Page 5 – DataToBiz

Manpreet Kaur
October 15, 2024
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Top 13 AI Implementation Partners to Consider in 2025

As businesses increasingly recognize the importance of AI in driving growth and efficiency, choosing the right AI consulting services partner is important. This blog highlights the top AI implementation partners based on their expertise and industry presence. According to Grand View Research, the global artificial intelligence market was valued at USD 196.63 billion in 2023 and is expected to grow at a CAGR of 36.6% in 2023. As businesses are adopting AI across different industry verticals, it is essential to partner with experienced AI implementation firms that offer the necessary expertise and resources. In this blog, we’ve compiled a list of the top AI implementation partners you must partner with to develop remarkable AI solutions. Top Companies Offering AI Consulting Services in 2025 DataToBiz DataToBiz is a leading AI implementation partner that helps you develop innovative products to solve your business challenges and achieve great results. Their team of experts offers robust AI consulting solutions, such as NLP, product development, and machine learning. The company uses the latest technology stack and offers real-time operational intelligence. Partnering with DataToBiz for AI development ensures you get a business solution that aligns with your objectives. The experts understand your queries, build strategy, collect data, and create models that fulfill the objectives, ensuring efficacy and efficiency. Client Collaborations: Flipkart, ICICI Bank, McDonald’s, NeilsenIQ, NPCI Industries: Manufacturing, healthcare, retail, ecommerce, and IT Average Ratings: 4.7 (Clutch) DataRobot DataRobot has been named a 2024 Gartner Magic Quadrant winner for data science and machine learning platforms. The company specializes in generative and predictive AI solutions and offers a comprehensive suite of tools designed to streamline the AI lifecycle, from data preparation to model deployment and governance. DataRobot offers tailored solutions for various industries like finance, healthcare, retail, and manufacturing. It integrates AI workflows into a single platform, enabling organizations to build, govern, and operate AI solutions efficiently with due consideration to government and compliance. Client Collaborations: MARS, Boston Children’s Hospital, Warner Bros, and Tokio Marine Kiln Industries: Food industry, healthcare, mass media, retail, and financial services Average Ratings: 4.6 (Gartner) Hugging Face Hugging Face Inc. is an American company that offers AI consulting for MNCs. Its main offerings include the Transformers library, which provides a vast collection of pre-trained models optimized that you can use for NLP tasks such as text classification, translation, and summarization. The company offers cloud-based services through its Hugging Face Hub platform, which allows users to host, share, and deploy AI models in the cloud. The platform integrates with machine learning frameworks such as TensorFlow and PyTorch, making it easier for developers to include models in their workflows. Industries: Text generation and classification Average Ratings: 4.0 on Gartner DataTech DataTech is an AI product development company that offers advanced solutions in AI and data analytics. It is popular for its notable work in machine learning, deep learning, and natural language processing. It uses a platform where developers can create contextual chat and offer customizable chat solutions tailored to specific business needs. Datatech is a great partner if you are looking for someone if you are looking for a company to handle image processing and social listening tools. The experts use features such as facial recognition and people counting to process images, making it easy to make valuable decisions. Client Collaborations: IT Motif Inc, Zydus Hospira Oncology, QX KPO Services, Meghamani Dyes and Intermediates Industries: IT, healthcare, BPO, manufacturing Average Ratings: 5.0 (Clutch) InData Labs InData Labs is a data science and analytics consulting firm that offers AI-powered solutions to businesses. It offers advanced and reliable solutions for AI, NLP, machine learning, generative AI, and data engineering solutions. The company offers AI solutions for different sectors, such as advertising, financial services, entertainment, retail, and ecommerce. InData Labs is a certified AWS Partner. The company builds and scales cloud solutions within the AWS ecosystem, solving data and analytics challenges. It offers a collaborative partnership with clients, ensuring easy alignment and communication throughout projects. Client Collaborations: GSMA, Entrance, Naexas, Asstra Industries: Telecommunications, IT, logistics, retail, and ecommerce Average Ratings: 4.9 (Clutch) Markovate Markovate is a generative AI consulting services company specializing in AI and digital transformation solutions. It is known for its modern AI-driven solutions tailored to specific business needs, such as predictive analytics and machine learning applications. The company also offers development services for decentralized applications and blockchain solutions. Markovate’s team has experienced professionals with expertise in mobile technology, AI, blockchain, and digital marketing. They follow a collaborative approach and implement agile methodologies to adapt to changing project requirements. Client Collaborations: Synervoz, Nown, Hawaii Revealed, Aisle24, Trapeze Industries: Software development, IT, travel and tourism, retail Average Ratings: 3.6 (Glassdoor) BrainPool.AI BrainPool.AI is an AI services company that provides comprehensive AI solutions tailored to various industries such as construction, finance, healthcare, real estate, retail, and marketing. It uses AI to empower businesses by using artificial intelligence to enhance operational efficiency and drive innovation. The developers tailor strategies to integrate AI into business operations, create prototypes, and provide expert advice on AI implementation. Some of its other services include custom GPT development, back-office automation, data structuring, design process optimization, data migration, data governance, and design architecture. Client Collaborations: Stair Craft Group, Ocula Technologies, Crown and Paw, Nvidia Industries: Construction, technology, Artificial Intelligence, retail Average Ratings: 4.8 (Clutch) EY (Ernst & Young) EY (Ernst & Young) is an AI implementation giant that offers a wide range of AI consulting services to help organizations use AI to drive growth. The company offers consulting and advisory services to organizations planning to integrate AI into their operations. Some of its core services include strategy development, use case identification, and implementation support for AI solutions. EY also focuses on RPA (Robotic Process Automation) and combines AI with RPA to automate complex business processes, making it easy to make data-driven decisions. It also offers cybersecurity solutions that detect threats, analyze data for unusual patterns, and improve threat response times. Client Collaborations: MNCs, NGOs, and startups Industries: Technology, healthcare, manufacturing, and financial services

Manpreet Kaur
October 14, 2024
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Modern Data Engineering 101 – Benefits, Use Cases, Examples!

