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AI as a Service Companies in Manufacturing – 10 Top Players

Industry 4.0 and smart manufacturing are not possible with artificial intelligence. AI offers numerous benefits for manufacturers. Here, we’ll discuss the top ten AI as a Service companies in the manufacturing industry and the benefits of using AI in enterprises.  With artificial intelligence becoming a part of every industry, it’s no surprise that it has a vital role in manufacturing. According to Meticulous Research, AI in manufacturing market is predicted to reach $84.5 billion by 2031 with a CAGR (compound annual growth rate) of 32.6% between 2024 and 2031. Meanwhile, the global AI market is estimated to be $1,811.8 billion by 2030.  Additionally, technological advancements have led to AI being offered as a cloud service. This is popularly known as AI as a Service (AIaaS). While Microsoft, Google, and Amazon (AWS) have the largest market share in this sector, many third-party companies offer AI as a service to manufacturers from different niches (automotive, aerospace, electrical, chemical, etc.). Partnering with the right AI product development company helps an enterprise seamlessly adopt smart manufacturing practices and gain an edge in competitive markets.  In this blog, we’ll read more about the role of artificial intelligence in manufacturing and the top ten companies offering AIaaS solutions to manufacturers. How Does AI as a Service Bring Efficiency in Manufacturing? The application of AI in manufacturing is diverse, varied, and innovative. Artificial intelligence can improve efficiency in manufacturing by automating repetitive tasks, enhancing quality control standards, shortening the production cycle, increasing supply chain visibility, reducing resource consumption, and making it easier to scale production according to market demand. AI can also make enterprises energy-efficient by identifying areas to reduce wastage. Data-driven decision-making based on insights derived from advanced analytics helps the top management navigate the complex and volatile markets in different countries.  Manufacturers don’t have to build the AI models from scratch or spend millions of dollars on developing applications in-house. AIaaS companies offer access to advanced tools and technology through cloud systems. Enterprises can migrate their processes to cloud servers and rely on the cloud IT infrastructure to streamline their internal operations. AIaaS is a cost-effective alternative for manufacturing businesses to adopt artificial intelligence.  So, what companies are providing AI services? Let’s check them out below!  10 Top Players Offering AI as a Service in Manufacturing DataToBiz DataToBiz is an AI-as-a-service company offering AI, ML, BI, data engineering, and cloud transformation solutions to MSMEs, SMBs, startups, and large enterprises. It has a strong presence in manufacturing, supply chain, transportation, and several other industries. The company’s tailored services for AI in manufacturing can help with strategy creation, product design and development, streamlining the supply chain, integrating cloud systems, connecting with IoT (Internet of Things) devices, setting up personalized dashboards, and aligning business processes with long-term objectives. The company follows a six-step approach to increase flexibility, scalability, and sustainability for clients. DataToBiz has won several awards for providing customer-centric end-to-end AI services to manufacturers from different parts of the world.  Glassdoor Rating: 4.8 Stars  IBM IBM is a popular multinational company offering AI consulting services for small, medium, and large enterprises from manufacturing, IT, and other industries. The company combines AI and hybrid technologies to help enterprises become more agile and scalable. IBM Watson’s platform is customized and deployed in manufacturing units to help clients unlock the true potential of business data and make data-driven decisions. It empowers businesses to become more aware of the market threats and opportunities. Additionally, IBM has many tools like Cloud Pak, Maximo, Supply Chain Intelligence Suite, etc., which can be integrated with the existing processes to deliver actionable insights and streamline internal operations in an enterprise.  The company assists clients in turning sustainability goals into tangible actions.  Glassdoor Rating: 4.0 Stars  Siemens Siemens is a global service provider with a presence in manufacturing, automotive, telecommunications, and other industries. The company has developed industrial AI solutions for large enterprises to adopt advanced technologies into their business. It is one of the leading examples of companies that offer AI as a service to clients from various regions. The AI services empower enterprises to standardize their processes, integrate machine learning algorithms, and streamline data and security requirements. The company develops a comprehensive AI framework for manufacturers to begin their Industry 4.0 journey and embrace smart manufacturing. Siemens follows a closed-loop model which includes all steps from planning to upgrading. The cycle is continuous and always active so that manufacturers can achieve their goals.  Glassdoor Rating: 4.2 Stars  C3.AI C3.ai is an enterprise AI service provider accelerating digital transformation in industries like manufacturing, defense, transportation, oil & gas, etc. The company’s AI development services address the key roadblocks and challenges in the manufacturing industry and help enterprises overcome the issues to become successful. Its C3 AI platform can be integrated with third-party apps to help manufacturers collect data from multiple sources, analyze the datasets, and derive meaningful insights for effective decision-making. The company offers extra tools for inventory optimization, supply chain risk assessment, transportation management, and more. The enterprise AI platform is 25 times faster and just as easy to use. From energy management to CRM, manufacturers can enhance all their operations by partnering with C3.ai.  Glassdoor Rating: 3.3 Stars  GE Vernova GE (General Electric) is a group of companies catering to the diverse needs of enterprises in the manufacturing, aerospace, and healthcare industries. GE Vernova is an AI platform developed for manufacturers to streamline their energy consumption and become a sustainable business. It focuses on ESG reporting and helps enterprises accelerate their adoption of reliable, sustainable, and affordable energy while enhancing customer experience and improving the quality of life for employees. The platform’s focus is on reducing the carbon intensity of the enterprises without compromising their access to advanced technology like, AI, ML, business intelligence, etc. GE works to empower the next generation of manufacturers to dream bigger, better, and bolder. The company has over 130 years of experience in the market.  Glassdoor Rating: 4.0 Stars  Rockwell Automation Rockwell Automation offers AI as

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IT Staff Augmentation Services for B2B Enterprises – Top 11 Market Players

