Data Engineering & Analytics Archives

Ankush Sharma
July 31, 2025
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Analytics as a Service: A Modern Approach to Data Engineering

Data analytics is a comprehensive solution for enterprises to convert their data into a valuable asset. Here, we’ll discuss the analytics as a service (AaaS) model and explore how a business can benefit from adopting this model to make informed data-driven decisions. In today’s data-driven world, a business cannot afford to ignore the advantages of leveraging data and insights to boost its revenue and enhance the customer experience. Data is not just a by-product but an asset to every organization. By using the latest data analytics and business intelligence tools, you can unlock the true potential of your business data and make informed decisions at all levels. Data analytics is no longer optional but a necessary part of all industries. According to Fortune Business Insights, the global data analytics market was $64.99 billion in 2024 and is expected to be $82.23 billion in 2025 with a projected CAGR (compound annual growth rate) of 25.5% to reach $402.70 billion by 2032. The same report shows that most industries have adopted data analytics in some form. The IT industry has the largest market share at 20% with healthcare, BSFI (banking and finance), retail, eCommerce, manufacturing, transport, and logistics (supply chain) also having prominent shares. There are various ways to integrate analytics into your business. Cloud-based analytics as a service (AaaS) has gained popularity for its cost-efficiency and ease of use in creating self-service systems. But what does analytics as a service mean? How does this delivery model help your enterprise in 2025? Let’s find out in this blog. What is Analytics as a Service (AaaS) Delivery Model? The analytics as-a-service model is a cloud-based solution where the related IT infrastructure, tools, and applications are hosted on the vendor’s server. Businesses pay for using these tools and the relevant services provided to set up the connections and troubleshoot the systems when necessary. Analytics as a service is also known as managed analytics as a service of BI as a service (BIaaS). Simply put, analytics as a service (AaaS) is a subscription-based model where you hire specific or end-to-end data analytics solutions from service providers. You use the resources, tools, technologies, and expertise of the service providers to derive meaningful analytical insights for decision-making. The data analytics platform is hosted on a cloud like Azure, AWS, or Google Cloud. The experts will integrate the tool with your existing data systems to provide insights through the dashboards. But what if you want to revamp your systems? The same data engineering company will provide end-to-end solutions to streamline data flow and connections between different tools to create a flexible and scalable IT infrastructure on the cloud or on-premises. Infrastructure as a service (IaaS) in cloud computing is a preferred choice for many organizations as it reduces the need for heavy on-premises hardware and migrates all major systems to the cloud. This allows your employees to work remotely and collaborate with others from different regions. Additionally, cloud services are future-proof and can be easily upgraded or downgraded to suit your needs. You only pay for the technologies and server space you add to your business account. The hosting, licensing, and other aspects are managed by the service provider. Popular business intelligence platforms like Power BI and Tableau can be used as web applications by integrating them with Azure PaaS services or other cloud-based solutions. PaaS stands for Platform as a Service, where the data analytics tool is hosted on a remote cloud and accessed by employees authorized to use it as part of their job. How Can a Business Benefit from the AaaS Model in 2025? When you opt for end-to-end analytics as a service delivery model, you hand over the responsibilities of data collection, data migration, ELT/ ETL, data warehousing/ data lake, data analytics, data visualization, and support services to a third-party offshore provider like a data engineering company and spend your resources on your core functions. But why should you choose analytics as a service to build a data-driven business model? Check out the benefits of using AaaS for enterprises: Reduce Workload By hiring a service provider to build, deploy, and manage the data-driven model, enterprises can reduce the pressure on internal teams and allow them to focus on growing the business. There’s no need to hire more employees on the payroll to take up the additional work. Talent gap issues can be addressed without spending more money on recruitment and training. This reduces the workload on HR teams. Resource Optimization By hiring a data warehousing company for AaaS solutions, an organization can ensure its limited resources are not distributed among various demands. The service providers use their own resources to deliver the desired outcomes for businesses. In exchange, you pay the company for the services it provides. This will prevent budget concerns and a lack of availability of resources for important projects. You can use analytical insights and have a competitive edge without compromising other departments or growth areas. Minimize Risk Building, maintaining, and regularly upgrading the AaaS framework is not an easy task. It requires domain-specific expertise as well as knowledge of the latest tools and technologies. Moreover, you should know exactly which tool to choose based on your current situation and future prospects. Taking up such a complex project entirely on your own and working on it from scratch is highly risky. A mistake could cause losses in millions, as well as excess consumption of other resources and delays. All these risks can be minimized by partnering with a service provider. Cost-Effectiveness As stated in the previous point, the greater the risk, the higher the possibility of monetary loss. Even large enterprises and multinational firms have to deal with budget restrictions. Analytics as a service is a cost-effective solution as it reduces the need for extensive research and development and in-house systems. You can pay for customized infrastructure as a service (IaaS) solutions to use a robust cloud-based IT infrastructure to run your business systems. This also reduces the need for replacing

Ankush Sharma
July 18, 2025
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Understanding the 5 Stages of the Data Maturity Framework

