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Recovery Testing
Recovery Testing is software testing technique which verifies software’s ability to recover from failures like software/hardware crashes, network failures etc. The purpose of Recovery Testing is to determine whether software operations can be continued after disaster or integrity loss. Recovery testing involves reverting back software to the point where integrity was known and reprocessing transactions to the failure point.
Recovery Testing ExampleWhen an application is receiving data from the network, unplug the connecting cable.
After some time, plug the cable back in and analyze the application’s ability to continue receiving data from the point at which the network connection was broken.
Restart the system while a browser has a definite number of sessions open and check whether the browser is able to recover all of them or not
In Software Engineering, Recoverability Testing is a type of Non- Functional Testing. (Non- functional testing refers to aspects of the software that may not be related to a specific function or user action such as scalability or security.)
The time taken to recover depends upon:
The number of restart points
A volume of the applications
Training and skills of people conducting recovery activities and tools available for recovery.
When there are a number of failures then instead of taking care of all failures, the recovery testing should be done in a structured fashion which means recovery testing should be carried out for one segment and then another.
It is done by professional testers. Before recovery testing, adequate backup data is kept in secure locations. This is done to ensure that the operation can be continued even after a disaster.
Life Cycle of Recovery Process
The life cycle of the recovery process can be classified into the following five steps:
Normal operation
Disaster occurrence
Disruption and failure of the operation
Disaster clearance through the recovery process
Reconstruction of all processes and information to bring the whole system to move to normal operation
Let’s discuss these 5 steps in detail-
A system consisting of hardware, software, and firmware integrated to achieve a common goal is made operational for carrying out a well-defined and stated goal. The system is called to perform the normal operation to carry out the designed job without any disruption within a stipulated period of time.
A disruption may occur due to malfunction of the software, due to various reasons like input initiated malfunction, software crashing due to hardware failure, damaged due to fire, theft, and strike.
If a backup plan and risk mitigation processes are at the right place before encountering disaster and disruption, then recovery can be done without much loss of time, effort and energy. A designated individual, along with his team with the assigned role of each of these persons should be defined to fix the responsibility and help the organization to save from long disruption period.
Reconstruction may involve multiple sessions of operation to rebuild all folders along with configuration files. There should be proper documentation and process of reconstruction for correct recovery.
Restoration StrategyThe recovery team should have their unique strategy for retrieving the important code and data to bring the operation of the agency back to normalcy.
The strategy can be unique to each organization based on the criticality of the systems they are handling.
The possible strategy for critical systems can be visualized as follows:
To have a single backup or more than one
To have multiple back-ups at one place or different places
To have an online backup or offline backup
Can the backup is done automatically based on a policy or to have it manually?
To have an independent restoration team or development team itself can be utilized for the work
Each of these strategies has cost factor associated with it and multiple resources required for multiple back-ups may consume more physical resources or may need an independent team.
Many companies may be affected due to their data and code dependency on the concerned developer agency. For instance, if Amazon AWS goes down its shuts 25 of the internet. Independent Restoration is crucial in such cases.
How to do Recovery TestingWhile performing recovery testing following things should be considered.
We must create a test bed as close to actual conditions of deployment as possible. Changes in interfacing, protocol, firmware, hardware, and software should be as close to the actual condition as possible if not the same condition.
Through exhaustive testing may be time-consuming and a costly affair, identical configuration, and complete check should be performed.
If possible, testing should be performed on the hardware we are finally going to restore. This is especially true if we are restoring to a different machine than the one that created the backup.
Some backup systems expect the hard drive to be exactly the same size as the one the backup was taken from.
Online backup systems are not an exception for testing. Most online backup service providers protect us from being directly exposed to media problems by the way they use fault-tolerant storage systems.
While online backup systems are extremely reliable, we must test the restore side of the system to make sure there are no problems with the retrieval functionality, security or encryption.
Testing procedure after restorationMost large corporations have independent auditors to perform recovery test exercises periodically.
The expense of maintaining and testing a comprehensive disaster recovery plan can be substantial, and it may be prohibitive for smaller businesses.
Smaller risks may rely on their data backups and off-site storage plans to save them in the case of a catastrophe.