Information technology is for everyone. Businesses can hire a talented team to achieve their IT goals. Here, we’ll discuss the top 11 IT staff augmentation services companies for B2B enterprises and the process of building an external team of experts to work with your business.  B2B or business-to-business is a model where a transaction occurs between two businesses, such as a manufacturer and a supplier, the manufacturer and a distributor, an IT company and its application providers, etc. B2B enterprises are business entities that primarily cater to other businesses. They may or may not offer products and services to general consumers. The changing demands in the global market have made many companies revamp their working models. Whether adopting new technologies, automating workflows, remote teams, or collaborations between internal and external employees, B2B enterprises have been streamlining their processes in recent times.  This led to an increase in demand for IT staff augmentation services. Instead of hiring employees on the payroll, enterprises collaborate with staff augmentation providers to build a team of experts in less than a week and complete a project in a short time. Statistics show that the global staff augmentation market size is likely to reach $145.12 billion by 2027, while the IT staff augmentation market is estimated to have a CAGR (compound annual growth rate) of 6.01% between 2020 and 2027.  In this blog, we’ll read more about IT staff augmentation and the top eleven service providers for B2B enterprises to strengthen their workforce temporarily and whenever necessary.  What is the Process Behind IT Staff Augmentation Services in B2B? Typically, IT staff augmentation services are a part of the umbrella term outsourcing. In staff augmentation, a business builds a temporary team of professionals by hiring from the service provider. The team works with the business as per the agreement. Once the project is complete, the team is dissolved. The business pays for its expertise by choosing a suitable package offered by the service provider. This could be hourly payments, project-based payments, etc. The duration also depends on what the business wants.  The process of hiring an IT staff augmentation team is usually mentioned on the staff augmentation services websites belonging to the service providers. This can change from one company to another. However, the following are the steps in the B2B staffing process.  This could be an in-house team or a remote team (even a combination of both). Similarly, the external team may or may not interact with the existing business employees. Once the project is complete and the agreement’s terms are met, the team is dissolved. A team can be formed whenever the business needs additional employees and doesn’t want to hire them permanently.  IT Staff Augmentation Services for B2B Enterprises- Top Market Players! DataToBiz DataToBiz is one of the leading AI, business intelligence, and data analytics staff augmentation firms in the market. The award-winning company has experienced, efficient, and friendly IT professionals from diverse domains. It offers nearshore and offshore IT staff augmentation services for clients from several industries. Businesses can hire data scientists for B2B projects through hourly-based, project-based, dedicated teams, or full-time hiring packages. The company partners with most businesses, be it a startup or a large enterprise. Moreover, it offers cost-effective pricing and tailored solutions for each client. Businesses can use the staff augmentation estimator tool to get a quote. DataToBiz believes in scalability, flexibility, transparency, and clear communication. The teams promise to deliver the expected results as per the schedule.  Clutch Rating: 4.8 Stars  eLuminous Technologies eLuminous Technologies is a custom web and mobile app development company offering IT staff augmentation services to B2B and B2C enterprises. The company has a presence in many industries and helps clients enhance their tech capabilities by hiring experts for various IT projects. It has short-term teams, conventional team augmentation, managed recruitment, long-term staffing, complete project outsourcing, and tailored IT staff augmentation models. Businesses can choose the most suitable model based on their requirements and budget. eLuminous Technologies follows a strict NDA policy and provides a dedicated project manager for each client, along with more add-on options. From app development to business intelligence, it covers many domains in the IT industry.  Clutch Rating: 4.7 Stars  Techahead Techahead is among the top IT staff augmentation providers in the industry and a one-stop solution for various tech services. The company handles many on-demand IT staff augmentation requirements by offering flexible and scalable teams for clients. It has experience in industries like healthcare, financial services, and eCommerce and has enough awards to prove its worth. The company specializes in customized staffing solutions and gives great importance to team compatibility as it affects the results. It also provides stringent security measures to ensure the project details are kept confidential. Techahead follows a six-step IT staff augmentation process that includes post-project support and continuous development solutions.  Clutch Rating: 4.8 Stars  Bacancy Technology  Bacancy Technologies is one of the top software development companies offering IT staff augmentation services to fill the resource gap in B2B enterprises and provide the necessary tech expertise. From UI/ UX testing to DevOps, AI/ ML engineering, etc., the company has IT teams for various project requirements. It has over a thousand and fifty agile developers working with clients from different industries. The company also offers dedicated teams, managed IT services, and full project outsourcing. Bacancy Technology has monthly and hourly pricing models for IT staff augmentation services. Businesses can also get a fixed quote for large and long-term staffing requirements. The company has a 15-day risk-free trial to onboard a developer into an existing team.  Clutch Rating: 4.7 Stars  Techminds Group  Techminds Group is a global and one of the top IT staff augmentation companies offering ISO and CMM-certified solutions to businesses from around the world. The company provides experienced and certified tech professionals to empower the existing teams of its clients and help them achieve their business objectives. B2B enterprises from many industries can choose from different staff augmentation models like contract (temporary), contract

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21 Biggest Cloud Transformation Trends in 2025

