2011. It was originally designed and created by Ron George in 2010, who released it with the name “Project Crescent”. Later in September of 2013, Microsoft changed the name to Power BI and launched it for the public.

This release was a Power BI for Office 365 and had Microsoft Excel add-ins, Power Pivot, Power View, Power Query in it. In later versions, Microsoft added advanced features like natural language Q&A, enterprise-level data security and connectivity, Power data gateways, etc.

Power BI’s first general public release was on July 24th, 2015. As of 2019, Power BI has been officially declared as one of the leading BI tools by 2019 Gartner Magic Quadrant for Analytics and Business Intelligence Platform.

Power BI Features

There are some of the most important and interesting features of Power BI:

  • Visualizations/ custom visualizations
  • GetData (Data sources)/data connections
  • Datasets
  • Dashboards
  • Filters
  • Ad hoc analysis
  • Reports/ ad hoc reporting
  • Trend indicators
  • Online Analytical Processing (OLAP)
  • Navigation pane
  • Natural language Q & A box
  • DAX functions and formula
  • Office 365 app launcher
  • Content packs
  • Authoring interactive reports

You must learn about these features in detail from our separate tutorial on features of Power BI

Power BI Components

Power BI is a business intelligence and data mining software suite which is a collection of different kinds of services by Microsoft. These services play a specific role and work in coordination with each other, to make Power BI function as a whole. In this section of the Power BI tutorial, we will learn about each of these Power BI services or components and their roles.

  • Power Query: We use this service to access, search and transform data from public or local/internal data sources.
  • Power Pivot: This service provides tools to model data taken from the in-memory data source to use it for analytics.
  • Power View: This service has many tools to graphically represent data using visuals and use them for analysis.
  • Power Map: It comes with tools and capabilities to visualize Geo-spatial data or information in the 3D model in a map. You can use these maps in a Power BI report.
  • Power BI Desktop: It is a companion development tool for Power View, Power Query, and Power Pivot. You can import data from a data source, prepare and transform it and use it in visualizations to create reports in Power BI Desktop.
  • Power BI Website: It is a web platform to view and share Power BI apps or solutions. Using Power BI Website, you can create dashboards from reports, share the dashboards with other Power BI users and slice and dice data within a report.
  • Power Service: The Power Service enables the sharing of workbooks and data views with other users. The data gets refreshed at regular intervals from the on-premises or/and cloud-based data sources.
  • Power Q&A: Using the Power Q&A option, you can search for your data or discover insights by entering queries in natural language. It instantly understands your query and returns relevant results.
  • Power BI Mobile apps: Business users view and interact with the reports and dashboards published on a cloud service through mobile hosted Power BI instances. Android, Windows and iOS mobile devices support the Power BI mobile apps.
  • Data Catalog: The Data Catalog option offers the capability to search and reuse queries.
  • Data Management Gateway: This component manages the periodic data refreshes, data feed viewing and table exposing.

Power BI Architecture

To have a better understanding of Power BI, we can divide the architecture into three parts or phases:

Power BI Architecture - Power BI Tutorial

1. Data Integration

In Power BI, we can import data from different kinds of data sources in different formats. In the data integration step, Power BI brings data together (extracted) from different data sources and converts it into a standard format. After data is integrated into Power BI, it is stored in a common storage area known as the staging area.

2. Data Processing

Once Power BI integrates and stores data at a secure place, the raw data requires some processing. Several processing or cleansing operations transform the raw data such as removing redundant values, etc. Later, we apply relevant business rules on the processed data that transforms it according to our business needs. This transformed data is loaded into the data warehouses. This completes a full process of ETL.

3. Data Presentation

In this final phase, the processed data moves from the warehouse and goes into the Power BI platforms like Power BI Desktop to create reports, dashboards, and scorecards. Power BI offers a wide range of visualizations. We can also import custom visualization from the marketplace. From the report development platforms, we can publish the reports on the web or mobile apps to share it with other business users.

Get a thorough understanding of Power BI Architecture

Users of Power BI

Power BI users are categorized into four sections according to the purpose of the usage of Power BI. These four types of users are Analysts, Business users, IT professionals and Developers. Let’s learn some more about them.

1. Analysts

Analysts use Power BI to develop reports, dashboards, data models and study them to discover valuable insights in the data. Power BI offers a wide range of data sources from which an analyst can extract data, make a common dataset, cleanse and prepare that data to make reports and conduct analysis.

2. Business Users

The business users are the common users who study the reports and dashboards available to share with them on the Power BI website or mobile app. Business users remain updated with the latest information which helps in taking important decisions in time. They can also set an alert notification for any change or abnormality in data (if occurs).

3. IT professionals

The IT professionals are mainly concerned with the scalability, availability, and security of data. They also centrally manage all the Power BI services and users.

4. Developers

Developers are responsible for all the technical work. Their key roles are to create custom visuals to be used in Power BI, embedding Power BI into other applications, creating reports, etc.

You must definitely learn to apply Filters in Power BI Reports

Data Connections in Power BI

There is a plethora of data sources from which you can extract data into Power BI. You can connect to data files on your local system, Excel files, Azure SQL Database, Facebook, Google Analytics, Power BI datasets, etc.

You can connect to cloud-based sources, on-premises data sources using gateways, online services, direct connects, etc. We have listed some commonly used data sources below.

