Let’s start with some industry examples where companies across the world have been working with legacy systems as they are critical to their business.

• Mainframe systems that airline companies use for reservations and ticketing systems
• Command-line interface systems based underwriting engine used by insurance companies
• Companies with a core platform on legacy systems (such as COBOL, DB2, etc.) and being used by numerous customers

Many companies are generating good revenues from their operations and supporting the business, but they are missing out on the opportunities for connected data across the ecosystem. The architecture is not supported for enabling seamless data porting, scalability, and speed. Moreover, these systems have a higher cost of operation and high maintenance; and are plagued with usability and security issues. Technological obsolescence and resource scarcity are a few more challenges.

Key challenges with the Legacy systems

Integration issues with other systems: Gone are the days of monolithic architecture; nowadays, solutions are designed to have benefited from multiple systems connected via APIs. Modular and SOA-based architecture are common with scalability benefits and integration with other 3rd party systems. Legacy systems are not designed to integrate with other heterogeneous systems and thus cannot achieve fully integrated systems.

Go to market is slower: Legacy systems usually follow the waterfall model of software delivery, and aligning with the market demand of releasing software features on the fly is not feasible.
These systems are heavily focused on a manual way of working with little or no integrations available for 3rd party build, configuration, and CI/CD tools, causing the entire process to become slow and error-prone.

Misalignment with customer requirements: In the current era of a customer being the king, legacy systems are not synchronized with customers’ needs, especially in terms of flexibility, agility, usability, and customer experience. Legacy systems are still tightly coupled with OS, interface, screen, etc. In the case of growth, these systems cannot scale at the same pace as that of business.

Artificial Intelligence and machine learning: Legacy systems are not built to integrate 3rd party data sources and are relatively inflexible. When the world moves towards NoSQL, a flat-file kind of database, legacy systems still run on relational DBMS, slowing the entire operation. Creating intelligent dashboards analytics is a cumbersome task, if not impossible, in these systems. These systems cannot harness the power of AI/ML and achieve predictability and digital transformation benefits.

Human resource challenges: The availability of skilled resources with expertise in legacy systems is seeing a decreasing trend. Universities and institutions, nowadays, focus more on newer technologies such as cloud, AI/ML, Internet of Things, Intelligent Automation, Blockchain, DevOps, etc. This means the engine to produce the team with legacy application skills is dying; moreover, the new generation of engineers is inclined to work on the latest technologies and not on legacy ones.

Compliance and Regulatory requirements: Nowadays, regulatory authorities and governments are very keen on adhering to compliances or heavy penalty is imposed on them. HIPPA, SOX, PCI, GDPR, etc., require your technology to be current and aligned with the regulations. These rules demand specific data to be shared with government bodies, and their implementations/modifications are very time-consuming in legacy systems.

Solutions to legacy technology challenges

We understand the challenges legacy technologies pose, like speed bumps, to leverage data modernization and use it to achieve higher revenue and innovative offerings at the utmost speed to enhance customer experience.

The question is, how can we resolve these legacy issues?

Digital transformation is the key to reducing the risk and unpredictability and improving the customer experience in the current market.
The implementation of Agile and DevOps coupled with a technological revolution comprises cloud, IoT, and AI/ML with enriched and interconnected data that will resolve the issues mentioned above.
Each organization is unique in the context of a market, customers, products, ecosystem, etc., so there can’t be an umbrella approach. The approach must be customized to different companies to harness the full benefits of digital transformation.

How can IGT Solutions help you?

With its technological expertise and process champions, IGT Solutions is well placed to implement methodologies and achieve a faster delivery cycle, cost optimization, and excellent customer experience.
IGT’s expertise in transitioning from legacy to cutting-edge technologies in a phased manner does not disrupt the ongoing business engagements. It can easily transition into the latest technologies fitting to customers’ needs. IGT’s expertise in Agile, DevOps, Cloud, and experience in implementing AI/ML, IoT, and Blockchain will help you ensure a smooth journey from legacy to latest technologies.

Author:

Yatender has 20+ years of experience in software test engineering. As the head of Testing Practice at IGT Solutions, Yatender is actively involved in innovations related to test engineering covering new tools, technologies, and solutions, and enabling IGT’s clients to achieve faster time to market quality improvement, and optimization of developer efforts in overall SDLC. A result-oriented leader, proficient in delivering high customer value and achieving excellence in service delivery management with proven skills in consulting and managing large and complex test programs. When away from work, he enjoys reading on a variety of topics and spending time with kids.

