Page 17 – DataToBiz

Manpreet Kaur
November 21, 2023
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Exploring Future Tech Trends in Data Science Managed Services

Find out how technologies including AI and ML have transformed the future of data science managed services. From tackling the “black-box” problem with Explainable AI to overcoming data governance and privacy concerns, the new technologies can help data scientists deliver value and success in an increasingly data-driven world. Businesses are generating massive amounts of data and there’s no doubt that harnessing the data potential is crucial for staying agile. Managed analytics services have emerged as a game-changer, enabling companies to make data-driven decisions, analyze customer behavior, and drive innovation. As we look ahead to the future of data science managed services, it’s essential to not only understand the emerging technologies and trends but also to foster a better and more professional relationship with our clients. Understanding the Role of Data Science Managed Services The global data science market reached a valuation of USD 95.3 billion in 2021 and is anticipated to increase at a CAGR of 27.7% from 2021 to 2026. Data science managed services have been widely adopted by organizations to access deep expertise and tailored solutions to extract insights from their unstructured data sources. Whether it’s augmented analytics or data management, these specialization services are designed to help clients derive actionable insights from their data, all while saving time and resources. To succeed in this field, it’s crucial to keep pace with the latest developments. Here are some of the emerging technologies and trends that will shape the future of data science managed services. Artificial Intelligence (AI) and Machine Learning (ML) Integration of AI and ML into business processes is becoming increasingly integral to data science-managed services. These technologies can analyze complex data sets, make predictions, and automate decision-making processes. By leveraging AI and ML, data scientists can deliver more accurate and efficient solutions to their clients. Explainable AI (XAI) Tools One of the challenges in AI and ML has been the “black box” problem, where models make predictions without providing insight into their decision-making process. Explainable AI is gaining traction as a trend, as it allows clients to understand and trust the decisions made by machine learning models. Data Governance and Privacy Increased focus on data governance, compliance, and privacy will continue to be crucial concerns within enterprises. Clients expect their data to be handled securely and ethically. Adhering to regulations like GDPR and evolving privacy standards is essential. As data scientists, it’s our responsibility to ensure the data we use is both legal and ethical. DataOps and MLOps DataOps and MLOps are emerging practices that bring DevOps principles to data science and machine learning. These approaches streamline the development, deployment, and maintenance of data science solutions, leading to more efficient and reliable processes. Automated Data Science Automated machine learning (AutoML) and other AI-driven tools are simplifying data science analytics. These tools allow for faster model development and deployment, making data science-managed services more accessible to a wider range of clients. Cloud Computing With data growing exponentially, cloud computing platforms are necessary to store and process this voluminous information. Organizations now have scalable and accessible solutions to store and process data more effectively, enabling quick decision-making through a comprehensive data analysis. The flexibility and scalability offered by cloud-based platforms help organizations to overcome the traditional data management challenges. Internet of Things (IoT) The Internet of Things plays an important role in improving the efficiency and effectiveness of data insights. IoT data analytics make it easy to handle large volumes of data and optimize data collection and decision-making processes to create an adaptable environment conducive to growth. IoT reduces human intervention, facilitating quick processing of raw data and full automation. Also, it offers a more holistic and real-time approach to data analysis, allowing for dynamic adjustments, and thereby improving client outcomes. In a nutshell, IoT ensures optimal business performance by tapping new opportunities, increasing sales, and offering an exceptional customer experience. Conclusion As the future of managed analytics services unfolds, we not only meet with exciting advancements but also new challenges. By embracing emerging technologies like data-as-a-service(DaaS), AI, ML, and Explainable AI, we have the potential to unlock even more value for our clients. However, it is equally important to foster strong professional relationships with our clients. This can be achieved through open communication and collaboration between data scientists, providing personalized solutions, and upholding ethical practices. As the landscape of data science managed services continually evolves, our focus should always be on creating value for our clients and aiding them in thriving in a data-driven world. Fact checked by –Akansha Rani ~ Content Creator & Copy WriterSunaina Meena ~ Digital Marketing Specialist

Yashica Chopra
November 17, 2023
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6 Top Managed Analytics Service Providers in Manufacturing