After folders and files are restored, following checks can be done to assure that files are recovered properly:
Rename the corrupted document folder
Count the files in the restored folders and match with it with an existing folder.
Open a few of the files and make sure they are accessible. Be sure to open them with the application that normally uses them. And make sure you can browse the data, update the data or whatever you normally do.
It is best to open several files of different types, pictures, mp3s, documents and some large and some small.
Most operating systems have utilities that you can use to compare files and directories.
Summary:In this tutorial, we have learned a various aspect of recovery testing that helps to understand whether the system or program meets its requirements after a failure.
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What Is Embedded Testing In Software Testing?
In this tutorial, you will learn
What are Embedded systems?Embedded systems are the electronically controlled devices where software and hardware are tightly coupled. Embedded systems may contain a variety of computing devices. These are PCs incorporated in other devices to operate application-specific functions. The end user usually is not even aware of their existence.
Embedded Testing is a testing process for checking functional and non-functional attributes of both software and hardware in an embedded system and ensuring that the final product is defect free. The main purpose of Embedded testing is to verify and validate whether the final product of embedded hardware and software fulfill the requirements of the client or not.
Embedded Software testing checks and ensure the concerned software is of good quality and complies with all the requirements it should meet. Embedded software testing is an excellent approach to guarantee security in critical applications like medical equipment, railways, aviation, vehicle industry, etc. Strict and careful testing is crucial to grant software certification.
How to perform Embedded Software TestingIn general, you test for four reasons:
To find bugs in software
Helps to reduce risk to both users and the company
Cut down development and maintenance costs
To improve performance
In Embedded Testing, the following activities are performed:
1. The software is provided with some inputs.
2. A Piece of the software is executed.
3. The software state is observed, and the outputs are checked for expected properties like whether the output matches the expected outcome, conformance to the requirements and absence of system crashes.
Embedded Software Testing TypesFundamentally, there are five levels of testing that can be applied to embedded software
Software Unit TestingThe unit module is either a function or class. Unit Testing is performed by the development team, primarily the developer and is usually carried out in a peer-review model. Based on the specification of the module test cases are developed.
Integration TestingIntegration testing can be classified into two segments:
Software integration testing
Software/hardware integration testing.
In the end, the interaction of the hardware domain and software components are tested. This can incorporate examining the interaction between built-in peripheral devices and software.
Embedded software development has a unique characteristic which focuses on the actual environment, in which the software is run, is generally created in parallel with the software. This causes inconvenience for testing since comprehensive testing cannot be performed in a simulated condition.
System Unit TestingNow the module to be tested is a full framework that consists of complete software code additionally all real-time operating system (RTOS) and platform-related pieces such as interrupts, tasking mechanisms, communications and so on. The Point of Control protocol is not anymore a call to a function or a method invocation, but rather a message sent/got utilizing the RTOS message queues.
System resources are observed to evaluate the system’s ability to support embedded system execution. For this aspect, gray-box testing is the favored testing method. Depending on the organization, system unit testing is either the duty of the developer or a dedicated system integration team.
System Integration TestingThe module to be tested begins from a set of components within a single node. The Points of Control and Observations (PCOs) are a mix of network related communication protocols and RTOS, such as network messages and RTOS events. Additionally to a component, a Virtual Tester can likewise play the role of a node.
System Validation TestingThe module to be tested is a subsystem with a complete implementation or the complete embedded system. The objective of this final test is to meet external entity functional requirements. Note that an external entity either be a person, or a device in a telecom network, or both.
Difference: Embedded testing and Software TestingSoftware Testing
Embedded Testing
Software testing is related to software only.
Embedded testing is related to both software as well as hardware.
On average 90% testing done in the world is purely manual black box testing.
Embedded testing is done on embedded systems or chips it can be a black box or white box testing.
Primary areas of testing are GUI checks, functionality, validation and some level of database testing.
Primary areas of testing are the behavior of the hardware for the no. of inputs given to it.
Software testing is majorly performed on client-server, web and mobile based applications.
Embedded testing generally performed on the Hardware.
e.g., Google Mail, Yahoo Mail, Android applications.
e.g., Machines of healthcare domain, Microcontrollers used in computers.