The post Modernization of Legacy Systems appeared first on IGT Solutions.

Building applications from scratch can be beneficial. It gives the flexibility to develop customized solutions specific to the organization’s requirements; this approach can sometimes backfire as the time to go live and achieve competitive advantages in the market may be delayed. Moreover, there can also be inherent technical debts which might remain unresolved due to other pressing priorities from time to time.

Another approach would be to have best of breed solutions where few or more applications or modes in the overall solutions are procured as Commercial-off-the-shelf (COTS) products.

The cloud policy of the organization may demand to move all the applications from on-premise to the cloud. It becomes difficult to move the solutions to the cloud for the heterogeneous solutions, where few components are COTS products and homegrown components.

Challenges in migration of cloud applications having COTS components

Many products are still in the process of having an authentic cloud version, especially if these applications have been in operation for several years and their vital licensing patterns are tied to hardware. These applications may become a hindrance to the cloud transformation journey for an organization. We need solutions for getting these applications to migrate to the cloud to realize cloud-based solutions fully. Service companies have also developed customized solutions to move COTS applications on the public cloud infrastructure, and few COTS vendors have created SaaS-based offerings.

Following are the few challenges companies encounter while transferring solutions with COTS applications to the cloud:

• Challenges associated with COTS applications which are highly dependent on underlying hardware for licensing and operations.
• End to end system showcasing higher latency when moved to the cloud.
• Technical challenges arising out of the existing architecture of shared storage, multicast and 3^rd party integrations.
• Non-availability of cloud approved version of a product.

These challenges become more severe considering the below objectives of cloud transformation:

• Cost optimizations
• Reducing maintenance
• Higher availability
• Better user experience

Need for migrating COTS to cloud

Organizations understand the need to have their applications available, anytime, everywhere, to everyone. There are definite benefits of moving its applications to the cloud (public cloud, private cloud and hybrid cloud) in terms of cost, availability and focusing on its core business.

Organizations are moving their COTS applications to the cloud, namely to:

• Achieve agility for the organization’s application landscape
• Enable automatic provisioning and DevOps for the organization
• Removing dependence on hardware, especially end of life servers
• Increase resiliency and enable disaster recovery capacities for mitigating risks
• Consolidation of data centers
• Reducing maintenance costs on tools, on-premise hardware and personnel

Following are the typical phases of migration of COTS applications to the cloud:

• Analyzing: Analyze product functionality, configuration, interfaces, infrastructure, data storage, as well as security and data privacy.
• Planning: Decide on target architecture in the cloud, compatible cloud services, cloud type and service provider, process and approach for migration, data identification, interfaces, DR requirements, and infrastructure capacity. Finalize monitoring plan, KPIs and alignment with business goals.
• Migration: Iteratively execute migration plan with data, applications, and interfaces. Ensure post-migration testing results align with expectations and the critical KPIs are aligned or are better than pre-migration workflows. Conduct end to end testing, performance and security testing to ensure migration has no adverse impact on the application’s functional and non-functional areas.
• Optimization: Continuously monitor, calibrate and fine-tune COTS application post-migration to the cloud to ensure its alignment with business requirements.

Assurance for Cloud Migration

A well thought out cloud testing strategy is essential for assuring smooth application migration to the cloud, as legacy functional and regression testing methods will not be aiding value here. In addition to practical aspects, organizations have to plan mitigation plans for availability, performance, latency, and security.

The risk of application failure or cloud server overrunning cost is the preeminent risk, which can be mitigated with a cloud-centric testing plan.

We recommend the below approach for cloud migration assurance:

• Analysis and planning: Planning the various phases of testing, coverage, test oracle, resources and scheduling.
• Pre-migration testing: Comprise baseline testing in existing applications and create benchmarks for functional and non-functional KPIs.
• Migration Execution: Providing validation during the migration process for different types of data and records from source to target.
• Post-migration testing: Ensure validation of end to end tests with respect to benchmarks of pre-migration testing. It covers functional, integration, performance, security, resilience and disaster recovery scenarios.
• Monitoring and maintenance: Covers post-go live testing, parallel run for a specified period, and resources monitoring.

These steps ensure a smooth transition to the cloud and mitigate the risks of application failure and performance degradation.

Conclusion

IGT Solutions helps you with faster, smoother, secure and cost-optimized cloud migration assurance for your business-critical applications.

