Page 3 – DataToBiz

Manpreet Kaur
March 17, 2025
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AIaaS Companies in Finance & Banking – Top 10 Industry Leaders

AI-as-a-service (AIaaS) has revolutionized the finance and banking industry by offering scalable, data-driven solutions. With its potential to streamline operations and improve risk management, AIaaS helps financial organizations to stay competitive. This blog highlights the top companies driving transformation in the financial sector with the help of generative AI services. “To serve these markets, you have to re-imagine how money can be managed and moved because there will be more change in the next five years in financial services than in the past 30.” said, Dan Schulma, President of PayPal Artificial intelligence as a service (AIaaS) has become a crucial component in the finance and banking sectors. “AI adoption in the finance function is advancing quickly,” said Marco Steecker, senior director of research in the Gartner finance practice. According to a report by Gartner, the adoption of AI as a service business model in the finance sector in 2024 has increased, with 58% of organizations using it, marking a 21% rise from 2023. Ai offers different benefits, such as personalized services, improved decision-making, and quick operations. In this blog, we’ll talk about the importance of AIaaS in finance, along with the top 10 AI services companies that use AI to transform their finance and banking services. AIaaS Companies in Finance & Banking – Top Industry Leaders DataToBiz DataToBiz is a leading AIaaS company in finance that is dedicated to helping organizations use AI to enhance business efficiency and decision-making. With a focus on integrating AI into business processes, DataToBiz offers a comprehensive suite of services, such as AI consulting, machine learning development, natural language processing, and computer vision solutions. The company offers end-to-end support, from strategy development to implementation, allowing organizations to use AI technologies for their specific needs. DataToBiz is committed to ethical AI practices and data security, prioritizing the fair use of AI and adhering to regulations such as GDPR. rue potential of the business through transparent and cost-effective end-to-end data warehousing services. Salesforce Salesforce is a leading AI consulting services company and offers AI-powered solutions for the finance and banking sector through its Financial Services Cloud. Salesforce Financial Services Cloud is a platform that helps financial institutions such as banks, wealth management firms, and insurance companies to connect with their customers, offer personalized service, and automate tasks. Some of the main use cases include transaction dispute management, insurance claims, client meeting preparation, and complaints management. The platform utilizes AI to streamline processes, enhance engagement, and improve productivity by integrating seamlessly with existing business workflows, driving faster results and better outcomes. Brighterion Brighterion is a leading AI product development company that offers an AI platform that uses AI and machine learning for the finance and banking sectors. It helps financial institutions extract insights, make decisions, improve customer experiences, and streamline operations. Some of its prime offerings include fraud detection and prevention, chargeback prevention, and digital customer experience. Brighterion serves more than 2000 companies to protect against fraud and risk. Its solutions allow fraud risk assessment for transactions and merchants. The company also offers resilient payment software, ensuring seamless and secure transactions. SAP SAP offers a comprehensive suite of AIaaS solutions tailored to the finance and banking industries. Its SAP Business AI platform uses machine learning and intelligent automation to manage financial processes, such as invoice matching, risk analysis, and predictive financial forecasting. Some of the key features of this platform include working capital, accounting and financial close, compliance optimization, planning and analytics, travel and expense, and intelligent invoice-matching automation. This platform makes it easy to detect potential risks but also improves decision-making by providing real-time insights and accurate predictions, Ocrolus Ocrolus is a leading AIaaS company in finance and provides AI-driven document automation solutions tailored for the finance and banking sectors. It uses advanced artificial intelligence to process and analyze a wide array of financial documents, such as bank statements, pay stubs, and tax forms, in different formats. By integrating Ocrolus’s technology, lenders can automate processes such as income verification, cash flow analysis, and fraud detection. This automation improves risk management and reduces the scope of errors by providing accurate data, allowing financial institutions to make informed decisions. AWS AWS is a reputed AIaaS company in finance and product development that offers comprehensive cloud solutions for the financial services industry, helping them to streamline operations, detect fraud, and enhance customer experiences. Using AWS’s secure cloud infrastructure, financial organizations can scale operations efficiently, reduce costs, and work strategically with AI-driven insights. AWS offers AI consulting services for personalized banking, fraud detection, and automated compliance. The platform supports global financial transformation, making it easier for firms to adopt digitalization and stay competitive. H2O.ai H2O.ai is one of the best AIaaS companies in finance and offers no-code and low-code tools, enabling faster deployment and operational efficiency. It also focuses on responsible AI practices, ensuring fairness and transparency in financial models. Its H2O AI Cloud allows organizations to create solutions and foster automation and transparency. One of its intriguing applications includes LIBOR clause detection, which allows the processing of financial contracts and documents. It further makes it easy to identify key sentences related to LIBOR and offers contextual insights to support informed decision-making. Workday Founded in 2005, Workday Inc. is a leading provider of AI consulting services and cloud-based enterprise software specializing in financial management, human capital management, and analytics. This AI product development company offers innovative solutions development that helps organizations streamline operations, improve decision-making, and adapt to changing business needs. Workday’s financial management solutions include features such as accounting and finance, close and consolidate, revenue management, accounting center, expenses, grants management, audits and internal controls, analytics and reporting, and services CPQ. DataRobot DataRobot provides robust AI tools for financial services helping organizations to increase efficiency and decision-making. Its platform uses generative AI services and predictive modeling to offer features such as automated credit scoring, fraud detection, and stress testing. The company uses targeted marketing strategies to personalize customer experiences and deliver better outcomes. DataRobot supports regulatory compliance through governance-focused AI,

Yashica Chopra
March 13, 2025
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Top 15 Data Warehousing Companies in Manufacturing – Features & Services