Challenges: Embedded Software TestingSome of the challenges that one can face during Embedded software testing:
Hardware DependencyHardware dependency is among the main difficulties faced during embedded software testing because of limited access to hardware. However, Emulators and Simulators may not precisely represent the behavior of the actual device and could give a wrong sense of system performance and application’s usability.
Open Source SoftwareThe majority of the embedded software components are open source in nature, not created in-house and absence of complete test available for it. There is a wide range of test combinations and resulting scenarios.
Software vs. Hardware DefectsAnother aspect is when software is being developed for a freshly created hardware, during this process high ratio of hardware defects can be identified. The found defect is just not limited to software. It may be related to hardware also.
Reproducible DefectsDefects are harder to reproduce/recreate in the case of the embedded system. That enforces the embedded testing procedure to value every defect occurrence substantially higher than in a standard case, other than to gather as much data as could sensibly be required to alter the system to find the foundation of the defect.
Continuous Software UpdatesEmbedded systems require regular software updates like the kernel upgrade, security fixes, different device drivers, etc. Constraints identified with the software updates influence makes bug identification difficult. Additionally, it increases the significance of build and deployment procedure.
Summary:There are some difficulties in testing embedded software testing that makes it more difficult than regular software testing. The most fundamental issue is the tight reliance on the hardware environment that is prepared simultaneously with the software, and that is regularly required to perform reliable software testing. Sometimes it is even difficult to test the software without custom tools, which effortlessly makes concentrating on testing in late stages exceptionally enticing.
One of the most important things is that you should think about is the fact that you should often opt for automated software testing. The embedded automated testing is a quicker process which would take some hours to complete, and in this way, the issue of your software is settled.
Complete Guide To What Is Laravel Artisan With Example
Introduction to Laravel Artisan
Web development, programming languages, Software testing & others
What is Laravel Artisan?
Laravel Artisan is one of the three command line interfaces found in the Laravel framework.
It is a helpful command line interface, assisting developers into developing applications though the numerous easy to read commands. One can also create custom codes in order to increase the efficiency of the applications.
But the efficacy of Artisan does not end here.
Developers can generate migrations, publish assets of packages and many similar tasks. Artisan has a whole lot of built in command which are a boon to the developer.
Though a whole lot generally work on custom commands, there are many who prefer the inbuilt ones.
php artisan list
This query will give a list of commands which certainly increases the efficiency of the entire process and saves a whole lot of time.
With these commands one can go ahead and create a plethora of functions, namely, controller, migration, create, mail, et al.
Laravel 3.7 was the initial offering from PHP and it was quite basic in its code structure. Though in spite of that, it had some excellent features to boot.
With the release of Laravel 5.7, added on to the newer sets of commands, some existing packages too are available.
Laravel 4, is primarily dependent on the Symfony framework.
How does Laravel Artisan work?
The Laravel Artisan serve command helps run application on the PHP Development Server.
A developer has the option to use Laravel Artisan serve for a variety of roles.
There are other two functions that the Laravel Artisan serve support. The change of application address by using the host and port.
The application’s port can be changed by using the port option.
The Laravel Artisan works in two ways, through the inbuilt commands and custom commands.
The Laravel Artisan has a robust set of inbuilt commands that can help one create a variety of functions.
On the other hand, developers also have the option of creating their custom made commands.
Example of Laravel ArtisanExample which will exemplify how a custom command can be created:
Create the custom command:
Execute the command on the terminal:
php artisan make:command CreateEmployeeNameA file is now created in the directory: app/console/Commands
The name of the file would be: CreateEmployeeName
The complete code would be:
<?php namespace AppConsoleCommands; use IlluminateConsoleCommand; class CreateEmployeeName extends Command { /** * The name and signature of the console command. * * @var string */ protected $user = 'command:name'; /** * The console command description. * * @var string */ protected $description = 'Command description'; /** * Create a new command instance. * * @return void */ public function __construct() { parent::__construct(); } /** * Execute the console command. * * @return mixed */ public function handle() { } }The immediate next step is to go ahead and update the Laravel command which we have just created.