Manufacturers can overcome business challenges and gain a competitive edge by investing in real-time analytics and reporting. Here, we will discuss the importance of managed analytics services and Top Managed Analytics Service Providers in Manufacturing to work with. Managed analytics services have become popular in the last few years. Many businesses, including enterprises from the manufacturing sector, are hiring third-party and offshore service providers to implement analytics and derive real-time insights for effective decision-making. According to Global News Wire, the global manufacturing analytics market grew at a CAGR (compound annual growth rate) of 20.7% from 2022 to 2023 to touch $11.98 billion. It is expected to touch $27.27 billion by 2027 at a CAGR of 22.8%. The manufacturing industry needs analytics and actionable insights to make proactive decisions about raw materials, vendors, supply chain, warehousing, marketing, shipping, customer service, etc. It helps in making faster decisions and gaining a competitive edge in the markets. In this blog, we’ll read more about managed analytics services and the top service providers in the manufacturing industry. What is Data Analytics in the Manufacturing Industry? Data analytics is a crucial part of the modern manufacturing industry or Industry 4.0. It helps enterprises effectively streamline production, quality control, warehousing, and distribution. Data analytics in manufacturing also helps with predictive maintenance to prevent unexpected machine breakdowns and supply chain disruptions. As per a report by Fortune Business Insights, the global smart manufacturing market is estimated to reach $658.41 billion by 2029 at a CAGR of 13.1%. Operational systems and machine data are used to improve the overall factory performance and increase the return on investment. Data analytics in the manufacturing industry is also called manufacturing analytics, where machines, people, and technology are aligned to get the best results. It minimizes the risk of errors, which can cause losses and unexpected expenditures. Top Managed Analytics Service Providers in Manufacturing Manufacturing analytics can be seamlessly provided by many leading service providers. Here’s a brief list of the top service providers in the international market. DataToBiz DataToBiz is one of the leading managed analytics service providers in the manufacturing sector. The company has a vast client base and a presence in different countries around the globe. It works with MSMEs and large enterprises to streamline factory operations, procurement, supply chain, and much more. The company builds customized OEE (Overall Equipment Effectiveness) dashboards to help manufacturers reduce production delays and increase factory performance. The company offers other services, such as the following: DataToBiz offers end-to-end managed predictive modeling and procurement analytics to reduce costs and optimize resources for an enterprise. It has certified and experienced data analysts, data scientists, AI and ML engineers, etc., to build, deploy, implement, integrate, monitor, maintain, and upgrade data-driven models according to clients’ requirements. DataToBiz has won several awards for being a trustworthy and transparent service provider. Alteryx Inc. Alteryx powers analytical solutions for clients from diverse industries. It provides manufacturers with the necessary setup to make faster and more confident decisions at all levels and departments. The company offers a low-code customizable platform with drag-and-drop features to access actionable insights through data visualizations. It specializes in analytics insights delivery with automated reporting. The company offers the following solutions: Alteryx automates different steps of the process to speed up data preparation and third-party system integration. From providing advanced analytics to creating predictive models based on event triggers and converting the analytical process into automated applications, the company offers tailor-made solutions for manufacturers to unlock the power of data. It has partners in over 90 countries and follows a client-centric approach to deliver the promised results. LatentView Analytics LatentView Analytics is a global pure-play data analytics company that helps businesses realize their potential and make the most of operational and customer data to maximize ROI. The company offers data analytics managed services in manufacturing through different analytical solutions using artificial intelligence and machine learning applications. These analytics increase the accuracy of demand forecasting reports to promote efficient decision-making. A few services offered by the company are listed below: Enterprises can use customer and marketing analytics to know what the users want. They can use connect vehicle analytics to streamline transportation and monitor distribution in real-time. Similarly, the supply chain and manufacturing analytics offered by the company help identify bottlenecks and overcome challenges. LatentView Analytics also offers enterprise-wide functional analytics to improve overall business performance and productivity. Oracle Corporation Oracle is a global tech company offering a range of services to help businesses use the latest technology for day-to-day work. It builds and deploys software, customizes databases, and sets up third-party integrations. The company empowers enterprises to rethink manufacturing by revamping supply chains, adopting advanced equipment, and running analytical reports. It promotes smart manufacturing by helping enterprises adopt Industry 4.0 solutions. The company offers the following services: Oracle provides data science managed services and real-time analytics through cloud-based applications. It accelerates innovation and boosts brand value in the market. The company helps manufacturers automate processes and scale applications based on their needs. It was named the Leader in 2023 Gartner® Magic Quadrant™ for Warehouse Management Systems for providing seamless data warehousing to small, medium, and large enterprises. Tredence Tredence is a data science company that helps enterprises bridge the gap between delivering insights and value realization. It offers analytics strategy and solutions to help clients reconsider limitations and overcome challenges. The company uses its powerful ecosystem to assist enterprises in achieving their business goals by leveraging data and analytics. It follows a vertical-first approach by developing AI and ML models to accelerate solutions. The company’s services are listed as follows: Tredence helps enterprises move from static to smart manufacturing by adopting artificial intelligence applications to derive actionable insights. The company fast-tracks value creation and promotes high-speed performance by generating reliable and accurate manufacturing analytics. It helps enterprises drive higher data efficiency and build a resilient system for decision-making. Sisense Sisense believes in innovation and accelerates growth by offering AI and ML capabilities to clients. It is a global analytics company working with startups, SMBs, and large enterprises. It streamlines the analytics lifecycle in a

Manpreet Kaur
November 13, 2023
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Choosing the Right Managed Analytics Service Provider: A Comprehensive Checklist

The blog highlights the importance of data analytics and the increasing demand for analytics service providers. It helps the readers understand the essential factors in choosing the right provider to fulfill data-driven goals and maximize the power of data analytics. Organizations across various industries are continually seeking ways to harness the power of data analytics. Data analytics is at the forefront of business decision-making, helping companies uncover valuable insights, improve operational efficiency, and drive innovation. The present big data analytics market is worth $307.52 billion. As the demand for data analytics services continues to grow, many businesses are turning to analytics service providers to meet their data-driven goals effectively. This article will guide you through the essential factors to consider when selecting the right provider, ensuring you make an informed decision for your organization’s future success. The Importance of Managed Analytics Services Before delving into the checklist for selecting the right service provider, let’s understand why organizations opt for these services. Managed Analytics service providers are companies specializing in delivering a range of data analytics solutions, offering both expertise and technology. Their primary goal is to help organizations leverage their data to make informed decisions, optimize processes, and gain a competitive edge. Managed Analytics services have become indispensable for businesses of all sizes due to several compelling reasons: Access to Expertise Service providers typically have teams of experienced data scientists, analysts, and engineers who can solve complex problems, design effective solutions, and bring industry best practices to the table. Cost-Efficiency Outsourcing analytics can be more cost-effective than building an in-house analytics team, especially for small and mid-sized businesses. It eliminates the need to hire, train, and retain specialized talent. Scalability Service providers can scale their services up or down based on your organization’s needs, making it easy to adapt to changing requirements without the hassle of recruitment. Focus on Core Competencies By outsourcing analytics, organizations can concentrate on their core operations and strategic initiatives while leaving the data analysis to the experts. Advanced Technologies Service providers often have access to state-of-the-art technologies and tools that may not be affordable for smaller organizations. Now that we’ve established the importance of analytics services, let’s dive into the comprehensive checklist for choosing the right provider. How do you choose the best Managed Analytics Service Provider? Choosing the best managed analytics service provider needs a strategic approach. A thoughtful selection process not only helps you to do the right data analytics but also pushes your business toward data-driven success. Define Your Objectives Before you start looking for an analytics service provider, it’s crucial to clearly define your objectives and what you want to achieve with analytics. Determine your specific business goals, the problems you want to solve, and the outcomes you expect from the services. This clarity will help you find a provider with the right expertise and capabilities. Expertise in Data Analytics and Data Science Look for providers with a strong track record in data analytics and data science. They should have a team of qualified professionals with expertise in machine learning, artificial intelligence, statistical analysis, and domain knowledge relevant to your industry. Check for relevant certifications, case studies, and client testimonials to ensure their proficiency. Range of Services Consider the scope of services offered by the provider. Ensure they cover the entire analytics lifecycle, from data collection and cleaning to model development and deployment. The services should align with your objectives and be adaptable to your changing needs. Data Management and Security Data management is a critical aspect of analytics. The provider should have robust data governance practices, ensuring the quality, security, and compliance of your data. Inquire about their data security measures, adherence to industry regulations, and disaster recovery plans in case of data breaches. Technology Stack Evaluate the technology stack used by the provider. They should be up-to-date with the latest analytics tools, platforms, and frameworks. Compatibility with your existing systems is essential to ensure seamless integration. Scalability Consider the provider’s ability to scale their services as your organization grows. They should have the flexibility to accommodate increased data volumes, expanded user base, and evolving analytics needs without disrupting your operations. Industry Experience Assess whether the provider has experience in your specific industry. Industry expertise is valuable, as it ensures they understand your unique challenges, regulatory requirements, and business dynamics. Customization Each organization has its own set of needs and preferences. A good service provider should offer customized solutions that align with your specific requirements. Avoid one-size-fits-all approaches. Data Intelligence Look for providers that not only analyze data but also offer data intelligence services. This involves extracting meaningful insights, identifying trends, and making proactive recommendations to drive strategic decisions. Data Visualization Effective communication of insights is crucial. Ensure the provider can create compelling data visualizations and dashboards that make complex data accessible to non-technical stakeholders. Monitoring and Support Check the provider’s ability to monitor analytics processes in real-time and provide support when issues arise. Service level agreements (SLAs) should be clear and aligned with your business goals. Cost Structure Understand the provider’s pricing model, whether it’s subscription-based, pay-as-you-go, or project-based. Transparent pricing will help you budget effectively and avoid unexpected costs. Case Studies and References Ask for case studies and client references to get insights into the provider’s past projects, success stories, and client satisfaction levels. This can help you gauge their capabilities and reliability. Performance Metrics Define key performance indicators (KPIs) that will measure the success of your analytics initiatives. Discuss these KPIs with the provider and ensure they align with your business objectives. Cultural Fit Lastly, assess whether there’s a cultural fit between your organization and the service provider. Effective collaboration and a shared vision are essential for a successful partnership. Conclusion In conclusion, choosing the right managed analytics service provider is a pivotal decision for your organization’s data-driven journey. By following this comprehensive checklist, you can select a provider that aligns with your business objectives, offers the expertise and technology required, and provides the necessary flexibility

