Page 30 – DataToBiz

Yashica Chopra
June 03, 2022
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Top 10 Machine Learning Companies to Watch Out (Updated for 2024)

Machine learning models play a vital role in digitally transforming a business. SMBs, SMEs, and large enterprises hire offshore ML companies to adopt the data-driven models and revamp their existing systems. Let’s take a look at the top machine learning companies in 2024 and their offerings. Machine learning is a field of artificial intelligence that helps machines learn to analyze data the way humans do. The algorithm teaches machines to process huge datasets and trace patterns to derive insights. The accuracy of a machine learning algorithm depends on the data fed to train the system. While AI is a technology that makes machines smarter, ML is a concept that applies AI in practical and real-time scenarios. According to GlobeNewswire, the global machine learning market was valued at 15.44 billion in 2021. It is expected to reach $209.91 billion by 2029 at a CAGR (Compound Annual Growth Rate) of 38.8%. Enterprises and organizations from various industries are increasing their budget allocations to adopt machine learning tools and digitally transform their systems. Statistics show that there is a 25% increase in the budget, especially in the banking, manufacturing, and IT industries. Half the companies are forming internal data science teams to build ML models, while the remaining are relying on offshore machine learning companies to revamp their business systems and operations. The market has various machine learning and AI companies helping SMBs, MSMEs, and large enterprises adopt machine learning models and gain a competitive edge over others in the market. So, which company is leading in machine learning? What are the top machine learning companies in the industry? Before you consider any of the companies from the curated list, let’s take a look at the values that ML provides. Advantages of Machine Learning Top Machine Learning Companies 1. DataToBiz DataToBiz has a tagline of ‘Simplifying the Complex’, and true to the word, the company offers a range of AI and ML solutions to businesses for multiple industries be manufacturing, retail, real estate, or fintech. DataToBiz works with small, mid-sized, and large enterprises, organizations, and establishments to adopt data engineering and AI-based automation solutions. Being continuously listed as the Top Artificial Intelligence company In India by Clutch, the company is a certified partner of Google Cloud, Microsoft Azure, and AWS. It helps businesses overcome their challenges by adopting data-driven models. DataToBiz offers end-to-end solutions to help a business achieve success through advanced technologies like machine learning, artificial intelligence, and data science. With newly added AI Product development solutions and startup AI prototyping, the company’s solutions are flexible, scalable, and cost-effective. The team has years of experience and a satisfaction rate of more than 97%. DataToBiz is one of the most promising AI companies in today’s times. 2. InData Labs InData Labs offers AI and big data consulting services to businesses from industries like Fintech, eCommerce, sports, entertainment, and digital health. The company has its own R&D center and helps enterprises scale their systems to expand into newer markets. InData Labs build customized AI solutions from scratch and fine-tunes existing products to suit the business requirements. CV and OCR are the core competencies of the company. It also offers services to use predictive analytics and natural language processing tools to streamline business intelligence in an enterprise. InData Labs works with establishments of all sizes to help them adopt the latest technologies in their business. 3. Itransition Itransition is a global software development company with over 20 years of experience. It delivers a full spectrum of artificial intelligence and machine learning consulting and development services, handling projects of any scale for SMEs and Fortune 500 companies from various sectors, including healthcare, finance, retail, automotive, telecommunications, and hi-tech. Itransition’s machine learning experts help companies deploy ML algorithms trained with vast amounts of corporate data to optimize and automate multiple business processes. The company’s extensive expertise in the AI/ML domain includes solutions powered by data mining, computer vision, and natural language processing aimed at intelligent business automation, smart recommendations and customer insights, and fraud and anomaly detection, among others. 4. Amazon Web Service Amazon Web Services (AWS) has been offering cloud computing services for more than a decade and a half. As a subsidiary of Amazon, it partners with other machine learning companies to offer cloud solutions in the global market. Amazon has a machine learning services center where they develop ML tools in the SageMaker line. Building datasets, automating training models, and augmenting predictions are some services offered by AWS. Netflix, Tinder, Yelp, Pinterest, etc., are a few noteworthy clients who use Amazon’s services to develop and integrate machine learning models into their systems. The transparent pricing makes it easy for businesses to choose the services they require. However, not keeping track of the usage might lead to additional expenditure. Actionable Advice for Data-Driven Leaders Struggling to reap the right kind of insights from your business data? Get expert tips, latest trends, insights, case studies, recommendations and more in your inbox. 5. MobiDev MobiDev offers custom software development services to clients from twenty countries around the world. Machine learning, data science, augmented reality, web development, etc., are some services provided by the large team of engineers and data scientists at MobiDev. The company has helped launch more than 300 software products into the market. It works with startups and large enterprises alike. MobiDev believes that a strong team can develop game-changing products and help businesses grow. The team focuses on minor details to ensure that there are no glitches or unexpected hurdles that cause trouble for enterprises and slow down their expansion. MobiDev offers full-cycle development services for the frontend and backend. It works on desktop, web, and mobile applications to assist clients. 6. Netguru Netguru is a custom software provider and digital consultancy company in the European Union. The company builds digital products for businesses that are involved in niches like languages, rental, and banking. The company’s main aim is to develop software products that help a business grow and make the world a better

Vikas Ojha
May 26, 2022
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How to Create Automated Information Extraction System for Document Images Using Graph Convolutional Networks

