7 Ways to Get Ahead of the Competition Using Big Data for Supply Chain

Supply chain management and logistics have always been complex. Be it sourcing raw materials or delivering finished goods and managing returns, everything comes under the supply chain. Vendors, manufacturers, warehouses, logistics service providers, etc., are all a part of the supply chain. Supply chain management (SCM) relied on ERP software and outdated data storage systems. With the traditional methods taking much time and not delivering enough results, enterprises are looking at data analytics and big data to streamline the supply chain, automate the recurring processes, and increase overall efficiency.  Data generated in the supply chain belongs to more than one enterprise (especially if the manufacturer uses third-party logistics services). This has made it rather hard to analyze a vast amount of data without using the latest technology. Big data for supply chain solves the problem.  What is Big Data Analytics? Data sets that are larger and complex than what a traditional data processing system can handle are called big data. To collect, store, and analyze such vast amounts of unstructured, semi-structured, and structured data, enterprises need to invest in advanced analytics. This use of advanced analytics on big data is called big data analytics.  In Big Data Analytics, data is collected in real-time from numerous sources in multiple formats. It has high volume, high velocity, more variety, etc., and is processed using artificial intelligence, predictive analytics, and other subsets of AI (like machine learning and natural language processing). You can also convert big data to smart data using data analytics. Many companies offer big data consulting services to help SMEs and large-scale enterprises process data and gather in-depth insights.  Importance of Big Data Analytics in Supply Chain So how does big data analytics help in supply chain management? What is the importance of investing in data analytics for SCM and logistics?  Compare and Match Data  As we mentioned earlier, data in a supply chain is produced within the enterprise and outside the enterprise. This data has to be collected, cleaned (remove duplicates, formatting, etc.), structured, and analyzed to derive insights. Both historical and real-time data need to be used to make decisions for improving the supply chain. Decisions about demand and supply, weather conditions, seasonal changes and their impact, etc., are made based on the insights gathered from big data.  Big Data and IoT  IoT (Internet of Things) makes it easy to share information among different devices connected to the network. It helps leverage data within the supply chain. And when this is combined with big data analytics, enterprises can create a network to facilitate continuous and live data exchange. Speed Up the Planning Process By integrating data across the supply chain, enterprises can use statistical models and predictive analytics to understand the coming trends in the market. This will help in planning the production, warehousing, and delivery of the finished goods. Data management services are used to process historical and real-time data and derive insights.  Sourcing Raw Materials  According to the Global CPO Survey 2016 by Deloitte, there’s no clear digital strategy for 60% of the procurement. Many SMEs can save costs by sourcing raw materials based on comprehensive data. Real-time data analytics will help enterprises in the following ways- Executing the Plans  Planning individual elements in the supply chain will not be enough. The success of a business lies in executing these plans with no errors or glitches. Big data analytics makes it possible to optimize the use of resources while increasing productivity and returns.  Delivering the Final Products  How does a manufacturer get back the return on investment and earn profits? It’s possible by delivering the finished goods to the end customer on time and keeping the customer happy. To avoid delays and uncertainties in deliveries caused by traffic, weather conditions, etc., enterprises can make the necessary changes to speed up deliveries.  Handling Returns  Regardless of how good a product is, every manufacturer will have to deal with returned goods. Reverse logistics is costly as it involves additional warehousing and transportation costs while returning the amount to the customers. Data analytics can help enterprises reduce returns by increasing data visibility and aligning the inventory and sales systems.  Customized Simulation Models  By using big data in the manufacturing industry, custom models can be designed to replicate real-life scenarios in a simulated environment. This helps enterprises try out various strategies, make changes to the model, and come up with the best solution to streamline the supply chain in the business.  Ways to Get Ahead of the Competition Using Big Data for Supply Chain  Big data analytics can tremendously help manufacturers, logistics providers, and enterprises to streamline their business operations on multiple levels. This empowers them to move ahead of their competitors and make a name for themselves in the market. In our highly competitive scenario, being equipped with the right kind of advanced technology can place a business on the top of the charts.  Improving Inventory Management  Managing inventory is one of the toughest jobs for any manufacturer or supplier. Even 3pl logistics and warehouse service providers need to be careful about how they manage the inventory in the warehouses. Big data analytics helps choose the nearest warehouse, the distribution of inventory, calculation of distribution and transportation costs, and helps track every move.  Also, by automating the process, enterprises do not have to manually check the stock each time to ensure if there is sufficient inventory to cater to customer demands. It is handled by the software, and the employees are alerted when the stock has to be moved.  Streamlining Online Retail and eCommerce  With more customers relying on online purchases, it has become important for businesses to streamline eCommerce operations and make the entire process as smooth as possible. Cloud data analytics services help retailers align the BI processes and tools to improve decision-making. Offshore companies offer cloud computing services to collect, store, clean, and analyze data to derive accurate insights. Today’s customers want their orders to be delivered in a single day. For this to work, enterprises need to have complete control over their supply chain.  Understanding Customer Behavior  The

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Top 15 Computer Vision Companies to Watch Out for in 2023