A data warehouse is a central repository that helps streamline and automate workflow in an enterprise and make data-driven decisions in real-time. Here, we’ll read about the top 15 data warehousing companies in manufacturing industry and the range of services and other features they provide. Data is the core of any business. Manufacturing enterprises have tons of data about vendors, raw materials, production, suppliers, distributors, end users, etc. Storing this data in independent silos can be cumbersome and result in duplication. A data warehouse is a comprehensive solution to streamline manufacturing data and help implement the data-driven decision-making model. According to The Business Research Company, the data warehousing market was $33.76 billion in 2024 and is expected to reach $37.73 billion in 2025 at a CAGR (compound annual growth rate) of 11.7%. It is projected to reach $69.64 billion by 2029 at a CAGR of 16.6%. Whether you want to invest in a data warehouse as a service (DWaaS) or build an on-premise repository for data warehousing, it is recommended to partner with a reliable and reputed service provider. Data warehousing is not limited to setting up a central database. It is a complex process of identifying data sources, cleaning, sorting, and formatting the data, storing it in a central repository, and creating third-party integration with data analytical tools to provide real-time insights to end users. Check out the blog to find out the best data warehousing companies in manufacturing that offer tailored solutions to streamline your processes and deliver the expected outcomes. 15 Top Data Warehousing Companies in Manufacturing DataToBiz DataToBiz is among the leading data warehousing companies in manufacturing and several other industries with a global client base. It is an award-winning artificial intelligence and business intelligence company with ISO and SOC 2 certifications. Be it real-time manufacturing analytics or OEE analytics, the company knows how to provide tailored solutions that align with the client’s requirements. The company is also a certified partner of Microsoft (Gold), AWS, and Google. This expertise makes it a reliable partner for cloud data warehousing or DWaaS. It empowers manufacturers to eliminate outdated data silos and replace them with a flexible and scalable central repository on a cloud server. DataToBiz creates streamlined workflows to automate data collection, cleaning, and analytics. The teams build customized data visualization dashboards for enterprises to use graphical reports for proactive decision-making. It helps unlock the true potential of the business through transparent and cost-effective end-to-end data warehousing services. IBM IBM is a global IT and AI company offering data warehousing services to clients from around the world. It provides scalable and high-quality solutions to manage enterprise data and derive actionable insights in real-time. The company uses AI and ML tools to set up a data warehouse with several third-party integrations. It offers cloud-native Db2 and Netezza data warehouse technologies designed by the company’s experts to manage, store, and analyze diverse datasets. Manufacturers can decide the cloud platform they want to use for hosting the system. IBM works with large enterprises to help them become more agile and flexible. From optimizing the production cycle to enhancing cybersecurity and improving customer experience, the company supports the manufacturer in several ways. Amazon Redshift Amazon Redshift is a part of AWS (Amazon Web Services) offered by the tech giant. It provides seamless data storage and analytics through a data warehouse as a service solution for SMBs and large enterprises. The data warehousing platform can be integrated with other apps in the AWS ecosystem or third-party tools by independent vendors. The company offers a specialist to work with each client and set up the necessary connections. Redshift can be integrated with data lakes to derive actionable insights using SQL tools and accelerate decision-making. The company also helps enterprises monetize their business data to increase revenue sources. Amazon offers industry-specific solutions for each client to maximize results, optimize the use of resources, and mitigate risks. It is a great choice for businesses that want to use AWS for managing all business processes. Cloudera Cloudera is one of the leading data warehousing companies in manufacturing and other sectors. It has clients from various parts of the world and simplifies analytics to make them accessible to every employee in the enterprise. The company’s data warehouse provides cloud-native solutions and self-servicing analytics to quickly derive meaningful insights in real-time, and that too for cost-effective prices. The solution is integrated with third-party apps and AI tools to create a consistent framework for managing workflows. Cloudera also takes care of data security and governance to prevent unauthorized access and creates guidelines for businesses to manage their systems. The company promotes smart manufacturing through intelligent systems. From setting up IoT connections to building resilient supply chains, the company knows how to assist the manufacturer at every step. Yellowbrick Data Yellowbrick is an SQL data platform and an enterprise data warehouse provider in the market. The company’s robust platforms are designed to handle the workload of growing enterprises. Its solutions for data warehousing in manufacturing are secure, efficient, and scalable. Moreover, the system can be built on minimal infrastructure to reduce management costs for the enterprise. The platforms can be run on public, private, and hybrid clouds and are powered by cloud-native Kubernetes architecture. By using advanced artificial intelligence tools, the team of experts makes the data warehousing setup more scalable, agile, and user-friendly. Yellowbrick’s enterprise data warehouse comes with reliable ecosystem support and works anywhere (cloud and on-premises). The company also consolidates databases from different vendors to create a central data warehouse with greater efficiency. Informatica Informatica is an AI and data engineering company for clients from various industries, including manufacturing. It offers custom solutions for data warehousing in the production line to ingest, integrate, and clean the manufacturing data and derive insights in real-time. The company reduces the complexity of using different applications by creating a unified interface on a single platform. Its AI-powered low-code and no-code applications can be used by employees to access tailored reports and make

Yashica Chopra
March 10, 2025
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Building a Cost Effective Data Pipeline – The Intelligent Approach