Author:

Yatender has 20+ years of experience in software test engineering. As the head of Testing Practice at IGT Solutions, Yatender is actively involved in innovations related to test engineering covering new tools, technologies, and solutions, and enabling IGT’s clients to achieve faster time to market quality improvement, and optimization of developer efforts in overall SDLC. A result-oriented leader, proficient in delivering high customer value and achieving excellence in service delivery management with proven skills in consulting and managing large and complex test programs. When away from work, he enjoys reading on a variety of topics and spending time with kids.

The post Cloud Migration Assurance appeared first on IGT Solutions.

Both the IT industry and software testing domains have different yet relevant trends that organizations should be focused on to remain relevant.

Let’s look at some key areas of software testing that will see a lot of traction in the year 2022

Hyper Automation Testing: The new trend around the block will take a platform-based approach where test automation of web applications, mobile apps and desktop apps will take place with a heterogeneous tool set. We will also see a growth in open source tools as well as low code platform for automation testing. The year 2022 will also see a greater focus on implementing In-Sprint Automation, wherein testing teams are performing functional testing in 1 to 2-week sprints, designing and executing automated test cases within a sprint is bound to provide benefits of shift-left, quality improvement and faster time to market.

Another trend is to take automation to next level, called as Hyper-automation, that combines the power of AI/ML and automation technologies. This will help achieve faster, scalable, high-quality product development.

The use of AI will help to automate the areas within the automation testing process, which are still performed manually such as functional test case writing, identifying regression, impact analysis etc.

Artificial Intelligence and Machine learning: Artificial intelligence is already implemented in several use cases in the industry and its effectiveness is increasing as the days pass:

There are several uses of artificial intelligence in software testing:

• Risk prediction in code-based historical data and key variables
• Optimization of testing efforts and timelines based on risk profiles
• Fine-tuning of regression suite and self-healing frameworks
• Analysis of application logs, identification of errors and automatically find failure reason
• Prediction of key test configurations and quality index levels

Security Testing: There has been an increase in the use of IT applications in the last few years, we have observed an exponential increase during the covid period, in addition to that, we also saw a sharp rise in cyber security incidents including ransomware. The positive effect of all these problems is the enhanced focus that organizations are now putting on the security testing of the applications, systems and infrastructure. The year 2022 will see greater traction on security testing right from pipeline security to DevSecOps to penetration testing. As security is picking up fast and has a large attack surface area, it’s important to perform security testing in layers. A customized security testing strategy will have a combination of DAST, SAST, IAST and API security tests spread across guidelines from NIST, COBIT, ISO 27001 and PCI DSS.

As per the world risk report 2022 of the World Economic Forum, the cyber vulnerability data trend is worrying:

• 435% increase in ransomware in 2020
• 3 million gaps in cyber professionals needed worldwide
• 800 billion estimated growth in value of digital commerce by 2024
• 95% of cybersecurity issues are traced to human error

Companies need to upgrade their infrastructure and ramp up staff skills to tackle cyber vulnerabilities. Security testing can help alleviate cyber risks by shift-left and implementing a continuous security testing pipeline.

UX/CX Testing: User experience is increasingly becoming a key factor in customer engagement and helps retain the existing user base. There will be an additional push to achieve a good user experience for applications used by organizations, especially websites and mobile apps. This is of utmost importance for organizations that use such web or mobile applications for business e.g. ecommerce, insurance, etc.

There is a plethora of tools available both in open source as well licensed categories spanning from user profiling, customer journey mapping, accessibility, user persona analysis, sentimental analysis etc. The best approach to UX/CX testing will be to understand the business flow, Application landscape, target customer segment, customer touchpoints, marketing mix and competitor analysis.

Performance Engineering: Performance of the application is imperative from a usability standpoint and users see it as a primary factor to either continue using the application or discard it. The organization’s focus will now be on the performance and results right from the architecture designing level to usage on production.

The market has seen some good traction in the performance testing tool segment, such as:

• Tricentis acquired Neotys
• Perfecto acquired Blazemeter

These trends are clearly giving good signs as performance testing is going to become a part of the DevOps cycle, performance as a pipeline. It will be great to see full pipeline testing for non-functional areas including security, accessibility and performance.

Agile, DevOps and Lean: Agile has helped organizations to bring the teams together and remove the compartments of BA, developer, tester, system engineer etc. and has set up expert teams that are working together to deliver good quality deliveries. Similarly, DevOps ensures that applications are deployed frequently and automatically thereby enabling faster value delivery to end users. Lean helps in the continuous improvement, removing the extra waste from the system and processes.