The AI revolution has changed the world drastically and its impact is felt in all the industries across the globe. It has changed the way companies operate their traditional business resulting in an immense productivity boost. Most of the organizations some way or the other have already implemented some form of AI or are at least thinking about it.  Computer vision, like human imagery, works as the perceptive part of the AI ecosystem. It enables the computers to see, observe and make out the meaning of a complex dataset that is fed into it and makes recommendations or acts appropriately based on the insights. Being the backbone of AI, it has the potential to grow exponentially. As per verified market research, in 2020 itself, it has a market share of 7.4 billion USD and has been estimated to be 144.46 billion USD by 2028. ( 45.64% of CAGR)  Here, we have compiled a list of a few companies that have significant contributions in the field of computer vision. They have established themselves in the Computer Vision domain and have already benefited multiple organizations in distinct ways.  Few areas we have considered as the measuring parameters:  Following are the top 15 computer vision companies to watch out for in 2023:- 1. Intel For the technology revolution that took place in AI, Intel is undoubtedly the market leader. Intel has a robust portfolio of computer vision products in the categories of general-purpose compute and accelerators. Intel has a product stack ready from the entire journey of prototype to production, from hardware to software. They are pioneers in open-source vision and AI software. With reference applications and sample code, orchestration, validation from the cloud service provider and an extensive set of tutorials – Intel has the complete toolkit required to accelerate computer vision for organizations. Intel has already leaped PhiSat-1 satellite by powering it through a vision processing unit. In addition, intel is also associated with Accenture for video analytics, FIFA World Cup Security enhancement, life sciences field, and many more. 2. Appen  Appen is a known name in the field of data annotation and collection services. It has made its stride by improving the AI ecosystem by enabling its customers with capabilities to swiftly deliver a huge chunk of images of high resolutions and video data about the computer vision program. Their commendable service in the field of image and video expands in the horizon of video annotation, pre-labeling the models to choose the best one, image transcription for accurate OCR training data, image annotation for different shapes and sizes, semantic segmentation for pixel-level image labeling, multiple types of point cloud annotation such as radar, sensors, LiDAR and many more. 3. DataToBiz DataToBiz, headquartered in India, has ventured into multiple domains such as Data Engineering & AI Solutions but their core strength lies in Computer Vision. With multiple industry expertise such as Telecom, Medical, Transportation, Retail Analytics, Education, HR, Securities and various other industries.  They have been serving distinct clients across the globe in different industries. A few service submodules are –   4. Clarifai  Based in the USA, Clarifai has already made its mark in visual recognition. They have garnered expertise in gathering business intelligence from images, text, and multitudes of sources to solve real-life business problems through their end-to-end AI lifecycle platform. Within a short span, they have made a commendable customer base of OpenTable, Samsung, Google, Deloitte, Red Bull, and many more. Also, they have secured 40 million USD funding for business expansion.  5. Telus International  When it comes to Intelligence Collective and having an AI platform powered by human intelligence, Telus International is one of the most preferred names in the industry. With a huge community of 1million plus linguists and annotators, they can provide data at scale in 500+ dialects and languages. From enhancing search results, expanding speech recognition to improve smart products, their AI solution is capable of harnessing human intelligence on a large scale. 6. Athena When it comes to securing the world with hidden threat detection with the alert platform, Athena is the name we look for. Elevated temperature detection to hidden gun detection, with extremely high accuracy, can stop miscreants from causing any trouble. Their clientele includes top names such as Memorial Hermann, Apple, Nodak insurance company, and many more. They have specifically designed the entire AI-based platform suitable for thermal imaging and people counting. A dedicated team of AI specialists has built this platform from scratch with proprietary neural networks backed by computer vision and deep learning. 7. Absolutdata Absolutdata has made its name in revenue management, AI and Analytics solutions.  By merging the right set of business data, AI and ML models they bring out the right set of intel recommendations for the businesses to increase their revenue significantly. With 300+ AI industry experts, they have created the NAVIK AI platform to process, analyze, monitor and recommend business-oriented insights to drive their clients’ revenue.  It is regarded as one of the top computer vision consulting firms in the business world with clientele such as Kia Motors, Adidas, Autodesk, and many more.  8. Fractal Analytics Recently being recognized as a leader in technology in the Forrester Wave™,  they have made significant contributions in areas like financial analytics, behavioral architecture, text analytics, revenue growth management, and many more. They are known for – It is renowned as one of the top computer vision technology companies in the market for customer centricity and forecasting at a large scale for business insights.  9. LeewayHertz With 10 plus years of experience and expertise, LeewayHertz is a known name in computer vision consulting firms. Their experienced team creates a video analysis toolkit for computer vision and machine vision systems. Their computer vision software is proficient in performing a huge set of tasks at a go such as real-time gesture recognition, movement analysis, face analysis, classification of images along with machine vision. They boast clientele including Disney, Pepsico, 3M, Siemens, and many more. 10. InData labs InData has been solving probing business problems and streamlining growth for organizations in

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Top 20 Artificial Intelligence Companies to Watch Out in 2025 (Updated)