Kavika Roy
November 10, 2023
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6 Top Managed Analytics Service Providers in 2024

Data analytics remains an integral part of digitally transforming a business to gain a competitive edge. Here, we’ll discuss the top managed analytics service providers and their role in enabling organizations to achieve long-term success. A managed analytics service provider is a third-party company offering a range of services to remotely manage the information technology, network, security, and analytical requirements of a business. While IT MSPs have been popular for many years, there’s an increasing demand for managed analytics services where the focus is on data analytics and reporting. Managed analytics services empower organizations to automate data collection, cleaning, storage, processing, analysis, and visualization to make data-driven decisions in a quick time. It helps businesses effectively manage and use data to understand customer requirements, market conditions, and competitors. According to Statista, the revenue generated by the managed services market is expected to touch $23.21 billion in 2023. It is expected to grow at a CAGR (compound annual growth rate) of 2.86% between 2023 and 2028 to reach $26.72 billion by 2028. The global managed services market is estimated to be over $400 billion by 2026. North America is the largest market, while Asia Pacific continues to be the fastest-growing market. Security and business apps are the most popular managed services provided in Europe and North America. In this blog, we’ll read more about the top companies offering managed analytics services in the global market. Top Managed Analytics Service Provider 2024 DataToBiz DataToBiz is one of the top managed analytics service providers to partner with for increasing precision in decision-making. With a global establishment itself, the company has clients from different continents like North America, Africa, Asia, Australia, etc. It caters to diverse industries and most businesses, be it a startup or a large enterprise. A few services offered by the company are as follows: DataToBiz is a high-level (top-tier) MSP offering end-to-end tailor-made services to plan, build, customize, implement, integrate, monitor, maintain, and upgrade the IT infrastructure and systems. It offers cloud and in-house services, along with remote collaborations. The company provides all four types of data analytics and data visualizations through customized and interactive dashboards. With years of domain experience, DataToBiz is a reliable, trustworthy, and affordable MSP in the global market. Wipro Wipro is one of the leading companies offering data analytics managed services and technology solutions to address complex digital transformation requirements of businesses from around the globe. The company has over 14,000 clients from various industries, collaborating on different projects and verticals. It helps clients become sustainable and adapt to the ever-changing market conditions. The company’s services are listed below: Wipro provides data operations and insights as a service to help organizations become more aware of data and its importance in making proactive business decisions. The company empowers businesses to transform their data analytics landscape and add value across the departments with real-time insights and reports. With next-gen technologies and robust processes, Wipro helps enterprises implement a data-driven culture. Deloitte Deloitte is one of the oldest IT service providers in the industry. It has a presence in over 150 countries and works with a range of clients with versatile needs. The company offers data science managed services with capabilities like artificial intelligence, deep learning, image recognition, etc., to get smarter and more accurate insights. More than 4,400 data scientists are working on delivering cognitive AI and ML-based analytics. The company’s other services are as follows: Deloitte has come up with an exclusive service, Insight in the Age of With™, which allows enterprises to master large volumes of data and control it effectively, detect in-depth patterns and innovation opportunities, and promote intelligent reinvention to gain an edge over competitors in the market. The company helps save around 4,000 hours of analytical time through automation and advanced technology. PwC PwC helps organizations unlock the power of data and analytics through customized data intelligence services. The company provides business strategies to convert insights into actionable decisions and drive the expected outcomes for the client. It enables data-driven digital transformation and builds custom digital products with scalability and flexibility. The company also helps businesses adapt and sustain in the long term. The following services are offered by the company: PwC promotes integrated data strategy and data modernization to empower enterprises to use analytics for effective decision-making. The company offers complete services to migrate business systems to the cloud and use AI tools for predictive analytics. It also enables automation to accelerate and re-engineer business processes. The company works with numerous clients from different sectors and continents. Infosys Infosys believes in amplifying human potential to create more business opportunities for growth. The company provides a wide range of services, including analytics and data management services using AI technology. It helps organizations unlock greater possibilities and change the business for the better. The company promotes more intelligence, more automation, and better connections. It offers the following services: The company creates an ecosystem for the clients to monetize data, navigate the competitive marketplaces, enable self-servicing systems, and transform the business to become an AI-first organization. It also offers hyperscaler solutions for fast-growing and multinational enterprises. Infosys has won the global Top Employer award for the third consecutive year. Ernst Young Ernst Young (EY) is a global organization with several member firms associated with it. The company aims to build a better world by providing trustworthy services. It is an MSP with multidisciplinary teams that work with clients to achieve their business goals. The company promotes sustainable economic growth and inclusivity while also giving importance to innovation and data-driven decision-making. The services offered by EY are listed below: Ernst Young helps enterprises derive and apply analytics throughout the business to grow, protect, and optimize operations. It uses the latest technology and delivers embedded analytics in real-time to support faster and more effective decisions at various levels. The company promotes fact-based decision-making by helping businesses understand the intricacies of analytics. What are the Different MSPs? A report by Canalys shows that 89% of partners who participated in the survey said that they expect their managed services revenue to increase in 2023.