  • File: Excel, Text/CSV, XML, PDF, JSON, Folder, SharePoint.
  • Database: SQL Server database, Access database, Oracle database, SAP HANA database, IBM, MySQL, Teradata, Impala, Amazon Redshift, Google BigQuery, etc.
  • Power BI: Power BI datasets and Power BI dataflows.
  • Azure: Azure SQL, Azure SQL Data Warehouse, Azure Analysis Services, Azure Data Lake, Azure Cosmos DB, etc.
  • Online Services: Salesforce, Azure DevOps, Google Analytics, Adobe Analytics, Dynamics 365, Facebook, GitHub, etc.
  • Others: Python script, R script, Web, Spark, Hadoop File (HDFS), ODBC, OLE DB, Active Directory, etc.

Deep dive into 100+ Spark Tutorials to master the cluster computing platform

Power BI Pricing

Now, we are sure you must have started liking Power BI after learning what all it has to offer. And so, you would also want to know its pricing and licensing costs. We will let you know about the prices of different versions with their features in this Power BI tutorial. Microsoft has put out three pricing plans for Power BI:

  • The basic version, Power BI Desktop is free of cost and includes tools for data visualization, data preparation, data modeling, data cleansing and publishing reports to Power BI Service.
  • Power BI Pro is available at a subscription price of $9.99 per user per month. You can try a 60 days free trial before purchasing the subscription. This plan for Power BI Pro includes tools for data collaboration, a 360 real-time view for dashboards, data governance, and the freedom to publish reports anywhere.
  • The Power BI Premium is available at a price of $4,995 per month for one dedicated storage resource and cloud computing facility.

Must Learn – How to Create Dashboard in Power BI

Companies using Power BI

Power BI is a relatively new business analytics software in the market and is gaining popularity very fast. It has gathered a huge customer base worldwide already and is rapidly expanding. Here is a list of a few big names that use Power BI as their business analysis software:

  • DELL
  • Capgemini
  • Nuevora
  • Accenture
  • Agile BI
  • Data Bear

Power BI Case Study on Rolls-Royce

In this section, we will briefly walk through a case study of Power BI. This will help us understand the role of Power BI in a real-life scenario. The case in the spotlight here is Rolls-Royce. This 20-year-old company needs no introduction.

As of this year, it is making more than 13,000 engines for commercial aircraft used around the world. This speaks for its massive and ever-increasing customer base. Now let us move further and see the challenges the company faced and how Power BI proved to be useful.

The problem

The most basic challenge of the company was to optimize maintenance costs, operational costs, fuel expenses, etc. This is only possible when the company is able to record, access and analyze the data produced by all the systems and equipment of the aircraft. With the advancing technology, the systems can record more and more signals which are the data from different aircraft sensors. This has resulted in a constant increase in data volumes. So, the company needed a good data management and analysis system that filter important signals or data and use them to generate insights.

In addition to this, Rolls-Royce launched a customer service and maintenance model known as “TotalCare Services”. It was a very successful initiative that involved engine maintenance services for the customer. For this also, the company needed proper insights into data so that they can establish a bond with their customers.

The change

Rolls-Royce chose the Microsoft Azure platform and Power BI to manage and analyze terabytes of data coming from the engines and maintenance systems. With the help of Microsoft Azure, the company was able to aggregate data from varied locations and sources. And with the help of Microsoft Power BI, they were able to carry out analysis on the extracted data.

With Power BI, they designed and created dashboards and reports having informative visuals and charts. Earlier, creating informative reports to gain insights into data was time-consuming. But with Power BI, it is the easiest step in the entire process. Thus, Power BI plays a crucial role in providing valuable insights into data so that the company can focus on improving operational efficiencies and establish long-lasting relationships with their customers.


This brings our introductory tutorial on Microsoft Power BI to an end. We hope it helped you lay a solid foundation about the technology. In the tutorials to come, we will expand on more interesting topics and tools of Power BI.

Next article lined up for you – Pros and Cons of Power BI

Any questions regarding the Power BI tutorial? Share your queries in the comment section.

Stay tuned and happy learning!

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Power BI Desktop April 2019 Feature Summary

Business Intelligence

April is an exciting month for Power BI Desktop! Our April update has major updates across the entire product.  This release adds the ability to define the titles of your visuals and the URLs of your buttons based on DAX expressions, which is only our first step towards making every property of a visual expression-based. You can also now easily link reports within a workspace together with across-report drillthrough support. We have a lot of new connectors this month and several  of our preview connectors are now generally available, including the Power BI dataflows and PDF connectors. Data prep gets some major updates as well with the GA of data profiling and M intellisense.

Here’s the complete list of April updates:





Data connectivity

Data preparation


For a summary of the major updates, you can watch the following video:


Filter pane improvements

Support for full filter pane editing

We’ve now added support for full editing of the new filter pane. You can add and remove fields to filter on, change the filter state, control the visibility of the pane and filter cards, and lock filter cards all within the new filter pane.

Since the new filter pane now has full editing ability, when you are using the preview, you no longer see the old filter pane at all in the visualization pane.

Ability to rename filters

When you’re editing the filter pane, you can now double click the title to edit it. This is a good way to make the filter card names more understandable for your end users.

Filter pane scales with the report page

To maintain proportion between the report page and the filter pane, the new filter pane will now scale with the report page and visuals.