Artificial intelligence is fast becoming an intrinsic part of every industry. From automobiles to manufacturing to finance, hospitality, media, and travel, AI technology is being adopted by SMEs and large enterprises. Giants such as Google, Amazon, and Facebook are investing heavily into researching AI and ML algorithms. It’s estimated that the global AI market will grow at a rate of 40.2% CAGR (Compound Annual Growth Rate) from the year 2021 to 2028. While the top names spend on research, the smaller organizations rely on offshore AI companies to embrace artificial intelligence and machine learning technology and integrate them into their business processes. Working with the right AI company can help streamline the business operations, optimize the resources, and increase returns by changing the way management and employees perform their day-to-day activities at work. We have not one or two but twenty such top artificial intelligence companies assisting MSMEs and large-scale enterprises to become a part of the AI revolution. These are the top 20 artificial intelligence companies to watch out for in 2025:- 1. Databricks  Databricks was founded in 2013 by the creators of Apache Spark™, Delta Lake, and MLflow. The company is based in San Francisco, US, with offices in different parts of the world. Databricks provides a simplified AI platform to seamlessly unify data, analytics, and business workloads. The open cloud platform has been built combining data warehouses and data lakes to help create a cost-effective and flexible solution for businesses. The company has experienced- Databricks works with enterprises from various industries and is a partner of Microsoft, Tableau, Amazon, and other solution providers. 2. DataToBiz DataToBiz was founded in 2018 with its headquarters in Chandigarh, India. The company helps unleash the potential of data and leverages AI technology in various aspects of a business. From automation to decision-making, DataToBiz works with MSMEs and multinational organizations at every stage. They have many US-based and UAE-based international MNCs as their clients. The company offers a range of solutions, starting from DataToBiz works with businesses from several industries to understand the challenges and overcome them using artificial intelligence and data science. The company has assisted various organizations to adopt and use AI solutions to help with security, surveillance, risk management, and many other services. DataToBiz has developed two popular AI-based solutions- PrepAI and SensiblyAI. PrepAI is a question generation platform designed for teachers and educators. SensiblyAI is a retail analytics solution developed for retail stores, supermarkets and shopping malls with an ability to scale for larger establishments. The company is much more than a service provider. DataToBiz is a team of problem solvers. 3. DataRobot DataRobot was established in 2012 with headquarters in Boston, US, and offices in five other countries. The company is one of the AI cloud leaders that provide unified platforms to collect, analyze, and process all data types and environments. DataRobots has clients from around the world, with many of them listed among the Fortune 50. The company offers- DataRobots provides services to handle data engineering, decision intelligence, machine learning, and MLOps. It uses a systematic approach to streamline business processes and improve the return rate. The company believes in the faster processing and scalability of AI systems. 4. Domino Data Lab  Domino Data Lab was set up in 2013 and has its headquarters in San Francisco, US. The company offers an Enterprise MLOps platform to help organizations revamp their systems and process and adopt the latest technology. From the medical industry to agriculture and automotive, Domino Data Lab has clients listed among the Fortune 100. The company specializes in- Domino Data Lab works with major IT Infrastructure and solution providers in the market. The artificial intelligence consulting firm believes in the potential of data science to help businesses become more productive and manage risk effectively. Automation and collaboration are the key capabilities of the company. 5. Automation Anywhere  Automation Anywhere was founded in 2003 in San Jose, US. The company offers an RPA cloud platform that helps digitize and analyze processes by integrating them on a single platform. Automation Anywhere is an expert in creating bots to automate the business processes and increase productivity by three times, that too at 1/5th of the original cost. Automation 360, developed by the company, happens to be the only cloud-native web platform powered by AI. The platform offers end-to-end automation using bots. The company offers- The company has clients from various countries and has been named as a 2021 Gartner® Magic Quadrant™ Leader. 6. Dataiku Dataiku was established in 2013 with its HQ in New York, US. The company believes in systemizing the use of data to achieve business goals with ease. It offers centralized solutions to design, deploy, and manage AI-based applications in the enterprise. Dataiku works to bring people and AI together and onto the same platform to deliver the expected results using advanced technology. The company provides services such as- The company was named the 2021 Gartner Peer Insights Customers’ Choice for Data Science & ML Platforms. Dataiku works with enterprises from different industries to provide artificial intelligence consulting solutions for everyday use. 7. Abnormal Security Abnormal Security was founded in 2018 and has its HQ in San Francisco, US. The company provides a comprehensive cloud security solution to help modern businesses combat email attacks. The integrated email security solution offered by the company works with cloud offices. Abnormal Security uses advanced behavioral data science and next-gen API architecture to work with different enterprise email applications such as Google Workspace, Microsoft 365, and SEGs. The cloud-native architecture of Abnormal Security can be deployed instantly and used for non-email data as well. It can be integrated with ticketing systems and detecting tools. 8. Atomwise  Atomwise was established in 2012 in San Francisco, US. The company plays a crucial role in the medical industry by helping enterprises with computer-aided drug design and drug discovery using AI and ML. The AtomNet® technology is being used by more than 250 businesses from across the globe. Atomwise is a part of 775+ collaborations to

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Scaling Up Deep Learning Model Serving Using OpenCV

According to one research, 80% of the models built by data scientists never make it to production. The reason for this is that the production environment has several constraints. It could be inference time or it could be hardware in some cases. Hence, to make the model ready for production, we need to first think about the model itself that we will use for production. For deciding the model different experiments are carried out and then a trade-off between the hardware and accuracy is compared. The accuracy term used here doesn’t only mean accuracy but includes any appropriate metric depending on the use case we are working on.  In this article, we will explore model optimization for CPU environments. Benefits of Model Optimization in Terms of Business Reduces Deployment Cost By doing model optimization, we can run our models efficiently using less memory and computational resources, which saves the cost of deploying our models in production. Model Optimization Boosts Your Earning       Model optimization reduces the latency of the model. Meaning that more requests could be served in less time. Meaning at the same deployment cost you can serve more users and get more revenue. Why Use OpenCV to Serve Our Model? Because It’s Fast and Memory-Efficient OpenCV is fast and memory-efficient. Memory consumption is often low in comparison to other frameworks when we are doing inference using OpenCV. The inference speed is also fast. Even the models trained with darknet framework run faster with OpenCV as the cv2.dnn module is optimized for inference using Intel CPUs. OpenCV is Optimized for Intel CPUs Since the OpenCV was originally designed by Intel, it is optimized for doing inference with Intel CPUs. Here in this case we will be optimizing an SSD mobile net model which has been trained on the coco dataset. Let’s Optimize Our Model We need to perform the following steps in order to optimize our model. Freezing and Optimizing the Model Freezing converts the weights in form of variables to constants so we can freeze the model and also optimize it. Fortunately, Tensorflow object detection API provides a single script for doing both things. The script is called export_tflite_ssd_graph.py. This script performs the optimizations like stripping unused and identity nodes, removing dropouts. Quantization option is also provided but that type of optimization is not suited for CPUs and they don’t support float16 operations. Although it is not true for all CPUs. For converting the model. We need to install the Tensorflow object detection API for Tensorflow 1.x and run the script with the following arguments. pipeline_config_path: This is the path to the configuration file used for training the network. trained_checkpoint_prefix: This is the path to the best checkpoint. output_directory: The path where the optimized model will be stored. The optimized model will be in protobuf(.pb) format. Let’s say our trained model checkpoints and configuration is stored at the trained_checkpoints folder then we can do the conversion using the following command. python object_detection/export_tflite_ssd_graph.py \ –pipeline_config_path trained_checkpoints/mobilenetv1.config \ –trained_checkpoint_prefix trained_checkpoints/model.ckpt \ –output_directory trained_checkpoints/optimized_model.pb Generating the pbtxt File for OpenCV Prediction In the case of TensorFlow models, the DNN module readNetFromTensorflow function expects both the protobuf(.pb) file which actually contains the weights and a configuration file which is in pbtxt format which contains the topology of the model. These configurations are called text graphs in technical terms. For writing text graphs OpenCV repository has some helper code. In this case, as we selected to use the SSD model. The script we would be using is called tf_text_graph_ssd.py. It will be different in the case of RCNN models. This script expects three arguments:- input: This is the path to the optimized model config: This is the path to the configuration file used for training the model. output: This is the path where the pbtxt file will be saved. Wow, the Script Was Great but Where to Find This Amazing Script? The script can be found as follows: Go to: https://github.com/opencv/opencv.git In this folder of repo:– samples/dnn Let’s say our optimized model resides in trained_checkpoints as well then we can generate pbtxt file using the following command: python tf_text_graph_ssd.py \ –input trained_checkpoints/optimized_model.pb \ –config trained_checkpoints/mobilenetv1.config \ –output trained_checkpoints/model_conf.pbtxt We Have Optimized Our Model. So What’s Next? Now let’s roll up the curtains and see the magic which is happening behind. Removal of Dropouts Any deep learning practitioner who has trained any neural network might be familiar with dropouts. They are implemented as layers in some deep learning frameworks like TensorFlow. The dropouts randomly turn off a certain percentage of neurons during training hence preventing the model to overfit the data. But during the inference, these are not needed and if they remain the neural network they will never be used and will still consume memory. Hence in this step, we remove those dropouts and make our model more efficient. Removal of Unused and Identity Nodes In some cases, there are some nodes in the model that never get used and they only increase the memory and computation footprint of the model and hence they have to be removed to optimize our model. There are also nodes in the model which just produce identity results and hence are redundant and can be removed. Conversion of Variables to Constants During the training time, the weights are in the form of variables. These weights are updated by backpropagation of the errors. But after training is done these weights have not to be changed hence there is no need to keep them as variables but instead, they could be converted to less memory-consuming constants. Pruning During training, some weights values approach near zero. The neurons corresponding to those weights are never fired and hence are redundant. By removing those neurons we can drastically reduce the size of the network. Quantization In quantization, we typecast the weights of the neural network to smaller data types. Like from float32 to float16 or int8. Quantization is hardware-specific i.e. some hardware support both float32 and float16 operation while others don’t. This