Kavika Roy
November 08, 2023
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The Ultimate Guide to Managed Analytics Services: Benefits and Best Practices

Organizations today are swimming in a sea of information. To make sense of this data and extract valuable insights, many companies are turning to data Managed Analytics Services. In this blog, we’ll take a deep dive into a guide to managed analytics services and help you determine if they’re a good fit for your organization. The world of data analytics is booming, with the global data analytics market projected to surge from USD 43.0 billion in 2020 to a staggering USD 167.3 billion by 2025, at a CAGR of 28.4% during this period. Organizations today are increasingly turning to data managed analytics services to make the most of their data. These services are like a guiding light, helping businesses harness the full potential of their data resources. In this comprehensive guide, we will dive into what data managed analytics services entail, explore the wide range of services they offer, highlight the benefits of embracing them, and outline best practices for forging partnerships with managed analytics services providers. What are managed analytics services? Data managed analytics services include a wide range of activities that empower businesses to extract valuable insights from their data, make informed decisions, and gain a competitive edge in their respective industries. These services can be categorized into various components: Data Warehousing Data warehousing involves the collection, storage, and retrieval of structured data from various sources. Managed services providers assist in designing, deploying, and maintaining data warehouses, ensuring that businesses can access data rapidly and with minimal downtime. Data Governance Data governance is the practice of managing data assets effectively. It involves defining policies, standards, and procedures for data management, ensuring data quality and security, and adhering to regulatory compliance. Big Data Analytics Big data analytics involves setting up and managing big data infrastructure, helping organizations process and analyze large datasets efficiently. Data Architecture Data architecture is the blueprint that defines how data is stored, organized, and accessed within an organization. It involves creating and managing data architectures that align with the organizational goals, allowing seamless data integration and analysis. Data Lake A data lake is a centralized repository for storing both structured and unstructured data. Designing and managing data lakes makes it easier for organizations to ingest, store, and analyze diverse data types without the constraints of a predefined schema. Benefits of Leveraging Data Managed Analytics Services Now that we’ve outlined what data managed analytics services offer, let’s delve into the benefits of partnering with a managed analytics services provider: Deep Expertise Managed analytics services bring in experts who have a profound understanding of data analytics, data science, and related technologies. Their knowledge keeps your organization updated with the latest trends and best practices. Focus on What Matters By delegating data analytics tasks to a managed services partner, organizations free up their in-house resources to concentrate on core business activities, boosting overall productivity. Data Security and Compliance Security and compliance are top priorities in the data analytics landscape. Managed service providers implement measures to safeguard data privacy, adhere to industry regulations, and protect against security threats, providing organizations with peace of mind. Accelerated Insights Leveraging managed analytics services speeds up the process of deriving meaningful insights from data. This accelerated insight generation can lead to faster decision-making and a competitive edge in the market. Best Practices for Partnering with Managed Analytics Services Providers To make the most of your collaboration with a managed analytics services partner, consider the following best practices: Define Clear Objectives Clearly define your business goals and the specific outcomes you expect from your partnership with a managed services provider. Having a well-defined scope will guide the service provider in delivering the services you need. Select the Right Partner Choose a managed services provider that aligns with your industry, understands your data-related challenges, and has a proven track record. Look for providers with expertise in your specific domain. Open Channel of Communication Effective communication is essential. Maintain an open and transparent channel of communication with your service provider to ensure they understand your evolving needs and can make adjustments accordingly. Data Access and Ownership Clarify data access and ownership agreements. Ensure you have control over your data and that it is used by your organization’s policies. Service-Level Agreements (SLAs) Establish SLAs that clearly define the service levels, response times, and performance metrics. SLAs help ensure that your provider delivers services consistently and reliably. Security and Compliance Verify that your provider follows robust security practices and complies with relevant regulations. Data security should be a top priority, and the provider should have appropriate safeguards in place. Continuous Monitoring Regularly assess the performance of your managed services provider and provide feedback for continuous improvement. This ensures that the partnership remains beneficial over time. In Conclusion In an era where data is often hailed as the new currency, data managed analytics services offer a strategic avenue for organizations to harness the power of data analytics and data science. By partnering with the right managed services provider, businesses can gain access to expertise, reduce costs, improve data security, and speed up the time-to-insights. Furthermore, adhering to best practices in your partnership ensures a successful and mutually beneficial collaboration. Embrace the advancements of data managed analytics services, and let your data work for you. Fact checked by –Akansha Rani ~ Content Creator & Copy WriterSunaina Meena ~ Digital Marketing Specialist

Yashica Chopra
November 06, 2023
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10 Top Deep Learning Companies in India