This blog talks about modern data engineering 101 and how organizations are using it to their advantage to extract the full potential of their data. We’ll discuss its benefits and relevant examples of how data engineering services have transformed various industries. Data engineering plays an important role due to the large data volumes and increasing dependence on data-driven decision-making. The global big data analytics market size was valued at USD 307.51 billion in 2023 and is expected to grow from USD 348.21 billion in 2024 to USD 924.39 billion by 2032 at a CAGR of 13%. “Data as a product is very different from data as an asset. What do you do with an asset? You collect and hoard it. With a product, it’s the other way around. You share it and make the experience of that data more delightful.” – Zhamak Dehghani, author of Data Mesh, Delivering Data Value at Scale. In this blog, we’ll discuss modern data engineering and how organizations are using it to make the most out of their data. What is Modern Data Engineering? Modern data engineering includes building, managing, and optimizing scalable data pipelines to handle large volumes of data from multiple sources. It processes data in real-time and uses cloud-based architectures and tools. These tools support data integration, transformation, and storage for advanced analytics and decision-making. Importance of Modern Data Engineering Data engineering helps organizations handle and organize data so that data analysts and scientists can easily analyze it. Here’s why data engineering services are important: The main part of data engineering involves managing data pipelines and ETL (Extract, Transform, and Load) processes. Data engineers build and maintain these pipelines to ensure clean and valid data is available to data analysts. This helps teams access data easily, gain insights, and make informed decisions, enhancing business growth and output. Benefits of Modern Data Engineering Imagine you’re trying to get the most out of your data, but it’s scattered all over the place. That’s where data engineering comes in. Now let’s understand some benefits data engineering solutions bring with them. Use Cases of Modern Data Engineering Some of the potential use cases of data engineering I’ve seen are: Personalized recommendations Subscription-based streaming services such as Netflix and Amazon Prime offer personalized recommendations to their viewers. These companies collect and organize user data and use machine learning to offer personalized recommendations. Fraud detection Banks and financial institutions use data engineering to prevent fraud. They gather vast amounts of transaction data, and with the help of advanced algorithms, they can spot suspicious patterns in real-time, preventing fraud before it even takes place. Predictive maintenance Manufacturing companies use data engineering to keep machines running smoothly. The sensors on equipment collect data continuously, and engineers use these insights data to predict when a machine may fail, preventing breakdowns. Customer behavior analysis eCommerce store owners can track customer purchases, their preferences, and browsing behavior. Further, they analyze these trends with the help of data engineering to create personalized marketing campaigns to offer personalized recommendations. This is the reason why you often see ads for things you’re interested in since the companies already have relevant data to target you. Real-time data analysis Businesses can collect, clean, and verify data through automated data pipelines. This makes it easy for data analysts to centralize large volumes of data by breaking down silos and making informed and strategic decisions. Businesses can detect trends, respond to market changes, and optimize their operations for better returns. Machine learning Machine learning uses large amounts of data to train artificial intelligence (AI) models and make them more accurate. Data engineers use data pipelines to transport data across different sources, ensuring it’s clean and ready for analysis. These data models are used in various applications, from personalized recommendations to fraud detection, and much more. Skill Set of Modern Data Engineer The skill set of a modern data engineer includes the following data engineering tools, technologies, programming languages, and frameworks: Database management systems: Data processing frameworks: ETL tools: Cloud platforms: Data warehousing solutions: Programming languages: Frameworks and methodologies: How do Data Teams Implement Modern Data Engineering? Data engineers integrate all your data into models that support operations and analytics, enabling your company to extract data-driven insights. Further, they understand existing infrastructure and data needs and offer personalized solutions and services to help you make the most of your data. They use different data engineering tools to consolidate data from multiple sources to manage data efficiently. The data teams create data models and algorithms that ensure these models are fully functional and work smoothly. How Does Modern Data Engineering Work? Data engineering involves designing and building data pipelines that convert raw, unstructured data into organized datasets. These pipelines are important elements for a reliable data system, built to meet specific needs of businesses. Data engineers manage data pipelines to ensure users get accurate and reliable data. The data integration pipeline has the following steps: Best Practices for Modern Data Engineering Conclusion Data engineering is no longer optional today, it has become a necessity. By converting data into meaningful actionable insights, data engineering helps businesses to make data-driven decisions. This not only enhances operational efficiency but also helps you deliver customized experiences quickly. Data teams not only add more value but also facilitate the development of the right solutions for various problems. People Also Ask (FAQs) What is modern data engineering? Modern data engineering includes designing, building, and maintaining scalable and efficient data systems. These systems support business intelligence, analytics, and data-driven decision-making by using advanced tools and practices to process vast amounts of data from various sources in real-time. What is the latest in modern data engineering? Some trends in data engineering that are popular now and will continue include: What is the salary of a modern data engineer? According to Glassdoor, the average salary for a data engineer in the United States is $1,31,939 per year. They typically get additional cash compensation, averaging $27,346 and ranging between $20,509 and

Yashica Chopra
October 09, 2024
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11 Best Staff Augmentation Services Firms for B2C Growth