The data maturity framework helps businesses assess how well they collect, manage, and use data. This blog explains the 5 stages of data maturity, from data collection to advanced AI-driven insights. Understanding these stages helps businesses see where they stand, spot gaps, and take steps to become data-driven. Businesses are producing more data than ever before. In fact, global data creation is expected to grow to more than 394 zettabytes by 2028. The McKinsey Global Institute estimates that data and analytics could generate approximately $1.2 trillion in value annually for the public and social sectors. Having data isn’t enough. The real challenge lies in understanding how mature your data capabilities are and how to improve them. As Dan Heath says, “Data are just summaries of thousands of stories—tell a few of those stories to help make the data meaningful.” That’s where the Data Maturity Framework comes in. In this blog, we’ll break down the 5 stages of data maturity and help you figure out where your business stands and what steps can help you use data optimally. What is Data Maturity? Data maturity refers to the process of collecting, managing, analyzing, and utilizing data to make smart decisions. The more mature your organization is with its data, the better you can use it to achieve goals and solve problems. It’s not just about having a lot of data. It’s about having the right systems, processes, and culture in place to turn data into actionable insights. A data-mature organization treats data as a strategic asset, ensuring it’s accurate, accessible, and aligned with business goals. What is the Data Maturity Model Framework? The Data Maturity Model is a step-by-step way to measure how well a business uses its data. It helps companies understand: The data maturity model has five stages of data maturity. It starts with basic data collection, moving to organizing and analyzing data, and advanced stages like automation, AI, and predictive analytics. The higher your data maturity, the better your business can use data to make faster, smarter decisions. What are the 5 Stages of the Data Maturity Model? The 5 stages of the data maturity model include: Stage 1: Initial What it looks like: Problem: You have data but no control. Nobody in your organization knows where the accurate data lives. What you need to do: Stage 2: Data Aware What it looks like: Problem: You collect data, but it’s not connected or unified. You fail to see the full picture. What you need to do: Stage 3: Data Managed What it looks like: Problem: You now have more data but need consistency, accuracy, and proper controls. What you need to do: Stage 4: Data Driven What it looks like: Problem: You have data power but need predictive insights to optimize actions. What you need to do: Stage 5: Optimized What it looks like: Problem: You need to fine-tune automation and scale responsibly while staying compliant. What you need to do: Data Maturity Model Steps for Assessing Data Maturity “We are surrounded by data, but starved for insights.”— Jay Baer This quote says it all. Here are simple steps to assess data maturity and find how ready your organization is for data-driven growth. Step 1: Is your company’s data organized? Find out how you are storing and managing it. The more centralized and structured your data, the more mature your system is. If you’re still fiddling with spreadsheets, you’re likely at an early stage. Step 2: Are you using data-driven tools like BI, AI, or machine learning? If you’re using advanced tools, it means you’re on the path to data-driven decision-making. Step 3: Do you find it difficult to make decisions due to data overload? If you’re stuck in reports and too much data, it means your system needs improvement. Mature data systems simplify information and help you focus on what matters most. Step 4: How do you store your data? You can either have a centralized data system or multiple separate storage systems. If you are at the beginning, get systems to store your data. Step 5: What are your biggest pain points? Find out what you are struggling with. It could be Step 6: Where do you need expert guidance? Is it: Knowing where you need to go will help you build a smart, focused plan to level up your data maturity. What Role Do Change Management and Culture Play in Achieving Data Maturity? “Culture eats strategy for breakfast”, says Peter Drucker. When it comes to becoming a data-mature organization, technology is only one part of the story. People, mindset, and habits are the real challenges. Why is Culture Important? Moving from instinct-driven decisions to data-driven decisions means people must be willing to: Even the best BI tools or AI models won’t work if your team doesn’t use them well. Why Do You Need Change Management? Implementing data maturity isn’t a one-time process. By bringing change management, you can Leaders can support this by emphasizing the importance of data and celebrating when the team makes good decisions using data. Always start with small pilot data engineering services projects that solve real business problems. Make data accessible to prevent silos and improve cross-functional collaboration. Share and reward wins that show how data improved KPIs or solved tough challenges. Conclusion Understanding the stage of the data maturity framework helps find out where your business stands today and what steps will add more value. Whether you’re just beginning to organize your data or exploring AI-powered decision-making, each stage gives you a chance to improve and grow. In order to use your data to the fullest, partnering with a trusted data engineering company is the best choice. With experience in data consulting and data engineering, they’ll build a strong foundation for your business, solve the right problems at each stage, and create a plan that connects your data progress with your business goals. People Also Ask How do I figure out which stage of data maturity my business is currently

Manpreet Kaur
July 10, 2025
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Best MLOps Companies in the USA- Top 10 for ML Engineering

