Month: June 2022

IoT Testing Approach on Devices

Posted on by Anusha A

Data has become pivotal to any business today. A huge amount of data is generated at various touch points through myriads of interconnected devices. Collecting this data is crucial; in this blog, we will discuss the Internet of Things (IoT), southbound–embedded devices, and gateways. These two parts of IoT are called southbound. The one from cloud and UI is called northbound.

Sensors and gateways fetch real-time data from multiple entities, including people, vehicles, machines, and drones. With the gateway failing to capture data, data is lost forever. So, robust gateway testing capabilities are paramount in south bound for the proper functioning of gateway.

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The importance of gateway testing lies in ensuring that the data is not lost and the gateway devices do not go into a dead state. Also, it helps to ensure that all the possible exceptional cases accounting for the loss of the internet connectivity to the cloud and the data from sensor are considered.

We will go in-depth into the technicalities of gateway testing. Before that, let’s glimpse different types of sensors and the importance of gateways in the IoT.

What are different sensors used across industries?

Embedded devices (sensors) are different for different domains, as we see below:

  • Healthcare: Health monitors from pressure, airflow, oxygen, pulse oximetry, temperature, and barcode sensing
  • Agriculture: Soil temperature sensors, location sensors, optical sensors, electro-chemical sensors, mechanical sensors, dielectric soil moisture sensors and airflow sensors.
  • Logistics: Temperature sensors, humidity sensors, infrared sensors, radar meters, road condition sensors, and visibility sensors.
  • Energy: Smart grid, EB reader, current & voltage sensors, position sensors, wind speed sensors, absolute motion and direction sensors, non-contract harsh position sensors
  • Mining: Intrusion detector, quality sensors, vibration sensors, temperature sensors, and magnetostrictive and inductive sensors.

Each embedded device is a data source, and there will be data either from a single source or from a large field. This data must be gathered and harnessed without any loss and sent to the cloud by Gateways devices. The information is then used in performing analysis. Hence, Gateways are the heart of the IoT system.

You might be interested in : IOT Testing Challenges and Approach

Gateway devices and their importance

Gateway devices are the central hubs for IoT devices, where data is collected at the edge device locally before sending it to the cloud. Gateways interconnect devices within the IoT. These are important bridges that connect the IoT sensor network and cloud. Reasons why gateway devices are key:

  • The data from the source needs to be collected, i.e., without any loss.
  • Irrespective of any kind of protocol or means of transport like wired or wireless.
  • Data comes from a single source or multiple sources.
  • Data can come from a closer location or a distant one.
  • Data from a sensor, or another control unit will be collected.

How to test gateway in the IoT?

Some gateways require a lot of synchronization, scheduling, and buffering mechanisms, mostly when huge data is collected. On the other hand, a few require nothing but a simple microcontroller unit with a GSM connection to send data. What is important is how the gateway firmware is managed to run all the time without losing any data. Gateways are different for different domains. Here we will go into the details of gateway testing by understanding different scenarios.

The sole purpose of gateway testing in IoT is to address as many worst cases as possible. Let’s take the example of cellular tower testing.

In the event of AC power loss, the diesel engine source will give power to the tower so that data is not lost. The gateway will monitor the sensor data from the AC power source, which will be collected continuously. Whenever there is no data from the source of AC, the possible scenarios are:

  • The sensor is faulty
  • The sensor collecting data, lost its connectivity to the gateway
  • There is an intrusion.
  • The sensor is removed.
  • There is no current passing through the sensor.

Let’s look at in detail:

  • Faulty sensor: In case of sensor fault, the alert for that fault is raised from the gateway to the cloud. The scenarios that needs to looked for are, it is not supposed to turn on the diesel engine source or should wait for the fault to be resolved as it is a critical alert and run-in diesel engine till then.
  • Gateway loses connectivity to the sensor: The controller is supposed to give exact information. The possibilities are that while the gateway was performing other data processing of high priority, it might have lost its regular listening, and so the gateway is supposed to try again to receive the latest data from the sensor. In such cases, there should be a business architecture that states the number of times the gateway can attempt to connect to the sensor and then declare that the communication to sensor is lost, following which the diesel engine turning on or waiting.
  • Intrusion: Intrusion occurs when it shows the diesel engine running, but in reality, it’s AC source. These activities take place at a lower field level to steal diesel. Testing becomes a high priority in such a case and is essential to prevent manual turning on of diesel engines. With a good testing procedure, the system sends a notification to the authorized person about possible manual intrusion and prevents unauthorized access.
  • Sensor removal: The sensor should be removed/replaced only by an authorized person, and the sensor must have a similar configuration as validated by the business. So, those things should be allowed only for limited users, and the information must be recorded in reports.
  • Other field cases: According to the climatic conditions, the sensor’s data collection might differ, which might require configuring accordingly. And there are certain temperature conditions where the sensor will enter a sleep state, maybe due to very cold or hot conditions. So, testing must consider these possibilities prior to designing or customer acceptance planning. The environment in which the sensor will be placed must be considered.

The above-described scenarios are mostly functional and related to data validation. So, the testing of the above involves functional validation and verification. There will be many test cases here in which a suitable automated tool to simulate all kinds of sensor data can be used to ensure smooth functionality.

Testing Gateway for IoT: Some facts to consider

Lately, the testing of IoT is test-driven on live data. There are specific tools used for simulating data as expected from the field and verifying the results and effects the data can cause to the existing system. There might be legacy systems where IoT sensor integration would have been done for value addition. For such cases, these tools shall help for testing and analyzing the results without disturbing the legacy system and live data. These are similar to a replica of the field environment which are also helpful for OEM.

In any case of data loss, the gateway must be intelligent enough to detect the exact reason and raise an alert accordingly. When thousands of data sources are there, the data handling capacity of the system can be ascertained by testing. Testing will be crucial in getting an idea about the maximum number of sensors the gateway can handle.

Apart from regular testing of sending message notifications from the SIM on the device, there are cases where the field person might install a SIM, which also allows for incoming data messages. The SIM will be receiving incoming messages, and the memory will be loaded on the SIM; and hence the actual functionality to send the data notification might not be possible. Such cases require boundary analysis and exploratory testing.

When a gateway has been running for years without any problems and stops working due to the Wi-Fi process, it reboots itself. For such single process cases, the gateway should not restart itself, but the process which stopped alone shall restart softly and ensure that there is a seamless connection. Testing in such cases shall add value to the performance of the gateway.

