Best Data Mining Techniques You Should Know About!

In this piece, we are going to discuss why one must study data mining and what are the best data mining techniques and concepts. Data scientists have a history in mathematics and analytics at their heart. Also, they are building advanced analytics out of that math history. We are developing machine learning algorithms and artificial intelligence at the end of that applied math. As with their colleagues in software engineering, data scientists will need to communicate with the business side. It requires a sufficient understanding of the subject to get perspectives. Data scientists often have the role of analyzing data to assist the company, and that requires a level of business acumen. Eventually, the company needs to be given its findings understandably. It requires the ability to express specific findings and conclusions orally and visually in such a manner that the company will appreciate and operate upon them. Therefore, you should practice data mining. It is the process where one constructs the raw data and formulates or recognizes the various patterns in the data via mathematical and computational algorithms. It will be precious for any aspiring data scientist, which allows us to generate new ideas and uncover relevant perspectives. Why Data Mining? Current technologies for data mining allow us to process vast amounts of data rapidly. The data is incredibly routine in many of these programs, and there’s enough opportunity to exploit parallelism. A modern generation of technologies has evolved to deal with problems like these. Such programming systems have been designed to derive their parallelism, not from a “super-computer,” but from “computing clusters”— vast arrays of commodity hardware, whether traditional Ethernet cable-connected processors or cheap switches. Data Mining Process Data mining is the practice of extracting useful insights from large data sets. This computational process involves the discovery of patterns in data sets using artificial intelligence, database systems, and statistics. The main idea of data mining is to make sense of large amounts of data and convert/ transform it into useful information.  The data mining process is divided into seven steps: Collecting & Integrating Data Data from different sources is consolidated in a single centralized database for storage and analytics. This process is known as data integration. It helps detect redundancies and further clean the data.  Cleaning the Data Incomplete and duplicate data is of little use to an enterprise. The collected data is first cleaned to improve its quality. Data cleaning can be done manually or automated, depending on the systems used by the business.  Reducing Data  Portions of data are extracted from the large database to run analytics and derive insights. Data is selected based on the query or the kind of results a business wants. Data reduction can be quantitative or dimensional.  Transforming Data  Data is transformed into a single accepted format for easy analytics. This is done based on the type of analytical tools used by the enterprise. Data science techniques such as data mapping, aggregation, etc., are used at this stage.  Data Mining  Data mining applications are used to understand data and derive valuable information. The derived information is presented in models like classification, clustering, etc., to ensure the accuracy of the insights.  Evaluating Patterns  The patterns detected through data mining are studied and understood to gain business knowledge. Usually, historical and real-time data is used to understand the patterns. These are then presented to the end-user.  Representation and Data Visualization  The derived patterns can be useful only when they are easily understood by the decision-makers. Hence, the patterns are represented in graphical reports using data visualization tools like Power BI, Tableau, etc.  Data Mining Applications  Data mining plays a crucial role in various industries. It helps organizations adopt the data-driven model to make better and faster decisions. Let’s look at some applications of data mining.  Finance Industry: From predicting loan payments to detecting fraud and managing risk, data mining helps banks, insurance companies, and financial institutions to use user data for reducing financial crimes and increasing customer experience.  Retail Industry: From managing inventory to analyzing PoS (Point of Sale) transactions and understanding buyer preferences, data mining helps retailers manage their stock, sales, and marketing campaigns.  Telecommunications Industry: Telecom companies use data mining to study internet usage and calling patterns to roll out new plans and packages for customers. Data mining also helps detect fraudsters and analyze group behaviors.  Education Industry: Colleges and universities can use data mining to identify courses with more demand and plan their enrollment programs accordingly. Educational institutions can improve the quality of education and services through data mining.  Crime Detection: Data mining is also used by crime branches and police to detect patterns, identify criminals, and solve cases faster.  Best Data Mining Techniques  The following are some of the best data mining techniques: 1. MapReduce Data Mining Technique The computing stack starts with a new form of a file system, termed a “distributed file system,” containing even larger units in a traditional operating system than the disk boxes. Spread file systems also provide data duplication or resilience protection from recurrent media errors arising as data is spread over thousands of low-cost compute nodes. Numerous different higher-level programming frameworks have been built on top of those file systems. A programming system called MapReduce is essential to the new Software Stack that is often used as one of the data mining techniques. It is a programming style that has been applied in several programs. It includes the internal implementation of Google and the typical open-source application Hadoop that can be downloaded, along with the Apache Foundation’s HDFS file system. You can use a MapReduce interface to handle several large-scale computations in a way that is hardware fault resistant. All you need to write is two features, called Map and Reduce. At the same time, the program handles concurrent execution, and synchronization of tasks executing Map or Reduce, and also tackles the risk of failing to complete one of those tasks. 2. Distance Measures A fundamental problem with data mining is the analysis of data for