This blog highlights the best MLOps companies that businesses can use for managing, deploying, and monitoring models. Businesses choose the right MLOps consulting services partner based on their unique needs, infrastructure, and budget. “We are entering a new world. The technologies of machine learning, speech recognition, and natural language understanding are reaching a nexus of capability. The result is that we’ll soon have artificially intelligent assistants to help us in every aspect of our lives”, says Amy Stapleton. Machine learning operations MLOps platforms are becoming an important part of data science and artificial intelligence (AI), making it easy to integrate machine learning models into production environments. The need for MLOps platforms and solutions has increased as businesses across various industries are implementing AI and machine learning initiatives. The global MLOps market size was valued at USD 1.58 billion in 2024 and is expected to grow to USD 19.55 billion by 2032 at a 35.5% CAGR. North America led the market with a 36.21% share in 2022. This rapid growth reflects the increasing demand for efficient machine learning deployment and management solutions across industries. Here is a list of the top MLOps companies in the USA that are helping businesses deploy, monitor, and manage machine learning models. 10 Top MLOps Companies in the USA for ML Engineering DataToBiz DataToBiz is a leading data engineering and analytics company that offers end-to-end data solutions. It provides a robust MLOps platform that makes it easy to deploy, monitor, and manage machine learning models in production environments. Their experts are adept at providing data engineering solutions such as creating custom data pipelines, setting data lakes, and providing advanced analytics platforms for actionable insights. DataToBiz offers comprehensive solutions for businesses looking to deploy AI models at scale, address challenges, manage models, and optimize them for performance. Their cloud-based platforms are designed to integrate seamlessly with existing IT infrastructure and support real-time data analysis. DataRobot DataRobot is an enterprise-grade MLOps platform designed to automate machine learning workflows. The platform helps businesses scale AI initiatives with ease, providing tools for model creation, deployment, and continuous monitoring. DataRobot’s solutions are ideal for companies looking to accelerate the deployment of ML models across their organizations. The company simplifies the creation and deployment of machine learning models with minimal user intervention. The experts manage the machine learning pipeline, from data preprocessing to model deployment and monitoring. Kubeflow Kubeflow is a comprehensive open-source MLOps framework built on Kubernetes, ideal for businesses that require a flexible and scalable approach to managing machine learning workflows. It’s widely adopted for handling large-scale ML operations with full transparency in model training, testing, and deployment. Kubeflow provides a customizable, open-source platform for machine learning model management and uses Kubernetes for scalable ML workloads. It also supports the full machine learning lifecycle from data ingestion to deployment. Domino Data Lab Domino Data Lab offers a collaborative platform for managing the end-to-end data science and machine learning lifecycle. It includes powerful tools for version control, model management, and reproducibility, ensuring that data science teams can build and deploy models efficiently and effectively. The company offers tools for managing the model lifecycle and ensuring data science teams collaborate. It also enables teams to work together on data science projects and version control for models and datasets. MLflow (by Databricks) MLflow is an open-source MLOps platform that provides robust features for tracking, versioning, and deploying machine learning models. It integrates seamlessly with cloud platforms and supports various ML workflows, making it a popular choice for businesses that want full control over their models. MLflow offers a flexible platform for managing the machine learning lifecycle, and tracks the development and performance of ML models over time to ensure accurate model deployment and monitoring. Tecton Tecton simplifies MLOps by automating data workflows for machine learning teams. It helps engineers build and manage features. It allows you to pull real-time or historical data, process it automatically, and serve it to models to offer accurate predictions for processes such as fraud detection or personalized recommendations. The company offers scalability and reliability so teams can focus on improving models cand ut costs by optimizing how data is stored and processed, making it easier to deploy AI faster. Hugging Face Hugging Face is a leading platform and community in AI known for making machine learning and natural language processing (NLP) more accessible and collaborative. It offers a vast library of pre-trained models, datasets, and tools that developers and businesses can use to build, fine-tune, and deploy AI applications. Hugging Face offers services such as model hosting, version control, deployment APIs, and automated training tools. The developers can easily integrate Hugging Face models into their pipelines, benefit from robust versioning and collaboration features, and deploy models at scale with minimal infrastructure management. Neudesic Neudesic offers cloud-native AI and MLOps solutions that help businesses scale AI and machine learning models efficiently. Their platform specializes in seamless model deployment, continuous monitoring, and scaling to reduce deployment times and minimize costs. The company uses Azure Data & AI platform accelerator which is a pre-configured framework (using Azure Databricks, Synapse, and Data Lake) to deploy AI/ML projects. It also offers end-to-end support for MLOps lifecycle management, including infrastructure monitoring, model governance, and cost optimization, helping clients streamline operations. Dataiku Dataiku is an end-to-end data science and machine learning platform designed to streamline the ML lifecycle, making MLOps accessible and efficient for organizations of all sizes. It provides a unified environment where teams can collaborate on everything from data preparation and model development to deployment, monitoring, and ongoing maintenance. The platform offers robust version control and collaboration tools, allowing multiple team members to work on models simultaneously, track changes, and maintain model integrity throughout the lifecycle. Rocket Software Rocket Software is a global IT leader, specializing in helping large organizations maximize the value of their legacy systems by integrating AI, machine learning, and cloud solutions. It supports AI/ML workflows through predictive analytics and AI tools, offers data integration & modernization. Rocket Software