There are performance conditions which shall lead to gateway crash, like when there is a huge request of data from the cloud, and in case the process fails to manage and send the full information as requested, those things can be simulated by tools and verified. Generally, the gateway functionality is tested by shell scripts and automating with the same.

Learn how Indium helped a semiconductor manufacture test their IoT analytics platform

With respect to configuring the firmware, what is important for the verification team is to ensure that only a valid configuration set is allowed while configuring and proper detailed guidance or information is given to complete the same. Here regression testing helps to verify all sets of configurations. Testing helps verify that the same configuration set has been configured on the cloud. So, there is smooth data transmission from the gateway to the cloud. These configuration sets are those which will help for the segregation of scheduled and alarm data from data sources. The historical data will help to build an AI-based predictive maintenance process. These are the actual IoT testing services which are of business value.

The value gateway testing brings to business

The testing of a gateway is like an endless ocean. But the key is approaching the test by categorizing its requirement according to the domain. However, the main tests are performed to give reports on failure (gateway down). As many exceptions and alerts due to gateway failure are known, it is required for us to reach the device remotely to fix the error. If the gateway runs on the filed for longer period, exceptions will keep adding as seen in the firmware updates.

Finally, what are the uses of reports and insights graphics if the data is not valid? Does invalid or lower performance data make sense to business? Instead of adding value, would it lead to a loss? Testing helps address all these questions. So, gateway in IoT requires robust testing.

Enable Increased Revenue using Rapid Application Development

Posted on by Adhithya S

Technology is constantly evolving in today’s competitive business environment; hence it is important to build new and innovative software where the features serve the customer better. The building and delivery of the software need to become faster, and the evolving needs of customers require to be catered to before your competitors.

According to BusinessWire, the rapid application development market is expected to reach a compound annual growth rate of 42.6% between the period 2021 and 2026. The next few years will see many enterprises adopting rapid application development.

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Rapid development means that the application development model provides prototypes at a high velocity. There is constant feedback from the customer which is used to extend the product’s life cycle on the market.

As building software is a very dynamic and constantly changing field, rapid application development services are built in a way that less stress is put on planning tasks, and more emphasis is put upon the rapid development of the prototypes. Let’s look at why rapid application development (RAD) is important in today’s IT environment, and the phases included in the process:

The Significance of Rapid Application Development (RAD)

Rapid application development is one of the most efficient software development program methodologies today. Discussed below are some advantages that come with adopting rapid application development services:

  • Flexibility to make Changes: Rapid application development is useful in a situation where the product needs to constantly go through quality checks and changes to the prototype. The client can give continuous feedback which can be incorporated into the application development cycle so the end product is satisfactory to the customer.
  • Comprehensive Knowledge of Features: The RAD model can be effective for software engineers and developers to gain knowledge on exploring different functionalities, user experience features, and graphics. Specific client requests can be met and the overall rejection levels can be reduced by receiving more comprehensive feedback from the customer.
  • Focus on Iterative Design and Teamwork: The overall structure of a rapid application development model allows to improve the teams’ efficiency. This is done by picking and choosing tasks according to the members’ specialties and past experiences. Thus, the process is streamlined, enabling constant communication between the team members and stakeholders, which significantly increases the quality and efficiency of the build process.

Phases of Rapid Application Development

There are four distinct phases in the process of rapid application development. The steps included are as listed down below:

  • Phase 1: The first step includes the requirement planning stage which helps to flesh out what the overall project scope is. In this stage, both developers and team members deliberate on the goals and expectations for the project. Hurdles that may arise are easily mitigated after this step. This is achieved, as research for defining requirements is done and finalized according to the stakeholders’ approvals.
  • Phase 2: When the project’s goals are listed down, it is finally time to get into the development and building stage of the application. This is done by getting different user designs by creating a number of prototype iterations. This interactive phase allows the product to take the right path and meets user expectations. Each product is tested and feedback is sent to the developers to fix any bugs or defects in the iterative process.
  • Phase 3: The construction phase converts the prototypes acquired from the user design phase into a working model. Users get to suggest improvements and changes as the software is developed in the pipeline. As the previous phase covers most of the issues that can arise, the construction of the model is a lot faster as compared to a traditional project management strategy.
  • Phase 4: The final phase in the rapid application development process is very similar to that of the implementation or final phase in a software development life cycle (SDLC). It includes a number of tasks such as the conversion of data, the changeover to newer systems, user training, and testing across platforms.

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How RAD can help your Business Boost Revenue

There are some key factors to consider before adopting the rapid application development (RAD) methodology. RAD works in models where the systems can be broken down into separate modules.

  • Increased Customization and Flexibility of Products: As systems can be distributed into separate forms, RAD allows frequent changes in the build of the prototype. This allows for the project to be broken down into smaller and more manageable activities for the team. The integrations can be applied from a very early stage which also helps to identify bugs early.
  • Skilled Capabilities in Workers & Technology: Highly skilled designers are sought out in order to build the perfect model for the client’s requirements. Code-generated tools make it cost-effective to create multiple prototypes as there require frequent changes to be made during the development process of the project.
  • Cost-Effective Models in Development: Rapid application development helps in reducing fixture and machine costs. This is alongside also making sure that the lead time is reduced by constantly setting up molds for products in real-time. Depending on the nature of the product, it can be expensive to purchase or create fixtures that can accommodate new technology. The reduced rates on material and lead creation can be utilized in conducting more marketing and testing activities.
  • Oversight with Collaboration until Deployment: The RAD process requires all the members of a team to be fully committed to the approach. As it is different from the traditional methodology of creating applications, there is always a need to ensure that all stakeholders are on-board with the strict timeline that needs to be adhered to. The success of any RAD process is fully dependent on the project manager’s capabilities to outline the development phase and have a successful communication channel established with the team members and stakeholders all in real-time.

Also Read: Taking advantage of Mendix’s Rapid Application Development Capabilities with MS Azure

What the Future Holds for RAD

Although rapid application development as a process is a few years old, it remains very relevant in the current state of IT. There is a need to deliver products to the market a lot faster to keep up with market trends. Traditional approaches come with strict planning and documentation which can be both time-consuming and expensive for businesses.

Since the product needs to be tailor-made to the customer’s expectations, the fast development needs to come with ample client participation. As the client can see the prototype at any point, RAD emphases on rapid prototyping where the process can be used on all project sizes.