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Data Analytics in Travel Industry: Stand Out in the Crowd

Data Analytics brings endless opportunities for the travel industry. A large amount of valuable data is generated at every stage of a trip and with a lot of people traveling around the globe, this data can offer significant insights. Travelers buy stuff online, create an itinerary, save dates on calendars, use GPS to reach their destination and so on. At every stage of their trip, they leave a data trail. The experts now analyze this data and infer various insights to enhance the customer traveling experience. However, collecting this data and connecting it is a  bit of a challenging task for data analysts, but the discernments obtained can revolutionize the travel industry and make this venture a more profitable business than it was ever before. There are several ways in which data analytics services are currently assisting the travel industry to do better and meet its goals. Business Data Analytics in Travel Industry Machine Learning and Artificial Intelligence are picking up traction in the travel industry to help airlines and hotels make data-driven decisions based on accurate and actionable insights. Business analytics provide real-time insights about customers using data from multiple sources. Data like flight bookings, hotel stays, schedule patterns, repeat bookings, flight preferences, and so on is collected from websites, apps, social media, customer accounts, etc., and stored in a centralized database. This data is cleaned and processed to avoid redundancy.  Analytical tools are used to analyze this data in real-time and share insights with the business to help make strategic decisions. Business analytics in the travel industry help airlines and hotels understand customer behavior and market trends. When the airline knows what a customer expects or wants, it can customize the services to enhance customer experience and thus inspire brand loyalty.   Business analytics helps in the data-driven transformation of the travel industry. The pandemic has pushed the travel industry into losses and havoc. Business analytics is a way to bring the necessary change and empower the airlines to recover from the dire situation and come out stronger.  Experts claim that the use of predictive and prescriptive analytics will be a game-changer in the industry. Data science and predictive modeling can help airlines uncover critical intelligence to provide real-time actionable insights that help in recovering and gearing up to make the most of new market opportunities.  An important aspect to consider is automation. Even though many businesses are already using data analytics, they spend too much time, energy, and money on cleaning the data rather than running analytics and using the insights. Artificial intelligence and machine learning make automation possible by streamlining data collection, cleaning, and storage processes. This gives airlines more time to run queries and use the insights without delay. Designing an Effective Business Strategy To build an effective business strategy it is important for an organization to be aware of its customer base and its preference. With the help of data analytics and predictive analytics tools, the data collected in the form of feedback, customer reviews, social media posts, etc. is utilized to infer the behavior pattern of the customers. This, thus, causes the organizations to comprehend their customer’s needs and offer services that will bring them more benefits. The bits of knowledge are utilized by the firm to tailor customized plans for its clients.  Better services offered by the firm not only help to retain loyal customers but also boost sales and improve reputation. Let’s You Stand Out in the Crowd When a traveling firm uses data analytics services to distinguish itself from its competitors, it is able to create lucrative offers that cater to the needs of the clients and at the same time help you to gain an edge over others. For instance, Amadeus (a Global Distribution System) allows its users to ask simple travel-related questions without entering any personal details. The questions can be as basic as – When will I get the cheapest flight to Italy? Or is it possible to travel somewhere for just 700 $? These types of services make it very convenient for users to clear their travel-related doubts and get reliable advice. Once a customer starts to trust the product, he/she will definitely return to use it and in the process become a loyal customer. However, retaining customers is another challenge that must be carefully dealt. Improve the Pricing Strategy While planning travel, money is one of the major concerns. People spend hours and days finding that one tour package that can offer them the best deals without cutting a hole in their pocket. But how would the firms know beforehand what the customer wants from them? The simple answer could be data analytics. All searches made by the visitors’ on a firm’s website can be used to infer the budget that a major chunk of people can afford to travel. Keeping this in mind a well-tailored travel plan can be devised and placed on the website. This is the strength of applying data analytics, you can predict what will happen next.  For instance, firms like KAYAK have been using data analytics to forecast the changes in flight prices for the coming seven days. Taking Better Decisions Taking upright decisions is one of the key aspects of improving a business. With the help of data analytics, travel planners are able to draw better choices based on real predictions and not just intuitions. Data analytics allows the firms to develop customers’ profiles and thereby helping them to accurately target the market campaigns. As per Forrester, data analytics tends to increase customer responsiveness up to 36%. Being able to comprehend the demand of the customer and recent ongoing trends in the market lets the companies peep into the future and perform accordingly. The famous Nippon Airways uses data analytics to optimize its cargo management system thus making it one of the largest airlines in Japan. Making the Tough Easy Having data analytics at your disposal prevents painful losses and enhances revenue for the travel industry. To understand this,