Restrict ability to change filter type

Under the Filtering experience section of the report settings you now have an option to control if consumers of your report can change the filter type. With this setting off, your report consumers won’t have access to the dropdown to switch between the basic and advanced types of filters.

Improved filter pane accessibility

We’ve improved the keyboard navigation for the new filter pane. You can tab through every part of the filter pane and use the context key on your keyboard or Shift+F10 to open the context menu.

Watch the following video to learn more about the filter pane improvements:

Conditional formatting for visual titles

Since the initial release of Power BI, you’ve been able to customize the titles of your visuals, but they’ve always been static text.

Since Power BI reports are interactive, it makes sense that you may want your titles to be dynamic and reflect the current state of the report. You can now use the conditional formatting dialog to change the text of your report based on a DAX expression in your model.

First, you’ll need to create a field in your model to use for your title. For example, here’s an expression that will change based on the filter context the visual receives for the product Brand name. One thing to note is that this field needs to be formatted as text.

 Then launch the conditional formatting dialog by right-clicking the “Title text” area in the property pane card and picking Conditional formatting.

Then in the dialog, select a text field from your model. This can be a column or a measure.

Now the visual’s title will respond to changes in the report.

Once such a title is set, you can re-launch the dialog by clicking the fx button in the property pane or revert to the default using the context menu.

Watch the following video to learn more about conditionally formatting titles:

Conditional formatting for web URL actions for buttons, shapes, and images

You can also use the same expression-bound formatting to make the URLs of your buttons dynamic! It’s set up the exact same way as titles. This can be very useful if you want users to navigate to other webpages with URL parameters based off their current selection.

Over the coming months we’ll be rolling these conditional formatting options out to more properties on more visuals and give you more ways to set the expression. The goal is that you’ll be able to use rules, a measure or enter an expression directly in the dialog and use the result to format any property.

Watch the following video to learn more about conditionally formatting URLs for buttons:


Drillthrough across reports

We are extending our drillthrough feature, which up until now only worked between pages of a single report, to also reference other reports in a given workspace as well. The power of this feature is that you can no easily link up multiple reports. For example, you could create a summary report connected to a slimmed down dataset and set up drillthrough to deep detailed reports.

To set up this experience you’ll need to:

  • Set up a drillthrough target page to be accessed from other reports within a workspace
  • Allow a report to opt into seeing drillthrough pages outside of the report

To set up a drillthrough page so it can accessed from other reports within a workspace, all you need to do is turn on the Cross-report toggle in the drillthrough section of the visualization pane.

After that, all you need to do is enable the Cross-report drillthrough setting for all reports within a workspace that you want to point that cross-report drillthrough page. You can find this setting in the Report settings for the current file section of the Options dialog.

Once you’ve done that, any report can see the cross-report drillthrough pages within its workspace or app. Right clicking in a visual in a report will show the drillthrough page from another report if the fields in the visual match the drillthrough fields setup on the target page. The matching needs to be identical by both table name and column name,  but doesn’t need to be the same dataset.

Watch the following video to learn more about drillthrough across reports:

Key Influencers visual now supports continuous analysis for numeric targets

You can now add numeric fields to the Analyze bucket of the field well and run a continuous analysis and find key influencers that cause that field to increase or decrease. This will work for numeric columns (including calculated columns) but measures are not yet supported.

In order to enable this analysis, you will need to explicitly turn it on after selecting the field to analyze. You can do this by going to the analysis card of the formatting pane and switching the analysis type to continuous instead of categorical.

Behind the scenes the visualization will run a linear regression and rank all the factors that the user selected as potential influencers. It will give insights about how much an explanatory factor increases or decreases the average of the metric being analyzing. In the example above we can see that when Class is Deluxe, the SalesAmount is on average $1.5K higher than when the Class is Regular or Economy.

To learn more about how to set up the key influencers visual and how it works, you can check out our documentation.

Watch the following video to learn more about continuous analysis for the Key Influencers visual:

Python support is now generally available

Thank you for all the great feedback during the preview period! Python is now generally available and can be used to create your models and visuals without needing to enable it.

Partial synonym matching for terms in Q&A

When using Q&A, you can now complete terms even if you only know part of it. Specifically, if you type a word or phrase that is part of a synonym of a field or table, you’ll see the synonym in the list of suggestions.

Watch the following video to learn more about partial synonym matching for Q&A:



We’ve added another new DAX function this month, ALLCROSSFILTERED. This function can be used to remove filters on a table from other tables across direct or indirect many-to-many relationships.



The rainbowGauge partner-developed visual lets report authors create a 3-state gauge with different colors to represent each stage. You can add a min, max, target, and a value and color for each stage to the visual.

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QlikView Case Study – The Game-Changer for Mercedes and Sony


We are well-versed about the features, pros & cons, capabilities, and many other things of QlikView, but are still unaware of the achievements of QlikView in the industry sector. The power and strength of QlikView make it different from other available BI tools. With these Best QlikView Case Studies, we are going to measure QlikView’s efficiency and success by looking at some real-time issues of renowned companies. Here, we will discuss how QlikView plays an essential role in solving the issues of CISCO, Mercedes Benz, Sony, and FILA.