These practices take place at various stages in the organization and this year will see further adoption among its teams.

Continuous and automated testing enabled with CI/CD pipelines augmented with AI will help achieve faster & quality software delivery thereby enhancing customer experience.

Conclusion

The future of testing in the year 2022 will be more technology-enabled. If you are looking for more updates on upcoming trends of testing and how to prepare your teams to harness it, you can connect with me at  Yatender.sharma@igtsolutions.azurewebsites.net

Source: Gartner

Author:

Yatender has 20+ years of experience in software test engineering. As the head of Testing Practice at IGT Solutions, Yatender is actively involved in innovations related to test engineering covering new tools, technologies, and solutions, and enabling IGT’s clients to achieve faster time to market quality improvement, and optimization of developer efforts in overall SDLC. A result-oriented leader, proficient in delivering high customer value and achieving excellence in service delivery management with proven skills in consulting and managing large and complex test programs. When away from work, he enjoys reading on a variety of topics and spending time with kids.

The post Software Testing Trends: What to expect in 2022 appeared first on IGT Solutions.

With more and more organizations adopting Agile and DevOps practices, the focus on continuous testing is increasing. The flexible approach to the delivery and deployment process makes it important to have access to relevant test data. If digital assurance is following functional testing, performance testing, model-based testing, using services virtualization, automation testing, or user acceptation testing, defining and managing the right test data can help improve the efficiency of the testing process.

Importance of Test Data Management for the Success of Digital Transformation Programs

The Test Data is the key to the overall digital transformation program. The testing done with obsolete or wrong test data will not provide adequate validation aligned with real world application usage by end users, thereby infusing the probability of application bugs, which might prove detrimental in the future.

For example, an insurance application if not tested with the right test data will have chances of throwing surprises once deployed on live.

Production data, synthetic data or hybrid approach?

Ideally, the application should be tested with real production data to get better validation of the application, but owing to the risk of personal data getting exposed and inviting the bitter side of the law, it is usually not used in test labs. Now, what should be the solution? Should we keep using synthetic data in test labs?

One solution would be to create a test data strategy as to how much and how old production data can be used, with specific guidelines.

Best practices for Test Data Management

Various organizations are at different stages of test data management maturity.

A few best practices for test data management are mentioned below:

• Create test data management strategy for creating, managing and purging test data
• Create role based access control for generation, modification, use and view of the data at various stages of the process
• Devise ways to have maximum reusability of test data
• Create and use accelerators for generating and validating test data
• Establish separate test data support team and enable test data as a service
• Use appropriate test data management tools
• Ensure compliance of data security and data privacy regulations
• Automate test data generation

The challenges of not managing test data properly

The testing process is the consumer of test data for its test cases, analysis and validation. If not properly used wrong selection and management of test data can have the below consequences:

• Longer testing cycles and higher testing costs
• High cost on data storage and maintenance
• Business risk owing to inefficient test process proving inaccurate results
• If using data directly from production, may add security risks
• Additional efforts on debugging

Apart from these challenges, great risk will be on account of the higher probability of application failure in production.

Conclusion

IGT’s state-of-the-art Test Data management practice has been providing test data management and optimization, data governance, and test harness services to our clients across the world.

We can help improve test effectiveness by implementing Test Data CoE, test data strategy, test data assessment process, test data generation, test data simulation, automation of data generation along with data security to protect personal information.

Author:

Yatender has 20+ years of experience in software test engineering. As the head of Testing Practice at IGT Solutions, Yatender is actively involved in innovations related to test engineering covering new tools, technologies, and solutions, and enabling IGT’s clients to achieve faster time to market quality improvement, and optimization of developer efforts in overall SDLC. A result-oriented leader, proficient in delivering high customer value and achieving excellence in service delivery management with proven skills in consulting and managing large and complex test programs. When away from work, he enjoys reading on a variety of topics and spending time with kids.

The post What are the key drivers enabling enhanced traction for Test Data Management? appeared first on IGT Solutions.

The proliferation of Agile and DevOps adaptation in order to improve efficiency, agility, customer experience, and profitability is the need of the hour to remain relevant and thrive in the current market dynamics.

Faster, Faster, and Faster is the mantra of digital strategies which leads to faster development, faster deployment, and faster delivery. This means shorter testing cycles while still covering all the risk areas.