Yashica Chopra
June 24, 2025
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Choosing the Best Data Lake Companies in 2025 – Our Top 5 Picks

Modern data lakes are built to handle the diverse requirements of organizations from different industries. The services are customized for each client. Here, we’ll discuss the top data lake companies in 2025 for businesses to partner with and achieve their objectives. Data is the key player in today’s world. It has changed how businesses manage their processes and make decisions. The digital-first approach and data-driven business models have become prominent as organizations strive to effectively use their data for various purposes. This data has to be stored in a central repository rather than in truncated departmental silos. A central database is a crucial element of the data-driven IT infrastructure. It is connected to several third-party software applications and can be accessed by employees across the enterprise. This central database can be a data warehouse or a data lake. A data lake is a preferred choice for many organizations as it is more flexible, scalable, and can store raw data in multiple formats. In the data lake vs. data warehouse debate, a data lake provides more opportunities for businesses to gain a competitive edge and is a future-proof solution. Statistics show that the data lake market would be $19.04 billion in 20525 and is expected to reach $88.78 billion by 2032 at a CAGR (compound annual growth rate) of 24.6%. The same report says North America will be the largest market with a 30% share, followed by Asia Pacific with 27%, and Europe with 23%. In this blog, we’ll look at the top data lake companies to partner with in 2025. Before that, let’s read a little more about data lake services. What are Data Lake Services? A data lake is explained as a central repository storing vast amounts of structured, unstructured, and semi-structured data belonging to your business. It can be built on cloud platforms and on-premises. It is connected to several input data sources (like CRM, ERP, HRMS, IoT devices, operational databases, etc.) as well as to analytical and output sources (like business intelligence tools, data visualization tools, customized dashboards, etc.). Data lake services include the tools, technologies, processes, skills, and expertise required to build, integrate, maintain, and upgrade a data lake in a business. It is an end-to-end solution consisting of various steps like data ingestion, data processing, data analytics, data security, data governance, and data visualization. The data lake services offered by companies are tailored to align with diverse business requirements, industry standards, budgets, and more. The companies can offer their proprietary platforms as data lakes or connect your systems with the ones developed by data lake vendors. Choosing the right data lake company ensures your business data is safe, accessible, and used to derive data-driven insights in real-time. 5 Top Data Lake Companies 2025 DataToBiz DataToBiz is a data lake engineering consulting company offering tailored services to clients from around the globe. As an award-winning service provider, it works with start-ups, SMBs, MSMEs, and large enterprises to help them streamline their data and processes using advanced technologies. The company is a certified partner of Microsoft (Gold), AWS, and Google to offer data lake as a service solution like Azure data lake for cloud-based secure and scalable requirements. It believes in transparency and ensures flexible price plans with no hidden costs. The company has a vast project portfolio and can customize the end-to-end data lake services to align with each client’s specifications, budget, and timeline. From data and system migration to building data architecture, setting up third-party integrations, and long-term support services, DataToBiz will empower an organization to manage its business data effectively and make data-driven decisions. Databricks Databricks is a data intelligence platform offering a range of solutions, including cloud data lake services, for clients with varied requirements. Over 60% of Fortune 500 companies use the company’s solutions in some form. It has developed a Lakehouse platform that can be seamlessly integrated with Azure, AWS, and Google Cloud to create a robust cloud-based IT infrastructure for data storage, analytics, and management. The company provides built-in data security and governance solutions to help clients comply with regulatory standards. Additionally, the Lakehouse platform can be connected with AI and ML tools for advanced analytics and real-time insights. The company’s modern data lake architecture provides greater reliability, performance, and data integrity for organizations to enjoy uninterrupted and scalable data services. Teradata Teradata is one of the best cloud analytics and data platform service providers in the global market. It is an AI company offering trusted solutions and faster innovation for data-driven decision-making. The company works with many large and multinational organizations to streamline their data systems and implement cloud-based infrastructure to accelerate processes. It offers a comprehensive lakehouse solution to provide the benefits of data lakes and data warehouses through its next-gen, cloud-native, VantageCloud Lake. This data lake platform can run independent workloads and be used as centralized storage for all data types. The platform offers transparent access to all users while optimizing resource consumption. Teradata’s VantageCloud Lake also has smart scaling technology for automating usage capabilities to ensure cost-effectiveness. IBM IBM is a multinational company offering enterprise data lake consulting services to clients from worldwide. Its data lakehouse solutions are designed to handle heavy loads without slowing down. The company connects the central repository with data analytical tools, advanced AI tools, visualization dashboards, power apps, etc., to create a comprehensive data architecture in the business and provide real-time and meaningful insights. Watsonx.data is the company’s solution to setting up an open data lakehouse, support querying and governance, and open data in multiple formats from any location. The experts customize the platform and implement it on-premises or via the cloud. It provides a data lake as a service solution through IBM Cloud and AWS. The company has also partnered with Cloudera to develop enterprise-grade data and AI services to empower clients to become successful in their digital transformation journey. Dremio Dremio is a hybrid data lakehouse platform that works with several businesses across the globe to help them