As everything is getting digital the demand for machine-based document digitization is growing higher than ever. Every organization wants its documents to be digitized as digital documents are easy to search. Maintaining hard copies of documents is both an expensive and tedious process. Also, these documents become unreadable after some years due to the fact that their papers get too old. The hard copy documents could be easily destroyed by criminals, natural disasters, etc in certain situations. All these facts make document digitization an inevitable thing to do. But digitizing documents manually can be both a very expensive and time taking process. So how can we digitize documents fast and cheaply? That’s where the deep learning systems come into place. The document pages can be captured using cameras and those images can be fed into a deep learning system to recognize texts and extract information from the images. After extracting information, it can be used to fill a predefined template for a particular document type and can be stored in the local system or in the cloud. Rule-Based Approach Our first attempt at the problem was very basic. We used an OCR system to perform text recognition of the document images. Once the text was extracted we employed certain hand-crafted rules based on regex to extract the information out of the documents. We know that the text in documents can have certain patterns like dates could be in formats like dd-mm-yyyy or yyyy-mm-dd, etc. The addresses can belong to texts separated by commas, etc. But what were the limitations of the first approach? Second Approach Having realized the limitations of the rule-based approach we decided to research a better approach. After doing some research we realized that the problem of information extraction can be represented in the form of graphs. The intuition around using graphs to solve the information extraction problem comes from the way in which we humans identify important information from documents. If you are given an identity card and you are told to identify the name of the person, DOB and address then you might look for a key-value pair in the card or you might be able to know about the information by the location of the text in the image. For example, the top most text in the card may be the organization for which the card is issued. Graph Convolutional Neural Network Exploring certain literature in the computer vision domain we found out GCN is a type of network that combines the visual and text information to create graphs. Then it performs classification on the graph nodes to identify the category of that text hence extracting the information from the document. The above diagram gives up a rough idea of our system. The whole work can be broken down into the below steps: Performing OCR: First, we perform OCR on the document image and extract the texts and corresponding bounding boxes from the image. Feature Extraction: The textual information is passed to the transformer module which converts the textual information into feature vectors. The bounding boxes obtained in the previous step are used to crop the image regions containing those texts. These cropped images are passed through the Convolutional neural network and features are extracted. Graph Convolution: Now we have textual features and visual features from transformer and CNN respectively. These features textual and visual are passed to the Graph Neural Network. The GNN component models the texts as nodes. The relationship between these nodes is established with the help of the visual feature obtained by the convolutional neural network. Once these nodes are established by the GNN. These nodes can be processed further and classified into labels. Node Classification: The BiLSTM layer and CRF layer follows the GNN layer which takes the graph feature and classified the nodes into their label e.g. Name, Company, etc. Say we want to extract the name of the organization from an ID Card then we would train our model to classify the text containing the name as the rest of the text. Challenges Like any other challenges we also faced certain challenges to create this project. Low Availability of Data One of the biggest problems with creating this type of project was getting a good amount of data to perform our experiments. We did an intensive amount of search but we couldn’t find any good dataset. In fact, even finding images of ID Cards in a good amount wasn’t possible. Hence we decided to create our own dataset with a mix of natural and synthetic images. We created certain tools to generate synthetic images of cards. We also created a tool to perform automatic annotation of these images. Soon after a few days, we had a good amount of data for doing experimentation. Model Size and Computational Requirement The model that we used for experimentation was based on Wenwen Yu et al. The model was too heavy to be deployed. Hence we needed to modify the architecture of the neural networks involved to make the model small and less computation demanding. We used the intuition that the textual features are less important than visual features for doing node classification and we modified the transformer and CNN blocks. We also modified the GNN model. After doing certain experimentations we were able to figure out the architecture that is both small and accurate. Results We tested our model on around 100 images of the cards. The metric that we used for evaluation was MEF(Mean Entity F1 score). The MEF of our model on test data was approximately 99.17 which seems to be pretty good. Conclusion In this article, we learned how Graph Convolutional Networks can be used to extract information from document images and can help us in the digitization of documents. Proper implementation of the approach can yield a pretty robust and accurate system which can save a lot of time and money for an organization in the digitization of documents.

Yashica Chopra
May 19, 2022
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Computer Vision in Manufacturing Industry: Explained With Use Cases