A data pipeline can bridge the gap between raw data and actionable insights by creating a comprehensive and multi-step infrastructure on-premises or cloud platforms. Here, we’ll discuss data pipelines, analyze their associated costs, and demonstrate how to construct profitable pipelines using modern data engineering techniques. Data is everything for a business, be it a startup or a multinational enterprise. Converting raw data into actionable insights helps an organization make decisions quickly and gain a competitive edge. The process of transforming data into insights happens in complex data pipelines, a system where data from multiple sources goes through various stages like cleaning, storage, transformation, formatting, analysis, and reporting. The data pipeline is vital to implement the data-driven model in an enterprise. Fortune Business Insights reports that the global data pipeline market will reach $33.87 billion by 2030 at a CAGR (compound annual growth rate) of 22.4%. Tools and technologies are an integral part of the data pipeline. According to a report by The Business Research Company, the global data pipeline tool market has grown from $11.24 billion in 2024 to $13.68 billion in 2025 at a CAGR of 21.8% and is expected to touch $29.63 billion in 2029 at a CAGR of 21.3%. The same report says that the increase in the adoption of cloud computing technologies and migration to cloud platforms contributes to the higher demand for data pipeline tools. Tech giants like Google, IBM, Microsoft, AWS, etc., are among the top companies whose data pipeline tools are used by enterprises from around the world. However, data pipelines come with a few complications, money being the biggest concern for businesses. Is your data warehousing setup draining your budget? You are not alone! Data pipelines that haven’t been optimized and managed effectively become costly over time and drain business money. In this blog, we’ll learn more about finding out if your data pipeline is expensive and how data pipeline management using cloud solutions can optimize costs. Building a Cost Effective Data Pipeline Microsoft Azure and AWS (Amazon Web Services) are the top two cloud platforms in the market, followed by Google Cloud. You can migrate your existing data pipeline and architecture to the cloud or build a new cloud-native data pipeline and optimize it to save costs from spiraling over the years. With help from data engineering companies, you can make informed decisions about how to use existing resources to maximize performance and get better results by investing in cloud solutions. Structuring the Pipeline Start with the basics. If the foundation is strong, the entire data infrastructure in your organization will be robust, scalable, and aligned with your objectives. Identify and define the goals of building the data pipeline. Set the path for data flow and check which processes can be run in parallel without consuming too many resources. Create comprehensive data security, governance, and compliance documentation to ensure no one who is not authorized can access the system or data. Parallelization Parallelization is the process of dividing data processing tasks into smaller units that can be executed in parallel or concurrently across distributed computing resources. This is done to make the data management system more effective and increase its speed. It also makes the data pipeline easier to scale as and when required. Data engineers use different techniques like parallel execution, batch processing, distributed computing, etc., to achieve the goals. Cloud platforms like Azure and AWS make parallelization simpler by allowing experts to choose the resources and programming language to set up concurrent processing. Increase the data pipeline performance without adding to the cost. Caching and Compressing Caching reduces the latency of data pipelines to promote near real-time data processing and insights. A high-performing data pipeline will use caching and compressing techniques. With caching, the data is temporarily stored in the memory. With data compression, the size of transferred data is reduced, thus limiting the load on the network. Together, the entire data processing model will be quicker and more effective while consuming fewer resources. This ultimately reduces the cost of maintaining and using the data pipeline in your organization. The data engineering team will balance the procedures to free up computational resources and allow the processing of large data volumes in quick time. Azure Spot Virtual Machines Azure data engineering services give you access to spot virtual machines (Spot VMs) which are available on an auction-based pricing model. It is cheaper than the pay-as-you-go subscription model though Azure has the right to reclaim them if other customers require the capacity. If you have non-critical workloads with flexible start and end times, a spot VM is the best place to run them. Businesses can benefit from unused Azure capacity by using it for their processes. The pricing is categorized into three models: achieve, cool, and hot. You can also automate the processes to speed up the results. Shut Down and Remove Unused Resources A common reason for increased costs is the presence of unused resources in your plan. Data engineers can identify such resources and shut them down to optimize costs. This can be easily done by using tools like Azure Advisor and Azure Cost Management. The cloud platform provides customers with numerous tools and applications for resource and cost optimization. It’s up to you to use them effectively to manage the data pipelines. Even after shutting down idle resources, they will still accumulate in your account. When you no longer require the resources, remove them and increase the storage capacity. It’s vital to know why a resource is not necessary and how removing it doesn’t affect other processes. Infrastructure as Code (IaC) AWS data engineering has a practice called IaC or infrastructure as a code. It is the process of setting up and managing the systems using code instead of manual processes. Simply put, the developer will write code for the infrastructure that will automatically be executed whenever necessary. It is similar to how a website or a mobile application works. IaC is a great choice for DevOps teams

Yashica Chopra
March 04, 2025
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Offshore Staff Augmentation in IT – What Nobody’s Telling You!

Staff augmentation is the process of strengthening a business by offering support services through augmented expert teams. Here, we’ll discuss offshore staff augmentation in IT and what nobody tells you about it. IT services are not limited to a single industry or certain types of businesses. A startup needs technology as much as a multinational company or a Fortune 500 firm. Similarly, a manufacturing enterprise uses technology just like a healthcare center, restaurant, or media house. While some organizations have in-house tech teams, others prefer a more cost-effective approach. They rely on IT staff augmentation companies to manage their tech needs. The staff augmentation services can be provided onshore, nearshore, and offshore. Offshore staff augmentation in IT has been in demand for years. Statistics show that the offshore software development market is expected to reach $389.7 billion by 2033. North America continues to hold a dominant market share in offshore IT and software development with $46.2 billion, which is 38.5% of the global market in 2023. Additionally, over 66% of US companies outsource at least one department to benefit from staff augmentation services. In this blog, we’ll understand how offshore IT staffing works and the benefits it provides. We’ll also look at the hidden challenges and ways to overcome them effectively by partnering with a reliable IT staff augmentation company. Offshore Staff Augmentation: How it Works Offshore is the term used to refer to service providers located on a different continent or across the seas from the client’s businesses. For example, an American business that hires an Asian or European service provider is said to be working with an offshore company. Offshore staff augmentation is the process of hiring an external team located in far away country to perform the required tasks for the business. The external employees can work independently or collaborate with existing in-house teams. They work remotely but follow the same timings as the client to be accessible when necessary. Offshore IT services can be temporary staffing solutions or for the long-term. They can be project-based, time-based, or expertise-based. While each offshore staff augmentation company has a different approach, the steps are more or less similar to the ones listed below: Features Offshore Staff Augmentation in IT The main features of IT staff augmentation services are as follows: Cost-effectiveness The best feature of offshore staff augmentation is cost efficiency. Implementing the full-scale hiring process is time-consuming and requires thousands of dollars. Moreover, the selected candidates could leave the job after a few months for various reasons. Then, the whole process has to be initiated again. Whereas with staff augmentation, you pay the service provider based on your contract terms. For example, businesses opt for hourly rates for small projects and can choose to pay monthly salaries for long-term projects. You can use the estimator tool to know how much it will cost. Moreover, additional employee benefits are provided by the IT staff augmentation company and not the client. Flexibility and Scalability Businesses have more flexibility in managing their activities and projects. For example, you can expand an internal team by adding experts from outside to work on specific tasks. The augmented team members can be replaced if you are not happy with the results. You can change the staff augmentation package by upgrading or downgrading the requirements. A team will be created based on your budget and time constraints. Access to Global Talent Another noteworthy feature of offshore staffing contract is the access to global talent. You don’t have to limit your human resources to people within the region or those from neighboring countries. You can hire an expert from the other side of the world and ensure your project gets the best results. Offshore staff augmentation eliminates geographical borders. This lets you attract the best talent to your organization without worrying about recruiting costs and other concerns. Remote Collaboration Remote working has become a norm in today’s world, especially after the Covid-19 pandemic. Employees from different locations use online platforms for video conferences, meetings, and real-time project management. Cloud-based tools simplify collaborations and provide authorized access to employees. Employees can either work in real-time or catch up with the developments/ updates when they log in on the platform. This is convenient and hassle-free. Delegation of Tasks Offshore staffing gives you the freedom to decide which tasks to delegate to the external teams. For example, some organizations might assign complex tasks to experts from outside. Others might prefer to hand over recurring activities to the outside teams and keep the core processes in-house. Both alternatives have their own advantages. The right choice depends on your business vision, objectives, budget, and growth plans. For example, startups might find it easier to access experts through offshore staff augmentation services. Specialized Skills Specialized skills or dedicated teams are offshore staff augmentation solutions where professionals with niche domain experience are hired temporarily to work on certain projects (or parts of a project). For example, you need a data scientist with ten years of experience for three months. You can add them to your team by approaching an AI and IT staff augmentation company. The data scientist will deliver the expected services and leave the team after their job is complete. Fresh Perspectives You aren’t just bringing new professionals into your business. You are also gaining fresh and different perspectives through them. This allows your team to look at the project in various ways. It boosts creativity and innovation. Business problems can be solved more effectively by considering them in a new light. Moreover, employees from staff augmentation companies are used to working with different people and have a wider range of experience, which can help your business. Challenges of Offshore Staffing While using an offshore augmented team is helpful in several ways, you should be aware of the challenges and know how to overcome them. So, what are the hidden challenges of managing an offshore IT team? How can you minimize these concerns and maximize the benefits? Let’s find out