Popular QlikView Case Studies
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Top QlikView Case Studies

In each of the following QlikView case studies, we’ll learn how the company has used QlikView to their benefit and have grown in the competitive market space. Let’s start with CISCO –

1. QlikView Case Study – CISCO

Cisco Systems, Inc is a multinational company based in the USA. The company develops and manufactures networking hardware, telecommunication hardware and provides high-tech services like network and technology architectures. It is spread worldwide having offices in more than 165 countries. It is a leading technology committed to providing people with efficient ways of communicating, connecting and collaborating.

CISCO QlikView Case Study

The challenge

To grow as a company and fulfill its purpose, the most important factor is customer relations and customer satisfaction. What Cisco needed was a means to establish a strong customer relationship by knowing the needs and behavior of its customers. For this, they needed an effective BI solution which enables people at Cisco to access sales and customer data from different data sources, transform that data into meaningful and actionable insights. The database of the company was as gigantic as nearly 500 million lines of complex data and installed base records. After researching for a few potential BI technologies, Cisco finalized QlikView as the perfect fit for their requirements.

QlikView’s role

With its sheer flexibility and user-friendly nature, QlikView is empowered in Cisco in more than one way. QlikView has enabled people at Cisco possessing any level of skill to access data and draw insights from it. It serves as a self-service BI tool used for innovative decision making. With the virtue of QlikView, Cisco sealed two major service deals with telecom giants, recorded as the biggest deals in the history of Cisco.

The change

  • Business users at Cisco created a QlikView dashboard that acted as an interface for employees at Cisco to access data from its large installed bases.
  • QlikView enabled Cisco to consolidate data from Oracle, Hadoop, and Teradata and put it together on a QlikView server.
  • Every user at Cisco was able to access and filter out the sales and customer data. It was also able to perform transformative and analytical operations on it by creating graphs, visuals, and reports.
  • Managers were able to gain better insights and precise information on sales opportunities, thus, improve sales and revenue.
  • Quicker detection and problem-solving in issues related to network operations.
  • Helped the marketing department to keep a track on customer’s current services and recommended them of the latest upgrades and available products to keep them up to date.
  • It is used for financial planning by executives.
  • Incredible business gains with ROI of about 4 million dollars and $100 million-dollar worth of additional revenue by support and service contracts renewals.

2. QlikView Case Study – FILA

FILA is a multinational sporting goods company. It was founded in 1011 in Biella, Italy. It is currently headquartered in South Korea due to the change of ownership and is spread in about 11 countries.

FILA case study

The challenge

The main challenge for Fila sales group was to access information lying in various systems and sites of operations. Another challenge was to circulate this information (data) to business users at different sites.

In addition to that, the company wanted to improve the data quality, data availability and provide easy-to-use analytic solutions to end users. Also, the focus was at providing system users with data about corporate processes so that they can understand the functioning and monitor activities, optimize decision making for the company.

QlikView’s role

Within no time after the requirements were surfaced, QlikView’s solution was deployed to 250 users. QlikView Server enabled the company to consolidate all the data residing at different sites and systems in one place. Also, QlikView offered FILA Europe with one common dashboard which was to be used from any Fila establishment across the globe and its suppliers. It helped to ensure better communication and maintained a solid network.

The clock is ticking, make your Career in QlikView. Get all the latest Career Opportunities in QlikView

The change

  • Using the QlikView dashboard, FILA can analyze aspects like production planning, pricing, invoicing, order management, and customer service support, etc.
  • The solution has helped FILA in terms of coordination and collaboration improvements; formalization, consolidation, and dissemination of FILA’s knowledge base to every employee for better insights and decision making.
  • Users at FILA were able to access and drill through the available data using user-friendly and interactive QlikView dashboards. Even non-technical users were able to surf and work smoothly in QlikView by using the data to filter important information, create visualizations and reports.
  • Ability to create in-depth reports and run queries on multi-dimensional data.
  • The associative and interactive nature of QlikView engine is best suited for the company’s requirements as it functions as per the natural way of thinking and decision making to bolster the company’s business.

3. QlikView Case Study – Mercedes Benz

Mercedes-Benz India was founded in 1994 and have grown in leaps and bounds since then. Mercedes-Benz is India’s one of the top luxury car manufacturers with a dense network spread across the country. It is currently present in about 31 cities of India having 72 touch points. Mercedes-Benz India has a plan of expanding its operations and business in the top-end luxury segment by 2022.

Mercedes Benz logo

The challenge

Like every successful corporation, building customer trust and solid customer relations are also the main focus of Mercedes-Benz India. The company wanted a solution that was easily able to understand customer requirements better and keep track of services provided by the various dealerships to the customers. In addition to this, a fast reporting system, improved decision-making and a user-friendly BI tool to gain valuable insights in almost real-time were vital concerns. To ensure, the company needed a robust CRM/DMS system to organize and manage data and an effective business intelligence solution to enable prompt and precise reporting.

After reviewing several potential BI solutions, Mercedes-Benz finalized on QlikView business discovery platform. QlikView’s speed to market, time to value, data analysis capabilities, and convincing proof of concepts impressed them.

It’s the right time to check your knowledge with our latest Free QlikView Quiz

QlikView’s role

Initially, Mercedes-Benz deployed QlikView just to the after sales department to analyze throughout, warranty, and revenue figures. After a successful trial on the Aftersales operations, QlikView was deployed across the company network for Sales, HR, and Plant operations in India has over 300 users.