IT staff augmentation is a time-saving and cost-effective solution to hire talented candidates to work with your business. Here, we’ll discuss staff augmentation services and the top eleven service providers catering to B2C enterprises through tailor-made solutions. B2C (Business-to-consumer) businesses are establishments that cater directly to the end users or consumers at large. These include retailers, eCommerce business owners, financial institutions, service providers (video streaming platforms, internet providers, etc.), and so on. The business has a large client base and needs to keep customers happy to ensure successful results. It has to stay relevant and change with the times by adopting the latest technology in the market. Digital transformation has made it vital for businesses to hire experienced candidates for data analytics, business intelligence, AI development, etc. However, this is easier said than done. Statistics show that 75% of employers find it hard to recruit the right employee for skilled work, and the percentage is higher for IT-based jobs. Staff augmentation services are a sure way for B2C businesses to access talent despite various constraints like budget, office space, company size, etc. In this blog, we’ll read more about B2C staffing services and the top companies providing IT staff augmentation services for B2C enterprises in the global market. What are Staff Augmentation Services in B2C? Staff augmentation services are a type of outsourcing model where talented employees from the service provider’s company will work in your business on a certain project or for a specific period. The external employee can work on-premises or remotely. You can bring in one employee or several of them, depending on your requirements. B2C staffing allows businesses to retain control and autonomy over the project and resources while hiring experienced candidates to tackle the job. It combines the best of both worlds and provides an effective solution to achieve your goals. Furthermore, you can decide staff augmentation model based on your budget, the timeline, and project complexities. What is the Rate of Staff Augmentation for B2Cs? Budget is one of the main reasons for enterprises to opt for staff augmentation or contract staffing, though it can also be expensive in the long term or for a large team of experts. The best way to invest in staff augmentation is to clearly define your needs and choose the right model. Typically, the rate scale for staff augmentation is wide and can be anywhere between $25 per hour and $200 per hour. Top IT staff augmentation companies provide an estimator tool for businesses to get a quote for their requirements. Select the details for specialists you want and provide the necessary information to get a customized quote. This will give you a proper idea of how much the staff augmentation services will cost for the project. You can discuss the details with the company before agreeing to the terms. Top Staff Augmentation Service Providers for B2C Enterprises DataToBiz DataToBiz is an artificial intelligence and data analytics company offering customized staff augmentation services for B2C enterprises and other businesses from different parts of the globe. It has experience in various industries, ranging from eCommerce to manufacturing and healthcare. The company’s tailored solutions are designed to suit the varying requirements of each business based on their niche, vision, mission, and objectives. With a vast project portfolio and multiple services to its name, the company helps B2C and B2B enterprises streamline their processes and embrace digital transformation. DataToBiz is a certified partner of Microsoft (Gold), Google, and AWS. Whether you want to hire Python developers, data scientists, business intelligence experts, cloud computing services, data engineers, AI and ML developers, or big data analysts, the company has them all. Choose from the four types of staff augmentation models –end-to-end staffing, dedicated development teams, project-based hiring, and specialized skill acquisition. Clutch Rating: 4.8 Stars Glassdoor Ratings: 4.8 Stars DreamSoft4u DreamSoft4u is a software development company that also offers IT staff augmentation services to businesses from healthcare, hospitality, education, and other industries. It helps clients scale their teams quickly by finding the right talent for the job. You can choose between project-wise, hourly, and fixed-price models. Its flexible workforce is located in the US and India and has worked with businesses from different parts of the world. The company follows an agile recruitment process to help clients hire experienced personnel for short-term or long-term. DreamSoft4u has a six-step staff augmentation process starting with an evaluation of your needs and providing post-recruitment support to ensure client satisfaction. It also has a pay-as-you-go model to offer budget-friendly services for product development, digital transformation, digital marketing, and many more. Clutch Rating: 4.6 Stars Glassdoor Ratings: 4.2 Stars TEKsystems TEKsystems is an IT service provider helping businesses accelerate digital transformation. The company offers staff augmentation services for B2C enterprises and other clients through direct communication and the ITSAC portal. It has access to the DIR markets (Texas, US) and knows how to find the top talent in the IT industry. Through local market research and business analysis, the company enables clients to connect with candidates who are the right fit for their projects. It offers a range of services like data analytics, DevOps automation, digital experience, etc., and has a presence in industries such as telecommunication, the public sector, financial services, CPG, and many more. TEKsystems is based in the US, with clients from many other countries. Be assured of customized onboarding and detailed monthly invoices when working with the company. Clutch Rating: None yet Glassdoor Ratings: 3.9 Stars Toptal Toptal is a reputed staff augmentation service provider offering digital transformation solutions by assisting businesses to connect and collaborate with industry experts. The company has clients in industries like IT, automotive, industrials, healthcare, life sciences, etc. Its services include edge computing, AR/VR solutions, deep data analytics, and more. You can hire from among the top 3% of developers to work on your projects for any duration. The company follows a three-step process where the client shares the requirement details, works with the representative to hire the

Yashica Chopra
October 07, 2024
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Data Engineering Services vs Warehousing vs Analytics: Pick Your Data Strategy

With data becoming a crucial part of the global industry, it is essential to unlock its full potential to boost your business. Here, we’ll discuss data engineering services, data warehousing, and data analytics to help determine the best choice. Data is the key to a successful business. Instead of storing the data in outdated setups like silos, you can create a central data repository and allow employees restricted access to the datasets. This makes it easier to use the business data for analytics and insights. Employees at all levels can make data-driven decisions by accessing the insights through their dashboards. Data analytics, data warehousing, and data engineering are different yet interlinked concepts used to streamline data collection, storage, and analysis in an enterprise. Statistics show that the global big data and data engineering market is expected to be $75.55 billion in 2024 and predicted to reach $169.9 billion by 2029 at a CAGR (Compound Annual Growth Rate) of 17.6%. However, you may have questions about which service to use for your business. Should you hire data engineering services, or will it be enough to pay for third-party or embedded data analytics solutions? Where does data warehousing fit into the grand scheme of things? Let’s find out in this blog. Is Data Warehousing the Same as Data Analytics? A data warehouse is a central repository or a large database containing massive amounts of business-related data. It can be built on-premises or on the cloud platform. A data warehouse is connected to several internal and external sources as well as third-party applications like business intelligence tools, data analytical dashboards, etc. Data warehousing services include setting up the repository, building data pipelines, streamlining data flow, maintaining the database, and periodically upgrading the systems. Data analytics is the process of converting raw data into actionable insights to make data-driven decisions. It helps see the hidden patterns, trends, and correlations in historical and present datasets. The insights derived are shared with end-users (employees) via data visualization dashboards. Data analytics help shape business processes to deliver better results while consuming fewer resources. It can be used to understand market trends, customer behavior, product performance, employee productivity, etc., and make the necessary changes to achieve business goals. In short, data warehousing is not the same as data analytics. While the data warehouse is used to store and clean data, analytical tools help to understand what the data means and how it can help empower the business. Creating a synergy between data warehouse and data analytics will certainly give you the best results. So, what is the difference between a Data Warehouse Engineer and a Data Analyst? A data warehouse engineer is responsible for managing the entire development lifecycle of a data warehouse. It is a backend process that includes many activities, such as building the warehouse, system connections, ETL, performance management, resource management, dimensional design, etc. A data warehouse engineer works with data scientists, data analysts, and data engineers to ensure the data flow is smooth and seamless across the enterprise. A data analyst uses the data stored in data warehouses and data lakes to review the information, detect patterns, and identify key insights useful for the business. The primary responsibility of a data analyst is to find solutions for various business problems by analyzing historical and real-time data and sharing insights with decision-makers. The data analyst has to collaborate with data warehouse engineers, software developers, and data scientists to run the data-driven model without interruptions or errors. What are Data Engineering Services? Data engineering encompasses various processes like data collection, data storage, data cleaning, and data analysis for large amounts of raw, structured, unstructured, and semi-structured data. It allows data scientists and data analysts to derive in-depth insights using various statistical and analytical methods. Data engineering also includes ensuring that the quality of the datasets is high to prevent inaccurate insights. Data engineering services cover a broader area and include many responsibilities. For example, it can also include data warehousing solutions or a part of the warehousing processes. Typically, data engineering involves the following activities: So, do data engineers do data warehousing? Yes. Data engineers play a role in designing, developing, and maintaining the data warehouse and its connections. However, note that data warehousing services are only a part of data engineering responsibilities. The top data engineering companies provide end-to-end services, right from planning the strategy to maintaining and upgrading the relevant systems, tools, and processes in your business. Data engineers collaborate with other experts like software developers, data warehouse engineers, data scientists, and data analysts to create a robust data model in the enterprise. Which is Better: Data Analytics or Data Engineering? Despite the overlap in some processes and data being the common factor, there are quite a few differences between data analytics and data engineering services. A business can invest in data analytical tools and derive insights to make important decisions. It can partner with a data analytics company to get embedded analytics through customized dashboards without setting up the IT infrastructure in the enterprise. Data analytics as a service is a cloud-based solution where third-party companies handle most of the backend work and share insights and reports with businesses. Data engineering is much more complex and extensive than data analytics. Data engineering consulting companies build data pipelines, set up system integrations, build data warehouses/ data lakes, connect the necessary data analytics and business intelligence tools, and maintain proper data flow across the IT infrastructure. Programming, database management, and cloud computing are part of the services. In today’s competitive scenario, investing in data engineering services is a better option than limiting your business to data analytics. This empowers you to unlock the full potential of data and gain an edge over competitors. It also keeps you one step ahead and capable of making proactive decisions to grab market opportunities or avoid pitfalls. Data Engineering Services vs. Data Warehousing vs. Data Analytics As you can see, the three aspects are different but interconnected on