Ankush Sharma
June 14, 2025
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Industry-Specific Analytics for Leaders: Key to Better Decision-making

Data analytics is essential to understand customers and markets and plan the business’s future steps to achieve its goals. Here, we’ll discuss the need for industry-specific analytics and how it can empower organizations to make better and profitable decisions. Data analytics is a keyword in today’s world. Every business wants to invest in analytics to gain a competitive edge. The market offers numerous data analytics and business intelligence tools for analyzing datasets and deriving insights. According to Fortune Business Insights, the global data analytics market was $64.99 billion in 2024, and is predicted to touch $82.23 billion in 2025. It is expected to grow at a CAGR (compound annual growth rate) of 25.5% to reach $402.70 billion by 2032. Artificial intelligence plays a vital role, as data analytics tools powered by AI and ML technologies can provide accurate and quick insights. However, with large amounts of data generated daily, how can organizations use this data for analytics? After all, statistics show that the global data creation will be 185 zettabytes by 2025. In such instances, the types of analytics you implement can determine your success. So, what kind of analytical insights should you generate and use for decision-making? Can general analytics provide the same results as industry-specific analytics? What is the difference between them? Let’s find out why industry-specific analytics are necessary for businesses in today’s scenario. Why is Generic Analytics Less Effective for Your Industry? Data analytics is the process of examining vast datasets to identify hidden patterns and trends and provide useful conclusions or interpretations. These are called insights and help in making data-driven decisions. Business intelligence, reporting tools, and advanced AI analytics come under data analytics. While the tools and technologies used are different, the central concept of data analysis remains the same. However, generic analytics are not as effective as analytics tailored for the business and industry. That’s because of the following reasons: Lack of Specifics Generic analytics are just that and offer one-size-fits-all insights that don’t go into specifics. They can be broadly applicable but miss the nuances of how things differ from one industry to another. Industry standards, business KPIs (key performance indicators), the organization’s mission and objectives, or even the target audiences are not considered in generic analytics. There is no specific indication that the insights will help your business handle a certain situation effectively. Misinterpretation or Inaccurate Data Without customized data analytics services, you have to rely on generic insights that may have misinterpreted the context or used a different dataset for the purpose. For example, a business manufactures and sells wooden kitchen appliances. To derive meaningful insights, it has to use data belonging to the kitchen appliances niche, especially items made of wood. Additionally, it should also consider the target markets. However, if it uses random data collected from the internet, the insights can be inaccurate and lead to wrong decisions. Risk of Biased Insights Since generic insights cannot offer nuance, they are not always actionable, as in, they are not always useful for decision-making. Moreover, there’s a higher risk of deriving biased insights since the data is not carefully collected or processed. For example, the insights might show that the sales haven’t been as expected, but fail to provide the real reason for this. Or, they could indicate a wrong reason, which ultimately results in extra expenses and losses for the organization. Lesser ROI When you hire a data analytics company, you want to get back the return on investment. The ROI is measured based on various metrics, like how actionable the insights are, whether the data-driven decisions helped achieve the business objectives, and so on. However, when the insights are generic, you cannot use all of them for decision-making. But you continue to spend money on the process. This reduces the ROI and indicates that your investment is not worth the money you spend on it. How Can Industry-Specific Insights Improve Your Forecasting Accuracy? Customized data analytics solutions for every business based on industry standards and requirements can increase forecasting accuracy and promote better decision-making at all levels in the enterprise. That’s why many data analytics companies offer tailored services that align with the mission, vision, and goals of each client. Here’s how industry-specific insights can help an organization be prepared for a better future: Targeted Insights Sector-wise data forecasting gives insights that target the industry, market, or customer base. This is done to get in-depth reports about how the various external factors influence the business and what can be done to make the best of the situation. When the insights derived are highly relevant, they help teams make empowered decisions to grow the business. For example, with targeted insights, you can understand why customers didn’t like a product or what can be done to increase sales. Strategic Decisions Since industry-specific analytics share insights about the patterns, trends, and correlations in historical data, they can be used to make informed decisions and build effective strategies to tackle various situations. For example, you can understand customer purchase patterns during different seasons to plan an effective marketing campaign and attract more sales. This increases the ROI for the amount spent on promotions and establishes the brand in the market. Market Expansion Every business aims to grow and expand into newer markets, increase its customer base, and achieve a higher share. For this, you should know which target audience to impress, how to convert them into customers, when to enter a new market, which products and services to promote, which marketing channels to use, and so on. The information to make these decisions can be provided by industry-specific insights. You can be ready for new opportunities and grow the business quickly. Customer Segmentation Customers are essential for any business to survive in competitive markets. However, retaining existing customers and attracting new ones requires a clear understanding of who they are, what they want, and how to convince them. For this, you should segment customers based on demographics, purchase preferences, likes, etc.,

Parindsheel Dhillon
June 14, 2025
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Why Strategies Fail Without a Data Maturity Assessment Framework?