Deep learning technologies are used to analyze large datasets to provide accurate and actionable insights quickly. We’ll discuss the top deep learning companies in India that offer end-to-end solutions and have clients from different industries. Deep learning forms a part of machine learning and artificial intelligence that mimics human emotions and thought processes to analyze the given data. Data scientists use deep learning and statistics for predictive modeling. It simplifies the process of collecting and analyzing large datasets in a quick time. Many artificial intelligence applications use deep learning algorithms for automation. It is a part of household AI applications like voice assistants and emerging technologies like self-driving cars. Deep learning cannot be separated from AI and ML. It is used to make an AI-based software/ app deliver more accurate results in an organization. India has a growing market demand for AI, ML, and DL technologies. New jobs are posted for data scientists, AI engineers, and DL experts. Many small, medium, and large enterprises are adopting artificial intelligence technologies to gain a competitive edge in the national and international markets. Educational centers like the Deep Learning Institute of India are actively producing more data scientists to meet the market demand. However, many businesses want an established deep learning company/ service provider that can take the responsibility of digitally transforming the enterprise and customizing the tools to suit their business requirements. Fortunately, India has several deep learning companies that offer several services to Indian and multinational clients. Let’s talk about the top deep-learning companies in India. Top 10 Deep Learning Companies in India DataToBiz DataToBiz is an Artificial Intelligence consulting company that works with advanced technologies like data science, AI, machine learning, deep learning, business intelligence, big data analytics, and more. The company offers data engineering services to SMBs, SMEs, and large enterprises. It has clients from India, the US, and the Middle East. DataToBiz is a B2B partner for many leading organizations. It offers flexible, scalable, and customized solutions to streamline digital transformation within an enterprise. The company is a certified partner of AWS, Google, and Microsoft. It provides in-house and cloud-based deep learning services to help businesses make better decisions and increase competitive advantage. From enhancing customer satisfaction to solving data-based issues and clearing roadblocks to adopting new technology, DataToBiz does it all. Among its clients are companies from diverse industries (including but not limited to) EdTech, FinTech, retail and eCommerce, transportation and logistics, security and surveillance, medical and healthcare, telecommunications, and hospitality. IDBI Bank, NPCI, Aditya Birla Group, Flipkart, GMR, Speero, etc., are some popular clients of the company. Trigma Trigma (Trigger your Imagination) is a custom software development and consulting company that offers AI/ ML, IoT, DevOps, and digital marketing services. The company has offices in India, the US, and Canada and offers smart technology, deep learning, and innovative IT solutions to its clients. Trigma has won various awards for being one of the leading software and mobile app developers. It has been recognized among the top artificial intelligence companies in 2021 by Clutch. Samsung, Whirlpool, Hero Motor Corp, Silverback Hosts, etc., are some of the company’s clients. Agile labs, data science, enterprise CRM, e-governance, and data engineering are the solutions offered by Trigma. It is a one-stop destination for businesses to work on their digital marketing and digital transformation strategies to streamline internal practices and attract end-users/ customers. Trigma has more than 200 professionals working on the team. Talentica Talentica has been offering its services for the last eighteen years. The company developed more than 170 software products and handles a large volume of work every day. It helps enterprises choose the right technologies to build and expand their businesses. Talentica deals with artificial intelligence, machine learning, augmented reality, wearables, big data, IoT, blockchain, DevOps, and user experience (front end). It also works with open-source technologies to develop customized applications for businesses. The company is reputed for providing fast and tangible results. True to its tagline, ‘we don’t deliberate, we deliver,’ Talentica can produce a minimum viable product for your business requirements in just 90 days. It follows an accelerated approach to ensure that you stay in the competition and gain an edge over other enterprises in your niche. The company has a presence in India and the USA. InApp InApp is a full-cycle software development service provider with clients from various countries. The company offers custom development services, cloud computing, DevOps, big data services, augmented and virtual reality services, etc. Innovation and quality services are the USP of this company. InApp has more than two decades of experience and 100+ clients. It builds world-class web and mobile apps for various businesses, including startups and large enterprises. The company is one of the leading SaaS and software vendors in the market. It helps organizations invest in the best technologies to digitally transform their businesses and streamline their processes. The company has expert teams that deal with emerging technologies like artificial intelligence, machine learning, IoT, blockchain, and more. InApp also offers testing and QA services. It has successfully delivered more than 1500 solutions to businesses from pharma, manufacturing, retail, eCommerce, construction, transportation, warehousing, and education. Prolitus Prolitus offers cutting-edge technologies to various businesses to increase sustainability. The company helps use innovation to develop internal methodologies for digital transformation. It believes that innovation is the way to add value to a business. Prolitus offers services in advanced technologies like AI, ML, blockchain, IoT, RPA, etc. It is an ISO 9001 & ISO/IEC 27001:2013 certified company with a special focus on blockchain technologies. DeFi, NFT marketplaces, smart contracts, P2P lending, and other solutions are provided by the company. It also offers enterprise ERP, mobile app development, eCommerce, and custom software solutions. Prolitus has its presence in India and the Middle East (Dubai and Qatar). The company is a partner of Hyperledger, Binance, AWS, etc., with clients like Dalmia, Apollo, Nasscom foundation, and more. Tokenization and NFT development are the highlights of the company. It also works with Metaverse. Prolitus has worked with 500+ clients

Kavika Roy
November 01, 2023
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Data Governance in Augmented Staff: Ensuring Data Quality and Compliance in IT Hires