Computer vision in manufacturing is streamlining the use of visual data to make faster and better decisions. Factories become safer, and the production quality increases when manufacturers use insights shared by computer vision systems. We’ll discuss the use of CV in the manufacturing industry in detail. The manufacturing industry is adopting the latest technology to modernize processes and increase production. Artificial intelligence, cloud computing, machine learning, the Internet of Things (IoT), Industry 4.0, and computer vision are now a part of the manufacturing industry. Computer vision is used at every stage of manufacturing, right from procuring raw materials to supplying and distributing the finished goods. It has a vital role in bringing flexibility and scalability to the manufacturing unit. It helps increase production while sustaining quality and minimizing the use of resources. Manufacturers hire AI developers and offshore solution providers to digitally transform their infrastructure and integrate it with the latest technology. What is Computer Vision? Computer vision is a field of artificial intelligence and computer science that empowers devices to read, understand, and analyze visual data as humans do. While AI makes machines think, computer vision makes machines see and understand visual information at the pixel level. Images, videos, and all forms of visual inputs can be read and processed through computer vision. The global computer vision market in 2020 was $11.32 billion and is expected to grow at a CAGR (Compound Annual Growth Rate) of 7.3% by 2028. The industrial segment was responsible for around 51% of the market revenue. Computer vision in a range of manufacturing processes contributed more than half the total global share. The Asia Pacific region had a high adoption rate and contributed 40% of the revenue in the same year. But how is computer vision used in the manufacturing industry? With more and more manufacturers planning to adopt computer vision and Industry 4.0, how do they intend to change their systems and processes? Let’s take a look. Use Cases For Computer Vision In Manufacturing Computer vision doesn’t require any physical contact with the machinery. It works simultaneously to process the input data from multiple sources and provide insights to the workers, supervisors, and managers. Product and Component Assembly Computer vision is used to automate the product assembly process. Computer-aided software is used to create 3D designs fed into the system to assemble and manufacture the products. The need for human intervention is minimized by allowing computer vision to assist the machinery in manufacturing the products with greater precision. Computer vision systems work continuously to monitor the assembly line so that defects are identified in the initial stages. This reduces the returns or rejected products and increases customer satisfaction. Every stage of the assembly process is monitored and tracked. The data is stored in the data warehouse or data lake. For example, Telsa manufacturing has automated almost 70% of the manufacturing processes using computer vision. The pharma industry uses computer vision to ensure that the quality of the product is consistent and adheres to industry-wide standards. Predictive Maintenance Predictive maintenance can save millions of dollars for the manufacturing industry. Machinery is the heart of a manufacturing unit. Even a minor breakdown can disrupt the entire process and cause a series of delays that lead to additional expenses. The supply chain will be disturbed when there’s an unexpected halt in production. Computer vision minimizes such disruptions and losses by constantly assessing the health and working condition of the machinery. Heavy machinery runs for long hours in unusual temperatures and environments. They are prone to breakdowns without proper maintenance and care. However, instead of being reactive and repairing the machinery after it breaks down, computer vision uses its algorithm to determine the best time to perform a maintenance check and repair the minor issues. This prevents the trouble from blowing up and causing a full-scale disruption. Furthermore, minor repairs take less time and money than major spare part replacements. 3D Vision to Identify Defects Defects are a bane of the manufacturing industry. The product assembly can’t manufacture with complete accuracy. However, the extent and percentage of defects can be a game-changer for the manufacturer. A computer vision algorithm is trained to identify defects at the micro and macro level. Instead of waiting until the quality testing/ inspection, the products are checked for defects right during the manufacturing process. Even the slightest difference is highlighted so that workers can remove the defective product. Corrective measures can be taken by easily identifying the cause of the defect. Data from the cameras and processed by the computer vision systems and compared with the database to test for quality and defects. This minimizes the loss of money and reputation by preventing defective products from reaching the end-user. Fine-Tune Die-Cutting Molds and dies are an inherent part of the manufacturing industry. Manufacturers spend large amounts of money creating dies to give the products the desired shape and structure. Laser and rotary are two famous methods used to create dies. While high-speed laser light provides precise dies, it cannot cut through tough materials. The rotary technique uses steel blades and physical tools to cut through hard material, but it can be less precise and time-consuming. Computer vision can fine-tune the rotary die-cutting method to make it as precise and fast as the laser technique. In short, computer vision gives the best of rotary and laser methods to increase accuracy and precision in the dies. Since these dies are used to manufacture spare parts and products, the end result will be of better quality. Offshore Artificial Intelligence software development companies can help manufacturers use a computer vision system to guide the die-cutting machine in the right manner. Inspect Packages Packaging is the final stage after manufacturing the product. The package should be strong and capable enough to protect the finished good from damage caused during the shipping and distribution. The package should also ensure that the product inside doesn’t get affected by the changes in the surrounding environment. Industries like pharma, food and beverage, cosmetics, etc., pay extra

Rafiuddin Khan
May 13, 2022
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Importance of Datasets in Machine Learning and AI Research