Data maturity determines the success of the data-driven decision-making model. Here, we’ll discuss data maturity assessment and how the framework is useful for a growing business in competitive markets. Data is the core of every business. You can no longer limit your data sources to internal systems and miss out on the valuable insights that external data can provide. However, collecting, storing, and using such large amounts of data can be a challenge for many organizations. After all, statistics show that the global data generated will reach 491 zettabytes by 2027. Whether you own a start-up, an emerging business, or belong to a multinational company, data management is a key process you cannot ignore. In today’s world, data-driven decisions give businesses a competitive edge. However, getting accurate insights requires high-quality data. This, in turn, requires standardized data engineering solutions, reliable data storage, and advanced analytical tools. Moreover, the entire process has to be streamlined and monitored to ensure consistent results. To summarize, the business requires a robust data maturity framework. If you have difficulties handling data or ask why your dashboards are not reflecting real-time or accurate metrics, this blog is for you. Let’s find out what the data maturity assessment framework is and how it is essential for your data strategies to be successful and deliver expected outcomes. What is the Data Maturity Assessment Framework? Simply put, data maturity is how well a business uses data to create value. It is about how integral data and insights are to the process of business decision-making. This is a primary part of initiating the digital transformation journey and adopting new technologies to boost your business. Data maturity assessment is the process of measuring your efforts in data management and usage. This assessment is used as a framework to evaluate the extent to which a business collects, stores, analyzes, and uses data to make data-driven decisions and whether or not the processes are aligned with one another and with the organization’s objectives. Data maturity assessment framework measures your data capabilities using different parameters, like data quality, data storage methods, data governance, data security measures, compliance standards, data literacy, data culture, the types of technologies used, etc. Before that, we’ll learn the reasons for data strategy failure due to the lack of a data maturity framework to guide the organization. Reasons Why Data Strategies Fail Without a Data Maturity Assessment Framework Creating data strategies is just one part of the process. The strategies can give results only when they are aligned with the business mission and objectives and are supported by the right technology. Lack of Understanding Do you have a clear picture of your business processes? Do you know the challenges your organization faces? Are the decision-makers aware of the concerns? The best way to get a detailed insight into your data management standards and processes is to fill out the data maturity assessment questionnaire. This helps evaluate the existing data and analytical systems in the business. No Communication The communication channels in an organization should go both ways. The top management and C-level executives should consider input from the middle-level managers and employees. They should keep employees updated about the changes being planned and how these will affect them. Open dialogue is essential to prevent misunderstandings and incorrect interpretations. Make clear communication a priority to build a data-driven culture in the business. Talent Gap New tools and technologies require new skills like data analysis, AI engineering, etc. If you are yet to begin the digital transformation journey, there’s a high possibility of a talent gap in the business. It implies that there’s a gap between the expertise of existing employees and what is required to strengthen the data-driven model. This gap can be filled by hiring new employees, augmented teams with external professionals, or partnering with a service provider who offers end-to-end, tailored solutions and long-term support. Lack of Data Literacy Data literacy is the ability to read, comprehend, process, and use data effectively in a business. A business that derives meaningful and actionable insights from data and makes decisions based on these in real time is said to have high data literacy. This includes employees’ and top management’s ability to work with data and technology for day-to-day activities. Employee training is the best way to increase data literacy. Outdated or Insufficient IT Infrastructure The IT infrastructure has to be upgraded regularly to prevent delays and mismatches of software. When a business doesn’t have the technology it requires, it loses opportunities to stride ahead in the markets. Legacy systems can be upgraded or replaced with cloud-based tools like Power BI to provide real-time insights and automated reports. However, you should choose the right technology. It should align with the business objectives. Resistance to Change It’s not uncommon for employees to resist change. Sometimes, even the top management is wary of new developments as they involve expensive upgrades. However, this can lead to stagnation, delays, and low performance. With many enterprises adopting new technologies, resisting change can increase competition and put the business in an unfavorable position. Talk to experts and reliable data engineering companies to understand how the right technology can give your business a fresh boost and a competitive edge. Low Data Quality Statistics show that businesses worldwide lose $15 million per year due to bad data quality. Poor data quality is when the data used by organizations is not cleaned. It has duplicates, missing details, and data in different formats. This can affect the accuracy of the insights. Data maturity assessment results indicate the extent of the loss. They also provide a clear picture of the current situation in the business. You can make the necessary changes to improve data quality by partnering with a service provider. No Regulatory Compliance Businesses should comply with data protection laws like GDPR, etc., to ensure confidential data is kept safe from unauthorized access. This is also necessary to avoid lawsuits and penalties. Lack of proper data strategies and management leads to

Parindsheel Dhillon
May 26, 2025
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How to Achieve Clean, Usable Datasets with Data Analytics?