Yashica Chopra
February 26, 2025
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Model-Based Systems Engineering: Is It for You?

MBSE is a new process that promotes the use of digital modeling and systems to enhance system lifecycle management. Here, we’ll discuss model-based systems engineering, its components, processes, tools, and benefits for enterprises in any industry in detail. Model-based systems engineering (MBSE) is a process or methodology in which different models and tools support a system’s lifecycle and track data through digital threads. It differs from traditional systems engineering, which uses text-based documentation and manual processes. In MBSE, digital modeling and simulations are used for interactions between various interfaces in the network. The global model-based systems engineering market is expected to reach $7310.9 million by the end of 2030 with a CAGR (compound annual growth rate) of 15.8%. A couple of years ago, North America led the global market with a share of 35%, followed by Asia Pacific at 30% and Europe at 20%. Another report shows that cloud-based MBSE software will be widely used compared to on-premises solutions by 2033. MBSE reduces errors, increases transparency, and improves system efficiency across the various stages of lifecycle development. It can be implemented in various industries like IT, manufacturing, healthcare (medical devices), automotive, aerospace, defense, electrical and electronics, etc. In this blog, we’ll read more about MBSE and how enterprises can benefit from partnering with data engineering consulting firms to implement model-based systems engineering in their processes. Main Components of Model-Based Systems Engineering Software MBSE makes analyzing, optimizing, and managing complex systems easier to achieve accurate designs and efficient outcomes. The MBSE framework has many components, tools, and languages. The main components are as follows: Modeling Language The modeling language is required to create the system models. Different modeling languages are available, such as SysML (systems modeling language) and UML (unified modeling language). Engineers may also use domain-specific language for better customization and accurate results. Model Management Tools Model management tools are used to create, organize, and manage the system models, view and analyze the results, set up collaborations between different team members and teams working on the project, and give engineers access to make changes to the data and system models. The model management tools allow experts to work together remotely and track developments. Simulation and Analysis Tools Simulations are a big part of MBSE. These tools allow engineers to create simulations for different combinations and record the outcomes. Then, analysis tools are used to understand the best-case scenario to optimize the system’s performance. Additionally, glitches and errors can be identified and eliminated in the early stages. Requirements Management Tools These tools are used to understand, monitor, and trace system requirements in the product lifecycle development process. Digital data sharing for system interaction is one of the model-based systems engineering fundamentals. Requirements management tools ensure data capturing and sharing are seamless and performed in real time. Integration Tools The MBSE software doesn’t exist in isolation. It has to be integrated with third-party tools and applications like a project management tool, configuration management tool, etc. The integration tools and APIs allow automated data flow between systems and create connections between interfaces to encourage better collaborations. Steps in Model-Based Systems Engineering Approach The MBSE process/ approach has a series of steps, where the enterprise or the service provider lists the requirements of the project. The last step is more of a continuous process where the MBSE consulting company provides long-term support to maintain and upgrade the tools whenever necessary. 1. Understand the Requirements Define the system requirements by identifying the needs of the stakeholders (management, employees, investors, customers, etc.). The system requirements should align with the end goal of the business. MBSE tools with built-in ‘requirements view’ can be used to sort and arrange the data for better understanding. Factors like types of resources, budget, timeline, expertise, etc., should also be factored into the requirements. Businesses should determine if they want an in-house team to work on the project or if they wish to collaborate with data engineering and top MBSE companies. 2. Design the System Based on the requirements model, the expert team will create the system design and workflow. This design has to be reliable, scalable, accurate, and cost-effective. It should also align with the long-term business objectives. For example, the design cannot be rigid or fixed. This increases costs over time as the enterprise has to start from scratch every time it needs to upgrade or enhance the model. Create a detailed flowchart with the components, required tools, and workflows. 3. Behavior Modeling For effective model-based systems engineering training, it is crucial to understand how the system will work in different scenarios. The engineers will develop a model to capture the system’s behavior in various conditions and store the data for further analysis. Having a robust data warehouse or a central data repository is essential to collect, clean, and store the data digitally. Typically, it is recommended to build a cloud-based data warehouse that’s compatible with third-party integrations and can adhere to data security regulations. 4. Analyze the Risks Risk analysis is another vital part of the MBSE process. This step should not be skipped. Here, various risks associated with the system (under development) are identified and recorded. The risks are analyzed by experts to find proper feasible solutions for enhancing the safety, accuracy, and efficiency of the system. Risk analysis helps the team to take the necessary measures in fortifying the process with proactive measures to mitigate risky scenarios. Ultimately, this increases the success rate and results in powerful systems. 5. Validate and Verify Once the models are designed, they have to be validated and verified before being implemented in the enterprise. This is done to ensure the model is accurate and aligns with the requirements, is capable of handling the workload, and can deliver the expected outcomes without affecting cost or quality. Different MBSE tools can be used in this step to create diverse environments to measure and validate the model’s performance. Factors like system requirements, model capacity, expected results, actual results, resources consumed, etc., are