Data Governance in Augmented Staff is crucial to maintain while opting for IT outsourcing or staffing services. This blog explores how to ensure data quality, compliance, and security in augmented staff. In today’s dynamic business landscape, the demand for IT or data engineering services is continuously growing. Many organizations rely on IT outsourcing to fulfill their technological requirements efficiently. These services offer flexibility, scalability, and cost savings. However, they also introduce challenges related to data quality, compliance, and security. This blog delves into the importance of data governance in augmented IT staff and how to ensure data quality and compliance in the process. According to Statista, the global IT staff augmentation market size was last valued at $132.9 billion. This shows that IT companies are significantly hiring staff on a project basis and for a short time. While staff outsourcing companies provide instant access to a pool of talented data-smart professionals, there’s often a need for governance to maintain data quality and regulatory compliance. Importance of Data Governance in Augmented Staff Data governance becomes crucial while working with outsourced staff, here’s why: Risk Mitigation Data governance reduces the risk of data breaches and non-compliance issues, protecting an organization’s reputation and finances. Data Quality Improvement By adhering to data governance principles, augmented teams can significantly enhance data quality, making it a reliable resource for decision-making. Regulatory Compliance Augmented IT teams must comply with various industry regulations. Data governance consulting helps align data practices with these requirements. Enhanced Collaboration Data governance fosters a collaborative environment by defining roles, responsibilities, and data ownership, which is crucial when working with augmented teams. Cost-Efficiency Effective data governance reduces data-related errors, leading to cost savings in data correction and maintenance. Challenges in Ensuring Data Governance While data governance is crucial, it’s not without its own set of challenges, however, the benefits far outweigh the challenges. While augmenting data professionals for a specified project, organizations often face these hurdles. But with the right set of instructions in hand, these challenges can be navigated seamlessly. Tips to Ensure Data Governance in Augmented Staff Effective data governance is essential to maintain data security and compliance. Hence, here are a few tips to ensure data governance in augmented teams and unlock the full potential of your data assets. Clearly Defined Roles and Responsibilities Start by defining clear roles and responsibilities within your augmented team of data engineering experts. Ensure that team members understand their specific roles in maintaining compliance. Data Ownership Assign data ownership responsibilities to individuals or teams within the organization. These data stewards will be responsible for data quality, and security, and ensuring compliance with regulations. Data governance consulting experts can help establish these roles effectively. Choosing the Right Staff Augmentation Partner Selecting the right staff augmentation partner is a pivotal step in ensuring effective data governance in augmented teams. When choosing a partner, consider their expertise in data governance consulting and their ability to align with your organization’s data quality and compliance objectives. Look for a partner who can provide skilled professionals in data engineering, data analytics, AI, BI, data science, and MLOps staff augmentation while adhering to your data governance framework. A well-chosen staff augmentation partner can greatly contribute to the success of your data governance initiatives, helping you maintain data quality and security while staying compliant with regulations. Data Governance Framework Implement a well-defined data governance framework that outlines the policies, standards, and practices for data management. This framework should cover data quality standards, security measures, and regulatory compliance requirements. Consistent Data Quality Monitoring Implement a robust data quality monitoring system to continuously assess and maintain data quality. Develop and manage unified data pipelines, ensuring data remains unscattered, accurate, and reliable. Regular Audits and Compliance Checks Conduct regular data audits and compliance checks to identify any discrepancies or potential threats. Ensure that your augmented teams are aware of the compliance regulations relevant to your industry or business and that they follow them diligently. Training and Awareness Invest in training programs and awareness campaigns to educate augmented team members about the importance of data governance. Everyone involved in data-related tasks should understand their role in maintaining data quality and security within your organization. Collaboration and Communication Foster a collaborative environment within augmented teams. Encourage open communication channels and a strong feedback loop between different team members, including data engineers, data scientists, and AI specialists. Effective communication ensures that everyone is aligned with data governance goals. Regular Updates Data governance is not static. It should evolve to meet changing business needs and regulatory requirements. Stay updated on industry best practices, and trends and adapt your data governance policies accordingly. Data Governance Metrics and Related KPIs Define key performance indicators (KPIs) and metrics to measure the effectiveness of your data governance efforts. Regularly monitor these KPIs to ensure that your augmented teams are meeting data quality and compliance goals. Conclusion Organizations must recognize the importance of data security and compliance when collaborating with an IT staff augmentation partner. Data governance is the safeguard that ensures data remains a trusted and valuable asset in the hands of augmented IT staff, whether they are involved in AI, BI, data science, MLOps, or any other specialized area. As the Information technology market continues to grow, the need for robust data governance becomes increasingly apparent. By embracing data governance principles and integrating them into their IT hires, organizations can harness the full potential of their data while minimizing risks and ensuring compliance with regulations. Fact-checked by –Akansha Rani ~ Content Creator & Copy Writer Sunaina Meena ~ Digital Marketing Specialist

Yashica Chopra
October 30, 2023
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5 Top MLOps Staff Outsourcing Companies to Partner With

MLOps is a powerful solution for effectively managing AI and ML models. It streamlines and standardizes the processes. Businesses can achieve their goals by hiring MLOps teams through staffing companies. Here, we’ll discuss the top MLOps staff outsourcing companies to partner with. MLOps stands for Machine Learning Operations, which is the core of ML functioning. It streamlines the process of creating ML models, deploying them in different environments, monitoring their performance, and maintaining the models over time to ensure seamless results. MLOps teams include data scientists, DevOps engineers, and information technology engineers who collaborate to keep the cycle running. The teams speed up the development and deployment of AI models in an enterprise through continuous integration and deployment (CI/CD), validation, and governance. According to a survey conducted with 200 US-based ML decision-makers, 86% of the businesses struggled with effectively utilizing ML to increase the commercial value of their establishments. Similarly, 71% said they were failing at creating value or generating revenue through large-scale ML models. The study also showed that 85% of the organizations had dedicated budgets for MLOps in 2022. This is a reflection of the growing demand for MLOps teams in the global market. However, a lack of in-house talent can lead to an increase in challenges and risk. These can be averted by partnering with MLOPs staffing companies to hire a team to work with your business or outsource the project to experts. In this blog, we’ll read more about MLOPs staff outsourcing companies to partner with. Is MLOps in Demand? According to a report by MarketsandMarkets, the MLOps market is expected to reach $5.9 billion by 2027 at a CAGR (Compound Annual Growth Rate) of 41%. The scalability and monitoring ability of MLOps has increased the demand for it across the globe. MLOps is also useful to standardize the ML processes, save time, reduce the risk of errors, and optimize resources. Based on a report released by Reportlinker.com, the global Machine Learning Model Operationalization Management (MLOps) market is projected to touch $8.5 billion by 2028 with a CAGR of 38.9%. Both reports prove that there is a high demand for MLOps, which will continue to increase in the coming years. Top MLOps Staff Outsourcing Companies to Consider DataToBiz DataToBiz is a reputed data engineering company offering one-stop MLOps staff outsourcing services to SMBs and large enterprises from around the world. The company has a talent pool with certified data analysts, data scientists, AI engineers, ML engineers, DevOps specialists, PowerBI/ Tableau developers, etc., to provide comprehensive end-to-end MLOps solutions. It is also a staff augmentation and outsourcing company specializing in MLOps development, big data analytics, BI, AI, ML, NLP, and LLM deployment based on clients’ requirements. Businesses looking to scale their MLOps teams can partner with DataToBiz to bridge the gap and gain access to expert ML engineers. The company helps save overhead costs and minimize expenses by streamlining business operations through temporary and remote teams. Organizations can choose a dedicated development team to work with their employees and seamlessly blend into the existing work culture. DataToBiz offers project-based staffing solutions to ensure timely deliveries and guaranteed results. It also has an ongoing feedback loop to promote continuous communication and collaboration. Unibrix Unibrix is a dedicated software development team that helps businesses find the best IT specialists for their requirements. It creates direct communications between the experts and enterprises to prevent the need for intermediaries. The company also provides customized packages to suit the varied needs of different businesses. Unibrix is a Poland-based IT staffing company with over 50 partners from several industries. It handles backend web development, DevOps, MLOps platforms, front-end mobile development, and cross-platform mobile development for small, medium, and large organizations in different continents. It provides complete transparency and high flexibility to build short-term and long-term teams for MLOps. SofterMii SofterMii is a US-based software development service provider with many certifications from IBM, AWS, Microsoft, Google, etc. The company mainly works with businesses from industries like real estate, communications, healthcare, and FinTech. It guarantees a 15% deviation in time and scope for the project delivery and provides continuous maintenance and support services to clients. SofterMii has flexible cooperation models for businesses to hire MLOps teams at various stages of development. An enterprise can extend an existing team or hand over the project to a new team. The company also takes care of the early stages of the project, such as conducting market analysis, creating strategies, and so on. It provides full-cycle software development, DevOps, cloud solutions, cybersecurity, webRTC development, and many other services necessary for an enterprise to stay competitive in today’s world. InfuseAI InfuseAI builds MLOps tools to help organizations streamline their ML projects. The company has developed an open-source MLOps platform called PrimeHub for businesses to build, deploy, and scale ML models. It provides IT leaders and data scientists for clients to adopt new AI and ML models and customize them accordingly. It works with establishments from different industries like healthcare, IT, construction, etc. InfuseAI offers AI-based SaaS solutions, making it quick and easy to adopt artificial intelligence and machine learning applications while saving costs up to 30%. The company’s MLOps tool can be trained and used online in just an hour. It provides a one-click installation to save time and minimize the risk of errors. The company optimizes data workflows and increases data observability, allowing the client’s in-house teams to focus on their core jobs. OpenGeeksLab OpenGeeksLab is an innovative service provider with 65+ experts from different domains and specializations. The company helps businesses scale their software products, applications, and models to gain a competitive edge in the market. Its engineers and developers are located in different countries and continents, making it easier to cater to a large and diverse client base. The company provides services to clients from the blockchain, healthcare, FinTech, and supply chain industries. It specializes in many IT technologies and has vast experience in software development. The company effectively handles the MLOps pipeline to deliver the promised results to the clients. OpenGeekLabs provides dedicated teams and staff