Most of us nowadays are focused on building machine learning models and solving problems with the existing datasets. But we need to first understand what a dataset is, its importance, and its role in building robust machine learning solutions. Today we have an abundance of open-source datasets to do research on or build an application to solve real-world problems in many fields. However, the lack of quality and quantitative datasets are a cause of concern. Data has grown tremendously and will continue to grow at a higher pace in the future. So, how do we use the huge volumes of data in AI research? Here we will discuss ways to smartly leverage the existing dataset or generate the right datasets for the given requirements. What is a Dataset in Machine Learning? Dataset is a collection of various types of data stored in a digital format. Data is the key component of any Machine Learning project. Datasets primarily consist of images, texts, audio, videos, numerical data points, etc., for solving various Artificial Intelligence challenges such as Why is Dataset Important? We can not have an Artificial Intelligence system with data. Deep Learning models are data-hungry and require a lot of data to create the best model or a system with high fidelity. The quality of data is as important as the quantity even if you have implemented great algorithms for machine learning models. The following quote best explains the working of a machine learning model. Garbage In Garbage Out (GIGO): If we feed low-quality data to ML Model it will deliver a similar result. According to The State of Data Science 2020 report, data preparation and understanding is one of the most important and time-consuming tasks of the Machine Learning project lifecycle. Survey shows that most Data Scientists and AI developers spend nearly 70% of their time analyzing datasets. The remaining time is spent on other processes such as model selection, training, testing, and deployment. Limitation of Datasets Finding a quality dataset is a fundamental requirement to build the foundation of any real-world AI application. However, the real-world datasets are complex, messier, and unstructured. The performance of any Machine Learning or Deep Learning model depends on the quantity, quality, and relevancy of the dataset. It’s not an easy task to find the right balance. We are privileged to have a large corpus of open-source datasets in the last decade which has motivated the AI community and researchers to do state-of-the-art research and work on AI-enabled products. Despite the abundance of datasets, it is always a challenge to solve a new problem statement. The following are the prominent challenges of datasets that limit data scientists from building better AI applications. How to Build Datasets for Your Machine Learning Projects? An Artificial Intelligence application flow is depicted in the diagram below. The first two components are the dataset acquisitions & data annotation section which are crucial to understanding for building a good Machine Learning application. Nowadays, we have ample resources where we can get datasets on the internet either open-source or paid. As you know data collection and preparation is the crux of any Machine Learning project, and most of our precious time is spent on this phase. To solve the problem statements using Machine Learning, we have two choices. Either we use the existing datasets or create a new one. For a highly specific problem statement, you have to create a dataset for a domain, clean it, visualize it, and understand the relevance to get the result. However, if the problem statement is common, you can use the following dataset platforms for research and gather data that best suits your requirements. Actionable Advice for Data-Driven Leaders Struggling to reap the right kind of insights from your business data? Get expert tips, latest trends, insights, case studies, recommendations and more in your inbox. Best Dataset Search Engine Platforms for a Machine Learning Challenge Below is the list of a few dataset platforms, that allow us to search and download data for Machine Learning projects and experiments. Most of the datasets are already cleaned and segregated for ML and AI project pipeline. However, we have to filter and utilize them according to our specifications. Custom Dataset can be created by collecting multiple datasets. For example, if we want to build an app to detect kitchen equipment, we need to collect and label images of relevant kitchen equipment. For labeling the images, we can run a campaign to collect data by encouraging users to submit or label images on a platform. They can be paid or rewarded for the task. Here are a few options that can be used to get data quickly for your requirements. A synthetic dataset is created using computer algorithms that mimic real-world datasets. This type of dataset has shown promising results in the experiments conducted to build Deep Learning models to create more generalized AI systems. Different techniques can be leveraged to generate a dataset. Nowadays, researchers and developers utilize game technology to render realistic scenarios. Game framework unity is used to create datasets of particular interest and then used in the production of real-world data. Unity report shows that the synthesized dataset can be used to improve models’ performance. For instance, computer vision models use synthetic images to iterate fast experiments and enhance accuracy. Generative Adversarial Networks (GANs) are also used to create synthetic datasets. These are neural network-based model architectures used for generating realistic datasets. Most use case requires data privacy and confidentiality. Hence, these networks are utilized to generate a sensitive dataset that is hard to acquire or collect from public sources. Data Augmentation is widely used by altering the existing dataset with minor changes to its pixels or orientations. It’s helpful when we are out of data to feed our Neural Network. However, we cannot apply the augmentation technique to every use case as it may alter the real result output. For instance, in the medical domain dataset, we cannot augment more data from the

Drishti Patel
May 09, 2022
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Role Of Artificial Intelligence In Web Development

Artificial Intelligence (AI) has evolved into a promising field in recent years, and now it’s an indispensable part of the web development world. Web development is an industry where user experience is everything. Any challenges regarding customized user experience can be easily solved using AI. The end question still remains, is hiring an AI/ML development company worth your web development needs? Around 38% of people will stop engaging with a website if the content or layout is unattractive. It means that you only get 0.5 seconds to make an impression on your visitor. Also, 90% of users leave a website because it is poorly designed. These are just a few stats that emphasizes the importance of having a website that has got everything from layout & design to loading speed right. AI/ML can make it much easier for web developers to deliver high-quality results faster. By using AI, you can improve the user experience of your website with chatbots, web design, marketing strategy, etc. How Artificial Intelligence Is Used In Web Development 1. For Faster Coding Coding is considered a problematic or challenging task, which it is. With AI, coding is way quicker and manageable. Artificial Intelligence in web development automates multiple tasks that help developers to create code more efficiently. Automated coding has massively cut down processing times and has allowed developers to build more intuitive apps in a short timeframe. 2. For More Personalized User Experience (UX) The whole fuss about getting everything right is about this one thing, providing a personalized and best user experience to all the visitors. AI-powered websites offer a personalized experience to all visitors, making the experience better for them and encouraging participation. 3. In Chatbots For 24*7 Support & Better Reply Predictions Not all businesses can afford to have support staff that work round the clock. Instead, you can hire a machine learning engineer to develop a chatbot for your website. Chatbots provide faster customer service and help manage customer requests better. On average, chatbots have increased sales by 67% for the companies who use them on their website. This is because chatbots make the customer experience better for every customer. 4. For Better Voice Search Optimization 58% of consumers are using voice search features to find information on local businesses. For a business website, voice search adaptability is an essential accessory if you want to gather more traffic and provide an easier way for your customers to find you. Virtual assistants such as Google Home, Siri, Cortana, and Alexa use AI to recognize and react to voice commands. Many people prefer to use voice search instead of typing their searches as this is a more convenient way. The Future of Artificial Intelligence In Web Development Even with all these benefits of AI in web development, in the end, it’s a machine. And a machine works best with human intervention every now and then. With AI, web developers can create better websites in terms of design and performance in a shorter period of time. But on the other hand, AI can provide better results with human intervention wherever possible. Creativity is something that only a human mind can have. This is the main reason why human intervention is necessary with AI to generate the best results, especially in a creative field like web development. Final Thoughts Artificial Intelligence and machine learning have evolved a lot since their initial discovery, but there’s still huge potential yet to be discovered. With the increase in the demand for AI in several aspects of human society, we will further see even more major developments and progress in this field. It still needs human intervention on a large scale to produce the best results, but it may be possible in the near future that AI alone will be enough. This technology will provide benefits to both businesses and their customers due to its fast learning abilities and speedy results. Would you use AI to help your web development team deliver better results?