Yashica Chopra
February 14, 2025
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Top MBSE Companies in 2025 : The 11 Industry Leaders

Model-based systems engineering is a modern and robust process of using digital systems and engineering models to streamline the product development lifecycle. Here, we’ll discuss the top eleven MBSE companies for enterprises to partner with in 2025. MBSE (Model-Based Systems Engineering) is an advanced system engineering process that uses intelligent digital models to document all the information about a system’s lifecycle. It uses digital and engineering domains to collect, store, and exchange various data (requirements, feedback, design information, etc.) about a system. It is different from the older static model that used analog documents and drawings, formulas, etc., which had to be stored and updated carefully. With MBSE tools, the developers working on a project have complete access to the data but cannot make changes to it on their own. This ensures that the single source of truth is undisturbed and remains secure. Systems Architect Model (SAM), Computer-Aided Design (CAD), and Computer-Aided Engineering (CAE) are used in MBSE to create digital threads that link all the data and models. It is a complex yet vital software to streamline various engineering projects. According to Global Growth Insights, the global MBSE tool market was $3,455.29 million in 2024 and is expected to reach $4,025.65 million in 2025, with a potential growth projected to touch $13,065.36 by 2033 at a CAGR (compound annual growth rate) of 16.5%. MBSE tools will play a major role in industries like aerospace, automotive, defense, telecommunications, and healthcare. While North America is a key player in the MBSE market, countries like India and China from Asia Pacific are seeing an increase in demand for MBSE tools to expand industrial capabilities. In this blog, we’ll find more about Model-Based Systems Engineering tools and the top companies that provide MBSE solutions to enterprises. About Model-Based Systems Engineering Software MBSE software is like a system of systems that helps optimize, streamline, and manage the product development cycle in industries like aerospace, automotive, healthcare, mechanical, engineering, electrical, software, etc. What is an example of an MBSE? A few helpful MBSE examples are listed below: Which companies use MBSE? Many leading global brands and government agencies use MBSE tools as a part of their internal processes. For example, Ford, BMW, the U.S. Department of Defense (DoD), etc., have been investing in MBSE technology for years. Airbus and Lockheed Martin are two other examples. Enterprises can partner with data engineering consulting firms to design their own MBSE framework or buy the Model-Based Systems Engineering software from vendors and customize it to suit their specifications. Both options can be combined to save costs and reduce risks. Hiring an experienced third-party service provider to personalize MBSE software and maintain is a cost-effective and time-saving solution for many businesses. Let’s look at the top MBSE companies to partner with! Top MBSE Companies To Partner With in 2025 DataToBiz DataToBiz is among the leading data engineering companies offering end-to-end services to startups, SMBs, MSMEs, and large enterprises from around the world. The company has ISO and SOC 2 certifications to ensure data security and compliance. It designs and maintains the MBSE framework that aligns with the client’s requirements. Be it Azure and AWS data engineering, the company’s certified experts will handle the process from start to finish and upgrade the systems in the long-term. Additionally, enterprises can benefit from customized cloud-based data warehousing services to build a central repository for better collaborations between teams. DataToBiz also customizes existing MBSE software tools provided by third-party vendors and takes care of the support and maintenance services. Siemens Siemens is a popular technology innovator with a global presence. Among various tech products and services, the company is famous for offering robust Model-Based Systems Engineering software for industries to effectively manage the product development lifecycle irrespective of its complexity. It promotes an ‘integrate and then build’ concept where manufacturers can rely on digital twins to streamline factory operations and create flexible and agile environments for better production. Additionally, Siemens and IBM have collaborated to bring together their powerful solutions and deliver greater results to businesses. The company works with large enterprises as well as SMBs to transform multi-domain development and enable cross-platform scalability. The company customizes the MBSE services based on the client’s industry and target market. IBM IBM is a global IT service provider with a presence in numerous nations. The company’s engineering lifecycle management product, Rhapsody, is a comprehensive and powerful MBSE software designed to help businesses from various sectors. It offers trustworthy modeling, seamless integrations, effortless code generation, digital thread, and simulations across different domains. IBM® Engineering Rhapsody® is great for collaborative design development and test environments. It is also effective in accelerating industry standards to improve production quality. From analyzing the project details to quickly implementing the design, supporting real-time agile engineering, and third-party integrations, Rhapsody is beneficial and must-use software for manufacturers from industries like aerospace, automotive, etc. Arcfield Arcfield is a US-based company offering services in the US and Canada with a focus on various forms of defense and space exploration. The company’s MBSE solution simplifies the complex challenges faced by industries in today’s world, be it cost, long-term efficiency, or decision-making. The platform’s innovative capabilities can empower businesses to handle volatile conditions, streamline the production lifecycle, and increase transparency. Arcfield has a team of certified experts with domain experience to use different existing MBSE platforms and integrate them to create seamless and high-fidelity digital twins in the enterprise. Its digital engineering ecosystem consists of all the required elements (from databases to analytics, visualization, and simulation) to deliver the promised results. Mercury Mercury Systems is a technology company offering services in the global aerospace and defense industries. Be it essential components or pre-integrated subsystems, the company provides innovative and scalable solutions based on clients’ requirements. It gives the utmost importance to safety certification and security. The company’s MBSE technology and services support the development lifecycle through cost-effective means. It considers MBSE as one of the four pillars of digital transformation and uses state-of-the-art technologies to provide

Manpreet Kaur
February 13, 2025
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Essential Elements of a Winning Data Analytics Management Strategy