While doing that a few queries have to defined for the command structure to properly function:
$user: Create admin
$description: Create the account of the user which would be having admin role.
The role field which is located in the user table also needs to be updated.
protected $commands = [ CommandsCreateEmployeeName::class, ];Once the chúng tôi has been updated, the custom command can now be run since it has become a part of the list. You can check the same by using the list command. However, if you still think this will work, you are wrong. It won’t. Commands work on logic, which has to be built.
Once the migration table is created update the model array:
protected $fillable = [ 'name', 'email', 'password', 'role' ];Now you can go ahead and update the handle() as per your wish:
Few quick examples to look at:
1. To begin a Laravel Artisan project
Code:
php artisan serveOutput:
2. To enable the caching mechanism
Code:
php artisan route:cache3. To view help, options and arguments
Code:
php artisan help serve4. Generating a new command class
Code:
php artisan make:console GoCommand ConclusionPHP Artisan has all the elements which will help the developer build a complete application. And as we have discussed, it is not simply limited to building of applications. One can do a host of other activities taking the help of the list of commands, that PHP Artisan holds. Also, with custom coding, the example of which we encountered above, this entire process of development becomes a lot more customized and personal. Needless to say, there is a rapid increase in the efficiency level too.
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Iot Testing Tutorial (What Is Process Challenges Tools)
IoT
IoT (Internet of Things) is a network of devices, vehicles, buildings or other connected electronic devices. This connection eases the collection and exchange of data. An IoT system has the following parts −
Sensor
Application
Network
Backend; also known as data centre
IoT is a connection of embedded devices along with the internet infrastructure. It is an era of smart, connected products that communicate and transfer massive amount of data and upload it to cloud.
Examples of IoT
Wearable tech − Wearable gadgets like smart watches, Fitbit bands, Apple watches, etc. easily connect with mobile devices in synchronization. They collect necessary information such as health, heart rate monitoring, sleeping activity, etc. These devices display data, notifications from mobile devices on them.
Infrastructure and development − With an application like CitySense, we can easily collect outdoor lightning data. On the basis of these data, the street lights are controlled. There are other applications also to control traffic signals and parking in a cosmopolitan city.
Healthcare − In healthcare sector, IoT is used to monitor the health conditions of the patients. On the basis of the benchmarked data, the dose of medicine at different times in a day is controlled. Applications (e.g., UroSense) track and monitor the fluid levels in the patient, and initiate fluid transfer as per the needs. The data can be simultaneously transferred wirelessly to the stakeholders.
IoT Technologies
RFID (radio frequency identification) tags and EPC (electronic product code)
NFC (near field communication) − It is used for 2-way interaction between electronic devices. It is mainly used for smartphones and for contactless payment transactions.
Bluetooth − This technology is used in cases where short-range communication is well enough to solve the problem. Bluetooth is mostly used in wearable technologies.
Z-Wave − This low power radio frequency communication technology is used in home automation, light controlling, and so on.
WiFi − This technology is the most common choice for Internet of Things. WiFi along with a LAN (Local Area Network) helps transfer files, data and messages easily.
IoT TestingIoT testing is a sub-category of testing to check IoT devices. We now need to provide better and faster services. There is a huge global demand to access, create, use and transfer data. The aim is to provide insight and control, of various interconnected devices. That is why IoT testing framework is so important.
Types of IoT TestingIoT testing generally revolves around security, analytics, devices, networks, processors, operating systems, platforms and standards.
Usability TestingUsers use many devices of varying shape and form factors. Also, the perception varies from user to user. This is why investigating the usability of the system is very important in IoT testing. The usability of each device used in IoT must be determined. In healthcare. The tracking devices used must be portable so that they can be moved to different divisions. The equipment used should be smart enough to push notifications, error messages, warnings, etc. The system must log all the events occurring to provide clarity to the end users.
Compatibility TestingA number of devices can be connected through the IoT system. Such devices have varying software configurations and hardware configurations. Therefore, there is a huge number of possible combinations, thereby making the compatibility of an IoT system important.