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AI for Law: Can AI Replace Lawyers?

How do we describe law? It’s complex, isn’t it, just like how algorithms work? There are a set of laws, regulations, and rules on one side and cases that need to be solved on the other. The lawyers and judges work within a framework using a process similar to how computer programming or machine learning algorithms work behind the scenes. Of course, the law cannot be that easily categorized into algorithms, but ML algorithms sure can be used to help implement the law and deliver justice. Before we talk about artificial intelligence in law firms, let’s first see what a law firm is and how it functions. A law firm is an association or a team of lawyers working together as a business entity and providing legal services to the public.  The lawyers of the firm share their clients and profits, depending on their schedule, expertise, and seniority. Law firms follow a hierarchy where the main partners or shareholders sit on the top tier. The senior associates follow next, with the junior lawyers working under senior lawyers and learning from them.  A law firm can be organized in several ways, based on where the practice is located. They can follow any of the below arrangements- Law firms also hire paralegals from time to time if required for the case. A firm can have both individuals and corporations as its clients. The primary step is to understand the client’s case, explain legal rights, responsibilities, and advise them on the best way forward.  The firm can accept civil and/ or criminal cases, cases that deal with financial transactions, business complications, or any other areas of law.  What are the Application Areas of Law?  Law is classified into several finer categories, each dealing with a specific aspect of society/ life. A lawyer usually chooses an area of expertise and specialization, which helps them take up cases specific to the category and help their clients.  Animal Rights Law An animal rights lawyer can save and protect the lives of those who cannot speak for themselves. Fighting against animal cruelty, getting better facilities for animal care shelters, and exposing animal abusers are some responsibilities of an animal rights lawyer. The lawyer takes up cases to protect domestic and wild animals.  Blockchain Law  This is a new area of expertise, thanks to the increasing popularity of Bitcoin and several other altcoins and digital currency. Though there aren’t many lawyers who specialize in this area, the field is set to grow and create many opportunities for upcoming lawyers looking for a new and profitable area of law.  Civil Rights Law Civil rights are basic human rights every human being in this world is entitled to. Being a civil rights lawyer is a crucial responsibility. Even though many civil rights lawyers work for nonprofit organizations, they also take up other cases of interest, especially when human rights are being violated.  Complex Litigation Law  Complex litigation cases are difficult to handle and can go on and on for years. Civil and corporate cases with their stakes high and involving some noted individuals or entities are dealt with by complex litigation lawyers. It’s a serious area of law that demands everything from the lawyer.  Corporate Law Corporate law deals with the everyday practices of a business and its other complex affairs. Corporate law enters the scene at the very beginning, right where the business is initially being set up.  Compliance, contracts, policies, rules, and regulations, etc., come under this area of law. Corporate lawyers also take care of business liabilities and bankruptcy. Criminal Law  A criminal lawyer can choose to be a defense lawyer or a prosecutor, or sometimes both. The role and priorities of the lawyer change based on whether they are defending the client or prosecuting an accused. However, the primary role of a criminal lawyer is to protect the basic rights of the client. Criminal lawyers deal with clients from all sections of society. Environmental Law  Lawyers who practice environmental law will see more demand as the fight against climate change gets serious. Setting up new regulations, amending existing laws, etc., help protect the environment. The lawyer plays a vital role in promoting these laws and helping individuals/ businesses understand the environmental laws and how these can impact their business. Family Law Family law deals with family-related aspects, be it good or bad. From divorce to inheritance disputes to adoption and child care, it encompasses an array of elements. A lawyer who practices family law has to deal with the emotional aspects of the case as well as the financial and social factors. Healthcare Law The healthcare law deals with the healthcare sector. It includes- The healthcare lawyer works with hospitals, medical centers, doctors, and insurance providers to advise them about their legal obligations and rights.  Immigration Law  Immigration laws protect immigrants and refugees from being subject to abuse/ neglect/ racism in the country they are taking shelter in. Immigration lawyers are currently in demand as (illegal) immigration, deportation, etc., are the most discussed topics. An immigration lawyer can help families get the justice they deserve in another country.  Intellectual Property Law  Intellectual property theft is becoming quite common in recent times, and the demand for IP lawyers has gone up. Intellectual property law protects ideas, concepts, theories, formulations, equations, designs, etc., from being duplicated and stolen by others. Copyrights, trademarks, patents, and other such applications are filed to protect intellectual property. Labor Law  Labor law deals with the rights of a laborer, worker, or employee of an enterprise. Lawyers who specialize in labor law are almost always in demand because of the varied nature of atrocities faced by employees. From hazardous working environments to sexual harassment and racism (among other things), the lawyer works with clients from the entry-level to top positions in an organization.  Sports and Entertainment Law  This area of law includes IP (intellectual property) laws, royalty disputes, ownership rights, contacts between different parties involved in the project, etc. The ‘entertainment’ part covers television and