Yashica Chopra
May 06, 2022
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Data Analytics in FMCG: Its Meaning, Importance & Use Cases

Data analytics help the FMCG industry to reduce costs, streamline production and supply chain, and take the products to the right target market at the right time. It provides companies with the necessary insights to adopt the data-driven model for decision-making. Let’s understand the effective role of data analytics in the FMCG sector. Data analytics is the practice of analyzing datasets to derive meaningful insights. It answers questions, gives patterns, and helps in making decisions based on proper evidence/ context. During the last few years, many establishments from various industries have adopted new technology to process data and derive insights. The FMCG (Fast Moving Consumer Goods) industry too followed suit to streamline its manufacturing, supplying, and customer care services. The FMCG industry is also known as Consumer Packed Goods (CPG) industry and happens to be the largest market internationally. The MENA (Middle East and North Africa) region saw high demand for consumer goods before the Covid-19 pandemic pushed things into turmoil. Statistics show that Saudi Arabia has a total retail consumer spending of $271919.41 million. Though there has been a 64% decrease in customers who went to supermarkets, the demand for goods hasn’t gone down. The market shifted from brick-and-mortar stores to online retailing. Moreover, the consumer trends in GCC (Gulf Corporation Council) have begun to resemble the trends in the US.There has been a polarization of consumers based on price, branding, quality, and convenience. In fact, the changes started after 2015, when major developments took place in the GCC, be it the introduction of VAT or an increase in working hours. A research report shows that 69% of UAE and 63% of Saudi Arabia consumers prefer to pay more if a brand guarantees quality products. Furthermore, modern trade is continuing to become famous in the region. In such instances, an FMCG manufacturer/seller needs to understand the market/ consumer requirements to become successful and gain a competitive edge over other players. This is possible when the establishment makes use of big data analytics to gather accurate and meaningful insights for decision-making. The Significance of Big Data Analytics in FMCG Data analytics in FMCG companies helps improve the scalability, flexibility, and value creation of the business. It is a dynamic industry that requires an equally dynamic approach to stay ahead of the trends and provide consumers with valuable services. For example, the omnichannel shopping experience can be best delivered when the enterprise leverages big data analytics. Running data analytics requires making changes to the existing business systems. Outdated technologies and legacy systems cannot work efficiently with advanced analytical tools. Businesses should first work on digital transformation and invest in data management solutions to build a streamlined data flow within the establishment. Knowing which tools and technology to use is vital to successfully implementing the data-driven model in the business. That’s where offshore solution providers step into the picture and offer end-to-end services to set up, integrate, implement, and manage big data analytical tools. The main purpose of using data analytics in the FMCG industry is to increase sales and profits by providing consumers with what they want. This requires flexibility, adaptability, and agility to align and realign the goals with the market demands. Understanding customers and working on real-time customer engagement will lead to an increase in customer loyalty. Why Data Analytics Is Important in FMCG Be it inventory, supply chain, or consumer experience, data analytics can help the FMCG industry use predictive intelligence in real-time to make the right decisions. Here, we’ll focus solely on how big data analytics companies assist businesses in dealing with consumer preferences and understanding the target market. Analyzing Target Customers There’s no denying the pressure of a competitive market. It’s hard to have loyal customers in the FMCG industry with new brands, products, and companies entering the market every day. Data analytics help you to understand your target audience. Who are your target customers? What do you know about them? What are the demographics? Such questions can be answered using data analytics. It helps identify potential customers for a long-term relationship with the business. Better Targeting Marketing and promotions play a significant role in today’s world. Businesses need to grab customers’ attention with marketing and wow them with their products. For that, the business should know which type of marketing and advertising strategies will deliver results. For example, having a business website and optimizing it for search engines has become compulsory to reach internet users. TV ads work for brands whose target customers spend more time watching the telly. Data analytics will give the necessary insights to plan marketing campaigns. Reduce the Risk of Fraud If attracting customers is one aspect, identifying fraudsters is another crucial issue. How does a business know that the person is a genuine customer and not a fraudster? The advent of online retailing and eCommerce has increased the rate of fraudulent transactions. Many businesses face losses due to a lack of awareness and control over such incidents. Data analytics can help define the weak spots and analyze a customer to determine the chances of a fraudulent transaction. Such transactions can be blocked, rejected, or prevented to avoid losses. Anticipate Customer Needs One way to inspire consumer loyalty is by anticipating their needs and delivering the products/ services. If a customer wants to go digital and make online purchases, an eCommerce website will help keep the customer instead of losing them to a competitor who offers this service. Data analytics in FMGC uses data from multiple sources and processes this data in real-time to anticipate what customer wants through predictive analytics. Plan Innovations What kind of products will the customers buy? What changes does a brand need to make to the existing products to drive sales? How can you redesign the store to make customers spend more time because of the attractive atmosphere and not because they can’t find what they want? Data analytics will help the business read the market trends accurately and be ready to grab opportunities before the competitors. Moreover,

Sunaina Agarwal
May 02, 2022
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How Can AI Transform Wealth Management?