Compatibility testing is also important due to the complex architecture of the IoT system. Testing items like OS versions, browser types, devices’ generation, communication modes is vital for compatibility testing.
Reliability and Scalability TestingReliability and scalability of any IoT system is important for setting up the IoT testing environment that involves simulation by using virtualization tools and technologies.
Data Integrity TestingData integrity testing of an IoT system is important as it includes massive amount of data and its applications.
Security TestingIn an IoT environment, a large number of users try to access a massive amount of data. This is why it becomes important to determine user validation through authentication, possess data privacy controls as in security testing.
IoT is data-centric, i.e., all the devices, equipment, system, etc. operate based on the available data. While the data is getting transferred between devices, it can always be read or accessed. The data must be checked to determine if the data is protected/encrypted while it is getting transferred between devices.
Performance TestingPerformance testing is essential for developing a strategic approach to develop and implement the IoT testing plan. The chart below is the applications of the different types of testing for various IoT components.
IoT Testing TypesSensorApplicationNetworkBackendFunctional testingTrueTrueFalseFalseUsability testingTrueTrueFalseFalseSecurity testingTrueTrueTrueTruePerformance testingFalseTrueTrueTrueCompatibility testingTrueTrueFalseFalseServices testingFalseTrueTrueTrueOperational testingTrueTrueTrueTrue
IoT Testing ProcessTest CategoriesSample Test ConditionsComponents Validation
Hardware
Embedded software
Cloud infrastructure
Network connectivity
Third-party software
Sensor testing
Command testing
Data format testing
Robustness testing
Safety testing
Function Validation
Basic device testing
Testing of IoT devices
Error handling
Valid calculation
Conditioning Validation
Manual conditioning
Automated conditioning
Conditioning profiles
Performance Validation
Data transit frequency
Multiple request handling
Synchronization
Interrupt testing
Device performance
Consistency validation
Security and Data Validation
Validation of data packets
Verification of data losses or corrupt packets
Data encryption or decryption
Data values
User roles and responsibilities and utility pattern.
Gateway Validation
Cloud interface testing
Device-to-cloud protocol testing
Latency testing
Analytics Validation
Sensor data analytics checking
IoT system operational analytics
System filter analytics
Rule verification
Communication Validation
Interoperability
Device to Device
Interrupt testing
Protocol
Challenges faced in IoT testing
Both, the network and internal communication, needs to be checked.
One of the biggest concerns in IoT testing is security and privacy because the tasks are done via Internet.
The software complexity as well as the system itself may conceal the bugs or defects found in the IoT technology.
There are limitations on memory, processing power, bandwidth, battery life, etc.
Suggestions to make IoT testing effective
Gray box testing and IoT testing should be performed simultaneously as it enables the designing of effective test cases. This helps us understand the operating system, architecture, third-party hardware, new connectivity, and hardware restrictions.
Real-time OS is vital to provide scalability, modularity, connectivity, and security, all of which are essential to IoT.
To make it effective, IoT testing can be automated.
Tools for IoT testing
Shodan − This tool can be used to determine which device/s is/are connected to the Internet. It helps track all the computers which can be directly accessed from the Internet. Shodan is also used in connectivity testing. It helps in the verification of the devices connected to the IoT hub. It provides the connected devices, their locations, user information, etc. It tracks and records all the computers connected to the network.
Thingful − This is a Search Engine for IoT. It helps keep interoperability between millions of objects through the Internet, secured. Thingful is used to control how tha data is used. It also helps take more decisive and valuable decisions.
Wireshark − This open-source tool is used to monitor the traffic in interfaces, source/destination host addresses, and so on.
Tcpdump − This tool is quite similar to Wireshark, but for the absence of GUI (Graphical User Interface). This tool is based on command line. It helps users display packets such as TCP/IP that are transmitted over a network.
JTAG Dongle − This tool is quite like a debugger in desktop applications. It is used in debugging the target platform or device code, and display variables step by step.
Digital Storage Oscilloscope − This tool is used to investigate the different events with time stamps, glitches in power supply, and signal integration.
Software Defined Radio − This tool is used to mimic receiver and transmitter for a wide range of wireless gateways.