Yashica Chopra
October 04, 2024
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11 Top Azure IaaS Companies for Scalable Cloud Solutions

Azure infrastructure as a service (IaaS) companies are third-party service providers that help startups, SMBs, and large enterprises build, deploy, and migrate IT infrastructure to the cloud. Here, we’ll discuss the leading Azure IaaS companies that offer scalable cloud solutions. IaaS stands for Infrastructure as a Service, a cloud solution where a majority (or the entire) IT infrastructure of a business is hosted on a remote cloud server. This reduces the load on the systems used on-premises and speeds up the processes to increase efficiency. Many companies offer IaaS solutions by migrating and building the IT infrastructure on public, private, and hybrid cloud platforms. According to Statista, the public cloud IaaS market is expected to be $180 billion in 2024 and predicted to reach $232.39 billion by 2025. Microsoft Azure, AWS (Amazon Web Services), and Google Cloud form the top three cloud providers offering IaaS, PaaS, and SaaS solutions in the global market. Azure IaaS is a flexible, scalable, and cost-effective option for businesses to host their IT infrastructure on the cloud. In this blog, we’ll read more about Azure data engineering and the top companies that help businesses build scalable IaaS solutions on Microsoft Azure. What is Azure IaaS? Microsoft Azure has grown exponentially since 2010, the year of its launch. It offers public and private cloud solutions and boasts a large ecosystem with over 200 apps, frameworks, tools, technologies, databases, etc. It is a suite of cloud services for enterprises to build virtual machines, networks, storage centers, applications, processes, etc., remotely and access them from any location at any time. Azure IaaS makes it easy to scale the processes as and when necessary and pay only for the solutions used by the business. Additionally, it supports big data analytics in real-time and offers effective capabilities like data security, data backup and recovery, risk management, and much more. What is an Example of Azure IaaS?- Key Components A few popular Azure infrastructure-as-a-service examples are listed below: VMS Azure Virtual Machines are used to deploy and manage Windows and Linux operating systems on a remote cloud server instead of on-premises. The virtual machines can be customized to suit the requirements of the business. For example, CPU, memory space, storage, etc., can be individually selected to customize the VM. This allows businesses to create powerful and super-fast virtual machines capable of processing large apps seamlessly. Networking A network is a set of connections between various components in the IT infrastructure. It also helps in managing resources. Azure’s network services consist of load balancers, virtual networks (VNet), VPN gateways (for greater data security and privacy), etc. Storage Not every business needs the same kind of storage options. Moreover, a single enterprise might use different storage models for different types of data. Azure IaaS solutions include a range of storage solutions like file storage, disk storage, queue storage, blob storage, etc. Security Data and system security is a huge concern in today’s scenario. Microsoft Azure has many options to ensure security and identity management on the platform. Key Vault, Active Directory, and Security Center are the top three features that help businesses manage user identities and provide/ limit access to data and resources. Leading Azure IaaS Companies for Scalable Cloud Solutions DataToBiz DataToBiz is among the leading Azure IaaS Companies offering tailored data engineering services, data analytics, business intelligence, and artificial intelligence solutions to clients from around the globe. The company has experience in working with SMBs, MSMEs, startups, and established enterprises from various industries like manufacturing, telecommunications, supply chain, transportation, real estate, hospitality, healthcare, finance and banking, education, surveillance, etc. The company is a certified Gold Partner of Microsoft (as well as a certified partner of AWS and Google Cloud) and provides scalable, IaaS solutions for cost-effective prices. Businesses can hire an Azure infra developer or a team of experts to set up their IT infrastructure on the Azure cloud. DataToBiz offers IP protection and guaranteed NDA to ensure confidential data is secured and encrypted. The award-winning company’s end-to-end customized services empower businesses to unlock the full potential of their data and achieve their goals. Clients can choose from the different hiring models offered by the company. Clutch Rating: 4.8 Stars Simform Simform is an AI and ML company offering AWS and Azure data engineering services. It has been listed as the top US software development company by Clutch and has clients from the Fortune 500 Companies list. It also works with startups and progressive enterprises. The company provides serverless development and consulting services to help businesses migrate to the cloud platforms and digitalize their processes. It builds stable, reliable, scalable, and secure solutions for businesses from industries like manufacturing, healthcare, logistics, financial services, retail, etc. Clients can integrate the serverless architecture with Azure, AWS, and Google Cloud to scale up the apps whenever required. Simform also offers serverless storage management, API gateway integration, and management and designs FaaS (financial accounting advisory services) based on client’s specifications. The company’s business-centric approach and delivery-oriented mindset ensure guaranteed results for clients. Clutch Rating: 4.9 Stars Persistent Systems Persistent Systems is a digital engineering and enterprise modernization company offering Azure infrastructure as a service (IaaS) solutions to various clients. It is a certified solution partner of Microsoft and provides diverse services for businesses to adopt digital technologies and invest in cloud solutions. Be it migration and management of M365, data security, modernizing legacy apps, or Azure modernization using generative AI, the company uses its vast experience and access to the Microsoft ecosystem to deliver the promised results. It follows a proven methodology to migrate the IT infrastructure to Azure and build custom-designed frameworks, apps, etc., to leverage the full potential of business data and processes. Enterprises can rely on Persistent Systems for Azure infrastructure diagram and end-to-end innovation and implementation on the cloud. The company has clients from industries like finance, banking, insurance, software, telecom, media, consumer tech, life sciences, etc. Clutch Rating: 4.5 Stars ValueCoders ValueCoders is a software company offering IT staff augmentation