This blog discusses how to create a winning data analytics management strategy to make the most of your company data and make informed decisions based on facts instead of assumptions. There were 5 exabytes of information created between the dawn of civilization through 2003, but that much information is now created every two days.” ~ Eric Schmidt, Executive Chairman at Google. This quote highlights the large amount of data that is produced today. A report by Statista reveals that global data creation will increase to 180 zettabytes. Organizations need a well-defined strategy to convert this data into actionable insights and make sense of the available information to drive better decisions. In this blog, we discuss data analytics management strategy and how you can create one to convert your data into a powerful asset. What is a Data Analytics Management Strategy? Data analytics management strategy can be defined as a structured approach to collecting, processing, storing, and analyzing data. It outlines how an organization will manage and use data to extract insights, optimize business operations, and make data-driven decisions. The strategy includes everything about data—its collection method, storage, and techniques. Data analytics management strategy ensures that organizations use their data effectively by converting raw data into actionable insights. This helps predict trends and identify growth opportunities. It also includes best practices for ensuring data, compliance, and governance to present a unified information view, making data a valuable asset for organizations. Centers can rely on AI agents to handle extra work instead of hiring more employees. Despite the need for initial investment, AI agents can be a cost-effective solution in the long term. Moreover, the service providers ensure the software follows the security and privacy regulations to protect confidential data. Why Work in Data Management Analytics Strategy? “As business leaders, we need to understand that lack of data is not the issue. Most businesses have more than enough data to use constructively; we just don’t know how to use it. The reality is that most businesses are already data-rich, but insight-poor. Those companies that view data as a strategic asset are the ones that will survive and thrive.” Bernard Marr, Big Data Having data is not sufficient for organizations. They need to harness the potential of the data and produce meaningful outputs aligned with the business goals and objectives. A data strategy helps overcome different types of data challenges, such as a lack of data-driven decision-making, misuse of data, inconsistent KPIs, manual data integration, and poor data quality. Concepts of Data Analytics Management Data analytics involves various practices and strategies that help users obtain insights and facilitate decision-making. Some of the main concepts of data analytics include: Data Governance This involves setting policies and standards that ensure privacy, security, and compliance of data across the organization. These regulations define who can access data and how it can be used, ensuring adherence to legal and regulatory rules. Data Integration Data integration unifies data from multiple sources and presents a cohesive view, making it easy to analyze data. It uses techniques such as ETL (Extract, Transform, Load) to combine data from different sources. Data Quality Management This aims to ensure high-quality data by identifying and removing inconsistencies, errors, and duplicates within data sets, ensuring reliable and accurate insights. Data Architecture Data architecture refers to the blueprint that defines how to collect, store, and manage data within an organization. This helps to align data management practices with business goals. Data Visualization This involves converting data into visual formats like graphs, charts, and dashboards, making it easier to understand insights and take action. Master Data Management (MDM) MDM creates a unified, accurate, and consistent version of various data entities, ensuring data elements are consistent across the organization and serve as a single source of truth. It prevents data silos, improves data quality, and ensures that everyone in the organization works with the same up-to-date information. How to Create a Data Analytics Management Strategy? Creating a data analytics management strategy is not a complex process if done correctly. Here are five essential steps to help you develop an effective strategy for your business: Know your business goals Start by identifying the key questions that need answers, such as: Once you find answers to these questions, you can start building a strategy and create a plan to implement it. Build data process Once you have figured out your objectives, it is time to create data processes for gathering, preparing, storing, and distributing the data. For each step, you must ask yourself a couple of questions mentioned below: Choose the right technology The third step is selecting the right tools and technology to build an effective data analytics management strategy. It involves choosing the hardware and software that will help you build a strong data infrastructure. Here’s how you can do it: Set data governance As data usage and infrastructure expand, it is important to pay close attention to data governance. Now you need to invest time and effort to create and share policies and procedures to ensure proper data management. You need to focus on ensuring the quality, security, transparency, and privacy of data. Share the policies with data owners, stakeholders, and everyone across the company to ensure the safe use of data. Train your team Train your team with the knowledge and skills to interpret and analyze data. This involves providing data analysis tools to departments beyond IT to ensure everyone understands the company’s data management strategy and knows how to do their part. Conclusion As data continues to increase in volume and complexity, new tools and techniques are emerging that help businesses extract insights. Therefore, it is recommended that you assess your needs and select a data analytics partner who can provide tailored solutions for your data analytics goals and help you convert your data into a strategic asset, driving growth and informed decision-making. More in Data Analytics Management… A data analytics management strategy is essential for driving business growth. It helps organizations convert raw

Yashica Chopra
February 09, 2025
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AI Agents in Healthcare – The Future of Medical AI