The change

  • QlikView was deployed across Mercedes-Benz in less than four weeks. The information to users was made available from the back-end ERP/DMS systems with fixed update frequencies. After a successful testing phase of centrally deploying the solution, it was rolled out for the whole dealership.
  • User adoption was at high rates and easy due to the user-friendly nature of QlikView. Also, QlikView easily integrated data from MB’s existing data sources; SAP and Siebel CRM tools.
  • Improved reporting by a centralized dashboard and reports created by QlikView. Analytical teams could easily work with the available data and draw insights important for the company. Every user was able to analyze data reports that helped them in understanding the processes and workflows ensuring better problem-solving.
  • Improved After-Sales operations by prompt reporting and close near real-time monitoring of customer interactions and related data. It helped in increasing the vehicle throughput of more than 60%.
  • Improved customer attention and problem-solving services.
  • Mercedes-Benz is planning on growing its QlikView application by deploying QlikView on mobile so that high management people have data and insights on their fingertips. In addition to a successful run in India, Mercedes-Benz will roll-out QlikView solutions for the APAC region and Europe as well.

4. QlikView Case Study – Sony Europe

SONY is a multinational manufacturer of electronic consumer products. It is a leading Japanese company. Sony Europe aims at utilizing valuable business information available as raw data and transforms it into meaningful insights.

Sony logo

The challenge

Sony Europe’s Spain division, having a turnover of 1.166 billion euros in 2009 felt the need for a BI system that can analyze multiple scenarios and analytical processes. Impressed by QlikView’s ease of deployment and adaptability, the company finalized QlikView as their BI solution.

Struggling for Interviews? Get Best 30 QlikView Interview Questions and Answers

QlikView’s role

By deploying QlikView, Sony instantly realized the potential of the tool and acknowledged the enhancements made by it in terms of visibility provided by QlikView for business information and dynamics. In addition to this, QlikView solutions were created to offer customer insights and for Sony Style Store Shops which benefited the company a great deal.

The change

  •  Integration of data relevant to Sony from various data sources such as Oracle, SAP NetWeaver BW, Microsoft Sharepoint, Microsoft Excel, MS Access, Sony’s planning files, etc., into a single QlikView BI environment.
  • Rapid application development and deployment followed by prompt assists in decision-making and analysis.
  • QlikView Desktop and Publisher deployed in Sony Spain for about 80 users.
  • Improved graphical analysis of key performance indicators like sales, visits, number of references, innovations, average amount, rate conversion, budget variations, stock rotation, indices, deviations, market share, expenditure, discounts and margins, innovations online presence, etc.
  • Creation of QlikView scorecards for internal departments in Sony.
  • The areas of business benefited from QlikView solution in Sony are marketing, management, finance, STIC, Sony Style Stores, customer insight, commercial sector, etc.
  • Increased visibility into business processes and consumer trends. It also improved resource management, risk analysis and threat detection for the company.


It is not the end; today many industries are using QlikView and, amongst all, these are the renowned QlikView Case Studies. With these four companies FILA, Mercedes-Benz, Sony, and Cisco we talked about the major role of QlikView in the betterment of the company’s business. However, Applications of QlikView are not just restricted to these companies but are also used in different sectors such as finance, insurance, etc.

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Qlik Advanced Analytics Integration – Analytic Connections in QlikView


This article is dedicated to the Qlik Advanced Analytics integration in QlikView and Qlik Sense. Here, we will start by understanding the basics of advanced analytics. Then we will proceed with learning how Qlik has added advanced analytics features in QlikView and how does it work.

Qlik Advanced Analytics Integration - Analytic Connections in QlikView

Qlik Advanced Analytics Integration – Analytic Connections in QlikView

1. What is Advanced Analytics?

Advanced analytics is a set of advanced operations or calculation performed on data by third-party engines. These advances techniques and operations can be anything like predictive analytics, location analytics, machine learning, big data analytics, data mining and visualizations.

Advanced analytics capabilities are needed by any BI tool as they offer a limited analytical capability to the users which in some scenarios prove to be insufficient to make the most meaning out of the data that exists. Thus, such capabilities enable a user to perform advanced and complex analytical operations on massive data sets and return correct results. The user can thus dive deeper into the data and gain unique insights and make predictions based on them.

Do you know What s Big Data & Its importance in IT Industry?

2. Qlik Advanced Analytics Integration

Qlik has always focused on improving their BI tools with the latest technology and trend. The integration of advanced analytics in Qlik Sense and QlikView is an effort made in the same direction. The capability of data integration and advanced analytics serves an enterprise by providing important insights like fraud detection, inventory optimization, sales forecasting and prediction, market analysis, pricing optimization and so on.

Qlik advanced analytics integration important for the two main reasons. Firstly, if we look at the traditional way of developing and designing the BI tools. Developers used to design a general analytical model based on some commonly asked queries for the available data. However, this makes a tool rigid and its analytical pathways static. That is, a user is restricted to analyse a given set of data in one or two ways. With the integration of advanced analytics, users have the flexibility to apply advanced analytical algorithms on the data rather than using the precomputed and pre-modelled ways.Secondly, the batch processing methods were used in query-based designs, resulting in the sending of big data sets to third-party engines as developers were unaware of the usefulness of the entire set

Most of the data loaded into such batches went unused. Thus, making it highly inefficient and slow.