Parindsheel Dhillon
October 29, 2023
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Power BI vs SAP BI- The better business intelligence solution?

Whether you’re a decision-maker in the business world seeking clarity or simply interested in the business intelligence landscape, this summary offers insights into the crucial distinctions between Power BI vs SAP BI. Use this information to make a well-informed choice when selecting the ideal BI solution for your enterprise. Power BI and SAP BI are two prominent players in the competitive space of business intelligence solutions. According to the reports, Microsoft Power BI has a market share of 14.31% in the global business intelligence (BI) market, while SAP Business Objects has a market share of 5.51%, each offering unique features and capabilities to help organizations derive meaningful insights from their data. Power BI, developed by Microsoft, is a user-friendly and highly accessible tool known for its intuitive interface and integration with other Microsoft products. It empowers users to easily create interactive reports and dashboards, making it a preferred choice for small to mid-sized businesses and individuals looking for a self-service BI solution. On the other hand, SAP BI, part of the SAP BusinessObjects suite, is lauded for its robustness and scalability. It’s a trusted choice for larger enterprises that require complex data modeling, extensive data governance, and high-level security. Both solutions have pros and cons, and the choice between Power BI and SAP BI ultimately depends on your organization’s specific needs, size, and IT infrastructure. In the upcoming sections, we’ll delve deeper into these differences to help you decide when selecting the best business intelligence solution for your enterprise. Challenges in opting for SAP BI For SAP BI users, navigating the world of business intelligence comes with its own set of challenges and It’s essential for organizations to assess if the advantages of SAP BI are worth the complexities it brings before deciding to adopt this powerful business intelligence solution. These hurdles include: Challenges in opting for Power BI While Power BI offers numerous advantages, understanding these challenges helps organizations make informed decisions regarding the adoption of Power BI, ensuring it aligns with their specific business needs and constraints. Power BI vs SAP BI- The right choice? Data Integration Power BI offers robust data integration capabilities and supports a wide range of data sources, including SAP systems. It provides seamless integration options, allowing users to connect to various databases, cloud services, on-premises data, and more. Power BI also offers Power Query, which enables users to transform and shape data from different sources. SAP BI, being part of the SAP ecosystem, naturally integrates well with SAP data sources and systems. It is optimized for accessing data from SAP applications and SAP HANA. However, its connectivity to non-SAP data sources may require additional configurations and connectors. Visualizations Power BI is known for its rich set of interactive visualizations and customizable dashboards. Users can create visually appealing reports using various chart types, graphs, maps, and custom visuals. Power BI also has a vast library of community-created visualizations available through its marketplace. SAP BI offers a range of visualizations, but it may not be as extensive or as user-friendly as Power BI in terms of customization and interactivity. It is more focused on traditional enterprise reporting and analytical templates. Self-Service Analytics Power BI empowers business users with self-service analytics capabilities. Users can create their own reports, dashboards, and analyses without heavy reliance on IT or data experts. The intuitive interface and natural language query feature make it accessible to a wide range of users. While SAP BI has self-service analytics features, it may require a steeper learning curve compared to Power BI. It often involves more complex data modeling and may be better suited for users with some technical background or training. Scalability Power BI supports building a scalable ecosystem that allows for enterprise-wide adoption. It offers cloud-based and on-premises deployment options to meet the needs of organizations of all sizes. The Power BI service enables collaboration, sharing, and distribution of reports and dashboards within the organization. SAP BI is designed for scalability and can handle the demands of large enterprises. It seamlessly integrates with other SAP solutions and can be part of a comprehensive SAP landscape. However, it may involve higher initial implementation costs and more complex configurations, which could make it better suited for large organizations. While both Power BI and SAP BI have their merits, Power BI provides distinct advantages in terms of user productivity, adaptability, collaboration, and cost-effectiveness. These advantages make Power BI an appealing choice for organizations, especially smaller to mid-sized ones, looking to maximize their business intelligence capabilities while keeping costs in check. However, the choice between the two still depends on specific business needs, existing IT infrastructure, and user expertise, as well as the scale and complexity of the organization’s data and reporting requirements. Conclusion When evaluating BI tools for an enterprise, it’s essential to consider various factors like the existing architecture, user expectations, data sources, fulfillment of requirements, and training complexity. Both SAP Analytics Cloud and Power BI have their strengths and are best suited for specific scenarios. But considering the discussed factors, it’s quite clear that Power BI is the top choice for existing Azure and MS SQL customers, as well as situations involving numerous data sources other than SAP. It’s also the preferred option when an organization primarily relies on non-SAP sources, values visually appealing presentations, permits data replication, and needs data transformation using DAX expressions. On the other hand, SAP Analytics Cloud is the ideal pick when your reports are primarily based on SAP data sources, necessitate real-time data, and require authorization and role structures to be set up at the source. Hence, the choice between Power BI and SAP Analytics Cloud hinges on the unique characteristics and requirements of your organization, ensuring that you can leverage the right tool to maximize your business intelligence capabilities.