Yashica Chopra
October 03, 2024
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Hire Data Scientists or Developers – Right Choice For Scaling Enterprises?

A data scientist deals with data, while a developer works with programming languages. However, there’s a lot more to know about their jobs. Here, we’ll discuss why businesses hire data scientists and developers and why they opt for IT staff augmentation services. Information technology (IT) is a huge part of our lives, personally and professionally. The IT industry is an independent entity that also plays a role in several other industries and sectors like manufacturing, finance, healthcare, EdTech, travel, entertainment, etc. As businesses grow and scale into larger enterprises, IT becomes crucial in running daily activities and making important decisions. Software developers, engineers, AI and ML developers, data scientists, data analysts, etc., become a part of enterprises in many industries. Naturally, there is a high demand and supply of software developers in the internal market. Statistics show that there are 28.7 million software developers globally in 2024. According to Gartner, worldwide spending on IT services is expected to touch $5 trillion in 2024, which would be a 6.8% increase from 2023. This makes IT services the largest segment of IT spending in the global market. Almost every startup, SMB, and large enterprise need some form of IT service – tools, technologies, and human expertise. This brings us to an important question. Should you hire a developer to scale your enterprise or should you invest in a data scientist? What roles and responsibilities do they have? How can data scientists boost your business? Let’s find out in this blog. Role of a Developer in an Enterprise In simple terms, a developer is a professional who writes code (programming language) to design, build, and develop software applications. Over the years, software development has gone much beyond writing games or basic computer programs. Typically, developers are categorized into two types: Developers are essential in many industries as they create software to help businesses overcome their challenges and find tech-based solutions. For example, a software developer can create a program or application to manage inventory in a manufacturing enterprise. They can customize open-source inventory software to align with the manufacturer’s requirements and industry standards. This enables the enterprise to optimize its resources and manage the stock effectively. However, not every enterprise needs to hire developers on the payroll. That’s where IT staff augmentation companies enter the picture. You can hire a software developer or a team to work on your project and deliver the results. It is easy to build a team of experts without going through the extensive recruitment process. The pricing is also cost-effective. Role of a Data Scientist Data science is one of the popular career paths in today’s world. A data scientist is a professional who combines expertise in different fields like computer science, statistics, mathematics, and modeling to study large amounts of data and unlock its full potential. In a modern scenario where data-driven decision-making is considered the best way to run a business, the presence of a data scientist in an enterprise is priceless. Generally speaking, when you hire data scientists, they are responsible for the following tasks: Data scientists deal with all types of data (raw, processed, structured, unstructured, semi-structured, etc.) containing textual, numerical, and multimedia elements. They use statistical models and advanced tools powered by artificial intelligence and machine learning to handle such vast amounts of data every day. There’s an increase in demand for data scientists, which makes it hard to hire top talent on short notice. However, IT staffing solutions make it easy to work with experienced data scientists to streamline your enterprise data and scale the business to suit your long-term goals. You can even get an estimate of how much it would cost to temporarily hire data scientists for your organization. Are Data Scientists Also Developers? A data scientist should have coding skills as well as in-depth knowledge of mathematics, statistics, machine learning, and analytics. However, a data scientist cannot be a developer since their role is centered on data and its uses. Developers are more focused on the systems, frameworks, software, application development and usage, etc. There is an overlap in the skills and qualifications but both roles are distinctive and have different purposes. Moreover, a bachelor’s degree is enough for a developer in many positions while an advanced (master’s) degree or diploma is preferred for a data scientist. So, is a developer better than a data scientist? There is no proper answer to this question. The specifications of both positions are different and equally important. A business needs developers, data scientists, data engineers, and data analysts to create a robust IT infrastructure and adopt the data-driven decision-making model. However, if you want a professional with developer skills and extra abilities, it is better to hire data engineers. A data engineer builds and develops the IT infrastructure and takes care of data management in the enterprise. It’s a role with more responsibilities and complexities than a developer. Fortunately, IT staffing services have made it easy to identify and choose the right professionals based on your changing requirements. Staff augmentation services allow businesses to expand internal teams or build new teams with diverse skill sets. You just have to share your requirements or project details with the staff augmentation company. It will assist in hiring talented candidates and building a customized team in less than a week. Whether you want a developer, data scientist, or both, the service provider will make it happen. What is IT Staff Augmentation? IT staff augmentation services have been mentioned multiple times so far. But are these services? Why should you hire Python developers, data scientists, or data engineers through staff augmentation companies? Let’s find out. Staff augmentation services fall under the umbrella term ‘outsourcing’. However, staff augmentation has a stark difference with outsourcing as it gives businesses more control over the teams, resources, and projects. In staff augmentation, you can hire one or more candidates to work on your project (on-premises or remote) for the given duration. The candidate will be your employee for that

Manpreet Kaur
September 24, 2024
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Top Data Warehousing Companies Revealed: 11 Players Shaping the Industry