With more organizations migrating their IT infrastructure to the cloud platforms, it is vital to understand what it means and how cloud transformation can help a business. Here, we’ll discuss the biggest cloud transformation trends in 2025.  Many organizations of various sizes are investing in cloud transformation solutions to revamp their business processes and benefit from cloud-based models to increase efficiency, productivity, and revenue. Gartner states global cloud computing spending will likely touch $679 billion.  Both public and private clouds are in demand. Additionally, businesses opt for a multi-cloud strategy to streamline their operations and enhance overall performance. Statistics show that over 70% of enterprises will use industry cloud platforms by 2027. While almost 80% of businesses use multiple public clouds, 60% use a minimum of one private cloud.  So, what are the recent trends in cloud computing? How is cloud transformation influencing the global markets? What do we need to know about cloud digital transformation in an enterprise?  Read on to know more! What is Cloud Transformation? Cloud transformation is the process of migrating or transferring the business systems, IT infrastructure, data, processes, etc., from on-premises to a remote cloud platform. It focuses on scalability, flexibility, and agility to ensure that the technologies used align with the business requirements and the latest market trends. For example, outdated legacy systems are modernized or replaced by lighter cloud-based applications for better usability and faster results.  What is the Market Trend for Cloud Computing? There are many trends in the cloud computing market, which directly and indirectly influence how businesses use the latest technologies to achieve their goals. According to Mordor Intelligence, the cloud computing market size is expected to be $0.68 trillion. It is predicted to grow at a CAGR (compound annual growth rate) of 16.40% to touch $1.44 trillion by 2029.  21 Biggest Cloud Transformation Trends in 2025 1. Serverless Computing  Serverless computing is a model where developers can build and deploy application code without managing backend servers. It is also called function as a service (FaaS) where cloud service providers maintain the servers. The term serverless doesn’t imply the lack of a server but indicates that the server is not hosted on-premises in an organization. This reduces the cost of building and upgrading the IT infrastructure in the enterprise and promotes greater scalability. It helps businesses be ready to tackle future developments quickly and efficiently.  2. Better Artificial Intelligence and Machine Learning  While AI and ML technologies are already being adopted by many businesses, the trend will continue in 2025 and beyond. Tech giants like Google, IBM, etc., are coming up with new products and services that will boost productivity in organizations and empower employees to use advanced tools. AI and ML are a part of cloud transformation and help in streamlining various activities in different departments, verticals, and industries. Be it procurement or customer service, AI applications can be used for analytics, automation, and more.  3. Edge Computing  Edge computing is another trend in cloud transformation services. It aims to reduce latency and improve data processing speed. In this method, data storage and computing tools are brought closer to the data source, or all applications are hosted on the same server. This allows real-time analytics and report generation as applications run faster. For example, manufacturers can benefit from edge computing by setting up analytical tools near IoT (Internet of Things) devices or in the same network.  4. Multi and Hybrid Cloud  Cloud transformation doesn’t mean a business has to move all its systems onto a single cloud. In fact, many organizations prefer multi-cloud architecture to save costs and increase efficiency. By using individual apps hosted on the respective vendor cloud servers, businesses can reduce the costs needed to build everything from scratch. Furthermore, the responsibility for data security and privacy lies with the vendor. Large enterprises use a combination of public and private clouds to spread the workflows on different platforms according to their priorities and security requirements.  5. Sustainability and Green Computing  Sustainability is a hot topic in many industries. How can one make the IT industry also sustainable? That’s where green computing comes into the picture. Sustainable technologies align with environmental protection goals and can help businesses reduce their consumption of natural resources without affecting their daily activities. The aim is to reduce carbon footprint and create a circular network for IT-based energy consumption.  6. Industry Cloud Platforms Industry cloud platforms (ICPs) combine the capabilities of software, platform, and infrastructure as a service to provide customized solutions based the business requirements. This creates greater flexibility for organizations to handle any range of workloads without worrying about system crashes or unexpected downtime. Oracle Fusion Cloud and SAP DMC SAP Leonardo are some examples of industry cloud platforms. Businesses from each industry can get services that are tailored to their sector as well as the specific requirements.  7. Disaster Recovery  When creating a strategy for digital transformation, businesses should include disaster recovery and data backup on the list. With many organizations migrating their systems to cloud platforms, it is vital to understand the importance of securing the data and applications from cyberattacks or natural disasters. Fortunately, the leading cloud service providers offer disaster recovery tools to quickly retrieve lost/ corrupted business data and apps to restart the processes without wasting too much time. Instead of worrying about data loss, the organization can revert to the latest previous update and take it from there.  8. Internet of Things (IoT) IoT or the Internet of Things are devices like sensors, connectors, etc., that extract data from the source and exchange it with other devices in the network. The data collected by IoT devices is sent to analytical tools to derive actionable insights. IoT has an important role in industries like manufacturing, mining, oil and gas, chemical, pharma, etc. IoT is a combination of different tools and technologies that collect varied data like temperature, movement, equipment motion, etc.  9. Security and Resilience  While effective cloud transformation processes are one aspect

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Parsing Dynamic JSON Data in Power Query: A Quick Coding Guide

Learn how to efficiently convert any JSON data into a fully expanded table format using Power Query, reducing the need for custom code and streamlining your data processing. Introduction: Understanding JSON Data and Power Query  JSON (JavaScript Object Notation) is a lightweight data-interchange format that is easy for humans to read and write and easy for machines to parse and generate. It’s widely used in web services and APIs to exchange data between clients and servers. JSON’s structure typically includes nested arrays and objects, making it flexible for various types of data but also challenging to work with when it comes to data transformation and analysis.  Power Query is a data connection technology that enables you to discover, connect, combine, and refine data across a wide variety of sources. It is integrated into Microsoft products like Excel and Power BI, providing users with a powerful tool to transform and prepare data for analysis. Power Query’s ability to automate data transformation tasks and handle large datasets makes it an essential tool for business intelligence professionals.  The Challenge with JSON Data  JSON (JavaScript Object Notation) is a common data format, especially in business contexts where data is exchanged between systems. While JSON’s nested structure is useful for organizing data, it can be challenging to work with tools like Power BI, where a flat table format is often required for analysis. Traditionally, handling varied JSON structures involves writing custom code for each case, which can be time-consuming and difficult to maintain. To address this, we developed a Power Query solution that dynamically converts any JSON data into a fully expanded table format. This blog explains how the solution works, provides the code, and highlights the key logic behind it.  The Problem: Handling Varied JSON Structures  JSON files often contain nested records and lists, making it difficult to transform them into a usable table format. Manually writing different code for each variation of JSON data can be tedious and error-prone, especially when dealing with data from multiple sources or systems.  Our goal was to create a solution that could handle any JSON structure, automatically expanding it into a flat table without the need for constant code adjustments.  The Solution: Dynamic JSON Parsing with Power Query  We created a two-part Power Query script—JsonParser and ColumnExpander—that dynamically parses and expands JSON data into a table. Here’s a brief overview of how each script works:  JsonParser: Loading and Parsing JSON Data  The JsonParser script is responsible for loading the JSON data from a SharePoint site, parsing it, and preparing it for expansion.  let JsonParser = (FileNameWithoutExtension as text) => let JsonFileName = FileNameWithoutExtension & “.json”, SharePointFilesList = SharePoint.Files(“https://YourSharepointSiteURL/”, [ApiVersion = 15]), FileContent = SharePointFilesList{[Name=JsonFileName, #”Folder Path”=”https://YourSharepointFolderURL”]}[Content], JsonParsedData = Json.Document(FileContent, 1252), DataAsTable = if JsonParsedData is record then Record.ToTable(JsonParsedData) else if JsonParsedData is list then Table.FromList(JsonParsedData, Splitter.SplitByNothing(), null, null, ExtraValues.Error) else null, ExpandedData = if DataAsTable <> null then ColumnExpander(DataAsTable, 0) else null in ExpandedData in JsonParser Key Points: ColumnExpander: Expanding All Columns  The ColumnExpander script is designed to recursively expand any nested lists or records in the table, ensuring that the JSON data is fully flattened into a table format.  let   ColumnExpander = (TableToExpand as table, ColumnToExpand as number) =>     let       CurrentColumnIndex = ColumnToExpand,       CurrentColumnName = Table.ColumnNames(TableToExpand){CurrentColumnIndex},       CurrentColumnContents = Table.Column(TableToExpand, CurrentColumnName),       HasRecords = List.Contains(List.Transform(CurrentColumnContents, each _ is record), true),       HasLists = List.Contains(List.Transform(CurrentColumnContents, each _ is list), true),       CanExpandColumn = HasRecords or HasLists,       IntermediateTable =         if CanExpandColumn then           if HasRecords then             let               RecordFieldsToExpand = List.Distinct(                 List.Combine(                   List.Transform(                     CurrentColumnContents,                     each if _ is record then Record.FieldNames(_) else {}                   )                 )               ),               NewExpandedColumnNames = List.Transform(                 RecordFieldsToExpand,                 each CurrentColumnName & “.” & _               )             in               Table.ExpandRecordColumn(                 TableToExpand,                 CurrentColumnName,                 RecordFieldsToExpand,                 NewExpandedColumnNames               )           else if HasLists then             Table.ExpandListColumn(TableToExpand, CurrentColumnName)           else             null         else           TableToExpand,       FullyExpandedTable =         if CanExpandColumn then           ColumnExpander(IntermediateTable, CurrentColumnIndex)         else if CurrentColumnIndex = (Table.ColumnCount(IntermediateTable) – 1) then           IntermediateTable         else           ColumnExpander(IntermediateTable, CurrentColumnIndex + 1)     in       FullyExpandedTable in   ColumnExpander Key Points: Why This Solution Matters  This Power Query solution simplifies the process of working with JSON data. Instead of writing new code for each variation in your data, you can use these scripts to automatically flatten any JSON structure into a table. This saves time, reduces errors, and streamlines your data processing workflow.  Conclusion  Transforming JSON data into a usable format doesn’t have to be complicated. With the JsonParser and ColumnExpander scripts, you can automate the process of expanding JSON data into a table, allowing you to focus on what really matters—analyzing and using your data.  If your business is dealing with complex JSON data and needs a reliable, scalable solution, contact us today to learn how we can help you streamline your data processing workflows.  FAQs Q1: What exactly is this solution designed to do?  The solution suggested above is designed to dynamically transform JSON data of varying structures into a flat, table format using Power Query. It automates the expansion of nested records and lists within the JSON, eliminating the need for manual adjustments or custom coding for each new JSON structure.  Q2: How does this solution differ from traditional JSON processing methods  Traditional JSON processing often involves writing specific code for each unique JSON structure, which can be time-consuming and prone to errors. This solution, however, is flexible and dynamic, allowing it to handle any JSON structure automatically. It simplifies the process by recursively expanding all nested elements, making the data ready for analysis without the need for manual intervention.  Q3: Can this solution handle any type of JSON data?  Yes, it can dynamically expand any JSON data, whether it contains nested records, lists, or a mix of both.  Q4: Is this solution scalable for large datasets?  Absolutely. Power Query is optimized for handling large datasets efficiently, although performance may vary depending on the complexity of the JSON.  Q5: How does this compare to other JSON parsing methods in Power BI?  Most methods require manual adjustments for different JSON structures. This solution automates that process, making it more efficient and reducing the risk of errors.  Q6: Can these scripts be customized for