Fast-project turnaround, a rapid working pace, and constant customer feedback loops are some integral parts of the rapid application development process. Leveraging rapid application development can help in reducing the overall planning time and help focus on multiple prototype iterations to satisfy customers at every step of the development process.

How to Leverage your Data and Analytics Resources for Innovation

Posted on by Adhithya S

Business intelligence and data analytics can provide deep insights into business operations. This can enable businesses to take a data-driven approach wherein they can integrate artificial intelligence, data and analytics, machine learning and data science to raise the standard of processes for future activities.

Technology is one of the key driving factors in the market for predictive analytics. Newer cloud-native solutions are being continually worked on leaving legacy data analytics solutions behind. This is done so that businesses can derive qualitative and faster intelligence by shifting to cloud-native data solutions.

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Discussed below are some best practices that businesses must follow while they leverage their data and analytics resources for better business insights:

Best Practices while Leveraging Data & Analytics Resources

  • Source Data with an Ample Strategy: Many companies refrain from adopting the right analytics processes as they believe the quality of the resources are not up to the mark. Data can be sourced or purchased through free open-source resources and other data providers. An organization should balance the cost of acquisition for the resources with what value the data brings to the analytics effort.
  • Transition from Analytics Projects to Products: Analytics projects more often than not are to be planned for the get-go and have a defined scope. There needs to be a strategy formed before-hand. Instead, if businesses focus on analytics products, they can generate a considerably higher amount of return on investment (ROI) along with obtaining business insights, thereby improving the overall business performance.
  • Maintain a Close Communication Channel with Stakeholders: Engagement and support can be facilitated by enlisting stakeholders onto the initial stages of the analytics processes. The best way to build questions is to clarify assumptions and to get the stakeholder to organically put across their requirements. Simply asking what the stakeholder wants will not suffice, as additional context will have to be provided. This helps to ensure that the key performance indicators (KPIs) and business goals are being met on a regular basis.
  • Build High-Performance Teams with Compliance as the Focus: The collection of data needs to be done with compliance being the main focus. Productive teams make for more efficient teams, as they work to integrating analytics into the company’s daily workflow. There needs to be a specific importance given to how compliance affects different factors such as internal business rules, industry standards, and government regulations.
  • New Infrastructure Technology with Advanced Analytics: There is a need to consider building an ecosystem that can host different technology types. These technologies can include in-memory computing for highly repetitive analytics. Companies that are measuring the best value for business are gravitating towards the use of advanced analytics. Predictive analytics is one step into the world of advanced analytics that makes use of machine learning and AI to predict future growth and success rates amongst other things.
  • Use Governance and Insights to Refine the Analytics Process: Dealing with increased amounts of data and team members accounts for governance to become a significant part of the analytics process. There needs to exist a formal procedure that helps to make certain the data that is captured is consistently of high-quality. There also needs to be a common understanding of the data’s nature across the entire organization.

Relevant Read: How a Well-Implemented Data Analytics Strategy Will Directly Impact Your Bottom Line

There are many forms of intelligence that can be used by a business to derive insights from. Let’s look at how a business can improve their customer service using trend analytics in social media using the Internet of Things (IoT)

Improving Customer Service Trend Analytics in social media

Digital marketing success can be sought out by using business analytics when working with new use cases:

  • The Internet of Things (IoT) opens up the possibility of intelligence to be distributed and consequently replenished in an automated fashion. This will surely change the essence of the overall supply chain and calls for companies to add new services that are relevant and of the right fit.
  • The usage of chatbots on a global level has been rising in recent years, as the data that is recorded from these conversations can highly enhance future communications. Chat automation powered by past insights can help in improving overall customer service and analyse trends.
  • Most companies are trying to leverage their presence and growth on social media to create a better brand image. Social media has an abundance of different types of data that can help an organization with customer service. The most important application of data from social media is analysing the public’s perception of a company’s product or services through reviews and feedback from customers. Social media data analysis also helps in determining the best time frames for company project and products to launch.
  • Online commerce and digital marketing are at the forefront of business. It is important to understand different customers, and how each new tool and technology can aid in the same.
  • When the marketplace is uneven and uncertain, customers inevitably end up paying more for solutions. There needs to be a certain maturity in the industry in question as the competition increases and the differentiators between businesses get more apparent.

Improve Insights from Business

Most of business cases requires business users to design, interpret, and deliver data that is produced by multiple applications to build technical and business analysis skills. The increasing complexity of the technological ecosystem, coupled with increasing number of data sources is rapidly changing what is considered cost-effective and practical to achieve.

Business intelligence and dashboards for analytics need to be created by business leaders while providing tactical requirements and constant inputs. It is difficult to find this exact combination of skills to make sure that the organization’s maturity is improved along with building competent capabilities to lead up to greater business needs. If you want to leverage the power of data and analytics for your business, you can consult our data engineering and data analytics experts now!

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IOT Testing Challenges and Approach

Posted on by Anusha A

The Internet of Things or IoT has been around for more than two decades, but its foray into the mainstream business has gained prominence in the last decade. IoT refers to a network of interconnected physical devices collecting and sharing data through a network. Organisations around the world are using IoT to operate efficiently, make informed decisions with real-time data, and provide enhanced customer experience. As per Statista, iPriopertyManagement.com, in 2021, the number of IoT devices is projected to surpass 75 billion by the end of 2025. Testing these devices will pose many challenges for IoT testers in the coming years.

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The main challenge which is faced in the testing of IoT is due to its vastness, integrating multiple devices, machines and sensors, like pulse-oximeter, electrocardiogram, a person with a heart monitor implant, a farm animal with a biochip transponder, an automobile that has built-in sensors to alert the driver when tire pressure is low, etc. These sensors and machines communicate in multiple protocols with an edge computing/gateway device which acts as a pipeline to collect the data from different sources. They send the collected information to the cloud, ensuring there is no loss of data, and the data is read in multiple ways by the user through phone/web interfaces etc.

In this article, we are going to discuss some of the common challenges in IoT that testers often encounter. We will also throw light on how to address them by offering an IoT testing approach before guiding you on how to select the right tools for IoT testing.

What are different IoT testing Challenges?

1.Devices/ Sensors: The data generated from a device/sensor doesn’t have intelligence on its own. The true challenge is to test data from ‘n’ number of devices that are sent at once. It can be wired/wireless, with a protocol that might differ as per business requirements. Hence, the verification of the data validation needs to be done in all these scenarios needs to be done to ensure only valid data flows in from the source. In the event of invalid data, there needs to be clarification of errors related to the same.