Data warehousing companies help enterprises capture lots of data from many sources for processing. Among the current excellent market leaders that offer an efficient method of managing data and therefore offering the market a competitive advantage for organizations are Snowflake, DataToBiz, Google BigQuery, and Amazon Redshift. Imagine you are flooded with data from the marketing department, the sales department, the finance department, and even the web analytics department. How would you make any sense of it all? If there were few data sources and the volume of data was small in each, then it is possible to combine them manually. What happens when the amount of data streaming in from all the sources becomes unmanageable, and the volumes are too big? That is where data warehousing services come in handy. This technology consolidates all your information into one point, which allows you to get more complete results from processing all interrelated fields at once, taking into account all the information and basing it on full data. Now, let’s examine what a data warehouse is and learn about the 11 leading data warehousing companies globally. This will help you get an overview of how it could streamline and improve the way you apply information within your organization. What is a Data Warehouse? A data warehouse is a central repository that initially supports and handles tremendous historical data collected from various parts of a company. These systems are designed for digital transformation strategies and enable businesses to define trends and patterns that could thus be of strategic importance in decision-making. This is not to say that data warehouses resemble regular databases; however, the former is more appropriate for analyzing large historical information sets. They are faster than traditional methods, enhance the quality of data, and offer rich information in comparison to other means. This helps businesses in several ways: Analysts estimate that the market for enterprise data warehouses (EDWs) shall grow further; the increase is estimated at $39.23 billion from 2024 to 2028. This growth is a result of the rising volume of data available within the different sectors. The increase in data calls for organizations to adopt state-of-the-art cloud data warehousing toolkits in the market to remain relevant. Top Data Warehousing Companies Shaping The Industry To help you pick the perfect solution for your business, let’s dive into some of the best data warehousing companies around the world: DataToBiz DataToBiz is a data intelligence firm based in India offering data warehousing services to manage, store, and analyze large volumes of data to make informed business decisions. Their team of experts offers advanced services in data warehousing consultation, development, integration, and migration, always prioritizing client needs and delivering tailored solutions. Key Features: Things to Consider: Amazon Redshift Amazon Redshift provides adaptable data warehousing services tailored for the AWS cloud, making it a budget-friendly choice for analyzing extensive datasets kept in S3. It provides a user-friendly interface that’s particularly convenient for those familiar with the AWS environment. Key Features: Things to Consider: Google Cloud Platform Google BigQuery stands out among data warehousing companies with its serverless setup, which eliminates the hassle of managing infrastructure. You only pay for the data processing you use, making it both affordable and efficient. It’s engineered to process extensive datasets rapidly and incorporates machine learning to delve deeply into data analysis. Key Features: Things to Consider: Snowflake Inc. Snowflake provides a cloud-based data warehouse software that scales easily with elastic computing for on-demand processing power. It uniquely separates storage from computing, enhancing cost efficiency. Thanks to Snowflake’s support for SQL queries, users already familiar with SQL will find it straightforward to perform data analysis. Key Features: Things to Consider: Microsoft Azure Azure Synapse Analytics, previously known as Azure Data Warehouse, is a modern cloud-based data warehouse software that works closely with other Azure services. This integration forms a cohesive data environment, simplifying the management of data across different platforms. Key Features: Things to Consider: IBM IBM Db2 Warehouse is a dependable and secure data warehousing platform tailored for seamless integration with the wider IBM analytics ecosystem. It’s built to support demanding data workloads with its scalable design and top-tier performance capabilities. Key Features: Things to Consider: Oracle Oracle Autonomous Data Warehouse provides a highly automated data warehousing platform hosted on Oracle Cloud. This platform utilizes machine learning to optimize workloads and efficiently allocate resources, ensuring seamless integration with Oracle’s suite of services. This advanced, self-managing architecture reduces the complexity of data warehouse product operations for businesses leveraging Oracle Cloud. Key Features: Things to Consider: Teradata Teradata stands out among data warehousing companies as a high-performance solution designed for crucial applications. It’s particularly known for its solid security measures, ensuring data safety and adherence to compliance standards—ideal for businesses handling sensitive information. Key Features: Things to Consider: SAP SAP HANA is particularly a data warehouse product that is designed to process data in-memory which makes it much easier for it to provide data as well as analysis faster. This makes it a perfect asset for organizations that require real-time access to information for quick decision-making. Key Features: Things to Consider: Cloudera Among the data warehousing companies, Cloudera has been noted as the best because it provides an open-source data platform capable of customization appropriately for the needs of its clients. It supports virtually any type of data format and data source and while it is relatively easy to use it does require some degree of technical skill to install and maintain. Key Features: Things to Consider: Firebolt Firebolt is a cutting-edge cloud data warehouse that’s highly favored by engineers due to its remarkable speed. It’s crafted to deliver an exceptional user experience through its intelligent storage solutions and efficient query handling. For those familiar with SQL, Firebolt will feel like second nature as it adheres to standard SQL protocols. Key Features: Things to Consider: Conclusion The applicability of the data warehousing system is most appropriate for companies that are interested in going deeper than just filtering information for

Yashica Chopra
September 20, 2024
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LLMs in AI Development- Key to AI’s Next Breakthrough?