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21 Latest AI Chatbot Solutions for B2B and B2C Enterprises

AI Chatbot solutions are intent based computer programs that specifically uses artificial intelligence to bring a conversational approach to B2B and B2C consumers. In this article, let’s know the latest AI chatbot solutions and how it will enhance your product development and offer personalization to your customers.   The development of AI has given birth to large language models and Generative AI. Both these show that the global LLM market is expected to grow at a CAGR of 79.80% and will touch $2598 million by 2030. The use of AI is not limited to one specific industry; however, it’s increasing to release quality products into the market. The development of chatbot solutions driven by LLM can process data to deliver responses and perform other tasks. By processing data and interpreting insights, LLMs help you discover dangers early on and devise solutions for problems. Innovation and originality in product development are further aided by the use of language models in AI.  AI chatbot development solutions play a significant role in the development of AI products and recommendation engines. LLM and generative AI chatbots can offer valuable insights and intelligence to streamline various aspects of the product development process. Before building actual prototypes, AI-powered systems may simulate a variety of scenarios, identify any design problems, and enhance product performance. This guarantees that the finished product meets or exceeds client expectations by cutting down on the time and expense of multiple iterations. In this blog, we will understand how AI chatbot solutions can transform your day-to-day business processes. We will also understand the latest 21 AI chatbot solutions you can use as a part of the AI product development process.  Latest AI Chatbot Solutions To Follow  AI-powered chatbot solutions that utilize cutting-edge AI technologies have the potential to revolutionize the product creation process. Here’s how the listed chatbots can help at different phases of the product development lifecycle: 1. ChatGPT Based on consumer demands and current trends, ChatGPT can help with brainstorming sessions by producing original ideas and suggestions for new features or enhancements to existing products. ChatGPT helps in analyzing consumer reviews and may gather useful information from a vast amount of consumer evaluations and feedback to improve the functionality and design of products. Using AI chatbot solutions product innovation can result in resolving several operational challenges in business. Thus, ensuring automotive processing and increasing efficiency.  2. Google Dialogflow Dialogflow may oversee user interactions to gather thorough input on how the product is used and performed. You can utilize this data to find areas that need improvement. It will also help in performing market research and collecting data from conversational surveys to validate product ideas and comprehend user preferences. With Google Dialogflow AI chatbot solutions, gathering consumer feedback to facilitate testing becomes easy. This tool can integrate a conversational user interface face and interactive voice response systems. Dialogflow chatbots can spot new trends and changes in the industry by keeping an eye on social media and news sources. 3. IBM Watson Assistant IBM Watson Assistant is a platform that leverages natural language understanding and machine learning to gather and analyze extensive customer feedback. This tool can allow product teams to make data-driven decisions and tailor their offerings to meet market demands. It can offer personalized recommendations to fine-tune product features to enhance customer satisfaction. Moreover, Watson Assistant streamlines project management eliminates repetitive processes, and promotes real-time development team engagement through its seamless interface. Watson Assistant helps companies stay ahead of the competition by spotting trends, keeping an eye on rival products, and providing ongoing learning and development.  4. Microsoft Bot Framework Microsoft Bot Framework is an AI chatbot development solution by Microsoft that covers a wide range of topics like data collection, model training, and model deployment. This bot framework is an open-source visual authoring canvas for developers and other team members to design and build conversational experiences for users with language understanding. With the help of this framework, chatbots can interact with users through a variety of channels, gathering varied input and information from different touchpoints to guide product development. Chatbots based on this framework can validate new features and evaluate their effect on user satisfaction by interacting with Azure AI services. 5. Amazon Lex Amazon Lex can be used to build conversational interfaces for applications using voice and text. It enables natural language chatbots into your new and existing applications. This AI product development tool offers deep functionality and automatic speech recognition to build a highly engaging user experience, create new categories of products, and facilitate conversational interactions. Before a product is released, Amazon Lex may oversee user testing and beta programs, gathering and evaluating user input to make sure it is intuitive and up to par. Because of its integration capabilities with other AWS services, the development process is streamlined by controlling workflows, automating repetitive operations, and enabling real-time team collaboration. This ultimately leads to improved customer satisfaction and product quality.  6. Rasa  Rasa is a generative AI service that helps in developing assistants. Rasa helps businesses create chatbots that can interact with users in a more human-like and intuitive manner. Through conversational engagement, this chatbot may collect specific and in-depth feedback from customers, offering valuable insights into user preferences, problems, and desired features. Product teams can use this input to inform decisions, improve current products, and create new features that appeal to their target market. Through its ability to streamline communication, automate repetitive operations, and offer profound insights into user behavior, Rasa enables product development teams to create and produce superior solutions that surpass customer expectations and fulfill market demands. 7. Zendesk Answer Bot  Zendesk Answer Bot is an AI chatbot solution that enhances product development by automating customer interactions and providing valuable insights. This answer bot leverages natural language processing to effectively handle customer inquiries by delivering relevant and accurate responses from the database. Zendesk Answer Bot collects and evaluates user input when developing new products. It is capable of recognizing frequently asked queries, requested features, and