MQTT Spy − If the device supports MQTT protocol, then this tool is the most useful. MQTT Spy is an efficient open-source tool for IoT testing. It is particularly useful for day-to-day usage.
Prerequisites of IoT Testing
Setting up IoT device − The IoT device must be turned on, and can be accessed and used in real life. E.g., while testing a smart watch, make sure to wear it on wrist. Placing it on a table would not be regarded as a real user case.
Setting up of IoT Hub − IoT hub is a server that can connect with IoT devices and gather information from them. An IoT hub may be an application in a mobile device or a web server on a cloud. The IoT hub must be set up properly.
Setting up network − We need a strong wireless connection to connect IoT hub and the IoT device together. This can be possible with a Wi-Fi, Bluetooth, satellite signals, NFC (near field communication), etc. While connecting wearable device with a mobile app, ensure the following −
The Bluetooth of both the devices is turned on.
Both the devices are paired together.
Both the devices are in range of each other.
What Is Data Analysis? Research, Types & Example
What is Data Analysis?
Data analysis is defined as a process of cleaning, transforming, and modeling data to discover useful information for business decision-making. The purpose of Data Analysis is to extract useful information from data and taking the decision based upon the data analysis.
A simple example of Data analysis is whenever we take any decision in our day-to-day life is by thinking about what happened last time or what will happen by choosing that particular decision. This is nothing but analyzing our past or future and making decisions based on it. For that, we gather memories of our past or dreams of our future. So that is nothing but data analysis. Now same thing analyst does for business purposes, is called Data Analysis.
In this Data Science Tutorial, you will learn:
Why Data Analysis?To grow your business even to grow in your life, sometimes all you need to do is Analysis!
If your business is not growing, then you have to look back and acknowledge your mistakes and make a plan again without repeating those mistakes. And even if your business is growing, then you have to look forward to making the business to grow more. All you need to do is analyze your business data and business processes.
Data Analysis ToolsData Analysis Tools
Data analysis tools make it easier for users to process and manipulate data, analyze the relationships and correlations between data sets, and it also helps to identify patterns and trends for interpretation. Here is a complete list of
Types of Data Analysis: Techniques and MethodsThere are several types of Data Analysis techniques that exist based on business and technology. However, the major Data Analysis methods are:
Text Analysis
Statistical Analysis
Diagnostic Analysis
Predictive Analysis
Prescriptive Analysis
Text AnalysisData analysis tools make it easier for users to process and manipulate data, analyze the relationships and correlations between data sets, and it also helps to identify patterns and trends for interpretation. Here is a complete list of tools used for data analysis in research.
Text Analysis is also referred to as Data Mining. It is one of the methods of data analysis to discover a pattern in large data sets using databases or data mining tools. It used to transform raw data into business information. Business Intelligence tools are present in the market which is used to take strategic business decisions. Overall it offers a way to extract and examine data and deriving patterns and finally interpretation of the data.
Statistical AnalysisStatistical Analysis shows “What happen?” by using past data in the form of dashboards. Statistical Analysis includes collection, Analysis, interpretation, presentation, and modeling of data. It analyses a set of data or a sample of data. There are two categories of this type of Analysis – Descriptive Analysis and Inferential Analysis.
Descriptive Analysisanalyses complete data or a sample of summarized numerical data. It shows mean and deviation for continuous data whereas percentage and frequency for categorical data.
Inferential Analysis Diagnostic AnalysisDiagnostic Analysis shows “Why did it happen?” by finding the cause from the insight found in Statistical Analysis. This Analysis is useful to identify behavior patterns of data. If a new problem arrives in your business process, then you can look into this Analysis to find similar patterns of that problem. And it may have chances to use similar prescriptions for the new problems.
Predictive AnalysisPredictive Analysis shows “what is likely to happen” by using previous data. The simplest data analysis example is like if last year I bought two dresses based on my savings and if this year my salary is increasing double then I can buy four dresses. But of course it’s not easy like this because you have to think about other circumstances like chances of prices of clothes is increased this year or maybe instead of dresses you want to buy a new bike, or you need to buy a house!