The existing ways of performing quality assurance of the products are no longer adequate in today’s time, where software applications are supposed to be available anywhere, on any device, on every screen size, on every browser, and provide amazing customer experience to its users.

Sharpen Digital Assurance further with AI

We all know that finding and fixing a defect in the software comes at an undesirable cost which we all want to minimize. Having said that, if defects remain undetected and are passed onto the production stage, it might cause a severe impact on the project delivery cost, not to mention a dent in brand image leading to customer attrition. The problem at hand is to optimize the testing process to achieve a balance of risk mitigation and cost/efforts/timeline incurred.

Tremendous improvements have been seen in the software quality assurance domain in the last decade, such as automation testing, shift-left, lean testing, and so on. At the same time, the application complexity, devices supported, and speed of delivery have also increased manifold. The gap has become wider between the current state and target state in the software quality assurance domain.

The Key approach here will be the utilization of AI/ML in software testing. The AI/ML based algorithm will train the model to predict the areas of maximum risk so efforts are optimally aligned to achieve maximum ROI.

The AI / ML model training itself is tricky, as it needs a large volume of data to train the model on, and any error in data if not identified at right time, would amplify the outcome of AI thus doing more wrong than good. Careful calibration of the model and availability of correct test data is going to be useful to enable AI in the Testing process.

The To Be state is when the model will provide a probability of defects in the area of the code, so automation testing (static and dynamic) is focused on a specific area, thus helping achieve results in a shorter time.

Why Digital Assurance function is best suited for AI modelling

Everyone is fascinated by the results promised by Artificial Intelligence (AI) and there is a lot of buzz in the media too. Let’s go a bit deeper and take a look at the four fundamental elements of AI:

Categorization: Categorization involves creating metrics specific to the problem, for example:

• There is a huge rework cost of defect identification, fixing, and revalidation post-deployment to production. Example metric: rework cost is 20% of overall project delivery cost
• Customers are finding it difficult to use the application and finding the right section on the browser / WebApp; this is driving the customer to move to a competitor product. Example metric: Customer Satisfaction surveys mention 3.1 (which is lower) on a scale of 1 to 5
• The delivery cycle of the organization is 4 weeks while several companies within the same domain/market have started adopting a two-week delivery cycle, thus having the advantage of delivering faster to market. Example metric: SDLC cycle takes 4 weeks from planning to production deployment.

Quality Assurance: Quality Assurance provides the data needed to create foundations of AI/ML applications, such as historical defect data categorized into modules, impacted code area, releases, developers, type of issues, etc.  QA also provides details on test cases executed/pass/fail, first-pass rate, etc. It helps join the dots and create problem statements.

Classification: Once a problem is categorized into various areas, the next step is to identify classifiers for each category to direct the user for analysis and conclusion. For example, in the airline travel domain, if the problem identified has to do with making a booking, the team needs to start classifying the possible causes of the problem: Web Application, Mobile App, Authentication, Authorization, Calendar, Pricing, Payment, and Reservation Factors and so on

Machine Learning: Now the problem is categorized and classified for domain-specific terms, the team can start feeding this data to machine learning.  There are various algorithms and techniques broadly divided into supervised learning and unsupervised learning. Supervised machine learning with neural networks is becoming popular. Few other applications of machine learning are feature discovery, event correlation, and time series anomaly detection.

As the quality assurance function is generating a huge volume of data such as test cases, code reviews, defects data, test execution data, etc. it is pertinent to use this huge volume of data related to code quality, testing results etc. in order to train the model.

Collaborative Filtering: It is used to sort through large volumes of data and starts using AI based solutions. This helps in turning data collection and analysis into meaningful insight or action.

Challenges of using AI/ML into Quality Assurance

The key requirements for an AI system are:

• Enormous sets of data
• Validity of testing data collected from various source
• Integrity of data

The challenge is with the availability of a large amount of verifiable data. If there are outliers in test data, those should be taken care of while massaging.

Another challenge is the non-availability of a continuous stream of data, as most testing is done on a discrete basis. In such a scenario, it would be difficult to find patterns in the QA data of one release and other releases.

Since there are various attributes involved in training ML models, and QA data of different types of industry/programs may have different outcomes, it becomes a bit difficult to have a single ML model for all projects.