Manpreet Kaur
October 27, 2023
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A Step-by-Step Guide to Choosing the Right Staff Augmentation Partner

Finding the perfect staff augmentation partner is essential for organizations planning to extend their workforces. This guide to choosing the right staff augmentation partner talks about the important parameters that companies must consider along with the steps that’ll help them partner with a trustworthy augmented staff. Companies face ever-increasing demands for IT expertise, data security, and cutting-edge technologies. According to the RioTimes, the IT staff augmentation market will expand to $81.87 billion in 2025. As these demands surge, organizations often find themselves in need of additional IT resources, whether for a short-term project or to support ongoing operations. This is where staff augmentation comes into play. To ensure you choose the right staff augmentation partner, this comprehensive guide will walk you through the process step by step, considering essential factors such as IT outsourcing, IT staffing, data security consulting, and more. Introduction In the dynamic world of IT, staying ahead of the curve is crucial for success. This often means embracing emerging technologies, adapting to market trends, and expanding your team’s capabilities. Sometimes, the solution isn’t hiring more in-house staff but rather partnering with a staff augmentation service provider. In this guide, we’ll explore how to choose the right staff augmentation partner to meet your company’s specific needs. Whether you’re seeking IT outsourcing, data security consulting, AI staff augmentation, or simply additional IT talent, we’ll help you navigate the process. Understanding IT Staff Augmentation Staff augmentation involves extending your team by hiring temporary personnel or experts for specific projects or to fill skill gaps. The services offered can span various areas, such as IT staff augmentation, data engineering staff augmentation, and AI staff augmentation. It’s a flexible solution that can be tailored to your unique requirements. Why Consider Data Engineering Staff Augmentation? Before we dive into the steps of choosing the right staff augmentation partner, let’s consider why it might be the ideal solution for your business: Cost-Effective Staff augmentation eliminates the overhead costs associated with hiring full-time employees, like benefits and office space. Faster Recruitment The process of finding and hiring freelance developers, contract developers, or IT experts is typically quicker than traditional hiring. Access to Specialized Skills Staff augmentation enables you to tap into niche skill sets that might not be readily available in your local talent pool. Scalability As your projects evolve, you can easily scale your remote team up or down based on your current needs. Reduced Risk By partnering with experts in the field, you reduce the risk of poor performance or skill gaps. A Step-by-Step Guide to Choosing the Right Staff Augmentation Partner Now that you understand the benefits, let’s walk through the steps of choosing the right staff augmentation partner for your organization. Step 1: Define Your Needs The first and foremost step in choosing the right staff augmentation partner is understanding your specific needs. Ask yourself the following questions: By clearly defining your needs, you set the stage for a successful partnership. This step is vital to ensure that you find a partner who can meet your expectations. Step 2: Research and Shortlist Once you’ve determined your requirements, start researching potential staff augmentation partners. Explore options ranging from outsourcing partners to dedicated teams, and remote teams to find the right fit. Make use of online resources, professional networks, and recommendations to create a shortlist of prospective companies. Step 3: Evaluate Experience and Expertise As you assess potential partners, pay close attention to their experience and expertise in the specific area you need assistance with. If you’re looking for AI staff augmentation, for instance, you want to collaborate with a partner that has a proven track record in artificial intelligence. Check their portfolio and projects to ensure they have the right skills and knowledge necessary to meet your requirements. Step 4: Check References and Reviews One of the best ways to gauge a partner’s capabilities is by talking to their previous clients. Ask for references and seek out online reviews and testimonials. This will provide valuable insights into the partner’s reliability, communication, and overall performance. It is advised to check with previous clients about their experiences working with the partner, the quality of delivered work, and their satisfaction levels. Step 5: Assess Data Security For many organizations, data security is of paramount importance. If your project involves sensitive data, consider data security consulting as part of your staff augmentation services. Ensure that your partner has robust security measures in place to protect your information. Request about their data security protocols, encryption methods, and compliance with industry standards to guarantee the safety and confidentiality of data. Step 6: Quality of Communication Clear and effective communication is a cornerstone of successful staff augmentation. Assess how well the partner communicates with you during the selection process. Open and transparent communication is crucial for a productive partnership. Step 7: Flexible Engagement Models Consider the engagement models offered by your potential partners. They should be willing to adapt to your preferred working style and align with your company’s culture. Step 8: Pricing and Budget Determine the pricing structure and ensure it aligns with your budget. Keep in mind that the cheapest option might not always be the best. Focus on the value you receive for your investment. Step 9: Legal and Compliance Verify that the staff augmentation partner complies with all relevant laws and regulations, especially if you’re considering international collaborations. Step 10: Final Selection After thorough research and evaluation, make your final selection. Ensure that the partner you choose aligns with your project’s needs, offers the necessary IT staffing or other services, and is a good fit for your organization. Conclusion Choosing the right staff augmentation partner is a critical decision for your business. Whether you require IT outsourcing, data security consulting, or a dedicated remote team, following the steps outlined in this guide will lead you to the best staff augmentation partner for your needs. Don’t rush the decision-making process, take your time to define your needs, conduct thorough research, and evaluate potential partners. Ultimately,