The healthcare and medical industry can vastly benefit from adopting advanced technologies like AI, ML, NLP, etc. Here we’ll discuss how AI agents in healthcare are transforming patient care, diagnostics, and hospital ops. Learn about the benefits, use cases, and future impact on medical AI. Artificial intelligence has become a part of many industries, including healthcare. AI has many uses in the medical and healthcare sector. According to Grand View Research, the global AI healthcare market is estimated to grow at a CAGR (compound annual growth rate) of 38.5% between 2024 and 2030. The AI healthcare market shot up by a whopping 233% between 2020 and 2023, the Covid-19 pandemic and post-pandemic years. Another report shows that over 60% of people from healthcare in South America and the Asia Pacific regions believe AI will play a prominent role in decision-making in the next ten years, with the Middle East in the third place. The clinicians from North America and Europe aren’t as convinced but AI is majorly used (at 29%) in clinical decision support tools in the US. AI in healthcare is helpful for doctors, surgeons, patients, medical/ lab technicians, and administrative teams. The popularity of generative AI and the development of AI agents has enhanced the use of new technologies in the industry. In this blog, we will read more about the uses, benefits, and need for AI agents in healthcare as well as the role of artificial intelligence in the medical sector. AI Agents in Healthcare: Key Components, Uses, and Benefits An AI agent in healthcare is an intelligent software program trained on patient and medical data to provide virtual assistance by processing input and delivering relevant outcomes. Key Components of AI Agents The AI agents’ framework consists of five key components listed below: AI agents use machine learning algorithms to identify patterns and discover trends or correlations in historical data to predict outcomes, diagnose diseases, recommend treatments, or determine the risk factors when considering a treatment/ surgery. By using NLP, the AI agents can accurately understand and interpret human languages. This reduces the risk of error caused by misunderstanding the content or context. NLP helps in answering queries by patients, filling their forms/ medical records, and generating reports. The AI agents’ architecture also includes technology to process audiovisual content like images and videos, X-rays, CT and MRI scan reports, etc., to identify patterns, detect early signs of diseases, and more. Tumors or cancerous cells can be identified before they affect the patient’s health adversely. Computer vision is also used when performing surgeries. Another role of AI agents is to regularly interact with humans (be it doctors, nurses, admin staff, or patients) and assist them however necessary. For this, the software is trained to have a user-friendly interface with adaptability and responsiveness. AI agents can be used to analyze datasets and derive actionable insights, future predictions, etc. From identifying the appropriate treatment method to post-care planning, the AI agent can help at every stage. It can provide the required insights and reports for the concerned parties to make the right decisions. Uses of AI Agents in Healthcare Medical treatments are diverse and personalized based on the patient’s medical history and overall health condition. Moreover, concerns like allergies to active ingredients in drugs should be considered when prescribing medication. By digitalizing patient records, hospitals can use AI agents to create a quick and accurate report about the patient’s condition and provide it to the doctor. This allows the doctor to prescribe an appropriate drug or a treatment plan. Even if the patient forgets to inform during the appointment, the AI agent can ensure the doctor has all the necessary information to make the right decision. IoT (Internet of Things) devices like sensors on wearables can be used to collect patient data in real-time and send alerts as necessary. For example, a patient recovering from a surgical procedure can be monitored through the AI agent that tracks their vitals like heartbeat, blood pressure, etc. The nurses can be quickly alerted in case of emergencies. Furthermore, patients recovering at home can also be monitored remotely through AI agents. The hospital can send someone to treat the patient or contact them immediately to provide support. Administrative processes and revenue cycle management (RCM) can be streamlined using AI agents in healthcare. From automatically updating patient records in the central database to scheduling appointments and follow-ups, sending alerts to reduce waiting time, and tracking appointments to provide a better experience, AI agents have varied roles in increasing operational efficiency. The admin staff can use AI agents to ensure the claim submissions are accurate and get quick reimbursements from payers. Generating invoices and printing bills is also easier. Sometimes, healthcare providers may have to come up with new treatments or drug combinations based on the patient’s condition and medical history. They can use AI agents to analyze the data and predict the possible outcomes of using a particular drug or procedure. This reduces the risk factor as they don’t have to rely only on guesswork or trial and error. It may lead to new discoveries and help more patients with similar conditions. Similarly, AI agents can provide insights about the benefits and potential side effects of a medicine on a patient. AI agents can assist surgeries in many ways. One method is the support provided to robotic arms. Another method is through read-aloud devices or communicator tools that list the patient’s history to the surgeon and help them make proactive decisions during emergency situations. Instead of waiting for someone to gather the information, the AI virtual assistant will deliver the reports in real time. Saving a few precious minutes during complex surgical processes is a matter of life and death. We cannot ignore the importance of providing mental health support to patients and medical staff. There are quite a few AI agents built on conversational AI that act as chatbots and virtual counselors. Patients can interact with these AI agents to share their worries/ troubles

Yashica Chopra
February 02, 2025
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Staff Augmentation Engagement Models: Smart Alternatives

IT staff augmentation services help organizations hire experienced professionals to handle various business tasks and projects. Here, we’ll discuss the underrated staff augmentation engagement models and how these can benefit your business. Businesses cannot afford to slow down or delay developments in competitive markets. Technological adoption and digital transformation are necessary to stay relevant and retain customer loyalty. However, this requires an experienced and trained talent pool. After all, it’s your employees who have to use innovations and technology to deliver the expected results. Since many organizations suffer from talent gaps, there’s a high demand for experts from different domains. Recruiting them as permanent employees is a cost-intensive and time-consuming process. Moreover, there’s no guarantee that the employees will continue to work with your business. An effective alternative is to use IT staff augmentation services. It is a strategic approach where businesses can temporarily hire experienced professionals to join their in-house teams and perform the necessary tasks (or complete a project). Staff augmentation companies handle the process of selecting and onboarding professionals based on your criteria and requirements. The staff augmentation procurement market is expected to reach $81.87 billion in 2025. It will continue to be the go-to method for recruiting experts for short-term and long-term needs. Naturally, there are different staff augmentation engagement models to cater to enterprises’ diverse specifications. The project-based model and the hourly pricing model are two such examples and commonly used options to hire talent. However, there are more models to choose from. The following are the underrated staff augmentation engagement models that offer flexibility, scalability, and cost-effectiveness to businesses. Underrated Staff Augmentation Engagement Models In 2025 Full-time Equivalent (FTE) Full-time Equivalent or FTE is a staff augmentation model that standardizes how you measure the work capacity of your human resources or talent pool. FTE is the unit of time used to measure the workload capacity of an employee irrespective of whether they work part-time or full-time. This engagement model is suitable when augmenting teams for large projects that span many months. It becomes easier to handle bigger teams and track their progress. What makes Full-time Equivalent (FTE) a worthy choice for businesses? Here are a few benefits it offers: PEO/Employee Leasing PEO stands for Professional Employer Organization. It is a staff augmentation engagement model similar to outsourcing but focuses on small and mid-sized businesses (SMBs). Co-employment is the basis of the PEO model. Simply put, you hire employees from the service provider company to perform the pre-defined tasks on your behalf. The employees will use the service provider’s resources for the job but deliver the results to your business. This frees up your business resources and allows you to focus on the core activities for faster growth and expansion. PEOs can cover admin tasks and routine IT jobs like maintenance, updating, debugging, etc. Here’s how the PEO or employee leasing engagement model is advantageous for your business: Contract Staffing Contract staffing is another type of IT staff augmentation services model that provides temporary employees through contracts. In this, you partner with a third-party offshore or nearshore staff augmentation company to fill the vacancies in your businesses with temporary candidates who fit the bill. A contract staffing provider also acts as an intermediary between your business and the professionals you want to hire. From selecting the right candidates to conducting interviews, onboarding, training, and payroll management, the service provider takes care of everything. Unsurprisingly, the IT sector is among the top industries that employ the contract staffing engagement model. Government and public sector agencies also use many contract employees due to the extensive nature of workplace requirements. A few reasons to adopt the contract staffing model are listed below: Contingent Staffing Contingent staffing is another engagement model offering team augmentation services through third-party companies. Though it sounds similar to contract staffing, contingent staffing is a different staff augmentation model. Professionals from outside the organization are hired usually on a short-term basis. They are paid hourly or per task instead of a monthly salary. Also, there’s no ‘contract’ between the candidate and the business as a contingent worker can handle multiple projects simultaneously. In short, a contingent worker steps in temporarily to handle a specific task and leaves afterward. They are not entitled to the same benefits as existing employees or contract workers. Legally too, it is easier to terminate a contingent worker when their role ends, which is why they work on more than one project at a time. Freelancers also come under contingent staffing. So, what are the advantages of opting for the contingent staff augmentation engagement model? Here are a few: Specialized Acquisition Skill The specialized skill augmentation engagement model deals with niche skills necessary for a business to complete a project or achieve an objective. This staff augmentation model doesn’t deal with general or basic-level skills. It is all about hiring experts with specific domain expertise and certifications. They are usually in high demand due to the lack of supply in the employment market. For example, if you want to hire a data scientist or an AI engineer with over ten years of experience, you can opt for analytics staff augmentation with a specialized skill set. The skill augmentation model is majorly used in the IT sector to bring experienced professionals to temporarily handle a project. Though many of them are called consultants, the term is not used in the traditional sense. Here, it implies that they have joined the in-house team for a definite reason and will deliver the results. Specialized skill acquisition model is beneficial for the following reasons: Conclusion With many staff augmentation engagement models to choose from, it’s vital to understand your business requirements, budget, project timeline, and market trends when making a decision. Discuss the case with the IT staff augmentation company and take their input to build a robust team with augmented staff. Choose a service provider with a diverse portfolio and a transparent pricing model. 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Yashica Chopra
January 30, 2025
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The Data Paralysis Trap – Are You Into One?