2. Edge computing/Gateway devices: Though this is a black box, the gateway devices are the heart of the IoT system. This has a complex configuration of the system, the schedulers, data processing and the business application firmware. The Gateway device needs to be verified to ensure that the device never goes offline. Even in case it goes offline, the reasons for failure need to be clear, and the data should not be lost but stored and then sent to the cloud as and when the device is On. The Gateway load needs to be verified to ensure that huge data transmission is not going to kill the memory of the device, but there is always a smooth transmission of data.

3.Data processing/Cloud: There are several cloud data processors available in the market. The verification of the cloud starts by ensuring that the configuration between the gateway device and the cloud is the  same and secured. Voluminous data can have the same or multiple protocol communication methods. The verification of valid data points is key so that no data loss becomes crucial in this layer. Failure scenarios are well possible in case of data loss, and so the cloud also needs to be tested.

4. User Interface (UI): There are again many user interfaces that can vary from smart devices (watches, health monitors etc.), mobile applications, web interfaces, desktop interfaces, Kiosk, TV to anything where a human can read the information. With businesses trying to provide a seamless omni-channel experience, the burden on testers is huge as there are endless software and hardware devices. Testers need to ensure that the application works seamlessly on the required software. For those that are not supported, proper information needs to be given.

5. Security: Data security is pivotal. With numerous devices connected through various networks, testing and fixing vulnerabilities that can pose a security risk for business is crucial. Let’s discuss the security aspect by considering the following example.

You might be interested in : IOT Testing Approach on devices

In a mall, a fire sensor was replaced as it was not working. The sensor which was replaced is of the same brand but a different version type.  In such a situation, testers need to ensure that:

  • Ensure the device cannot be configured during replacement.
  • While removing the previous device, there should be a proper uninstallation process, or the past data should be recorded.
  • While configuring the new device, appropriate information stating that the device does not support should be provided in the respective UI.
  • Ensure the brand device of the same version is replaced and there is proper data transmission.
  • From a UI perspective, only installers who have access can do the replacement
  • Alarm messages are sent only to the configured users.

IoT Testing approaches to follow for perfectly addressing the challenges

From the above challenges, you must have understood that test scenarios are huge for functionality testing. But testers don’t always have time to test all the scenarios in an exploratory model, which may lead to delivery delays. So, it is important for testers to intelligently plan the test approach according to the product/project and minimize the time of test, and finish testing on time.

The following table gives an idea of what testing types will be applicable for the IoT layers.

IoT Testing TypesSensorApplicationNetworkBackend
Functional testingTRUETRUEFALSEFALSE
Usability testingTRUETRUEFALSEFALSE
Security testingTRUETRUETRUETRUE
Performance testingFALSETRUETRUETRUE
Compatibility testingTRUETRUEFALSEFALSE
Services testingFALSETRUETRUETRUE
Operational testingTRUETRUETRUETRUE

The best practice to plan testing is to have a checklist or an acceptance criterion clearly defined for the whole product. This helps to set a clear boundary for the testing team and to avoid testing other scenarios. 

The following are a few samples questionaries that must be considered here:

  1. 1. IoT testing services involves myriad end-users, and they are important. So, we need to address questions surrounding end users, business users, and actors (includes support/maintenance/installers). These questions include:
    1. a. Who are the end-users (includes specification of age group and family type)? It is good to start testing with a clear persona detail in hand.
    1. b. Who are the Actors (includes trained installers and field operation men)?
    1. c. Who are the business users (includes financial team, CEO, and OEMs)?

2. For Software and Hardware, it is good to start testing once the requirements are clear.  Points that need to be considered here are:

  • If it is a mobile application, ensure to get clarity on the mobile OS types, resolution, and supported software.b. For the hardware, ensure that the manufacturer’s detailed version is known.

c. As for the cloud, ensure that there will always be upgrades, and those upgrades are captured in the system. The lower version support also needs to be known.

  • 3. For Industrial Internet of Things (IIOT), it is the field which matters, and the final product should work in the field. So, it is always mandatory to understand all the environmental facts with respect to climatic conditions and the specifications and behavior of end devices, too, with respect to the environment. It is advisable to have compliance certification as per the geographical location.

How to choose the appropriate tools for IoT Testing?

Several open as well as paid tools are available in the market. Choosing the right tool depends on the design strategy of the IoT product, Saas, Paas or IaaS.

It is also difficult to have a single tool for a complete product and to design an automation framework for a product as a whole. So, it is good to split up testing according to the layers and use separate testing methods and automation tools.

However, among all the integration, testing will play a major role. While there are CI/CD pipeline models, integration testing brings in lots of possibilities and metadata and meaningful testing to the product. The Northbound of IoT (Cloud-> communication network ->UI) can be chosen to be automated separately. Whereas testing gateway and devices require apt tools according to the product/domain and architecture. As the investment in the hardware is quite high, choosing virtual simulators and emulators can prove helpful in generating the data points.

Another important aspect of IoT testing is pilot testing. In this, the product runs directly on the field post the verification runs are executed in the sophisticated lab environment with virtual simulations. Once the product is installed in the field, testing with the real data is very important. Only when this is done the reliability of the product is assured. In order to understand and collect the data, pilot testing ranging from 1 month to 6 months is advisable.

Conclusion

Even for a single IoT product, several components have to be tested. Hence it is always good to initiate testing in the design phase itself. Though exploratory testing takes a longer time compared to other testing methods, it is important in IoT as there are several users, and the same data is read by various users.

Remember that testing with requirements is never sufficient for the IoT, but exploring data and the reports and insights from the data source actually brings a great useful product in the end.

Data Visualization Testing

Posted on by Senthil kumaran.G

Introduction

Data visualization takes a very important part in the business intelligence / data analytics since this is the part where the customer will view their required outputs / results. Data visualization is usually called as the pictorial / graphical representation of any data. In Big Data, the data visualization tools are used to research and analyze the huge or massive data / information and make it as pictorial representation for end user to access them for their further research or to get their desired output through drill-downs.

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Here are a few main items / elements present in the Data visualization:

  • Graphs / Charts
  • Tables
  • Filters
  • Navigation buttons
  • Notes

Etc.

Here are some techniques in Data Visualization:

  • Box plots
  • Heat maps
  • Histograms
  • Charts
  • Tree maps
  • Network diagram

Etc.