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8 Insanely Useful Ways How Data Warehouse Can Improve Your Business Reporting

These days, multiple startups and companies make use of different software applications to run their business finance and reporting effectively and efficiently. From buzzword-heavy projects to advanced analytics, companies dive hard to get ahead of their competitors. But they often forget to realize the value data can bring to their organization, in terms of effective storage and strategic approach.  Data warehousing is a strong foundation for Business Intelligence programs. A warehouse works as the central location and fastens the lifelong storage space for different data sources, which can be further used for integral analysis, reporting, justification, data mining, and more. This article takes you through a step-by-step guide on how a data warehouse can improve your business performance.  What Is a Data Warehouse? In simple words, a data warehouse collects and stores data, very similar to how we save our documents and photos on the cloud. Having a location to store or keep data makes it extremely easy to use for all, providing insights and reporting on a large scale.  The database collects constructed data from the entire organization, pulls it together from different sources, and organizes and aggregates data for effective comparison and analytics.  Data Warehouse maintains a strict processing ETL (Extract, Transform, Load), a process to load data in batches and transfer it into an appropriate structure.  Ways How Data Warehouse Improves Business Reporting Organizations use tons of different software applications, for example, ERP, CRM, and Finance frameworks to maintain their business smoothly. These applications produce information that, whenever accessed, provides important understanding into business execution. However, accessing information and reporting from frequently complex frameworks, like Microsoft Dynamics, Sage, Salesforce, and SAP Business One, can be troublesome and time-consuming.  At times, to such an extent that it isn’t even sought in any way. Here are the key points on how a data warehouse can improve financial and operational reporting.  1. Report From Structured Data The characteristics of a good data warehouse signify that an organization can store its data in a constructed format, a structure that can change itself in different formats, especially for reporting and analytics.  Prime data warehouse gears are known for their ELT performance tuning, that is Extract, Load and Transform processes. The job of ELT data warehouses is taking or extracting the data from a particular source (extract), turning or transforming it into a format that is effective (transform), and then saving or adding it in the warehouse (load). ETL processing is also considered as it uses metadata from the primary transactional database. Metadata is what communicates to a particular person working on data about what the data is regarding, making it easier to locate and understand.  Acknowledging data and reporting it becomes much more comforting when it has been transformed in a defined structure and language, using metadata.  2. Report From More Than One Source at the Same Time Businesses these days do not rely on one application in order to solve their numerous objectives. While one data source collects information on its own, like ERP or CRM, other lines of business applications like Excel, CSV, etc., are created by users based on specific formats of exchanging information.  Utilizing the ETL process to convert different data sources, that are structured or framed in discrete ways, into one familiar constructed format allows to draft reports from multiple data sources. For example, now it is possible to create one unified report that includes marketing analytics to online sales records.  3. Enables Historical Reporting and Trend Analysis Comparing historical data with the latest trends that change over tweets is impossible to keep up with. While a data warehouse stores historic data it becomes completely effortless to carry pace with trend reporting.  With data warehouses businesses can step up their analytics and reporting game with an all-time overview as they are now free from, only a current view option, making it easier to show how the data has evolved over time. 4. Saves Time & Reduces Errors ETL data orchestration makes it an easy job to draft or produce business reports swiftly. With manual tasks being erased from different steps, reports produced with data warehouses present almost zero human errors. Moreover, they save time in finding and accessing data from different systems.  With a data warehouse, businesses can update and refresh the data from all business systems regularly leading to scheduled and improvised reports with accurate numbers. 5. Enhanced Business Intelligence Data warehouse provides access to different information from multiple sources on one particular platform, managers, executives, and decision-makers no longer need to depend on limited data or their intuition for making business decisions. Also, data warehouses can be easily applied to various business processes, from financial management to inventory organizing and market segmentation. 6. Generates Return on Investment Data is the new diamond, referring to the value of data in today’s world.  Accurate data can lead businesses to significant revenue gains and building quality data in the most structured format with a data warehouse can lead to better work decisions. Turning these data-led decisions to create strategies can further result in a higher return on investments across different sectors of business strengthening the organization.  In simple words, data warehousing is an investment rather than a closing cost on maintenance.  7. Data Security Numerous advances have enhanced the security benefits in the data warehouse. With creating and storing data from different wide sources for business growth, maintaining its safe security was one great concern. Advance improvements in techniques that block malicious SQL code and encrypted columns have led to improved security in maintaining confidential data.  8. Higher Query Performance & Insight Continuous business intelligence queries have become a routine part of businesses in today’s world, putting indefinite strain on analytics foundations from databases to data marts. A good data warehouse can efficiently manage queries eliminating several pressures from the system.  How Data Warehouse Benefits Business To begin with, data mining is one principal factor for most modern businesses today. Constructive data not only provides effective planning

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How Embedded Analytics Provides Value for Your Business