An overload of data can cause confusion and conflict, resulting in the inability to make a proper decision. This is data paralysis. Here, we’ll discuss the causes of data paralysis and how tailored data engineering services can help overcome analytics paralysis in an organization. Data is the core of a business in today’s world. Just about everything depends on data and analytics in some form. Moreover, 149 zettabytes of data were generated in 2024 thanks to technology. This is said to increase to 185 zettabytes in 2025. To simplify the math, a zettabyte is approximately equal to 250 billion DVDs worth of content. This is an overwhelming amount of data generated, consumed, and shared by people worldwide. Since most of this data is readily available on the Internet, businesses began to find it easier to adopt data-driven analytical models for streamlined decision-making. This requires data collection, data warehousing, and data engineering services to create a comprehensive analytical model in the enterprise. According to The Business Research Company, the global data collection and labeling market has grown from $3.55 billion in 2024 to $4.44 billion in 2025 at a CAGR (compound annual growth rate) of 2.25%. However, the availability of large volumes of data comes with its share of challenges. The biggest concern is data paralysis. Simply put, data paralysis is a situation where you cannot decide due to overthinking or access to too much data. When you have much more information than what’s necessary, you start to double-guess the decisions or consider too many metrics. This leads to a sense of uncertainty and a state of limbo where you cannot decide what to do. Data paralysis is an end businesses should avoid. However, it is easy to fall into this trap. Here, we’ll read more about data and analysis paralysis, the causes, and ways to overcome the challenge by partnering with data analytics and data engineering service providers. What Causes Analysis Paralysis? Various reasons/ causes contribute to analytics paralysis in an organization. Accumulation of excess data, lack of proper data governance policies, outdated data storage systems, inadequate data management tools, etc., are some crucial causes of data paralysis. But what is the main reason for data paralysis? Data overload is the main reason for data paralysis, which results in analytics paralysis and troubles with decision-making. However, this doesn’t happen overnight. Gradually, over time, you might realize that the data-driven model has become a hindrance rather than a facilitator. The sooner you realize the symptoms, the easier it will be to reverse the situation and streamline the models to help you the way they should. Generally speaking, the path of analytics paralysis has three stages. When a business identifies the problem in the first stage, finding solutions will be simpler, quicker, and cost-effective. Stages of Analysis Paralysis 1. Data Distrust Data distrust is when an employee/ stakeholder or a team is skeptical of the quality of data collected by the business and doesn’t want to use it for making decisions. They are wary of using incorrect and incomplete data as these may lead to wrong decisions. However, emphasizing data quality excessively can lead to increasing data distrust across the enterprise. This creates a tense work environment and can prevent the management from making positive changes and developments to the models. The best way to handle data distrust is to get to the root of the problem. Hire expert data analysts and data scientists to handle the business data. Give them full control over the project for data cleaning, labeling, storage, etc. There has to be a balance to ensure good data quality but not at the cost of the returns. Setting too high standards increases the expenses and can still have a variance rate of 1-3%. The resources spent on the process need to be justified. You can achieve the balance by investing in data warehousing as a service from reputed data engineering companies. The cloud platforms like Azure and AWS provide the necessary tools and framework to improve data quality and reduce data distrust. 2. Data Daze Data daze is the stage before data paralysis. Here, you accumulate so much data that it starts to feel threatening. For example, asking an employee to create a project report might give them anxiety due to the sheer volume of data they have to process, even if they are using analytical tools. The work doubles and triples since they have to consider a long list of metrics and generate reports for multiple combinations. It feels like a neverending task and can be draining. When data overload becomes a daily occurrence, it changes the work environment and makes everyone stressed 24*7. This can also affect their personal lives and lead to a higher attrition rate. The best way to overcome data daze and prevent it from becoming analytics paralysis is to hire AWS data engineering services. Data engineering is a continuous and end-to-end process of managing data collection, cleaning, storage, analysis, and visualization. The workflows are streamlined and automated using advanced tools to ensure only the required and relevant data is used to derive insights and generate reports. Here, experienced data engineers will choose the KPIs and divide datasets into neat layers or groups based on your business activities and goals. They will train employees to properly identify and visualize data reports as per the requirements. 3. Data and Analysis Paralysis The final stage is analytics paralysis, where the management or team heads cannot decide because they over-analyze the information. For example, consider data analytics to derive insights about the prospects for a new product. Here, the focus should be on the type of product you want to release into the market and whether or not the target audience will like it. You can also look at some must-have features to make the product special or different from existing options. However, if you expand the metrics and target market to include various variables, the insights will be all over the place. This makes it