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Digital Transformation for Banks: How Can Banks Thrive in a Digital World?

Digitization gives banks a competitive edge by promoting innovation. Here, we’ll discuss the importance of investing in digital transformation for banks and how partnering with consulting companies can help the industry embrace the digital-first model.  The banking industry is embracing new technology and keeping up with the technological changes in the world. We have come a long way from visiting the bank for every transaction to managing our accounts online 24*7. Banks are now adopting digital transformation strategies to further enhance their financial products and relevant services.  According to a study report by CU Insight, the percentage of active digital banking users had increased to 77% in 2024. Mobile deposits increased from 52% in 2023 to 54% in 2024. 42% of financial institutions are already using machine learning algorithms to streamline operations.  Digital transformation for banks empowers the institutions to gain a competitive edge and thrive in today’s digital world. It helps banks strengthen their position in the market by increasing profits and improving customer experience. For this, banks partner with digital transformation consulting companies and implement a long-term plan to achieve their objectives.  Let’s read more about the importance of digitalization and digital transformation in the banking sector. Why are Banks Moving to Digital? Banks and financial institutions deal with a lot of paperwork and complex procedures. This is time-consuming, cost-intensive, and stressful. As the internet became more accessible, banks slowly upgraded their services to offer digital alternatives for customers. Now, with technology advancing quickly, the industry has no option but to continuously upgrade and cater to customer demands. Digital transformation in banking and financial services is not a choice any longer but a necessity. The increase in private banks and market competition has also played a role.  Moreover, digitization decreases the workload while reducing the risk of human error. There is a digital trail for transactions, making it easy to maintain a proper record. Automation has helped banks and customers make payments online from any place and at any time. The interconnectivity between bank branches and banks has increased. With such benefits, it is easy to see why digital transformation in retail banking has become popular around the globe. With countries like India promoting cashless transactions by encouraging UPI and online payments, we can say that digital banking is here to stay.  How Does Digital Technology Affect Banks? Digital technology offers many benefits for banks. While the institutions should be aware of the challenges, there’s no denying the importance of adopting new and digital technology to revamp banking services. Customers no longer have to visit the bank branch for minor transactions. They can send and deposit money online. They can check their account balance online. Net banking and mobile banking services have increased efficiency and customer satisfaction.  Digital transformation for banks has further enhanced the results. Now, banks can automate repetitive internal tasks, increase operational efficiency, use real-time analytics to make better decisions, improve data security, detect and prevent fraud, and deliver greater satisfaction to customers. It allows banks to offer personalized services to customers based on their preferences and transaction patterns.  What is Digital Transformation in a Bank? The digital transformation definition states that it is a strategic and continuous process of adopting and implementing digital technologies in an organization to streamline operations, optimize resources, create and modify products/ services, and improve customer experience. This is done by converting the processes into digital formats. Digital transformation and changes to banking services also include redefining the cultural aspects of the establishment. Adopting digital technology requires changes in the work culture. Banks need to bridge the talent gap and help existing employees navigate the changes without feeling stressed.  What is the digital transformation of the bank branch? Generally speaking, the digital transformation of a bank branch is where the institution converts analog data to digital data and invests in new tools and technologies like artificial intelligence, machine learning, big data analytics, data visualization, data warehousing, etc. This requires a compressive cloud–based digital transformation strategy and support from expert professionals. By partnering with reliable service providers, banks can overcome the challenges of digital transformation and amplify the benefits. Long-term support from digital transformation companies also ensures that banks regularly upgrade their technologies and minimize the risk of glitches or downtime.  Benefits of Digital Transformation in Banking  Digital transformation for banks is much more than enabling banks to offer online banking services to their customers. It offers various benefits such as the following:  What are the Four Pillars of Digital Transformation in Banking? The banking digital transformation framework is built on the following four pillars:  What is Digitalization in Banking? Digitalization in banking is the transformation of the IT infrastructure in banks to adopt modern technologies. A report by Markets and Markets shows that the global digital banking platform market is expected to reach $13.9 billion by 2026. Digitalization allows banks to build and implement custom applications to overcome roadblocks and streamline workflows across the institution. The front-end and back-end are given equal importance. Website and mobile app development, digital marketing strategy for banks, phone banking services, automating data, data analytical dashboards for decision-making, shortening the application processing cycle, developing customer-friendly financial products, AI chatbots for 24*7*365 support services, etc., are some examples. What is Digital Strategy in Banking? Digital acquisition strategy for banks refers to the blueprint describing the steps the bank should take to acquire digital technologies in its day-to-day operations. In today’s world, banks are encouraged to follow a digital-first approach where they prioritize digitalization and new technologies.  The success of digital transformation depends on the strategy and its implementation. That’s why many banks are hiring digital transformation consultants to handle the process from start to finish.  The end-to-end digital transformation strategy includes the following:  Digital Transformation Challenges Faced by Banks Digital transformation for banks is not without a few barriers or roadblocks. Banks should have a proper plan to tackle these issues to avoid facing losses.  Lack of Clarity in Estimating Costs  When calculating the costs, make

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How to Scale Your Startup With Managed Data Analytics?