Recommended Reading – Unique Capabilities of Qlik Sense

But, with Qlik’s associative indexing engine technology, data updates itself quickly according to the context and selections. As soon as you make a selection on the app, the associative engine quickly forms new associations between relevant data units and analyses. It is the current context. Here, only a small set of relevant rows and columns send to the third-party engine. Which performs advanced analytics on it and sends it back to the client app in real-time.

3. How Qlik Advanced Analytics Integration Works?

Advanced analytics provides a set of new analytical capabilities. Which enables users of QlikView and Qlik Sense to explore and analyse their data even better. This facility is leveraged by connecting QlikView with third-party engines such as R, Python, MATLAB, Spark, Regex etc. All of the analytical engines contain a unique set of functions, expressions and algorithms, can apply on the data sets within QlikView. We can do this by using connectors specific to analytical engines. However, Qlik also provides open-source APIs for the users to create connectors of their choice and use case.

Now, let us see on a technical front, what happens when a BI tool like QlikView integrates with third-party advanced analytics engines.

Do you know What is R Programming and its Features? 

Qlik Advanced Analytics Integration
  1. When a user interacts with the QlikView desktop, they generally make selections or search data. This triggers the associative engine into making relevant data association according to the selections made.
  2. Due to the selections made, the associative engines refines the data contextually and updates it for further advanced analytics. We can store the set of data, which is select in the in-memory.
  3. Here, we can send the in-context or relevant data to the third-party analytic engine connect via a connector or plug-in.
  4. The analytics engine processes the data and applies advanced calculations and algorithms on the received data and send it back to the Qlik engine.
  5. Resultant data will combine with the data residing in-memory of QlikView.
  6. The transformed data will receive after advance analysis instantly use for the visualization tools and represented on the dashboard.
QlikView Quiz

5. Analytic Connections in QlikView

In QlikView Jargon, the advanced analytics capabilities referring to as server-side extended analytic connections. It offers a built-in library of expressions and functions of different language like R, Python etc. If the user needs algorithms and functions for statistical analysis, then the R engine is suitable. For example, you might need a specific forecasting function from R which can be called within the QlikView script and computed on the go, instantly producing results. Whereas, Python and MATLAB are more general purpose and machine learning oriented languages. The analytical engines for different languages are called Server-side Extensions (SSE) and can be configured into QlikView Desktop and QlikView Server using the Settings.ini file.

So, this was all about Qlik Advanced Analytics Integration. Hope you liked our explanation.

6. Summary of Qlik Advanced Analytics Integration

Thus, providing advanced analytics capabilities, users are able to process their data in different ways. This gives them the flexibility and unique insights, which may be miss when we use Qlik tools for query-based and batch approaches.

Feel free to drop your queries and suggestions in the comment box below.

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Qlik Approach to Big Data – 5 Amazing Methods to Utilize Big Data in Qlik


Let us make an effort and shift our focus from the specific BI tools like QlikView and QlikSense to Qlik as a technology to Big data. Also, in this article, we are going to keep our discussion focused on Qlik approach to big data. Here we will discuss different methods of utilizing Big Data in Qlik and several benefits of Qlik for Big Data.

So, let’s start with understanding how Qlik approach to big data and what it has to offer its users.

Different Approaches for Big data by Qlik

Qlik Approach to Big Data

Qlik is known to be a pioneer in data analytics and management. As it has reached a point where its user base has grown to be of 48,000 customers spread across 100 countries. Qlik provides efficient BI solutions for both tech and non-tech users. Who use it to explore data and comprehend the story behind it. Qlik solutions offer data exploration, visualization, discovering insights. Also, lets users make informed decisions based on their analysis to foster their businesses.

As we know, billions of terabytes of data are being generated by all sorts of businesses worldwide which is referred to as big data in common terms. Businesses see big data as a potential source of information regarding the requirements and behavior of their customers, suppliers, products, partners, and markets. This makes big data a strategic and economic asset for organizations. Thus, organizations are always in search of efficient business intelligence technologies. Which lets everyone in the enterprise work with data and give them user-friendly tools to transform and visualize data to provide it relevance and context. And for the same reason, enterprises have turned to Qlik and the solutions offered by it. Qlik has ties with over 1700 partners, many of which are big data-centric tools such as Cloudera, Hadoop, Google BigQuery, and AMS.

Do you know What is Big data and its Importance in the IT Industry?

What are the Methods of Utilizing Big Data in Qlik?

When it comes to dealing with big data, a common concern is that not all the employees in an organization are adept data scientists or even close to knowing anything about it. So, there must be such big data analytics and techniques which enables as well as empowers every user to conduct proper operations on data and generate informative reports from using it. In other words, they need a simple, user-friendly guided analytics environment to help them manage and understand the big data.

 Methods of Utilizing Big Data in Qlik

Another challenge faced by users while working with big data is they were expected to know which part or segment of the entire big data repository they want to work with, which is not an easy task for non-technical users. To overcome such issues, Qlik has come up with certain methods which can be used individually or in combination to work with big data.

1. In-memory (QIX Engine)

Qlik’s Indexing Engine (QIX) has the capability of compressing big data to ten percent of its original size which is enough for some customer’s use. Qlik solutions work by accommodating the compressed data into the in-memory and load it from there.