Embedded analytics adds real-time data analytics reports to websites and software applications. It empowers enterprises to adopt the data-driven decision-making model and add value to the business. Data analytics is the process of acquiring, transforming, and using AI-based business intelligence tools to convert real-time raw data into structured data to derive actionable insights. Data analytics are performed in separate environments and are then shared with employees and management to make decisions. Employees have to work on different systems and move back and forth to use these analytics at work. Data visualization tools have simplified the process through embedded analytics and third-party integrations. This enables you to use the insights in the same system on which you are working. It provides a seamless workflow with little or no disruptions so that business intelligence analytics can be used to their maximum potential. The issue with the underuse of insights in the traditional method is solved through embedded analytics. When the analytical insights are a separate entity, some of it may be lost or not considered for decision-making. By embedded analytics to other software and platforms, this loss can be minimized and even nullified. Every important piece of information is effectively used by businesses. What is Embedded Analytics?  Embedded analytics is the integration of data analytics reports to websites, third-party software, and applications. The dashboard of the BI tool is embedded (included and presented) on another software or website so that the insights can be directly shared and used from the main source. You can see changes in real-time when using embedded analytics. The report will change as data is processed and insights are generated. These changes are visible in real-time when you look at the website or the app. News portals, public health reporting websites, research platforms, etc., are a few embedded analytics examples where the dashboard of a BI tool is embedded into the website to share information with the public.  Inserting the dashboard is a great way to present information to the management and the end-user. Be it the sales team, the marketing team, the accounts department, or the production team, every department in the enterprise can be provided with embedded analytics to use the latest insights to make the right decisions at work. It is an effective way to adopt the data-driven model in the business. Relationship Between Embedded Analytics and Business Intelligence Embedded analytics solutions effectively use business intelligence tools and insights by fully integrating them into the system. Instead of restricting BI to its isolated framework and accessing the insights when needed, embedded analytics helps you understand data with more clarity. Once you take data insights outside the traditional BI tools, they can be presented anywhere. In fact, the insights generated by your business can be shared with or sold to another organization and embedded into their systems. They can either use it for internal decision-making or share it with their end-users. Using embedded analytics can increase your business value and give you a definite edge over your competitors. It also helps improve your B2B connections and expand your business.  How to Use Embedded Analytics There are three ways to use embedded analytics within and outside the business and take data insights to the users. Internal Tools  Within an SME or a large-scale enterprise, embedded analytics software is used to streamline workflows in various departments and align them to achieve organizational goals. Even though software like CRM has in-built data visualization capabilities, the features are limited and do not provide all the necessary information to the teams. By integrating embedded analytics with these systems, various teams across the enterprise can access the data required for their projects and use the insights directly to create better strategies. What happens here is that if a team is working on a CRM system, they don’t have to move to another software or application to access data analytics. Whatever they require can be found in the same system.  Web Embedded analytics on the website is used to take the information to the B2B audience and end-users. It helps engage the audiences and share complex data in an easy-to-understand manner. You can plug tools like Chartio with most HTML frames without much fuss. Instead of detailing a report on the blog or website, you can rely on graphical presentation and illustrations to make the post interactive and fun. Buy-In  Now, what if you want to monetize embedded analytics? Simply make it a premium feature and sell it. Interested parties can opt for the premium feature (either by paying a one-time fee or monthly subscriptions) to access embedded analytics and use it in their systems. This will enable data analytics to happen at the same place where the decisions are being made. In case of any error, you can fix it within the same system instead of going to a reporting tool or visualization tool. Steps to Start Using Embedded Analytics  Employing business intelligence consulting services in an enterprise will make embedded analytics easy. Embedded analytics needs people, processes, and technology to be aligned with each other and with the organizational goals. Let’s look at how to start using embedded analytics in brief.  Use Cases of Embedded Analytics There are many ways in which embedded analytics can help your organization. The following are some embedded analytics use cases- Helps Monetize Data Analytics Embedded analytics allows you to monetize data by providing customers access to the insights. You can do this by embedding the data visualization tools to the front end or the user interface and allow only paid or subscribed customers to view the details. Can Optimize Mobile Workforce If your employees work using mobile devices, it can be frustrating to move between different apps and tabs. Embedded analytics makes it easy for them to work on the same app. It helps employees finish work faster and increases the quality of work.  Promote and Increase Use Adoption Since analytics are integrated into the existing workflow, employees will be more open to adopting the latest technology. This will increase productivity and add value to the business. The money you spend on adopting the data-driven model will be well-spent when employees embrace technology. Reduce the Risk of Errors There is always a risk of misrepresenting data when

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9 Uses of Machine Learning in Business Communications