Large language models can provide a transformative experience in various sectors, be it real estate, healthcare, entertainment, or manufacturing. Here, we’ll discuss the future of LLMs in AI development and how it can help businesses enhance their processes, products, and services. Artificial intelligence has seen great advancements in recent years. It is a part of everyday life, be it social or professional. From smartphones and voice assistants to commercial chatbots, content generators, and workflow automation tools, AI has diverse applications and uses. According to Grand View Research, the global AI market is estimated to touch $1,811.8 billion by 2030. Large Language Models (LLMs) are a part of artificial intelligence and play a vital role in generative AI. These have shot to fame with the success of ChatGPT and other generative AI tools (generative AI apps and built on LLMs and other foundation models, so LLM is a part of generative AI and not GenAI on the whole). Statistics show that the global LLM market was $159.0 million in 2023 and is expected to grow at a CAGR (compound annual growth rate) of 79.80% to reach $259.8 million in 2030. It is predicted that 750 million applications will use LLMs by 2025 to automate 50% of digital work. In this blog, we’ll talk about what LLM stands for in AI, their working methodology, and the possible future of LLMs in AI development. What are LLMs in AI Development? Large Language Models(LLMs) are used to build generative AI applications for various purposes. So, is ChatGPT LLM? Yes, ChatGPT comes under LLMs, but it is actually a GenAI tool. LLMs are massive deep learning models pre-trained on huge amounts of data to provide better quality output by understanding the context of the user’s input. The large language models have powerful transformers, which are a set of neural networks with encoders and decoders that can analyze the input data to interpret the meaning and provide a relevant and (relatively) accurate output. LLMs can handle unsupervised data and work with hundreds of parameters, which makes them highly suited for handling complex tasks. They are versatile, flexible, and customizable. For example, LLMs can support generative AI tools that convert input text into images, videos, or audio sounds. It can scan, read, edit, and summarize several pages of text in a few minutes. This makes LLMs an important part of AI product development. As per the Datanami August 2023 Survey, 58% of companies work with LLMs but a majority of them are only experimenting with it. This shows that even though large language models are gaining popularity, businesses taking time to explore the technology and understand how it can help their establishments. The diverse role of LLMs in AI development makes it clear that the models will have a profound impact on the future. Future of LLMs in AI Development AI researchers want to build culturally and linguistically diverse and inclusive LLMs to make the models user-friendly for people around the world. Predicting Next-Gen AI-Language Models LLMs in AI language models can help in providing more human-like interactions with chatbots. The LLMs can power AI chatbot solutions to be more context-aware and learn from the interactions with users to offer better responses. Additionally, it could also make AI more capable of understanding the subtle nuances in text. This can make the language models more efficient and accurate for a wide range of communication purposes. Cross-Disciplinary Usage What if we say, LLM in AI development can promote the integration of two or more technologies for developing applications for different fields? For example, AI language models can be integrated with robotics or computer vision to build robots that understand verbal instructions and respond more effectively to human interactions. Another example of cross-disciplinary application is how the LLMs can help AI tools simultaneously analyze visual and auditory data for enhanced security and surveillance. Breakthrough in Algorithms Large language models can streamline AI algorithms to enable the models to process more data in less time and with fewer resources. This reduces response times and empowers the models to offer better real-time capabilities. It could lead to AI applications that minimize energy consumption while optimizing user experiences. Businesses can redefine their processes to make AI an integral part of their establishment and get enhanced results. Apps with Greater Efficiency AI-powered innovation strategies that actively use LLMs in AI development will result in applications that are not only bigger but also more efficient and diverse in handling a plethora of tasks. For example, the larger models could work even on smaller devices (like smartphones) which will enable users to work on the go. Addressing Ethical and Bias Concerns Ethical concerns and bias are two major challenges faced when adopting LLMs in a business. However, in the future, the same models could help overcome these concerns. AI researchers and developers are working on building models that can detect and mitigate bias in data. They are also focusing on developing LLMs that can be used ethically. While this could take some time, it is definitely something to look forward to in the future. Generating Personalized Content LLM advancements can further help AI tools to personalize content for various purposes like articles, news snippets, listicles, ads, target marketing, etc. Though there are already applications that offer such services, the content still feels like it is written by a machine. In the future, the LLMs used in AI development will understand the intricacies of language better to create text that aligns with the user’s requirements and read as if it has been created by humans. Domain-Specific Applications While businesses from different industries can use many large language models, future models can cater to specific domains. For example, AI developers can build LLMs for healthcare (patient management), finance (streamline payments and detect fraudulent transactions), law (read the reports and summarize them without misinterpretations), etc. Such models can be highly advantageous for businesses as they are trained on data from the industry and give more accurate results. Real-Time Query

Manpreet Kaur
September 17, 2024
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Why Contract Staffing is the New Normal in IT?

IT contract staffing is transforming the future of work by offering flexibility, access to specialized talent, and cost efficiency. It allows companies to quickly adapt to changing project needs while workers enjoy varied experiences and work-life balance. This model is essential for meeting modern IT challenges and achieving business success. Hiring in the field of technology has its challenges, especially for top or highly specialized positions. Here, hiring is a simple factor since it calls for the selection of talented individuals. But engineering jobs are some of the hardest to fill in the current job market, taking an average of 49 days to hire. But here’s a more interesting fact: Each vacancy costs roughly $33,251 per annum, and expenses can soar to hundreds or thousands of dollars while waiting for the dream candidate. Therefore, what can companies do to fast-track the process of finding the right talent? Some employ individuals without the right qualifications, which can result in major losses to the companies in the long run. However, others address the issue right from the beginning by opting for a flexible solution. It is also commonly referred to as IT contract staffing or IT recruitment. The first strategy is a short-term strategy that involves identifying talent and getting them into the organization as soon as possible; the second strategy seeks to identify talent for permanent positions within the organization. Read on to learn about this staffing model, its benefits, legal compliance, and so on. What is Contract Staffing? Contract staffing is a model whereby organizations employ their employees under fixed-term contracts rather than permanent employment. This modality is so popular because it is inexpensive and easy to control, and organizational leaders can easily access the right talent when it is needed. IT contracting is also much more favorable for many workers as it provides more flexibility and is usually paid better. Furthermore, such contracts can also be temporary but offer the option to turn them into a fixed-term contract for the employee’s advantage and their employer’s benefit. A recent market report estimated that the world market for IT staff augmentation would increase by 3.66% by 2029. Types of IT Contract Staffing There are four main types of IT contract staffing: Steps to Follow in the IT Contract Staffing Process IT contract staffing is a powerful strategy for organizations needing extra support for their team. By following these steps, you can ensure a smooth process and successful delivery of quality IT solutions. 1. Identify Your Needs and Requirements First, determine what specific skills or expertise your project requires. Whether you’re launching a new software development project, handling a system upgrade, or managing a sudden workload spike, knowing where your team might need a boost is essential. Once you’ve established the need, define the exact skills, experience, and qualifications required to ensure that the contracted IT professional is the right fit. 2. Rely on Specialized IT Staffing Agencies Realize your need to see how the right IT professionals may be secured. You have two main options: Staffing Agencies: Many MNCs still hire IT staffing companies that deal with the contractual employment of IT professionals. These agencies make work easier by generating a list of qualified candidates who have undergone standard screenings. Recruitment outsourcing firms assume the tasks of the first levels of recruitment, such as search and selection. Direct Recruitment: If you want to coordinate the entire procedure independently, then recruitment can be done on your own. This includes advertising for the position available, sorting through the pile of applicants, making a list of those candidates who will be offered an interview, and even the interviewing process and hiring of the candidate all by yourself. 3. Contract Negotiation So, if you have tapped the right candidate, there is no better time to negotiate than the IT contracting provisions. Here’s what typically gets covered: 4. Build an Onboarding Process After finalizing the contract, the next step is onboarding the IT professional. This involves introducing them to your company’s policies, project goals, and team dynamics to ensure they fit seamlessly into your workflow. Here’s what effective IT contract staffing usually includes: 5. Project Execution and Evaluation Once onboarding is complete, the technology contract staffing can start contributing to the project. They’ll work within the agreed-upon timeline, meeting milestones and objectives. Monitoring their performance throughout the contract is crucial to ensure they’re meeting expectations and delivering quality work. How Can IT Contract Staffing Propel Your Business Forward? IT contract staff may be recruited to provide narrow technical skills for certain engagements; this gives businesses the flexibility to respond to the dynamic needs of an organization while making it cost-effective. Here’s how: Flexible Staffing One of the benefits of IT contract staffing is that it is very flexible. Employers can easily hire a lot of workers or few workers depending on the projects at hand without necessarily having to contract the workers’ employment. This has made it possible for companies to manage change and be able to respond to any conditions that arise promptly. Access to Specialized Talent Specialized or right technical skills are difficult to find, as stated by employers from IT and technology. 77% of employers find it difficult to find talent in the field of technology. IT staffing services minimize this risk by providing businesses with a wider talent pool, helping to ensure that the right talent is hired. Cost-Efficiency IT outsourcing also opens new opportunities to rationalize labor costs. Compared to full-time employees who require the provision of benefits, stock options, and other employee privileges, contract professionals are only paid for the duration of their project. Meets Immediate Business Needs Contract IT professionals are usually willing to go to work immediately and start producing; hence there, little time is spent on training and incorporating them into ongoing projects. This ease in the establishment of the workforce is particularly helpful for those organizations with short working time frames or received an unexpected influx of work. Future Trends of IT Contract Staffing The