QIX Engine

2. Segmentation

Big data get easy to handle and visualize by dividing a large application into small segments based on categories. For instance, if an application is showing the geographic data of the entire world, you can break it down into small segments per country. Thus, this process is called segmentation.

Example of Segmentation

Latest Career and Job Roles in Big Data for Fresher and Experienced

3. Chaining

Chaining is contrary to segmenting. Chaining is linking different segments or subject-specific views to one another. It forms a logical association between application segments. Big data analysis and handling become easy by first segmenting larger applications into subject-specific views and then linking them with each other through chaining.

Example of Chaining

4. Direct Discovery

Another way to handle big data is through direct discovery. Here, some data (small tables) still resides in the in-memory but a large chunk (large tables) resides in the database. A user gets to directly access the external database when in need of the large tables. It is known as a hybrid approach as it brings together the in-memory system with the external database storage system.

Qlik Data Discovery

5. On Demand App Generation (ODAG)

A unique method, where an on-purpose application generates having only the section or data set that you require to work with. There are two divisions of this process:

First, you have a Selection app, which is a portal where data is been sorted into categories and sections such as customers, product, vendor, time period, geography etc. You can select the data set of your choice.

On demand App Generation (ODAG)

Then, in the second part, a new application is launched having only the section of data that you selected from the database through the selection app. You can work with the data set as you like and create reports and dashboards. You can always go back to the selection app and work with new sets of data.

Advantages of Qlik for Big Data

Qlik Approach to Big Data is incomplete without discussing its benefits for it. One can think of many advantages of using Qlik solutions for big data. We have tried to list out a few for you.

  1. Qlik gives an associative and augmented experience to the user by assimilating data from various data sources and associating it logically. The associative engine efficiently gathers data from different data sources and indexes it for a better understanding of the data structure.
  2. Qlik supports a wide range of user base and offers a lot of services such as guided analytics, self-service visualization, and exploration, collaboration and reporting, geographic and advanced analytics, AI capabilities (Qlik Cognitive Engine), data integration etc.
  3. It helps in making sense of big data by empowering the users with capabilities to fetch data from varies sources and use it to extract meaning from it.
  4. Big data is a reservoir of important information and insights in business. Technologies like Qlik helps the business users to access that data, model and structure it, then finally represent it visually and explore it better.
  5. Qlik tools empower every user in the enterprise, regardless of their skill set, explore and analyze big data efficiently.
  6. It can connect to different types of data sources like Excel, XML. Or, big data sources such as Cloudera, Hadoop, Teradata. App-specific sources such as SAP, Salesforce etc.
  7. Qlik’s associative engine enables associations between data tables which makes navigating within large data sets very easy. The user doesn’t have to drill-down in complex rows and columns of large data tables. Thus, it makes big data much more manageable.

Do you want to make the career in QlikView? See Some Latest Career Opportunites in QlikView

QlikView Quiz

Solutions Offered by Qlik for Big Data

Qlik currently offers many platform-based solutions aimed at aiding developers or purposes like data management, data visualization, etc. Some latest tools offer by Qlik are-

  1. QlikView: A highly interactive tool for data discovery and guided analytics.
  2. QlikSense: A self-service, user-friendly data analytics and, visualization platform.
  3. QlikCore: Development platform for customizable and embedded applications.
  4. Qlik Data Catalyst: An enterprise data management solution. There are some value-added products provide, to enhance the capabilities of existing tools.
  5. Qlik NPrinting: Report generation and distribution tool for Qlik Sense and QlikView.
  6. Qlik GeoAnalytics: An advanced mapping and location-based analytics platform for Qlik Sense and QlikView.
  7. Qlik Associative Big Data Index: Enables binary indexing on data stored in Hadoop clusters or data lakes for fast data discovery.
  8. Qlik DataMarket: Provides Data as-a-service (DaaS) from comprehensive data libraries from different data sources on a subscription basis.
  9. Qlik Connectors: Provides connectivity to internal and external data sources. It enables connection with web-based, app-based (Salesforce, SAP), file-based, cloud-based data sources.


Qlik is a robust technology aiming at providing as many customers a hassle-free data analytics, management, and visualization experience. One major advantage of using Qlik solutions amidst various other BI platforms is Qlik’s scalability and flexibility to adapt to changing big data landscape.

We hope this explanation of “Qlik Approach to Big Data” was helpful to you and answered all your questions. If you have any query, tell us through comments.

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QlikView Vs Qlik Sense – Which is Better BI Tool for 2019?


Qlik provides its customers with a wide range of products. Two such popular BI tools are QlikView and Qlik Sense. Some of you might be familiar with either or both tools. Here, we are going to discuss QlikView Vs Qlik Sense. Although, both the tools are business intelligence platforms and more or less serve the same purpose, there are certain things that make them distinctly different from each other.

Let us start out discussion on how QlikView is different from Qlik Sense and perhaps find which one is better Business Intelligence tool.

QlikView Vs Qlik Sense

QlikView Vs Qlik Sense: Overview

Before we start the difference between QliView Vs QlikSense, we will discuss the introduction of these two terms.

i. What is Qlik View?

QlikView is a data analysis and visualization tool which enables users to fetch, integrate, process and analyze data from varied sources. We can use it for developing data models, analytical applications, dashboards, visualizations to create analytical reports and deliver it to end-users via Access point.