Before getting these items / elements, we have a process called ETL to get the required table to visualize. We have three stages in the ETL process. They are:

  • Extract – Extract the data from the source / raw database
  • Transform – Improve the quality of the data and make it consistent
  • Load – Load the data to the target database

We get the target database to visualize the required outputs / results using the BI tools like Tableau, Power BI, etc.. We can call the output / results as “Dashboard” / “View” in the BI tools.

Data visualization testing

The main aim of any validation is to meet the requirement and we can follow the same testing process which we are following for the web testing, but the testing methodology differs here. Frankly speaking, it is very hard to validate the data and analytics when the respective persons who are going to validate are not subject matter expects on how the dashboards / views / data are developed and most of the time, the data by itself is incorrect. Usually, this can be overcome by understanding the proper requirements from the respective experts like business analyst, business economist, data scientist, etc..

There are many approaches to validate the dashboards / views. In general, we have two parts to validate the dashboards are UI and Data.

UI:

Here are a few common scenarios to validate the UI part:

  • The default elements (for example: title, sub-title, filters, tooltip info etc.)
  • Type of the filters (for example: multi-select drop-down or single select drop-down)
  • Behaviour of the filters (for example: Context filter)
  • Resolution / Font style & size / Colors
  • Legends order
  • Notes / valid error message

Etc.

Data:

Here are a few common scenarios to validate the Data part:

  • Compare the values between the database and dashboard
  • Validate the calculations and logics used
  • Check decimal places / rounding

Etc.

In the Data part, for validating / comparing the values between the source and the output, we have multiple approaches which we can choose based on the project need:

Raw database vs. Dashboard:

We must build the output / source table from the Raw data using SQL (we have to replicate whatever logics are used by the ETL team to get the Aggregated / Output table) to validate the dashboard. Once we have the source table for the dashboard, we need to build the further logic / calculation to validate the specific charts / tables in the dashboard using Excel Pivots or SQL.

Aggregated / Results table vs. Dashboard:

We just need to build the output / source table from the Aggregated / Results table (no need for creating the logics since it is already done by the ETL team) to validate the dashboard. Once we have the source table for the dashboard, we need to build the further logic / calculation to validate the specific charts / tables in the dashboard using Excel Pivots or SQL.

Previous version vs. latest version:

In some projects, they are planning to improve the performance or upgrade the BI tool version. In this case, we should validate the data between the versions. Just downloading the values from both the versions for the same combinations and compare the values between them using Excel (usually it is called as Test Harness – it has source table in the left and latest table in the right and using the formula (source – latest = 0) to get the output.) Etc.

Note: The above methodologies are used commonly but we might have more methodologies than these.

Common bugs in the Data visualization testing

UI bugs

• Mis-order of legends
• Irrelevant color
• Missing data in tooltip
• Missing data labels
Etc.

Data bugs

• Mistakes in calculation / logic
• Issues in Decimal / rounding
• Backend filter is not applied
Etc.

Conclusion

The World is already transforming to digitalization and the data is already taking a vital role in any businesses and making the data as pictorial / graphical (i.e. Data Visualization) which makes the digitization faster than before. If we need to create the quality product to make the brand, there should be quality check before handing over the product to the respective client or customer. Welcome to our digitized world and eagerly waiting for the upcoming features in digitization / visualization.

Migration of App testing from Cloud to Decentralized server / Decentralizing Apps

Posted on by Venkatesh V

App testing solutions were traditionally carried out in local environments, but with time, the cloud helped overcome limitations posed by this approach. However, even the cloud is not a panacea for rightfully addressing the challenges that app testing poses.

To streamline app testing, migrating app testing from the cloud to decentralized server or decentralized apps has been emerging as the way out. There are several advantages that decentralized apps (dApps) offer for app testing when compared to the cloud.

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In this blog, we intend to offer you insightful details about the relevance of migrating app testing from the cloud to decentralized apps (dApps). This blog will cover how application testing happens on dApps, their importance and advantages. To begin with, first, let’s understand the cloud, how it helpz test applications and the benefits and challenges associated with application testing over the cloud.

What is cloud testing and what’s it’s importance?

Cloud testing refers to the testing process that is used to assess the performance, scalability, reliability, and security of applications. This testing procedure is carried out in the cloud environment of the quality assurance tester.

Testing on cloud greatly assists in shared and decentralized setup, where teams are dispersed geographically.

With cloud testing, testing becomes faster, easier, and more manageable.

Since testing involves multiple environments/devices, it is more complicated to set up and proceed with the test execution over a single device. Here BrowserStack comes in to aid developers/testers.

It provides them quick access to the cloud, which allows in comprehensively testing the websites and mobile apps. Such a testing can be carried out on over 2500 browsers and devices. This relaces the need to have an in-house test infrastructure.

You might be interested in this: Testing a bank application: A Success Story

Application testing over the cloud

Cloud Testing uses a Centralized server Architecture which is operated from a centralised server. If users need access, they can download a copy of the app, and the app works by sending and receiving information from this server. Below is the pictorial representation of typical client-server architecture.

Benefits of Cloud Testing / How cloud testing benefits

There are many benefits that can be reaped through cloud testing. Discussed here are crucial ones.

  • ● Cost Effectiveness: Today, it is hard for every organisation to hold every device in the market to test their product. And also, due to rapidly changing user expectations and standards, organizations continuously invest money and human resources, escalating project budgets and maintenance cost. Cloud testing tools solve this problem by providing a real-world testing environment that closely mirrors the production environment . Testers simply have to sign up, select the real devices they want to start tests on, and start flagging bugs.

Availability: Resources can be accessed from any computer with a network connection. Since most cloud testing applications work as a subscription model, testers with access to a browser can register anytime and start testing immediately. Moreover, efforts are not limited by  the physical presence of testers.

  • ● Customization: A variety of testing environments can often be simulated.
  • ● Scalability: Based on testing demands, resources can be scaled up and down.

Challenges in Cloud Testing

Cloud testing isn’t without its  challenges. Let’s look  at the major challenges that are encountered in cloud testing.

  • ● Bandwidth Issues: Bandwidth plays a major role in accessing and utilising the cloud testing tools as cloud resources are accessed only with a proper network connection. If a user is unable to maintain a consistent internet connection, then it will be difficult to carry out testing in cloud systems.        
  • ● Security: As a subscriber to the cloud testing application, the tester/developer will be in a position to hand over the data or information to the outside part.
  • ● Redundancy: There is no monitoring of redundant test plans, which results in charging for every retest application or website.
  • ● Feature Coverage: If new features are added to your application constantly, then the cloud testing tool will not provide adequate coverage, which leads to inadequate coverage test execution.