So here, this Analysis makes predictions about future outcomes based on current or past data. Forecasting is just an estimate. Its accuracy is based on how much detailed information you have and how much you dig in it.
Prescriptive AnalysisPrescriptive Analysis combines the insight from all previous Analysis to determine which action to take in a current problem or decision. Most data-driven companies are utilizing Prescriptive Analysis because predictive and descriptive Analysis are not enough to improve data performance. Based on current situations and problems, they analyze the data and make decisions.
Data Analysis ProcessThe Data Analysis Process is nothing but gathering information by using a proper application or tool which allows you to explore the data and find a pattern in it. Based on that information and data, you can make decisions, or you can get ultimate conclusions.
Data Analysis consists of the following phases:
Data Requirement Gathering
Data Collection
Data Cleaning
Data Analysis
Data Interpretation
Data Visualization
Data Requirement GatheringFirst of all, you have to think about why do you want to do this data analysis? All you need to find out the purpose or aim of doing the Analysis of data. You have to decide which type of data analysis you wanted to do! In this phase, you have to decide what to analyze and how to measure it, you have to understand why you are investigating and what measures you have to use to do this Analysis.
Data CollectionAfter requirement gathering, you will get a clear idea about what things you have to measure and what should be your findings. Now it’s time to collect your data based on requirements. Once you collect your data, remember that the collected data must be processed or organized for Analysis. As you collected data from various sources, you must have to keep a log with a collection date and source of the data.
Data CleaningNow whatever data is collected may not be useful or irrelevant to your aim of Analysis, hence it should be cleaned. The data which is collected may contain duplicate records, white spaces or errors. The data should be cleaned and error free. This phase must be done before Analysis because based on data cleaning, your output of Analysis will be closer to your expected outcome.
Data AnalysisOnce the data is collected, cleaned, and processed, it is ready for Analysis. As you manipulate data, you may find you have the exact information you need, or you might need to collect more data. During this phase, you can use data analysis tools and software which will help you to understand, interpret, and derive conclusions based on the requirements.
Data Interpretation Data VisualizationData visualization is very common in your day to day life; they often appear in the form of charts and graphs. In other words, data shown graphically so that it will be easier for the human brain to understand and process it. Data visualization often used to discover unknown facts and trends. By observing relationships and comparing datasets, you can find a way to find out meaningful information.
Summary:
Data analysis means a process of cleaning, transforming and modeling data to discover useful information for business decision-making
Types of Data Analysis are Text, Statistical, Diagnostic, Predictive, Prescriptive Analysis
Data Analysis consists of Data Requirement Gathering, Data Collection, Data Cleaning, Data Analysis, Data Interpretation, Data Visualization
What Is Business Intelligence? Bi Definition, Meaning & Example
What is Business Intelligence?
BI(Business Intelligence) is a set of processes, architectures, and technologies that convert raw data into meaningful information that drives profitable business actions. It is a suite of software and services to transform data into actionable intelligence and knowledge.
BI has a direct impact on organization’s strategic, tactical and operational business decisions. BI supports fact-based decision making using historical data rather than assumptions and gut feeling.
BI tools perform data analysis and create reports, summaries, dashboards, maps, graphs, and charts to provide users with detailed intelligence about the nature of the business.
In this tutorial, you will learn-
Why is BI important?
Measurement: creating KPI (Key Performance Indicators) based on historic data
Identify and set benchmarks for varied processes.
With BI systems organizations can identify market trends and spot business problems that need to be addressed.
BI helps on data visualization that enhances the data quality and thereby the quality of decision making.
BI systems can be used not just by enterprises but SME (Small and Medium Enterprises)
How Business Intelligence systems are implemented?Here are the steps:
Step 1) Raw Data from corporate databases is extracted. The data could be spread across multiple systems heterogeneous systems.
Step 2) The data is cleaned and transformed into the data warehouse. The table can be linked, and data cubes are formed.
Step 3) Using BI system the user can ask quires, request ad-hoc reports or conduct any other analysis.