Through the access point, end-users can access data, carry out searches, create data models, associations, visualizations etc. to analyze data and discover data trends.

Features of QlikView

  • Dynamic BI ecosystem (Interaction with dynamic apps and dashboards).
  • Default and custom connectors
  • Data visualizations
  • Capable of building guided analytics applications and dashboards.
  • Guided and advanced analytics

ii. What is Qlik Sense?

Qlik Sense is a self-service data discovery and analysis tool which focuses on ease of use for the user. It provides a modern and interactive user interface where you can use the tools for modeling and managing data, creating visualizations, layouts, and stories. It is not very technical in its approach and thus very user-friendly.

Features of Qlik Sense:

  • Smart search options like Google and associative functions.
  • Fast and reliable connections to multiple data sources.
  • Drag and drop visualizations
  • Generate personalized reports and detailed, interactive dashboards.
  • Self-service data discovery

Difference Between QlikView and Qlik Sense

Let us start QlikView Vs Qlik Sense based on what features they do or do not provide.

i. OLAP functionality

QlikView provides Online Analytical Processing capabilities to perform analytical operations on the data whereas, Qlik Sense does not have OLAP functionality.

ii. ETL capabilities

Both Qlik Sense and QlikView provides Extract, Transform and Load (ELT) capabilities.

iii. Self-service visualizations

QlikView does not offer self-service visualization capabilities whereas Qlik Sense does.

iv. Guided analytics

QlikView provides guided analytics functionalities whereas Qlik Sense does not.

v. Filter requirements

Data filters are required in QlikView whereas, they are not needed in Qlik Sense.

vi. Data storytelling

Data storytelling is a feature unique to Qlik Sense and is not available in QlikView.

vii. Data mining & analytics

Data mining & analytics capabilities are provided in Qlik Sense only.

viii. Modern visualizations

Qlik Sense is known for its attractive as well as interactive user interface. Thus, Qlik Sense provides modern visualizations to represent data with an easy to use drag-and-drop functionality. Whereas, QlikView offers basic visualizations.

ix. Associative indexing engine

Both Qlik Sense and QlikView have a robust associative indexing engine (QIX Engine) at its core.

x. Mobile support

Both Qlik Sense and QlikView offers mobile compatibility. Qlik Sense has a mobile app and is supported by iOS 11.0 or later. Whereas, QlikView supports Apple Mobile Safari (iOS 10 or later) which is a browser support.

xii. Advanced analytics and integration

Qlik is working to provide efficient features and capabilities on advanced analytics and integration. However, better advanced analytics integration capabilities are offered in Qlik Sense than in QlikView.

xiii. Integrated app development environment

QlikView is a development-based tool as provides an integrated app development environment which is not so in Qlik Sense as it a visualization centric tool.

QlikView Vs Qlik Sense – Verdict

QlikView is more of a traditional, technical tool for shared business intelligence, data analytics and reporting. Whereas, Qlik Sense can be regarded as a modern data exploration platform. Although, both the tools are created on the same ground, which is to serve as a BI tool. They are distinct from each other in many ways.

So, instead of declaring a clear winner as to which tool is better, it is only fair to say that both the tools are useful for respective purposes. On one hand, where Qlik Sense might be more popular in the market for its sheer ease of use and simplicity as it is a self-service and data exploration (drag-and-drop functionality) tool which can be used by users of any caliber and skillset. While on the other hand, QlikView focuses more on application development and guided analytics and less on user interaction and responsiveness.

So, this makes the users or the need of the organization entirely responsible for which tool amongst the two (QlikView Vs Qlik Sense) is the best choice for them. If they want to perform technical operations and work with data on the ground level, then QlikView is the choice for you. And, if you wish to just design and create visually appealing data reports and dashboards, then Qlik Sense is the answer. Both of them can also be used simultaneously for the respective services they are good in providing at.

Also, See – Latest Job Trends and Salaries in QlikView

QlikView Quiz

QlikView and Qlik Sense – Extra Points

Next, we take an elaborative dig at the matter and provide a detailed pointwise comparison.

  • Data loading is slower in QlikView as compared to that in Qlik Sense.
  • If using QlikView, organizations need to make an investment in recruiting specially trained QlikView developers. Whereas, in the case of Qlik Sense, there is no condition of being technically trained and skilled as Qlik Sense is more user-friendly in embedded analytics.
  • Licensing and purchasing process is a little more complex for QlikView than for Qlik Sense.
  • QlikView supports only English as a language whereas Qlik Sense supports English, Chinese, Japanese and German.
  • The target customer size for QlikView is small, moderate and large industries whereas, for Qlik Sense it is small and moderate industries.
  • QlikView offers greater and better control to developers over the design and control of applications thus, objects are more customizable. Whereas, Qlik Sense does not give users much customization and development options. Rather it is user-friendly and easy to use.

Must refer – Latest Job Trends and Salaries in Qlik Sense


Qlik has kept its focus on developing and improving Qlik Sense as a tool, investing a large chunk of their time and resources on it. However, they are also dedicated to make QlikView a better product for the user base that still exist and serve them to their best of capabilities. Also, as the purpose behind both these BI tools was to give users a good and complete data visualization and analytic software, efforts are made by Qlik to decrease the seeming differences between the two and make them the best versions of them.

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