Artificial Intelligence (AI) and Machine Learning (ML) are becoming an integral part of our lives, at work or home. Enterprises use AI and ML to streamline the business processes and help employees become more productive. AI and ML are used by social media sites, search engines, and OTT platforms to assist users in finding what they want. At home, we use AL-based voice assistants like Alexa, Siri, and Google Home Assistant for several purposes. As days pass, we see ML being extensively adopted by businesses. North America grabs the first place for ML adoption, with 80% of the companies using artificial intelligence and machine learning in some way or another. According to GlobeNewswire, the global market value of machine learning is said to touch $117 billion by 2027 at a CAGR of 39%. So, what do companies use machine learning for? Why are AI and ML so crucial in today’s scenario that it has become necessary to invest in machine learning to stay competitive? Let’s find the answer to this question and more. Importance of Machine Learning in Business Machine learning is a subset of artificial intelligence. It analyses data sets to trace patterns and identify trends that are otherwise difficult to find. It allows a business to automate data analytics and save resources. Understand Customers Businesses have become customer-centric, thanks to the increasing competition. Understanding what the customer wants is essential if you wish to retain them. Remember that your competitors are putting in more effort to attract your customers to their business. Machine learning will analyze customer data to help you understand their preferences, likes, and dislikes. Automate Business Processes  How is machine learning used in business? When a task can be completed by a machine in less time, with more efficiency, why dump it on employees? Let machine learning automate recurring tasks so that your employees will have more time to focus on the core projects. Personalization of Ads   Customers like variety. They also like to be provided with several options of products/ services they want. How do you attract a customer to your business? Personalized ads are the result of machine learning. You can reach out to a user who wants a product/ service you provide by analyzing their search history and purchase preferences. Increase Business Security Cybersecurity has been a cause of concern for every business. A startup or a multinational company, no one is safe from hackers. But AI and ML-based antivirus software can safeguard your business and prevent cyberattacks by providing multilayer security. Of course, it is expected that hackers will also use the same technology to get in. But machine learning can help identify the weak spots in advance and strengthen the overall security system. Human Resource Management  Can machine learning enhance human learning in business working environments? Absolutely! Machine learning is used for human resource management in several ways. From identifying the talent gap to screening applicants and assessing the value of an employee to providing customized training options, ML can help employees become better at work. Every employee’s career growth can be mapped in parallel to the company’s growth. Manufacturing and Logistics  AI and ML are being used to streamline inventory and shorten the delivery time. Machine learning uses the existing data to provide valuable insights, whether predictive maintenance or alternative routes to reach the destination earlier. This helps you in making better decisions. What is Artificial Intelligence in Business Communication? What role does AI play in business communication? Artificial intelligence combines machine learning, deep learning, natural language processing, and more such technologies that effectively understand, analyze, and process data to provide meaningful insights. Artificial intelligence has been used to facilitate better communication in recent years. Here’s how AI is used in communication: Chatbots for Customer Service  Chatbots have changed the way businesses and customers interact with each other. There is no need for customers to wait long hours, hoping the representative will respond soon. These chatbots are found not only on business websites but also on other communication channels. You can develop a chatbot for Facebook Messenger to respond to followers on that platform. Using chatbots also reduced the cost of expenditure incurred by the customer service department. Smart Campaigns  Artificial intelligence solutions can create smart campaigns for marketing and promoting the brand among the target audiences. Customers are segmented and categorized based on their online data. This allows you to create hyper-targeted ads for each customer group and every customer. It can increase the chances of converting a prospective user into a successful lead and customer. Filtering Emails  Do we need to tell you about the nuisance of spam emails? They keep coming, don’t they? Unless you have AI-based filters will effectively keep phishing emails out and prevent your employees from falling prey to cyberattacks. Though Gmail is effective, using your own ML-based filters and spamware will better protect against phishing attacks. Sending Automated Smart Replies  Natural Language Processing helps understand the text and the intent behind the words. Instead of asking employees to send replies to every mail, this can be automated using AI and ML. The automated smart replies use the appropriate wording to frame the response for every email. Self-Servicing for Employees Similar to how chatbots communicate with customers, the same helpdesk system can be set up for internal customers, aka employees. Machine learning in business applications can help employees answer their queries by contacting the chatbot instead of a human agent. This saves time for both sets of employees. How to Apply Machine Learning to Business Problems? Machine learning can solve several business problems if you know how to use it. Of course, adopting AI and ML has its own set of challenges to deal with. That’s why most enterprises rely on offshore machine learning consulting companies to help with the adoption process. Prediction vs. Decision You need to determine if you want to use machine learning for prediction or decision-making. Using ML for the wrong purpose will result in more loss than profits. Data Processing and Analytics  While data is available in abundance, not

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11 Extremely Helpful Use Cases of Computer Vision in Medicine

Computer vision identifies patterns in data using artificial neural networks. It helps doctors accurately diagnose complex health conditions and provide effective treatment to patients. Computer vision improves the quality of medical services. Science and technology are a part of our lives. But there’s one area that relies exclusively on them, and that’s the medical industry. Be it for research or healthcare, the medical industry is dependent on science and technology. The computer vision in the healthcare market in 2019 was valued at $445.6 million. It is expected to have a CAGR of 23.6% and touch $2.50 billion by 2028. Over the years, we have seen how new equipment and advanced tools are helping doctors and scientists to diagnose diseases accurately and provide better treatment. Artificial intelligence has begun to make a lot of difference in the medical and healthcare industry. From using robotic arms to perform surgeries to relying on computer vision for medical imaging, AI is enhancing the quality of healthcare services offered to the public.  Artificial intelligence is an umbrella term that has various subfields interconnected with each other. If machine learning is one, computer vision is another. Artificial neural networks, natural language processing, and deep learning are other areas of AI used in various industries, either independently or in sync with different subfields.  What is Computer Vision? We have mentioned computer vision as a part of AI. But what exactly is computer vision, and how does it help the medical industry?  Computer vision has been designed to empower computers to recognize and understand images so that actions that are usually performed by humans are now done by computers. It is the process of automating tasks that were previously handled by humans.  A computer vision consulting firm works on training and enabling computers to replicate human sight and understanding processes the way humans do. It is much more than simply copying human actions without understanding the reasons for it.  Computer vision is already being used by platforms like Facebook and Amazon to provide personalized services. The technology is making progress in the medical industry to assist doctors and healthcare personnel in identifying patterns, detecting anomalies, and making accurate diagnoses to provide correct treatment to the patients.  Evolution of Computer Vision  Do you know that it took less than a decade for computer vision technology to be 99% accurate from being just 50% accurate back then? Computer vision was first used in the 1950s to distinguish between typed and handwritten text. Today, technology can process and analyze images to understand which action has to be performed.  One of the primary reasons for the growth of computer vision is the increase in data. With billions of images being shared online every day, it has become necessary to use technology like computer vision to process these images and identify the objects/ data in them.   How Does Computer Vision Work?  In a way, the process is similar to recognizing and tracing patterns. We could say that computer vision relies on neural networks and algorithms that mimic human minds to observe images the way humans do. Facial recognition is one example of computer vision.  Earlier, creating something like this needed a lot of manual work. And yet, the margin of error was high. But with machine learning algorithms and deep learning becoming more precise, the need for human intervention has reduced.  However, if you want to invest in computer vision technology, you will need to hire CV experts to handle the systems. Many organizations, including the medical industry, are hiring computer vision services to enhance their processes and deliver high-quality services.  Medical Fields Where Computer Vision is Used  The medical industry is made up of several subfields, each of which focuses on different specializations. For example, dermatology deals with skin, cardiology is about the heart, and so on. Computer vision is used in each field in a different way to assist doctors to detect early signs of symptoms and prevent the disease from affecting the patient after they have recovered from it.  Radiology  Computer vision assists physicians to make the correct diagnosis, especially if he hasn’t come across a case like that before. The job of a radiologist is to- Dermatology  Computer vision applications help dermatologists in three ways- Cardiology  Computer vision applications and virtual reality hardware are used together by cardiologists so that they can- Embryology  Computer vision is transforming IVF (In Vitro Fertilization) through- Use Cases of Computer Vision in Medicine  1. Accurate Measurement of Blood Loss Computer vision is now accurate enough (more than 99%) and is known to predict and measure data by analyzing images with great clarity. Gauss Surgical has developed a computer vision application that helps measure blood loss during childbirth. Excessive bleeding during childbirth has caused a lot of complications and the death of new mothers for centuries.  This application helps measure the amount of blood loss so that the doctors will know when things will get risky for the patient. The CV tool uses studies of images of surgical sponges and suction canisters used during the delivery to determine if the patient is at the risk of suffering from excessive blood loss.  2. Minimizing False Positives  False positives can prove to be costly, not only in terms of money but also in terms of life. Treating someone for a disease they don’t have but have been tested positive because of faulty analysis can lead to short-term and long-term ill-effects.  AI Vision Consultancy services help healthcare centers develop computer vision tools that can process a vast amount of data and identify subtle changes in patterns. There are two advantages of using CV applications for testing patients.  One is the reduction in the number of false positives, and another is the detection of minute symptoms that could, in all probability, be missed when using traditional testing methods. Instead of going through multiple tests to confirm whether they are really positive for a disease, a patient can rely on the reports generated using CV tools.  3. Timely Detection of Symptoms  Patients suffering from cancer, Alzheimer’s, Parkinson’s,