Examples of Business Intelligence System used in PracticeExample 1:
In an Online Transaction Processing (OLTP) system information that could be fed into product database could be
add a product line
change a product price
Correspondingly, in a Business Intelligence system query that would beexecuted for the product subject area could be did the addition of new product line or change in product price increase revenues
Increase radio budget
Correspondigly, in BI system query that could be executed would be how many new clients added due to change in radio budget
In OLTP system dealing with customer demographic data bases data that could be fed would be
increase customer credit limit
change in customer salary level
Correspondingly in the OLAP system query that could be executed would be can customer profile changes support support higher product price
Example 2:
A hotel owner uses BI analytical applications to gather statistical information regarding average occupancy and room rate. It helps to find aggregate revenue generated per room.
It also collects statistics on market share and data from customer surveys from each hotel to decides its competitive position in various markets.
By analyzing these trends year by year, month by month and day by day helps management to offer discounts on room rentals.
Example 3:
A bank gives branch managers access to BI applications. It helps branch manager to determine who are the most profitable customers and which customers they should work on.
The use of BI tools frees information technology staff from the task of generating analytical reports for the departments. It also gives department personnel access to a richer data source.
Four types of BI usersFollowing given are the four key players who are used Business Intelligence System:
1. The Professional Data Analyst:
The data analyst is a statistician who always needs to drill deep down into data. BI system helps them to get fresh insights to develop unique business strategies.
2. The IT users:
The IT user also plays a dominant role in maintaining the BI infrastructure.
3. The head of the company:
CEO or CXO can increase the profit of their business by improving operational efficiency in their business.
4. The Business Users”
Business intelligence users can be found from across the organization. There are mainly two types of business users
Casual business intelligence user
The power user.
The difference between both of them is that a power user has the capability of working with complex data sets, while the casual user need will make him use dashboards to evaluate predefined sets of data.
Advantages of Business Intelligence1. Boost productivity
2. To improve visibility
BI also helps to improve the visibility of these processes and make it possible to identify any areas which need attention.
3. Fix Accountability
BI system assigns accountability in the organization as there must be someone who should own accountability and ownership for the organization’s performance against its set goals.
4. It gives a bird’s eye view:
BI system also helps organizations as decision makers get an overall bird’s eye view through typical BI features like dashboards and scorecards.
5. It streamlines business processes:
BI takes out all complexity associated with business processes. It also automates analytics by offering predictive analysis, computer modeling, benchmarking and other methodologies.
6. It allows for easy analytics.
BI software has democratized its usage, allowing even nontechnical or non-analysts users to collect and process data quickly. This also allows putting the power of analytics from the hand’s many people.
1. Cost:
Business intelligence can prove costly for small as well as for medium-sized enterprises. The use of such type of system may be expensive for routine business transactions.
2. Complexity:
Another drawback of BI is its complexity in implementation of datawarehouse. It can be so complex that it can make business techniques rigid to deal with.
3. Limited use
Like all improved technologies, BI was first established keeping in consideration the buying competence of rich firms. Therefore, BI system is yet not affordable for many small and medium size companies.
4. Time Consuming Implementation
It takes almost one and half year for data warehousing system to be completely implemented. Therefore, it is a time-consuming process.
Trends in Business IntelligenceThe following are some business intelligence and analytics trends that you should be aware of.
Artificial Intelligence: Gartner’ report indicates that AI and machine learning now take on complex tasks done by human intelligence. This capability is being leveraged to come up with real-time data analysis and dashboard reporting.
Collaborative BI: BI software combined with collaboration tools, including social media, and other latest technologies enhance the working and sharing by teams for collaborative decision making.
Embedded BI: Embedded BI allows the integration of BI software or some of its features into another business application for enhancing and extending it’s reporting functionality.
Cloud Analytics: BI applications will be soon offered in the cloud, and more businesses will be shifting to this technology. As per their predictions within a couple of years, the spending on cloud-based analytics will grow 4.5 times faster.
Summary:
BI is a set of processes, architectures, and technologies that convert raw data into meaningful information that drives profitable business actions.
BI systems help businesses to identify market trends and spot business problems that need to be addressed.
BI technology can be used by Data analyst, IT people, business users and head of the company.
BI system helps organization to improve visibility, productivity and fix accountability.
The draw-backs of BI is that it is time-consuming costly and very complex process.
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