Data quality is a major concern for businesses and has to be dealt with effectively to promote decision-making based on a data-driven model. Here, we’ll discuss how to clean datasets and make them more usable to derive actionable data analytics insights. Data is the core of every business in today’s world. With about 402.74 million terabytes of data being created each day, you cannot ignore the importance of identifying useful insights by collecting and analyzing relevant parts of this data. From social media posts to generative AI tools, business transactions, consumer searches, promotions, and just about everything else, a business has multiple data sources to track and connect with its systems. Additionally, the ERP, HRM, CRM, and other business management software also have vital data about markets, customers, products, services, competitors, and more. However, to set up high-quality data analytics in your organization, you need more than data and tools. You need clean and usable data that can provide reliable insights and help in decision-making. The data collected from sources is not clean. It is raw data in multiple formats and has duplicates, missing information, incorrect tags, etc. So, a successful business doesn’t just require data. It should have clean, refined, and enriched data to give accurate insights and promote data-driven decision-making. How do you achieve this? How to determine if your business data is of good quality? How to enrich data and why? Let’s find out in this blog. What are the Business Risks of Using Unclean or Raw Data? Do you know that poor data quality costs $12.9 million every year on average? According to Salesforce, poor data quality can cost a business 30% of its average revenue. This is a high number to ignore. Yet, some businesses don’t implement data cleaning and refinement processes due to the costs and struggle with low-quality and incorrect insights. But what are the risks of using unclean data? Why should you invest in data cleaning techniques to improve the quality of your business datasets? Inaccurate Forecasting Historical business data is analyzed to identify hidden trends and patterns and provide predictions for future planning. Sales forecasting is useful to measure the possible interest in a product or service among various markets. It helps identify the demand vs. supply ratio and determine the production capacity, promotional campaigns, sales targets, etc. If poor-quality data is used for forecasting, you will end up with incorrect insights and wrong planning. This could literally benefit your competitors as you struggle to make last-minute changes. Incorrect Customer Segmentation Customer segmentation is necessary for personalized marketing. You should know where your customers are from, their purchase habits, behavior patterns, preferences, etc., to target them with tailored ads and promotional offers. With missing or outdated customer data, your marketing campaigns will not give the expected results. Imagine spending thousands of dollars on ads only to get the bare minimum returns. Such data analytics errors can be avoided if your business datasets are clean. Compliance and Legal Concerns Apart from financial issues, poor data quality also results in compliance risk. Industries like insurance have to follow stringent data policies for greater transparency and accountability. Moreover, depending on the geographical locations, you have to adhere to different data security and privacy laws when using customer data for analytics. A misstep at any point can lead to lawsuits and other complications. It could affect the brand name and push customers away from the business. Mismatch in Resource Allocation No enterprise has unlimited resources. You should allocate resources carefully based on the requirements of each department or process. Wrong insights due to unclean datasets can negatively affect resource allocation. This could result in wastage of precious resources or bottlenecks due to a lack of sufficient resources for critical processes. The money spent on the entire process can end up as a loss in either instance. High-quality datasets mitigate such risks and play a role in optimizing operations for greater efficiency. In short, we can summarize the risks using a popular statement, ‘garbage in = garbage out’. If you use poor-quality data, the outcome will be equally poor and lead to a multitude of losses for the business. The sooner you fix the issue, the less the risk of affecting your organization in the long run. That’s why end-to-end data engineering services include data cleaning and refinement using different techniques. How can the organization assess if it needs professional data analytics and enrichment services? Every business that uses data for analytics needs professional data cleaning and enrichment services. Here are a few ways to assess the business datasets to hire a reputed data engineering company for streamlining the entire process. Data Audit Data auditing is the process of carefully and thoroughly reviewing the datasets to identify inconsistencies, missing values, duplication, etc. The audit report provides insights into how much effort is required for data refinement. Data Profiling Data profiling is the process of analyzing data to examine its quality, understand the structure and the content, identify anomalies, etc. It helps highlight inconsistencies and errors that result in low-quality data. Data Validation Data validation is the process of ensuring that the business data is clean, accurate, and reliable to derive meaningful insights. It helps in preventing invalid data from being used for analytics and promotes data enrichment to improve the overall data quality. While these processes require resources like time and money, they are necessary to get a clear picture of where things stand in your business. You can partner with data analytics or data engineering companies to perform these assessments and provide recommendations for data cleaning. Typically, this is the first step to implementing the data-driven model in an organization. How Can Data Cleaning Improve Decision-Making in an Organization? Data cleaning is a part of data refinement, which can ensure high-quality datasets for analytical insights. Simply put, data refinement is the process of transforming raw data into usable and quality datasets to support data-driven decision-making. It involves multiple processes, such as the following: Data

Yashica Chopra
May 07, 2025
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Top MBSE Consulting Companies Driving Transformation in the USA