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Machine Learning is Right for Your Business: Is it True?

Machine learning can make an enterprise an industry leader by streamlining the processes and increasing returns. ML is right for every organization, and here’s why. Have you heard of the terms artificial intelligence and machine learning? Or, the correct question would be how many of you have not heard of them. Isn’t it? The interest in AI and ML has been steadily growing over the years. Many organizations have already started investing in ML technologies to improve their business operations.  However, it is vital to understand what ML is, why your business needs machine learning, and what kind of technologies you should adopt. To make things easy for you, we’ll explain the various facets of ML in this blog. In today’s world, there is no shortage of data. The world has generated around 2.5 quintillion data bytes every day in 2020. Every user created 1.7 MB of data per second. Data is available in real-time for processing and analysis. This helps organizations around the world to derive accurate insights and use them to make decisions.  But how is this data processed? Manually collecting, cleaning, and analyzing big data is not really possible and takes too much time and effort. Artificial intelligence tools and machine learning algorithms help replace the manual processing of data.  Machine learning models can process data in real-time, identify patterns, and share valuable insights that help enterprises streamline their day-to-day business operations. In recent years, machine learning adoption rates have improved in all parts of the world. The machine learning global adoption rate is 45%, with another 21% exploring ML methods to get started soon.  There are several practical applications and benefits of using machine learning in the business. It has become necessary to invest in artificial intelligence and machine learning tools to revamp the business process and compete in the market. Both SMEs and large-scale enterprises need to understand what machine learning is and how it can change their business for the better.  But first, let’s start with the basics. In the first section, we’ll read more about machine learning, the definition, model, purpose, and more.  Overview of Machine Learning Machine learning is an AI application that helps systems read and understand vast amounts of data. Machine learning is used to develop programs that can process and analyze data to deliver predictions and insights.  The main aim of using machine learning is to empower a computer to learn and understand data in various forms. In short, machine learning helps computers process data the way a human mind does.  Enterprises develop machine learning models customized to work with the type of data collected to make the business successful.  Is machine learning AI or is machine learning the same as Artificial Intelligence? Machine learning is a subset of AI. The concept of machine learning is to empower machines to learn and adapt through self-training. It deals with the statistical methods and algorithms that teach a computer to understand and process data. This means that while machine learning can be called AI, the reverse is not true. It is an application of artificial intelligence where a machine is trained to think and make decisions like a human.  AI has more components than machine learning. It is a broader subject. Artificial intelligence aims to make computers smart enough to solve complex problems without human intervention. In other words, Artificial intelligence creates intelligent machines, while machine learning trains these machines to become efficient and accurate. Machine Learning Models The machine learning algorithms are widely classified into supervised and unsupervised learning, semi-supervised learning, and reinforcement machine learning. 1. Supervised Learning This model uses the learning of the past and implements it on new data through labeled examples. It is used to predict future outcomes and trends. The input is mapped to output based on a given example of input-output pair.  Regression Model:  A regression model is where the output is continuous and is further subcategorized into the following- 1. Linear Regression This is where you find a line that best fits that data when you draw a graph with the X and Y-axis. Multiple Linear Regression (where a plane of best fit is determined) and Polynomial Regression (where a curve of best fit is found) are an extension of this model.  2. Neural Network We hear about neural networks more often than other models. It is a series of algorithms to recognize the several relationships and underlying connections between datasets. Neural networks are vital for deep learning. Each node is connected to the other to send data across the network. The network is trained using huge data sets and becomes accurate over time. Google’s search algorithm is the best example of a neural network. This model has been developed to resemble the operations and thought processes of the human mind.  3. Decision Tree This is a popular model where every question/ assumption is a node. The probable answers to each question will lead to more questions (nodes), and you finally have the answers (called leaves). This method considers all possible conditions before coming up with the final answer. The more nodes you have, the better will be your decision. However, this method is not always accurate due to the various factors involved.  4. Random Forest This is a collection of decision trees. However, random subsets of variables are selected at each step of the decision tree. Then the mean of predictions for each tree is selected. The final decision is selected depending on how many predictions are similar (same). This is a majority-based model that tries to eliminate the risk of individual error.  Classification Model:  A classification model is where the output is not continuous but discrete. It uses logistic regression the probability of outcomes in two. The output value should either be 0 or 1. Decision trees, random forests, and neural networks are a part of the classification model as well. The only difference is the change in output.  2. Unsupervised Learning  The information used to train the algorithm is neither labeled

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