Application testing over a Decentralized App

Decentralized App or DApp runs on a Blockchain network. Decentralised applications are outside the single authority– and can be developed for a variety of purposes, including gaming, finance, and social media. Let’s get some insights into blockchain to understand this better.

  • ● A Blockchain is basically a chain of blocks. These blocks contain certain information, and store and transfer it in a secure manner across a network. Blockchain can also be viewed as a network of computers that are interconnected instead of being linked to a central server. So, essentially, this is a decentralized network.
  • ● Architecturally, blockchain requires each participant in the network to maintain, update and approve fresh transactions. There’s no separate team or individual to control the blockchain network. Rather, each participant in the network controls the systems.

Migrating from Cloud to Decentralized Server / Decentralizing Apps

Moving from centralized server to decentralized server is no easy task as it involves architectural changes. However, moving to decentralized server provides more stable application/product as the system is distributed along the nodes. But when considered traditional system or centralized server, any problem that affects the central server can generate problem throughout the system.

Also, each transaction is verified by the peer-to-peer network in a random order which results in a higher level of security in decentralized server, whereas in centralized server verification happens in higher level and lacks security.

Since the information is not stored in central location, accessing data by large user is possible and can be done without any lack of speed in decentralized system. On the other hand, centralized systems will fail and can cause waiting scenarios, which can result in slowing down of the system.

DApp Testing

Much like a common application, DApp, too, consists of frontend and backend layers. The frontend exhibits not much difference when compared to traditional apps and can be built using a programming language of choice. The backend exhibits a different structure and is Blockchain-based. Users hardly recognize if they are using DApp.

Testing DApp doesn’t much differ when compared to testing of traditional apps. QA engineers typically leverage functional testing to ensure that a DApp complies with functional specifications. Similarly, testers leverage non-functional testing to gauge DApp’s performance, reliability, usability, scalability and assure its secure. Testing the application in decentralized servers involves testing the application locally first. Once application tester locally tests, a testnet such as Ropsten or Rinkeby needs to be used.. This is similar to Ethereum network’s scaled-down version, having own tokens (valueless). For testing transactions on one of the test nets, you will have to have some test ETH (This you can obtain from respective faucets.)

Importance of Migrating to Decentralized Server / Decentralizing Apps

Users don’t have to put trust in a central authority: As there is no central authority, user doesn’t need to trust a single authority as there are multiple nodes which provide a secured network.

Very little risk of single point of failure: In a decentralized server, irrespective how many users come and go, application will be up and running. Risk of failure of multiple nodes is very little or zero failure which results in more qualified and stable application.

No Censorship: Centralized servers can be easily tracked and shut down by shutting down the traffic to central servers. Since there are no central servers in DApps, censorship is not possible in the apps developed in decentralized servers.

Advantages of DApps

User Privacy: User does not need to produce any personal information.

Anonymous: Smart contract used in DApps in transaction between two anonymous parties without the need of any central authority.

No Censorship: Since there is no centralized authority, any app built on Decentralized network will have no censor.

Obstacles in moving to Decentralized Server / Decentralizing Apps

  1. Application development is costly
  2. Transaction Fees is costlier in crypto platform where DApps transactions takes place
  3. Scalability
  4. Usability

Cypress – A Modern Trend in Web Automation

Posted on by Vignesh R

Do you remember the good old days of setting up an Automation framework? For running end-to-end integration tests, we must install Selenium, a wrapper for selenium, drivers, assertion libraries and other libraries for reports, stubbing, mocking., etc. It would be a tedious task for testers to configure all these requirements and build up a framework to meet their needs. All these configuration tortuousness can be avoided by using Cypress, an all-rounder which has entered the market.

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Cypress is a one stop solution for all your web test automation solution needs as you get all the libraries installed at a stretch when you add cypress to your machine.

In this blog, we help you comprehensively understand Cypress and how it is proving to be a difference-maker in web automation space. Let’s first take a look at how Cypress evolved.

Evolution of Cypress

Cypress gained immense popularity soon after its launch, when it was offering support for only Chrome testing. Later as versions progressed, it added cross-browser testing for Edge and Electron. Finally, the popularity of cypress hit off the charts when protractor framework announced its deprecation. Let’s see the factors which led to Cypress’s burst of popularity .

What makes Cypress special?

 Some factors that have contributed significantly to the success of Cypress. We’ll see them one-by-one.

Easy installation

Unlike other automation frameworks, cypress hardly takes 5 mins to install.  After installation of Cypress, you need to launch the application where your framework is already setup. Basic end-to-end testcases on different websites are given as examples.

Automatic waits and Retries

It is difficult for the application to cope up with the speed of automation framework, hence QA engineers will add waits to the framework to make it stable, which in turn leads to code inefficiencies. Cypress automatically waits for the webelements and even retries searching for it if not found.

Excellent Documentation and Great Community

Unlike other frameworks, Cypress has the most understandable and simple, yet a more detailed explanation of the components and functions available. The community of Cypress is also a more active one which updates the version of Cypress with constant upgrades and bug fixes.

Total Control over your tests

Automation framework with Cypress doesn’t require any third party libraries for controlling and monitoring the function flows. Cypress comes auto bundled with functions for handling clocks, spying the network traffics and stubbing the behaviours of the functions

Stable – Non flaky tests

Other Web automation frameworks require a webdriver and commands will be sent from the test runner to perform the steps in webdriver. This continuous to and fro motion from test runner to webdriver may result in flakiness of the tests. Cypress has an integrated platform in which the test runner and the web interface is in the same place and avoids communication gaps between the two.

API Testing in Cypress

Cypress also works great for API testing. Cypress has an inbuilt “request()”  function which accepts three parameters for API testing – Method, URL and the payload. By passing these parameters, we would get the response body which can be validated using the inbuilt chai assertions.

If you want to observe the requests and responses in browser window, you could make use of         cy – api plugin which renders the details of API into browser window.