Data analytics is the process of collecting, storing, and analyzing large datasets to derive actionable insights in real-time. Here, we’ll discuss the role of managed data analytics in scaling a startup and the benefits of adopting the data-driven model for decision-making. Startups usually begin on a small scale and expand over the years. However, scaling a startup is easier said than done. As per a study by the Kauffman Foundation, just one startup in every ten scales is successful. That’s when startups are worth $1215 billion in 2024, and the value of unicorns around the globe is estimated to be $4.3 trillion.  So how do you scale your startup and ensure success in 2024 and beyond?  Managed data analytics and artificial intelligence are the answers. Startups need to be proactive and take advantage of the data and technologies available in the market to establish themselves. Instead of following conventions and orthodox business models, startups should adopt data-driven models and advanced tools (cloud solutions, AI, ML, etc.) to reduce the risk of failure. Fortunately, plenty of third-party companies offer onshore, near-shore, and offshore managed analytics services tailored to suit the business requirements.  In this blog, we’ll read more about how to successfully scale a startup by investing in data analytics and the benefits of using managed analytics in a new business. How do You Scale up Your Startup?  Scaling a business is the process of growing or expanding the operations, products and services, market share, customer base, and returns. Startups scale to invite more customers which brings higher revenue and creates a brand image in the market. However, scalability can be limited to one or specific avenues as well. Not every startup scales all aspects together.  Typically, you can scale a startup in any of the following ways:  What is an example of a Scalable Startup? Is Managed Data Analytics Involved? Facebook, Twitter (now X), and Amazon are great examples of a scalable startup. These started as small companies run by the entrepreneur and a couple of friends or team members. Over the years, Amazon has become the number one international marketplace and Facebook is one of the tech giants. They heavily invested in business analytics (gradually) to make decisions backed by data and insights. This allowed them to quickly adapt to the changing market conditions and gain a competitive edge. How do Startups Use Managed Data Analytics? Startups cannot underestimate the importance of data analytics in modern times. From streamlining internal processes to increasing customer satisfaction, analytics can be helpful in several ways. It converts raw data from multiple sources into actionable insights that employees can access to complete daily tasks more effectively.  Understand Target Markets  Startups don’t have the luxury of taking major risks or investing all the money into a project without basic research. Nine out of ten startups fail and shut shop in less than a decade. This can be due to lack of funds, incorrect investments, wrong decisions, etc. Understanding the target market conditions can reduce the risk of losses and keep the business afloat. For this, you can rely on data analytics to process the datasets related to the market and derive meaningful insights. Third-party companies offer managed analytics solutions and embedded analytics to provide real-time insights whenever you need them.  Analyze Customer Behavior  Customer feedback is vital for every business, and startups need it more than ever. However, you cannot afford to spend your time and resources on manually analyzing this feedback. Data analytics simplifies the process by using powerful tools and sharing the reports through data visualization dashboards. Platforms like Power BI, Tableau, etc., are popular data analytics tools that can be integrated with various input and output systems to provide insights 24*7.  Evaluate Competitors  How else do startups use data analytics to grow their business? Managed data analytics is not limited to analyzing the internal aspects of a business. You can use public data to determine who your competitors are and how they are faring in the market. This helps in identifying weak areas in your startup and strengthening them to survive the competition. Similarly, you can track the performance of your competitors and identify growth patterns to plan your strategies accordingly.  Improve Products and Services  Startups face severe competition from existing and new businesses. This means you have to continuously tweak your products and services to increase sales and bring more customers. Data analytics helps by highlighting the purchase patterns and preferences for your offerings and in the market. It provides information about what customers like and how you can improve your products and services to generate more sales or get the desired number of subscriptions.  Streamline Internal Operations  As a startup, you don’t have access to unlimited funds. You need to make some strict decisions about where to invest, what to prioritize, etc. In such instances, managed data analytics can help by identifying processes or steps that consume excess resources but are not necessary for the business. You can shorten the cycles, automate recurring tasks, and trim areas without compromising quality. It also helps in negotiating better deals with suppliers, storing your inventory carefully to reduce wastage, increasing or decreasing production/ procurement to align with the market demand, etc.  Increase Conversion Rate  Marketing is another area where data management services and analytics can help your startup. A new business has to invest in offline and online marketing campaigns to reach the target audience. Generating leads is one aspect, and converting them into sales is another. Startups tend to have a hard time converting leads to sales. Data analytics can be used to create a marketing strategy that suits customer preferences. Identifying the right kind of audience and sending the right message to the right customer at the right time through the right channel is possible when you use data analytics.  Identify More Avenues for Revenue  Revenue cycle analytics gives startups in the healthcare and finance industries a clear idea to streamline their revenue generation avenues. It tracks every step

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Startup Fundamentals Checklist : 11 Essentials New Businesses Need to Get Right!