A Gartner report suggests that by the end of 2024, nearly three-quarters of companies will shift from pilot to operational AI. Artificial Intelligence has been changing the dynamics of different industries. And the finance industry is no exception here. AI is also fast becoming an integral part of wealth and asset management companies. Financial advisors or wealth managers have been providing effective investment solutions to the wealthiest individuals and businesses for ages now. Yet, according to eMarketer: 37% of clients worldwide prefer their fund managers to choose hybrid advisory engagement for leveraging automation in wealth management. Thus, managers are adopting artificial intelligence in wealth management for advanced projects. It also helps them provide enhanced customer experience, investment decisions, and risk management. How Does AI Help in Asset Management? Several financial institutions and wealth managers are discussing AI and its soaring benefits. Yet, a lot are reluctant to invest in AI for asset management. As asset means large holdings, it also means significant responsibility. This is where AI asset management can support managers in many facets. Automating Task Artificial intelligence is becoming a valuable addition to wealth management. It can help identify trends in market performance and suggest portfolio composition. Furthermore, AI can help automate tasks such as investment research and portfolio management. This analysis helps to reduce costs for clients. It also increases the accuracy and timeliness of the advice provided. AI asset management can result in tremendous innovations which would not happen otherwise. It can also encourage tech-savvy strategies for managers to deliver value. Thus, wealth managers can make a client’s financial journey more successful. Improved Decision Making Wealth management is a complex and challenging task. It requires the manager’s ability to make sound judgments about financial investments. This is where AI comes to their rescue. It offers immense potential to automate and streamline processes for advisors. It also supports them with insights that may not be able to detect. For example, an AI-powered platform can scan large data sets and identify trends. These custom-made platforms are particularly designed by AI companies to provide greater efficiency and accuracy in investment decisions. Thus, improving decision-making. Identifying Risk The use of artificial intelligence is gaining much popularity. This is because AI in investment management can identify risk in a portfolio. Also, it helps in offering suggestions for improvement. With this, advisors can ensure that a client’s money is being managed responsibly. And also avoid putting them at risk for financial instability or loss. It also provides guidance on best practices for saving and effective investment strategy. Data Insights AI can support managers identify integral data points. These include account balances and transactions. Further, advisors can manage their client’s data, identify patterns, and predict future behavior. This allows advisors to provide better service and recommendations. Also, make better investment decisions and stay ahead of the competition. It also helps advisors find new customer segments and identify fraudsters. Can AI Replace Fund Managers? There is no doubt that the incorporation of AI is revolutionary and it accelerates the overall functioning of asset management. It also makes data processing and investment strategizing proficient. However, a complete replacement of manual labor with machine learning might not be the future. The reason is, that artificial intelligence needs to undergo certain scrutiny and security assessment to make these solutions reliable. We also need businesses to develop a sturdy infrastructure that welcomes innovations for optimum usage. Regardless of AI’s advantages like higher efficiency, and reduced speed, complete replacement wouldn’t take place. Where and How to Adopt AI in Wealth Management? If you want to get started with artificial intelligence in wealth management, there are a few things that every manager must analyze before starting. Hence, by automating tasks and providing personalized recommendations, AI can support managers in providing efficient portfolio management with wiser recommendations. Advisors who embrace AI will be in a competitive position and offer quality services.

Pawan Chabra
April 29, 2022
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How Computer Vision is Transforming the Global Transportation Industry?

Technology has played a critical role in transforming the way individuals interact with each other over the past few decades. The changes may have started with communication, the technological advancements have quickly moved into other areas like manufacturing, after-sales service, and logistics, among others. For instance, mobility is one of the most important pillars that contribute to economic activity in any economy. Modern technological solutions combined with the power of artificial intelligence and machine learning are transforming the global transportation industry. One of the critical components of this tech machine is computer vision. Experts believe that computer vision in transportation is a necessity that has multi-pronged benefits and applications across many use cases. The level of success of computer vision is increasingly playing a big role in changing the status quo in the transportation industry. What is Computer Vision? Computer vision is a part of artificial intelligence solutions that helps computers to derive actionable meaningful information from various input sources like images, videos, etc. In other words, if AI systems allow computers to think like humans, computer vision enables computers to see, observe and understand visuals like humans. The benefits of computer vision capabilities are not only limited to the transportation industry. Computer vision systems are already playing a big role in transforming many other functions like healthcare, manufacturing, agriculture, retail, etc. Benefits of Computer Vision Computer vision can help in a variety of ways. Some important benefits of computer vision are as follows: Computer Vision in Transportation Industry Computer vision or vision transport tracking is critical to the future of the transportation industry mainly because of its multi-use-cases across areas like self-driving cars, traffic management, parking management, road condition monitoring, and more. For instance, according to a CDC report, around 1.35 million people across the world are killed on roadways each year. In fact, crash injuries are the 8th leading cause of death globally. Computer vision is at the center of driving intelligent transportation systems across markets. Computer vision is playing a critical role in the following areas in the transportation industry: Self-Driving Cars We are living in an era where self-driving driving or autonomous vehicles are a reality. As companies are working on improving the capability, reliability, and safety of self-driving cars, computer vision is driving this change from the front. As vision transportation tracking helps in identifying and classifying static and moving objects, computer vision has played an integral role in making self-driving cars a reality. Traffic Management The advancement in the field of computer vision has paved the way for efficient traffic management and flow analysis. As computer vision helps in providing accurate information like traffic density, freeway traffic count, etc., it results in better traffic management and improved road safety. Computer vision is also expected to play an integral role in futuristic public transportation going forward. Parking Management Computer vision is widely used as the solution for parking management in place of costly sensor technology that requires regular maintenance. It is expected that the parking management solution would soon be used along with the license plate recognition solution to identify which vehicle is occupying which parking spot. Road Condition Monitoring Computer vision has been found useful in monitoring road conditions. This helps in decreasing safety risks for vehicles and pedestrians and improving road maintenance efficiency. Many countries are using computer vision to trace, track and improve road conditions for improved mobility. Governments usually hire computer vision as a service for road condition monitoring. To sum up Computer vision has provided several groundbreaking innovations to the transportation industry. It is expected to play an increasingly important role in coming up with solutions that are expected to decide the roadmap for how transportation will change in the future.