Performance Testing in Cypress

We can also check the performance metrics of our application in cypress by using the Cypress – Lighthouse plugin. We are familiar with how Google Lighthouse service helps in checking the performance metrics of our application. We can leverage the same lighthouse service in our Cypress framework and we could do an effective performance testing in our application

Conclusion

This is not the first time we are seeing a software related product reaching high popularity due to its user-friendly interface. Computers, the internet, operating systems etc. all began with complex interfaces, but later evolved into user-friendly elements with focus on customer centricity. QA-centric, stable, and enabled with modern framework, Cypress shares the same story. It has evolved from complex web automation frameworks into a simple-to-use, highly efficient web automation tool.

Checklist for Validation of Data Privacy Augmentation Computation in Social Media

Posted on by Premalatha S

We are all into social media nowadays. A day is not spent well unless we spend some time in social media. According to a report there are more than 5 billion active social media users worldwide. With such a high number of users connected, it has created a lot of possibilities with accessibility. Social media has its advantages as well as more disadvantages.

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With the booming number of users Data privacy is at its stake. Content creators of social media are a lot but are they equipped enough with data security? Managing the privacy of customer data is one of the biggest challenges faced in social media, according to a report by Hoot suite Social Trends Survey 2022.

As per Gartner’s report, 75% of the world’s population will have its personal data covered under modern privacy regulations, by end of 2024. This regulatory component is the critical catalyst for the operationalization of privacy.

With the privacy regulations across the globe, organizations should focus on privacy enhancing computation techniques to meet the challenges of protecting the data.

Increasing complexity of analytics engines and architectures mandates that social media owners incorporate a by-design privacy capability. AI models and techniques helps in designing this. Unlike common data-at-rest safety measures, privacy enhancing computation protects data in use.

While incorporating the algorithm is one side of the spectrum, validating the data logic plays a crucial role to ensure that the desired outcome is achieved.

Working with one of the top social media creators, we have created a checklist to help validating the data privacy augmentation.

Here is a glimpse of the control points for which we have a checklist created for validation:

  • Web / DNS control points
  • Email control points
  • Executable control points
  • Content control points

We have created a 3 stage model to achieve this.

Setup 

The Setupphase includes foundational capabilities of a privacy management. Identifying control points, defining business rules, record keeping. These are needed for any customer-facing organization that processes personal information. These include discovery and enrichment to establish and maintain privacy risk registers

Maintain

The maintain phase is to scale and focus on ongoing administration and resource management. Categorization of the controls and actions on the incidents observed, thereby bringing automaton to privacy impact considerations. Tools are used to identify the contents and block based on the rules.

Progress

The progress phase is updating the rules based on the controls and mitigating the risks identified. Its more of a continuous improvement phase.

Now lets move on the checklist for validation.

  • Web / DNS control points

This is more of filtering on the unauthorized access from DNS / web addresses. List of control points used in this validation with multifold techniques. Repository of unauthorized DNS addresses created with responsive actions. DNS control points blocks content or network access from potentially harmful sources. Control point will have a block list or allowlist to filter harmful unwanted content. API will identify the blocked domains on the CMS and restricts user to create account, add email using those domains.

  • Email control points

Screening of emails for spam and objectionable content forms this email control points repository created with rules of actions. API will identify and restrict banned users from login to the application.

  • Executable control points

Few files while opening or executing triggers unwanted actions and those are added in executable control points. Rules are programmed to stop execution of the malicious files.

  • Content control points

Content based controls use pre moderation and post moderation techniques to filter and bypass the unwanted content. API identifies the word configured in CMS and restricts user from posting those words.

Pre-Moderation Server will identify and restrict users to create account using configured words, will identify configured words on posts, comments and quote posts and will block users from posting. Post-Moderation Server will identify and delete words/images/video which meets the AI score

These controls are configured to exclude undesirable unwanted type of content that violates the product’s acceptable use policies.

Above checklists helps to validate the data privacy augmentation rules and protects the organizations and individuals against harmful content.

Validation helps to minimize unwanted access and content thereby making the social media safe for the ever increasing users.

Live Stream Testing

Posted on by Sasikanth P

What is video live Streaming?

Video livestreaming is streaming multimedia content in real time over the Internet. Using the technology, users can create, share & view live videos through the support of an internet enabled platform. There is also non-live media stream which technically streams but do not broadcast in real time. Live streaming has phenomenal ability to connect with audience with real time across the globe in cost efficient way which earlier was possible only through TV broadcasts.

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Live streaming contributes to over 30% of the total video content that is being watched globally.  Post Covid-19, it is estimated that there is a 300% increase in live streaming and it has become the most important forms of communication opening doors for multiple opportunities like students attending live classes, online doctor consultation, artists performing virtual concerts, live commerce shopping, retail customers shopping online, video conferencing, etc.

Live streaming in social media

Even though the concept of live streaming prevailed earlier, it was the social media with its incredible power of connecting peoples across started using it in a much larger scale along with numerous other features like live chatting, closed caption with multi language support, play back speed controls, multi resolution, video replay, controlling audience, applying effects, etc. Statistics mentions that around 23% of social media users are using live streaming to connect with others

However, the platform capability to engage a live audience is challenging as even a minor glitch for a few seconds during the live stream could impact the interest of the audience. The most important aspect of live streaming is to provide high quality end user experience without any imperfections. Is there an efficient way to validate the quality of live streaming? Here are some notable testing points to ensure quality live streaming.

Live stream Test Process

The digital assurance of the live stream testing is a necessity and partnering with an efficient digital assurance services partner is equally important. The following are the process steps for live stream testing.

  • ● User feedback

Planning and preparation play a vital role in validating live streaming. The test team should be aware of the items that could potentially cause issues. It is best to maintain a checklist that is created through analysing patterns. One way to create this is through carefully looking into user feedback of similar application and understand what the users are concerned about. A collection of these items will indicate the areas of additional focus.

  • ● QA Tools

There are multiple cross platform screen casting and streaming tools available to test different dimensions of live stream. Using various broadcasting, video and audio controls, live streams can be setup and tested.

  • ● Standards & Protocols

Each live streaming uses different protocols based on their business needs. However, the efficiency of the video stream depends on the multiple factors. All individual factors contribute to the overall quality and thus be carefully taken care during development and testing. There are various protocol like HTTP Live Streaming (HLS), Real-Time Messaging Protocol (RTMP), WebRTC, Secure Reliable Transport (SRT), etc. Each protocol has its own advantages/disadvantages and knowing it would really understand the scope and expectations. 