Model-based systems engineering revamps the engineering models through digital modeling and system design. It is an effective solution to simplify systems for making informed decisions. Here, we’ll discuss the top MBSE consulting companies offering expert services in the US. Data and design systems are complex and contain many elements that power the entire setup. As businesses expand, these systems become harder to maintain and track, especially if you don’t use new technology. MBSE (Model-Based Systems Engineering) is a relatively new solution that simplifies engineering systems by introducing digital modeling to improve transparency, increase collaboration, enhance communication, and promote efficiency. While MBSE is useful in most industries, it is actively adopted by enterprises from aerospace, aviation, automotive, manufacturing, defense, government, etc. Statistics show that the global MBSE tool market is expected to be $2.5 billion in 2025 and is projected to grow at a CAGR (compound annual growth rate) of 15%. The same report indicates that the market share is widely divided between top players like Siemens, IBM, and Dassault Systèmes (CATIA Magic), though other vendors also offer comprehensive MBSE software. According to Business Research Insights, the global model-based systems engineering market was $3.31 billion in 2024 and is projected to reach $13.09 billion in 2033 at a CAGR of 16.5%. In this blog, we’ll read more about model-based systems engineering and the top ten MBSE consulting companies in the USA you can partner with. What is MBSE and Why is it Important for Businesses? MBSE uses digital modeling and simulation to design systems, create connections, and build a network of applications, software, and interfaces. This is done to increase its efficiency and maintain clear documentation of the processes. It also reduces the risk of errors and promotes better collaboration between teams. Furthermore, MBSE makes the systems and outcomes more consistent and increases overall quality. Unlike traditional systems, it is easy to upgrade and maintain on-premises and on cloud platforms. Typically, MBSE consulting companies in the USA offer tailored solutions to help enterprises implement model-based systems engineering in their processes. They use the advanced MBSE tools and frameworks developed by tech giants to provide relevant services to clients. With MBSE, you can focus on the core operations and factors like product design, safety standards, efficiency, resource optimization, risk management, etc., instead of managing complex systems and writing lengthy documentation. A few MBSE examples are as follows: Top MBSE Consulting Companies in the USA DataToBiz DataToBiz is one of the leading data engineering companies with a global client base. The award-winning company offers custom MBSE consulting and end-to-end services for businesses from diverse industries like automotive, manufacturing, healthcare, and many more. As a certified partner of Microsoft (Gold), Google, and AWS, the company specializes in building tailored data warehouses to create a single source of truth to power the model-based systems engineering solution. It has a team of experienced and certified professionals to set up and implement the latest MBSE software solutions available in the market. The company can build the infrastructure on Microsoft Azure or AWS to create a reliable, flexible, scalable, and agile cloud-based model to introduce digital engineering in businesses. DataToBiz also provides data analytics, advanced analytics, and data visualizations through powerful AI and ML tools like Power BI, Tableau, etc. The company’s expertise in IaaS solutions has helped numerous enterprises seamlessly adopt MBSE solutions to achieve their objectives. STC Arcfield STC is an Arcfield company working with Model-Based Systems Engineering tools to help clients increase understanding and make informed decisions. Its model engineering and digital engineering practitioners have years of experience in providing advanced MBSE solutions in the government and commercial sectors. The company revolutionizes systems engineering through digital modeling to simplify complex systems and streamline the decision-making process. It provides the required tools, services, and training to clients in the US to drive innovation and growth. STC offers MBSE as a service along with consulting solutions to leverage the digital engineering ecosystem in a range of disciplines. The company provides complete support to clients to use cutting-edge technologies and custom-designed MBSE tools in their organizations to achieve their goals. It also develops next-gen digital engineering solutions for the US Army. Intercax Intercax is one of the well-known MBSE consulting companies in the USA. It is a pioneer global innovator in the field of digital engineering. The experts have worked on creating a robust solution to streamline and link engineering models for varied requirements. The company launched Syndeia, its digital thread platform for integrated digital engineering, in 2014. The software connects, compares, and synchronizes existing engineering tools in a business. It can be integrated using Rest APIs and has many server-based applications along with user interfaces to share the outcomes with end users (employees). The company also provides training services to use the software. Additionally, it offers IT support services and custom software development solutions for clients. Intercax has more products like ParaMagic®, a plug-in for Magic Draw to enable dynamic SysML models. It has a presence in the aerospace, automotive, healthcare, defense, IoT (Internet of Things), manufacturing, and other industries. SSA Systems Strategies and Analysis (SSA, Inc.) is a small business owned by women and minorities. It is a systems engineering and program management company offering cost-effective services and MBSE consulting solutions for clients from aerospace, IT, and other industries. The company helps with designing, building, integrating, and operating enterprise-wide complex MBSE software to handle the ever-changing requirements of a business. Expert engineering can identify potential problems in the early stages of design to reduce the risk rate. The company collaborates with Intercax to provide self-paced online training programs for MBSE consulting. SSA, Inc. empowers clients to increase system performance and quality standards, boost revenue, and get into better collaborative partnerships with other businesses. Its consulting services focus on MBSE engineering best practices and the ways to implement effective tactics to achieve business objectives. ETAS ETAS empowers tomorrow’s automotive software with its next-gen solutions and tailored services for Model-Based Systems Engineering adoption and implementation in the industry.

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