Rajat Mehrotra
April 27, 2022
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Artificial Intelligence in HR: Human Resources Reinvented

A company’s success depends upon how effectively and efficiently it manages its 3Ts, time, talent, and technology. Given the plethora of jobs today, it is humanely impossible to find the right candidate at the right time. That’s where the third ‘T,’ technology comes to the rescue. Artificial intelligence is one such advanced technology that constantly changes the HR domain. From hunting to onboarding, artificial intelligence is now playing a significant role. But before we understand how AI is transforming human resource management, let’s first understand why and what is Artificial intelligence in hr or the human resources department. AI refers to the ability of machines to interpret and process information like humans. In HR, AI can not only manage the recruitment process but can also help onboard candidates smoothly. Why AI in HR? With the changing dynamics of the business environment, there is a need to manage the organizational workforce. We, as humans, have a limited capacity to do everything, and doing routine tasks might become tedious. This eventually affects the overall productivity and efficiency. That’s where the magic of Artificial Intelligence works the best. Here’s how AI and human resources go hand-in-hand: 24*7 Access With the 24*7 availability of AI Chatbots, any potential talent can easily understand the procedure and other relevant details about the company or particular topic. The use of conversational AI in human resources promotes connection and relationship building. Enhances Efficiency The repetitive task can be shifted from manual mode to automation. This shift gives the workforce more time to concentrate on additional tasks, eventually boosting the overall productivity and efficiency. Personalized Communications AI can ensure personalized communication, especially with the newly recruited talent. With AI tools like Talmundo, where the talent can break the ice through a chatbot. Analytics AI can use real-time data to identify emerging problems when employees raise similar concerns and address them before escalating. Role of Artificial Intelligence in Transforming Human Resource Management The role of artificial intelligence in HR functions is endless, as it is constantly changing the HR working dynamics. The following explains how is AI being used in HR and recruitment: Screening Out Potential Candidates As soon as any company calls for an application, a large number of applicants start forwarding their application to the respective HR. Here, scanning the resumes manually may overlook the best potential candidates, given the number of applicants. However, artificial intelligence HR software like HireLakeAI can be leveraged to manage the hiring process. From shortlisting the resume of candidates to the final round of interviews, this AI-based solution helps HR in making ideal recruiting decisions. Creating a Diverse Work Environment While scanning and shortlisting profiles, HR may unknowingly be biased based on age, gender, etc., making the recruitment process unfair. But AI is trained and programmed to filter out any kind of bias during the screening process, making the procedure fair, unbiased and equal. This emerging role of artificial intelligence in HR functions like screening and finalizing potential candidates helps build a diverse team, leading to a better and more productive work environment. Building an Interactive Candidate Experience Usually, it is pretty difficult for HR to communicate with all the candidates. This leads to a bad impression of the company. But AI chatbots are now used to build an interactive candidate experience. For example, chatbots can guide the candidate for further process, schedule interviews, etc., ensuring a smooth and interactive recruitment procedure. Smooth Onboarding Traditionally, the onboarding process was time-consuming once a candidate was recruited, as the candidate had to manage documentation. However, AI has made this process easier by digitizing and automating documents. They also help properly organize the documents and store them in zip files. Moreover, AI can also track when the hired candidate has gone through these documents and remind them to provide a digital signature. Application of AI in HR Hunting Ideal Candidates Artificial intelligence in HR can help in attracting potential candidates by analyzing the user’s search patterns and advertising companies that are open to recruiting such specific job roles. For example, LinkedIn uses simple AI to target the potential candidate. It attracts them using some recommendation algorithms, that is “related jobs for you” feature. Effective Communication Not having proper communication might lead to a stressful work environment. Therefore, a better communication process is important to have clarity on the job responsibilities. Using AI solutions, the HR department can streamline proper and productive communication in the company, efficiently comprehending texts and voice messages and detecting their tone. Moreover, this AI solution in HR can highlight the present tone so that the person communicates professionally, without any bias, and in a non-offensive tone. Data Collection There is more to a candidate than what his resume displays. Therefore, it is equally important to dive more into the candidate’s profile by digging the social media, past company reviews, etc., to look out for any scope of concern. Artificial Intelligence tools provide efficient data aggregation techniques to collect information. For example, eBay and IBM use AI-powered data aggregation to extract data from a variety of sources, store them and analyze them to conclude the work experience and market value of the potential candidate. Talent Management Hiring a candidate through resume shortlisting and interviews is just one of the many ways a company acquires a candidate. However, the candidate is more likely to join a company if he sees the potential for growth and advancement in his career by working in the company. Artificial intelligence for HR can benefit here by streamlining the process of connecting him/her to someone in the company who already has proficiency in that skill. Why Should You Opt For AI-Based HR Solutions? Enhances Productivity Managing the human workforce of the company can be a nerve-wracking task. But using AI-based solutions in HR, the HR team can easily plan out a proper workflow from start to end rather than shifting a task to a particular day. This will eventually lead to a faster process without compromising on the