Establishing a startup is much more than naming a business and pitching to people in your network. It is a long-term commitment and needs proper planning. Here, we’ll discuss various aspects of setting up a business and the startup fundamentals checklist to help you plan ahead. Entrepreneurship and startups have gained prominence in recent years. Many youngsters and experienced professionals are taking the risk of establishing their own businesses instead of working for others. This is a great development as it creates more job opportunities and keeps the market healthy with new competition.  According to Small Business Trends, only two out of five startups turn profitable, while one in three could break even and one in three could end up in losses. Running a startup is not easy and requires a lot of planning (and funding) along with experience and support from different parties. Many people ask if they can start a startup alone. It is certainly possible, but it is also important to find third-party partners to sustain the business and survive market competition.  Following a fundamental checklist will help create the initial plans and give you a clear idea of what needs to be done to establish and run a startup. Let’s find out more about the startup fundamentals in this blog. What are the Essentials for Starting a Business- Startup Fundamentals?  So, what are the fundamentals of startup? What is required for a startup to become successful? Let’s find out. 1. Business Plan  A business plan is a detailed document with information about your startup, like the idea, industry, goals, expectations, potential investors, target markets, etc. It is a roadmap you draw to convert your ideas into actions. So, why does every startup needs a business concept statement? Because a business cannot run for long on vague ideas and random decisions. Moreover, if you apply for a loan or pitch the business to investors, they ask for a business plan to make their decision. It is a compulsory document for many fundraisers.  But how do you create a business plan? Is there software for it?  Yes! Business plan software can be used for this purpose. The software helps refine your ideas and structure them into a polished plan that will appeal to investors and banks. However, you can create one from scratch by following the existing templates. The following are the common elements to include in a business plan:  As you can see, much of this requires serious effort from entrepreneurs. You can save time by investing in data analytics in startups to gather the required information. This also ensures that your business plan is data-driven and based on solid groundwork.  2. Cost Estimation  Another important part of startup fundamentals is budget. What are your financial prospects? How much will it cost to establish the startup? How much working capital do you need to run it? What salaries will you pay? Can you buy the latest tools? What kind of funding will you require?  These are just some questions you need to answer when estimating the cost of a startup. These calculations should be ready long before the startup opens. Use budget templates meant for startups or small businesses to plan your finances. Additionally, don’t rely only on outsiders to raise funds. You should have enough money saved to pay for the initial foundation work, like creating a business plan, pitching the idea to investors, getting expert opinions from financial and legal advisors, etc.  How you set up a startup also depends on how you plan and manage money. Opt for small business loans and angel investors instead of mortgaging your existing properties. Many legitimate platforms allow entrepreneurs to run crowdfunding campaigns. This helps in generating funds and assessing the viability of your business idea.  3. Researching a Managed Service Partner  Though you have yet to register and run the startup, you have the necessary information to determine the business operations. This will give you an idea to search for a budget-friendly managed data analytics provider for collaboration. The company can also help create the business plan, plan daily operations, build the IT infrastructure, and manage it to reduce downtime. In short, you not only gain access to data-driven insights but can also rely on an expert to manage your business system end to end.  4. Registering the Startup  Another crucial part of the basics of startup essentials is the registration process. This depends on the geographical location of your business, the industry standards and requirements, etc. It is recommended to hire a professional to oversee the registration to avoid last-minute complications. For example, some business models require additional permissions and documents. The owners may have to sign certain agreements which could clash with their existing jobs (if any). This can be risky if you are working for a company and want to build a startup simultaneously. In such instances, you should select a model with more relaxed terms and conditions.  5. Finding the Right Business Model  As mentioned above, the business fundamentals checklist includes the type of structure/ partnership you want in your startup. This also determines how you get your funds or approach investors. By default, the business models are categorized as below:  Study each model carefully and choose the best one for your startup. Take expert advice and consider your long-term goals.  6. Naming the Startup Giving a good name for your business may not seem like a big deal to some, but it is! That’s why it is listed as one of the fundamentals of a business. The name you choose becomes your brand image and identity. Make a list of all your ideas and suggestions from others. Analyze the names to see if they suit the business idea and convey a message to the target audience. If you wish to trademark the business name, it should comply with certain criteria, which will be available on the official website. Moreover, the name should be easy to pronounce, spell, and recollect. Go through

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Team Augmentation Services In Finance: Guide To Hire Data Scientists in Finance Companies

In this blog, we’ll discuss team augmentation services in finance, covering their benefits, types, working, key considerations, and when to use them. It also talks about the pros and cons, highlighting the importance of hiring specialized skills.  Suppose you are manufacturing a tech product with your internal engineers at the software firm, but you want to speed up the product creation process. You can also hire one group of engineers for a particular part of your product and require additional professionals to work on another part. However, the recruitment of qualified human capital in the local market is a major challenge due to scarcity, and the entire process is rather cumbersome and costly. What options do you have?  Lately, big and small firms have used staff augmentation with the teams to overcome this problem. Employers can correct this situation by outsourcing talents from a pool of candidates through flexible and efficient engagements for full-time employees. Team augmentation services are widely utilized, and the global staffing industry—a key provider of augmentation talent—is estimated to contribute to a $490 billion annual market. According to another research by PR Newswire, the IT staff augmentation services market is projected to reach $81.87 billion at a CAGR of 3.53%.  Here’s a comprehensive overview of staff and team augmentation, its key benefits, and how it can be effectively applied to your business. What are Team Augmentation Services? Team augmentation services in finance involve temporarily filling a company’s need for human resources or specialized skills without recruiting permanent employees. These services enable the firm to outsource professionals to join the firm’s workforce as employees but with less legal responsibility and often at a lower rate. These professionals can work within the company or from a distance, on a permanent/ temporary/ regular/ occasional basis, depending on the company’s needs and the resources required by the employees. Considering the described services, it is imperative to state that the advancement of team augmentation services is observed in almost all industries, including the finance market. This has turned out to be a common practice mostly for companies that are in the process of searching for top talents to attend to their needs in the short term and at a considerably cheaper cost than hiring full-time employees with similar competency levels. This strategy is especially helpful for dealing with workload fluctuations and mitigating gaps that arise when the workload is beyond the present staff’s capacity for some period but not significant enough to warrant hiring new employees. How Team Augmentation Services Work? When your financial organization faces specific project requirements and tight deadlines, team augmentation services might provide the competitive advantage you need. Here’s how IT team augmentation works: Step 1: Identify Skill Gaps Begin by pinpointing the areas where your company lacks expertise. Assess the resources that could enhance your organization or support your current employees. Determine which specialized skills or knowledge are essential to achieving your project objectives and deadlines. This analysis helps your team augmentation solutions provider match you with the best candidates for the roles you need to fill. Step 2: Search for Temporary Talent The next phase involves finding temporary professionals to meet your organization’s needs. While it is possible to handle this process internally, many larger companies prefer to use team augmentation services. These providers offer a time-efficient, cost-effective way to supplement your staff with the necessary expertise. Step 3: Onboarding Once you’ve identified the ideal candidates, the final step is to integrate them into your organization through a structured onboarding process. This ensures they are well-prepared to contribute effectively to your projects from the start. Types of Team Augmentation Services in Finance Team augmentation in finance involves bolstering an existing financial team with external experts to enhance capabilities, address skill gaps, or manage increased workloads. Here are some common types of team augmentation: 1. Project-Based Team Augmentation In this model, external finance professionals are brought in to work on specific projects or initiatives. They collaborate with the internal team to complete the project within a defined timeline and scope. This approach is particularly useful when organizations require specialized skills or need to accelerate project delivery. 2. Dedicated Financial Team Here, an external team is formed with the special focus given to a particular financial project which is, therefore, carried out for a long time. This devoted team works separately maintaining the organization’s standards and procedures to blend with the internal environment. 3. On-Demand Expertise There can be situations when specific issues have to be solved, or specific approaches have to be applied, and to do it, financial teams can turn to SMEs or staff augmentation partners. Outsourcing experts means that organizations can have access to outside specialists who can contribute to the resolution of specific issues or assist in the solution of specific questions. 4. Nearshore and Offshore Team Augmentation This requires a team augmentation company, which are situated in various geographical locations. Nearshore teams work in the neighboring time zones while offshore teams work in other time zones. Such arrangements provide cost efficiency, increased opportunities for finding personnel, and the flexibility of expanding the coverage of a team through the different time zones. 5. Freelance Platforms There is a primary characteristic of gig marketplaces such as Upwork or Guru. This kind of augmentation is good for task-specific needs and is structured to allow businesses to hire data engineers hourly or at a fixed rate. Freelancers, one of the main sources of augmentation talent, have been around for quite some time but have only recently grown by leaps and bounds with the help of international freelancer platforms. Each type of team augmentation in finance offers unique advantages and considerations. The choice depends on project requirements, budget, time constraints, and the specific skills needed to complement the existing team. What Should You Consider For Team Augmentation Services? IT Staff augmentation services offer unique benefits but require careful consideration. Here’s a look at key factors and common misconceptions. 1. Project Duration Team augmentation can uniquely meet