  • ● Beyond the reach

When a match is broadcasted in television, it is very much live that there is no delay in the telecast even for a few seconds. This is also applicable to live streaming. Latency is the time difference between the video sent from host to the receiver. The ultra low latency ensures video is streaming without any delays and reaches wider audience.

  • ● Widest possibility

The compatibility factor in live streaming is responsible for delivering the video content to a larger audience. The technology should make sure that live streaming is possible across multiple devices, platforms and browsers. It would be helpful to understand the audience location and have trending list of popular devices, platforms & browsers based on statistics. These primary platforms should be at least covered during the tests.

  • ● Subjective Video quality

Bitrate, video resolution & image sharpness contributes larger part of the video quality. Bitrate is associated with delivering best video quality dynamically to users based on different network capabilities. The video resolution adaptiveness is to be validated with different internet speed to check whether the   system automatically adapts to the best quality video under various conditions. 

  • ● User Experience

Whatever technology or features that is developed, it really matters only when audience is convenient in using it. UX/UI and UI/UX testing play a major role in engaging the users with the application. Usability tests should be performed to understand the application usage and issues.

  • ● Security & Privacy

The scale of security and privacy related issues in live stream are much higher than other technologies. As the content is targeted to a wider audience across the globe, there should be much responsibility involved to avoid problems. There are content management systems and other security features which restrict text, image, video using AI programmed filters to keep the content safe. The security and privacy aspect of live stream should be extensively tested.

In addition to the above points discussed that are other areas to focus which depends on individual business. There is much scope for live streaming in the coming years as new players and domains have started to use this technology to are Overall 99.999% availability, stability & premium quality are three key customer success metrics that a tester should look for ensure the purpose of the technology is fulfilled.

AI & ML: Forecasts and Trends for 2022 and beyond

Posted on by Adhithya S

A Crucial Year for AI/ML

The way we work and live has been constantly changing in the last few years. Google CEO Sundar Pichai predicts that the advancement in artificial intelligence and machine learning will be even more revolutionary than the invention of fire.

According to Comptia, 86% of CEOs report that AI is considered mainstream technology in their offices as of 2021. Businesses across the globe are battling labour shortages, economic crises, and many other hurdles that affect business efficiency. Intelligent and comprehensive digital solutions include the use of artificial intelligence and machine learning as they are referred to as the ‘brains’ of smart machines that will help businesses deliver increased business productivity & constructive solutions. Many predictions in the field of artificial intelligence and machine learning are being made that we will see below:

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Predictions about AI/ML in Business

  • Accessibility and Democratization of Processes: Artificial intelligence and machine learning are no longer the responsibility of a single employee in the IT department. It is available to engineers, support representatives, sales engineers, and other professionals that can make use of it to solve everyday business problems. Machine learning will soon emerge to be the standard tool that is used to solve certain complex computational problems. It will help in personalizing customer experiences and provide an enhanced insight into customer behaviours.
  • Enhanced Security for Data Access: AI & ML tools can track and analyze higher network traffics and recognize threat patterns to prevent cyber-attacks. This can be done in conjunction with monitoring the networks in question, detecting malware activities, and other related practices. Enterprises can adopt advanced AI solutions to both monitor data and construct special security mechanisms in their AI models. AI can help by recognizing patterns and suggesting business intentions using smart algorithms. AI-powered security will reach new heights in the days to come.
  • Deep Learning to Aid Data Analysis: Deep learning happens after the creation of multiple layers of artificial neural networks to use for processing large amounts of unstructured data. This allows the machine to learn how to analyze and categorize inputs without being specifically instructed on how to handle the task. The use cases for deep learning range from industries such as predictive maintenance to product strategies in software development companies. Some autonomous locomotive and automobile enterprises are already implementing deep learning capabilities into their products. In the future, businesses across industries will increasingly leverage deep learning for data analysis.
  • Natural Language Processing Enhancing Use Cases: Natural Language Processing involves both computational linguistics, and the general model of the human knowledge- paired with machine learning, statistical learning, and deep learning models all working closely with each other. NLP can help in making one aware of the subconscious patterns in the organization’s processes- this can help identify strategies to boost business efficiency. It is used both in the legal and commercial space, as dense legal contracts and documents and can be analyzed with speed.

Having got an insight into the probable trends for Artificial intelligence and machine learning, here we discuss a few use cases that are driving the use of AI/ML forward:

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Use Cases for AI/ML in 2022

  • Machine Learning in Finance: Machine learning techniques are paramount to enhancing the security of transactions by detecting patterns and possibilities of fraud in advance. Credit card fraud detection is an example of improving transactional and financial security through machine learning. These solutions work in real-time to constantly ensure security and generate alerts. Organizations across the globe use machine learning techniques to conduct sentiment analysis for stock market price predictions. In this instance, business trading is aided by the algorithm, where various data sources such as social media data help to perform sentiment analysis.
  • Machine Learning in Marketing: Machine learning can aid with considering customer and business objectives while considering purchase patterns, pricing, comparison with other businesses, and mapping marketing points that can align with customer objectives. Content curation and development is an essential component in an era of digital marketing. There are tools that can help to customize the content as per the customer’s preferences and also tools that can help effectively organize content for customers for better engagement. Customization, understanding customers, and creating a memorable experience are all aided by machine learning as seen in the examples of chatbots that use AI technologies.
  • Machine Learning in Healthcare: Administrative tasks can be delegated to natural language processing software, which can effectively reduce the physician’s and other healthcare staff’s overall workload. This can help the healthcare staff concentrate better on the patient’s health and spend less time going through legal and manual administrative work. NLP tools can help generate electronic health records and with managing critical administrative tasks in the healthcare industry. The tools would automatically find words and phrases to include in the electronic health record at the patient’s visit. They can create visual charts and graphs that can help the physician understand the patient’s health better.

Also Read: 10 Promising Enterprise AI Trends 2022

AI/ML Paving the Road Ahead for Growth

In 2022, along with the help of artificial intelligence and machine learning technologies, businesses will increasingly try to automate repetitive tasks and processes that involve sifting through large volumes of data and information. It is also possible that businesses will bring down their dependence on the human workforce to improve the overall accuracy, speed, and reliability of the information that is being processed.

AI/ML is usually called disruptive technologies as they are powerful enough to elevate industry practices by assisting organizations in achieving business objectives, making important decisions, and developing innovative services and products. Data specialists, analysts, CIOs, and CTOs alike should consider using these opportunities to efficiently scale their business capabilities to have an edge in the business.