Yashica Chopra
April 21, 2022
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Business Intelligence in Manufacturing: Transforming the Industry

Data-driven manufacturing is the need of the day. Digital transformation has entered the manufacturing industry and is changing the sector through data analytics. Business intelligence is the key to revamping manufacturing and improving quality, production, and returns. The manufacturing industry has been looking at business intelligence with renewed interest. Data is usually scattered in the factory and manually fed into the system; it’s also offline until it is entered into the database. Business intelligence provides a solution to bring the data together with minimum effort. It promises data collection, cleaning, and analytics to improve the manufacturing processes and increase returns. The manufacturing industry contributed 18% of the GDP in the Middle East in 2018. The countries have marked manufacturing as an integral part of their economic strategies to achieve their 2030 visions. The ME region has the necessary infrastructure and connectivity to boost the manufacturing industry. Oxford Economics said that UAE’s industrial sector grew more than a third during the 2007-17 decade. Innovations and technological adaptations have been game-changers. Artificial intelligence, business intelligence, data analytics, etc., are said to take the manufacturing industry in the Middle East to newer heights. The Gulf countries are using business intelligence services offered by offshore companies to revamp their existing systems and make the necessary changes to stay active in the competitive market. Let’s read in detail about the role of business intelligence in manufacturing and how data analytics can help manufacturing industries fulfill their role in contributing to the 2030 economic vision of the Middle East. Types of Manufacturing Processes In simple terms, manufacturing is the process of producing products/goods using raw materials and tools. Biological or chemical processing may also be used to obtain the final product. Be it the production of a tiny toy, household appliances, or an automobile, converting raw materials into finished goods by following a systematic and pre-defined procedure is known as manufacturing. However, the reality is much more complicated and involves a range of factors such as quality, cost, production cycle, duration, supply chain, and more. The path from acquiring raw materials to supplying finished goods to the market is filled with various elements that are not always under our control. Furthermore, the manufacturing processes are classified into three types: Make to Stock MTS is where the finished goods are made in large numbers and held as stock in stores. These are widely used and always in demand. That said, manufacturing too much in surplus can lead to blocked capital and losses. Make to Order MTO is where the manufacturer waits for an order to start the production process. The clients/ customers have to wait until the production cycle is complete and the end product is handed over to them. Make to Assemble MTA is a combination of MTS and MTO. The manufacturer has a stock of the parts required to assemble the final product. The assembling will be done only when an order is placed. This not only reduces the time to deliver the product to the customers but can leave the manufacturer with excess parts if there are no orders. How a manufacturer manages the processes and stock determines the success of the business. Business intelligence helps manufacturers use historical and real-time data to correctly anticipate the market demands and adjust production accordingly. What is Business Intelligence? Business intelligence is a set of procedures, techniques, and software used to convert raw data into meaningful insights. BI allows manufacturers and enterprises to increase their business efficiency by making required changes to their systems and processes. Every industry requires business intelligence to analyze data and derive actionable insights. These insights help businesses to make better decisions in a quick time and stay relevant in the competitive markets. Business intelligence is the use of data and data analytics to gather valuable information. BI for the manufacturing industry is also called manufacturing intelligence or manufacturing analytics. Business Intelligence in Manufacturing Industry The manufacturing industry needs to streamline and balance machine operations, supply chain, and logistics to get the desired results. The main aim of the manufacturing industry will be to reduce production costs without compromising the quality standards or delaying the production cycle. To achieve the goal of reducing costs and increasing production, the manufacturer needs to rely on the data-driven model to make decisions. This is possible when the data flow is aligned within the manufacturing unit and bridges the gap between data, systems, and employees. BI and data analytics give manufacturers the necessary tools and insights to use data effectively and make the right decisions for the business. How is Business Intelligence Used in Manufacturing? The use of business intelligence in manufacturing can be seen at every stage, from sourcing raw materials to supplying the final goods to end-users. Since most manufacturers already have the ERP system in place, adopting a BI and data-driven model is not too complicated. Hiring the services of a business intelligence developer will help manufacturers understand the ways to implement BI and data analytics to derive accurate insights. Improve Operational Efficiency Operational efficiency is a must for a manufacturer to survive the competition in the markets. However, it is possible only when a business knows what changes to make and which processes to continue. While data is available in abundance, using the data efficiently to gain in-depth insights into the existing operations of the manufacturer can be achieved through business intelligence. BI reduces the time taken to analyze data and derive reports, thus providing the management with the necessary information to make changes on time. Manage and Streamline Supply Chain A manufacturer works with various third-party vendors, suppliers, carriers, distributors, etc. A delay at any point in the supply chain will lead to disruptions, postponements, and losses throughout the production cycle. It could lead to product failure or put the manufacturer at a disadvantage. Business intelligence gives insights into suppliers, transporters, deliveries, expenses incurred during distribution, and so on. The reports can be used to choose reliable suppliers and freight partners and plan the production