Manpreet Kaur
September 12, 2024
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Top 10 Reasons to Prioritize Data Warehousing Services

Data warehousing is like building an organized library for datasets in your organization. It integrates data from various sources, improves data quality, and makes it easy to analyze and make smart decisions upon it. Investing in data warehousing services ensures you can make well-informed decisions that keep you ahead of the competition. As decision-making moves towards relying on data and analytics, the demand for data solutions has increased. Incidentally, 31% of respondents are eager to quickly scale up their analytics spend to support more users and handle bigger data. Additionally, 28% are aiming to focus on improvement in the infrastructure which would support the cloud, on-premise as well and regional computing. But how can you efficiently handle petabytes of data? The answer is the data warehouse. Data warehouses are a kind of central repositories designed for storing and processing huge quantities of information from various segments of an organization. When approaching the question of investing money in data warehousing services, you should be acquainted with what gets you the maximal profit. In this blog, let’s try to break down what is important to know but might be too obvious to think of when getting started with data warehousing services. But first, let’s start with the basics. What is a Data Warehouse? A data warehouse, often called an enterprise data warehouse (EDW), is a central system where businesses keep important information like customer and sales data. This information is stored for analysis and reporting. Data warehouses are essential for generating insights and supporting decision-making through business intelligence (BI). They typically hold both current and historical data that has been collected, transformed, and loaded (ETL) from various sources, including internal and external databases. Essentially, a data warehouse serves as a business’s single source of truth (SSOT) by consolidating data into a stable, standardized system that relevant employees can easily access. These systems are designed for online analytical processing (OLAP) and enable fast, efficient analysis of data from multiple angles. Data warehouses can store vast amounts of summarized data, sometimes reaching several petabytes. Why Should You Have a Data Warehouse? The main benefit of a data warehouse is that it brings together data from various sources into a unified format. This consistency ensures that the data is accurate, leading to well-informed decisions. When data is standardized across the business, every department can generate consistent and reliable results. Traditionally, data warehouses were located on servers within a company’s premises (on-premise). However, nowadays, many data warehouses have moved to the cloud, where they can store and analyze vast data sets. Some popular cloud-based data warehousing platforms include: Data warehousing services often serve as a single, reliable source of truth for businesses, centralizing big data in a secure, stable, and standardized system that’s accessible to the right teams and employees. Reasons To Invest in Data Warehousing Services It is important for an organization that requires information in real time to make the right decisions to use a data warehouse. Still, these reasons can help you determine whether, in your case, data warehousing services are worth the investment. 1. Unlock Data-Driven Potential Decisions are no longer hove on guesswork or instinct – at least they shouldn’t be. Indeed the modern-day leader has the great fortune of having a basis for decision making informed by current data and this is made possible through a data warehouse. For information power to be realized optimally, there must be a loss of data ownership where one department usually owns most of the data. A data warehouse can rectify this scenario, and those in need of some specific details do not need to go through all sorts of hoops or even other departments. When it is set up as a single source organizing all other material, data warehousing services ensure that information seekers are well-equipped to find what they require and use it decisively to chart the destiny of the organization. 2. Harness the Power of Automation Data warehousing services open up opportunities for businesses to explore automation. Automating different parts of operations is gaining traction, especially as people see how it can help avoid costly errors and speed up processes. Market studies suggest that the global industrial automation market could reach $265 billion by 2025, a significant jump from $175 billion in 2020. Data warehouses play a crucial role in supporting these automation efforts. Businesses can use software-driven workflows to automate tasks like data access and transfer, reducing the time needed to gather information for auditors, investors, or other stakeholders. Automation can also speed up data analysis, helping uncover insights much faster. Additionally, it’s possible to automate error detection and logging, making it easier to spot potential issues and address them quickly. By understanding how data warehousing services are used within an organization, businesses can identify the best areas to implement automation. 3. Keep Your Data Secure When data is scattered across multiple locations, makes your security more challenging. Many leaders don’t even know how much data they have or where it’s stored. With data warehousing services, everything is stored in one place, making it easier to track and secure information. Plus, most data warehousing platforms come with built-in security features. Some can block harmful SQL code from outside attacks, while others limit how much data someone can view at once, reducing the risk of unauthorized use. Organizations can also control who accesses the data warehouse and why. This ensures that people only see what’s relevant to their job. Additionally, some data warehouses lock out users who try to log in from unusual locations, making it harder for hackers to take advantage. To effectively implement data warehousing services in your company, it is advisable to leverage digital transformation consulting services for a well-guided and strategic implementation. 4. Enhance Data Quality and Consistency Sustaining high quality and consistency of the data is an essential requirement for your organization. Worse the cloud data is unstructured or inaccurate and hence will not be of much use. A data warehousing system can aid in