Conclusion

Future belongs to AI/ML and we should be ready to embrace the changes. At the same time, we also need to ensure authenticity, integrity, and availability of correct data. If the above-mentioned challenges are taken care of, AI can be very beneficial if used along with digital assurance to work and deliver solutions, thereby achieving predictive threat modeling. This will provide benefits such as shorter delivery cycles, improved risk management, and cost optimization.

Author:

Yatender has 20+ years of experience in software test engineering. As the head of Testing Practice at IGT Solutions, Yatender is actively involved in innovations related to test engineering covering new tools, technologies, and solutions, and enabling IGT’s clients to achieve faster time to market quality improvement, and optimization of developer efforts in overall SDLC. A result-oriented leader, proficient in delivering high customer value and achieving excellence in service delivery management with proven skills in consulting and managing large and complex test programs. When away from work, he enjoys reading on a variety of topics and spending time with kids.

The post Deliver Insightful Results with AI and Digital Assurance appeared first on IGT Solutions.

Being a gadget geek myself, I’ve made multiple purchases that are lying around the house without being used. This New Year, I made a resolution not to buy one, and a few days later, I was already itching to make the next tech purchase. So my wife suggested that I list down the reason for my next purchase and how it will benefit me, which inspired me to write this blog- making the right decision when it comes to Data Modernization and Cloud Services for the long term benefit of an organization.

Why do companies choose to modernize? Just because a service like Cloud is available and provides 24X7 infrastructure, does it imply that all companies need to implement it? Let’s understand both these services to understand better whether your business needs it and to what extent. Data modernization and Cloud Computing have emerged as critical factors in the Digital Transformation Journey. Data Modernization refers to “state of the art” data management tools that enable organizations to manage and process a large amount of structured and unstructured data from heterogeneous sources. Conventional data management tools are not equipped to harness large volumes of social media and IoT (Internet of Things) datasets. With modern data management tools, organizations would be able to capture and harness such information to understand actionable intelligence. Whereas, Cloud Computing enables access to IT resources such as Storage and Computing Cycles etc. that are much needed for implementing data-driven applications at scale.

The key to successful data modernization implementation is dependent on your business objectives. Following are some common business objectives that are part of most of our conversations with customers & prospects:

1. Cost Reduction: Rationalizing Operational spend on the applications
2. Improve response time for the end-users of the application
3. Reduce time to market for changes & onboarding new data sources
4. Auto-scaling: Ability to scale up & down the IT infrastructure automatically based on market demand while ensuring service levels remain intact
5. Ensuring high availability
6. Future Extendible: Ability to have an evolving system which extends to future data types & sources without the need for high maintenance

Now that we have identified the usual suspects, the next step is to identify which methodology is the right fit for the data modernization undertaking.

Re-architecting the solution:

1. Performance in terms of utilization of resources like memory, compute, storage.
2. Enablement of the current solution to take advantage of the autoscaling factor
3. Relook at the solution from the lens of DevOps, micro-services & containerization to make use of current technology in the solution. This helps to make it future-ready
4. Define naming standards, best practices and standard operating procedures
5. Define & decide data latency needs, security needs (including GDPR & other privacy laws), data governance including data retention, archival & operational management.
6. Define & decide encryption levels, agility & reliability needs, VPC pairing
7. Define & decide process around metadata management, data quality, cost monitoring

IGT plays a critical role in the data modernization and digital transformation for its customers. Whether it is defining a business/technical strategy roadmap, a high-level approach, or actual implementation of services and value management through support services, IGT has the needed expertise and experience to ensure successful digital transformation through data modernization on the cloud!

Our Approach:

To summarize, data modernization is an absolute must for a successful digital transformation of the organization and Cloud is both the means as well as a consequence of data modernization. They both go hand in hand. Data modernization gives rise to increased cloud spending and vice versa. IGT has helped many organizations in their Digital Transformation Journey.

Learn more about our services or reach out to mktg@igtsolutions.azurewebsites.net to understand how we can partner with you!

Authors:

Bhushan Gangurde is passionate about solving business problems  using AI/ML, Analytics, Data & Automation and Digital technologies in that order. With more than 17 years of experience in field, he loves exploring new technologies and reading books. He heads IGT’s Center of Excellence for AI/ML & Automation.

Kiran Dongre is Analytics Presales head at IGT Solutions. He has more than 20 years of Analytics experience and have serviced many customers in their Digital Transformation journey through data modernization. Kiran is passionate to travel around the globe as well as loves playing Badminton. He can be reached at kiran.dongre@igtsolutions.azurewebsites.net

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