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From ETL to ELT: Evolving Data Integration Practices 

What it really took for us to transform from ETL(Extract, Transform, and Load) to ELT(Extract, Load, Transform). This article covers the foundational and evolving data integration practices among enterprises. Introduction Businesses are generating data at an accelerated pace now; there’s no stopping it, and there never will be. Consider a large retail chain trying to keep track of customer preferences, a manufacturing firm managing procurement data, or a financial institution handling client information—all in real time. The challenge? Making sense of this massive amount of data from multiple sources quickly enough to make informed decisions in a given duration, be it a project deadline, a product launch, or a client collaboration. Traditional data processing methods, like Extract, Transform, and Load (ETL), are struggling to keep up with the volume, velocity, and variety of today’s data bulk. But there’s something new and advanced in town—one that’s transforming how businesses approach data integration: Enter ELT (Extract, Load, Transform). Seems like just a word shift, but this orientation leads to a higher impact for any enterprise out there- Yes, yours too! Visiting the Past – What’s ETL? To simplify, ETL or Extract Transform Load is a data integration process that involves extracting data from various sources, transforming it into a suitable format(arranging it), and loading it into a target data warehouse or data hub. As the name suggests, it involves: Extract: This phase involves retrieving data from disparate sources such as databases, flat files, or APIs. Transform: Data is cleaned, standardized, aggregated, and manipulated to meet business requirements. This includes data cleansing, formatting, calculations, and data enrichment. Load: The transformed data is transferred into the target system, often a data warehouse, for analysis and reporting. ETL processes are critical for building data warehouses and enabling business intelligence and advanced analytics capabilities. What’s New – Defining ELT! ELT is a data integration process where raw data is extracted from various sources and loaded into a data lake or data warehouse without immediate transformation(that’s done later). The data is transformed only when needed for specific analysis or reporting. As the name suggests, it involves: Extract: Data is pulled from disparate sources. Load: Raw data is stored in a data lake or data warehouse in its original format. Transform: Data is transformed and processed as needed for specific queries or reports. This approach uses cloud computing and big data technologies to handle large volumes of data efficiently and at the right time. ELT is often associated with cloud-based data warehousing and big data analytics platforms. The Shift from ETL to ELT: Evolving Data Integration The shift from ETL to ELT represents more than just a change in process—it’s a fundamental shift in how businesses handle their data. Data analytics companies understand that the future is digital, and staying a step ahead requires not just adapting to new technologies, but leading the way. Our mission is to help businesses like yours use the power of data, ensuring that every data point contributes to your business sustainability.  For decades, ETL has been the front face of data integration. As explained above, the process involves extracting data from various sources, transforming it into a suitable format, and then loading it into a data warehouse or other system for analysis. While ETL has served us well, it comes with significant limitations.  Real-World Applications of ELT It’s quite surprising to see the quick change in process and the prioritisation of activities, with ELT making a difference in every industry. It suits workflows, adapting to the types of activities involved, and enhancing overall efficiency. Retail A global retail chain uses ELT to process massive amounts of transactional data daily. By loading data first, they can quickly analyze purchasing patterns and optimize inventory in near real-time. Finance In the financial sector, ELT enables institutions to load raw transaction data into a data lake and then perform complex risk assessments and fraud detection, ensuring compliance with changing regulations. Healthcare Healthcare organizations use ELT to handle patient records, lab results, and treatment data. This allows for more timely insights into patient care and operational efficiency. As Ankush Sharma, CEO of DataToBiz, mentions, “We’re not just in the business of delivering solutions—we’re in the business of building futures. With the shift to ELT, we’re enabling our clients to turn every data point into a strategic advantage, without a hefty investment. Overcoming Challenges in ELT Implementation While ELT offers many benefits, it also presents challenges such as ensuring data quality, maintaining security, and managing performance. Poor data quality can lead to inaccurate insights sometimes while loading raw data into a central repository before transformation can raise security concerns.  To overcome these hurdles, it’s important to implement strong data governance, enforce security protocols, partner with analytics firms, and optimize your data architecture. In the meantime, trends like data virtualization, AI-powered pipelines, and cloud-native platforms will continue to shape the future. The Future of Data Integration Practices: Beyond ELT Data transformation technologies are never at rest! As data integration continues to evolve, new trends are emerging that promise to further transform the landscape: Data Virtualization This approach allows businesses to access and query data from multiple sources without the need to move or replicate it. AI-Backed Data Pipelines AI is increasingly being used to automate data integration processes, making them more efficient and less prone to error. Cloud-Native Data Platforms As more businesses move to the cloud, the demand for platforms designed specifically for cloud environments will continue to grow. Conclusion The shift from ETL to ELT marks an evolution in how businesses approach data integration. Using this new model, companies can achieve greater agility, scalability, and cost-efficiency—all while aligning with the broader trends shaping the future of data. All we can help with is guiding you through this transformation, helping you turn every data point into a strategic asset.  Ready to explore how ELT can sustain your digital future? Let’s start the conversation. Fact checked by –Akansha Rani ~ Content Creator & Copy Writer

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