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Outsourcing AI Requirement To AI Companies Is a New Emergent Trend: An Analysis Justifying It

Are you thinking of outsourcing AI requirement, when you are not sure of the value it can add to your business, during its initial phase of R&D. Whether it is the e-Commerce retail giants Amazon, eBay or an emerging startup, they all have one thing in common, the acceptance to technological advancements and the willingness to adopt it in their process automation. In their visions, AI’s role has been crucial. On a larger scale, Amazon has been automating its godown and warehouses with RPAs or (Robotic Process Automation) by signing a deal with Kiva Systems, a Massachusetts based startup that has been working on making AI robots and software. The report from PwC, a professional service network specify that nearly 45% of the current work is automated in many organizations. Such an approach leads to an annual $2 trillion in savings. Even the emerging startups have started to integrate chatbots in their process management for simplifying the customer engagement process. All these businesses have focussed on outsourcing their AI needs to other companies having the domain expertise in AI. Therefore, it is evident that such a trend has been persistent and will sustain for long in the near future. Let’s look at why this trend is becoming mainstream and why it is beneficial for companies to outsource their AI requirements to other domain experts. Benefits Companies Receive When They Outsource Their AI Access to Top Level Resources or also known as Connoisseurs in AI Companies/corporations work at different wavelengths, and domain expertise differs for all. For example, a company in the retail, supply chain, or logistics might not be an expert in technology. But they do need smart technological solutions that can automate tasks, eliminate the need for workers for menial jobs, and ways that can cut down the operational budget. Though they have full knowledge of their process and domain, having experts to sit in-house for programming, development, and deployment will cost them fortunes. When these companies outsource to AI-oriented companies with expertise in Robotic Process Automation, Business Intelligence, Data Mining, and Visualization, it helps them save additional expenditure from setting up a new tech process and face mental hassles to manage the same. As a result, top companies, whether SMEs, startups, or even MNCs prefer to outsource their AI needs to domain experts in the market. On-Time Delivery of Services & Products On-time delivery is a pressing challenge when you have an in-house team to manage the development, testing and delivery process. For example, a retail giant like Amazon or eBay is more interested in improvising their delivery system, product quality and price optimization rather than spending time manufacturing robots or managing data of consumers on their own. At such instance, they need the support of data management and manufacturing companies on the AI domain to help create feasible solutions for them. Having an expert AI company can assure them of on-time delivery without compromising on the quality. The result would be satisfied and happy customers for the companies hiring AI service provider for their niche based requirement. Setting Up Smooth Business Process Smooth business process using AI solution works best when you have the customized solution provider in the market working on solving your challenges. Most AI driven applications need prevailing market analytics and trends to be incorporated for better performance. Companies who decide to build and manage their AI applications on their own if they excel in different sectors won’t meet the desired results when compared to AI oriented solution providers. Those companies whose main product is AI solutions are continuously monitoring trends and upgrades. They partner with numerous AI based companies, volunteer in AI workshops and programs to further enrich their knowledge base. Thus, ending up as best for companies who want to integrate AI solutions in their scheme of work. These AI based startups, or established companies understand the process of their clients and customize the product to best fit into their requirements. For example, Apple’s Siri, or NetFlix customized content shown to users are best use cases to show how AI can simplify the user experience and set-up a smooth process as per the changing needs of the business. But for banks, pharmaceutical or logistics sectors to develop their own solutions like Apple’s Siri or Netflix’s customized AI data analytics would be a tough job to achieve. Even if they do invest into it, the time investment required to keep things in order might disrupt their natural business process. Hence, they find it much more feasible and cost effective to outsource it to an AI company and develop the solutions on their behalf. Save Expenses In A Big Way For sustainability, businesses have to understand the challenges, market dynamics and adapt to the changes every now and then. Such an approach requires a lot of time investment and spending time to create AI based solutions to simplify their process will be an added liability for resource and time. When companies in other sectors outsource their AI based requirements to a technology company excelling in AI, they save the time and cost. As a result, most companies are willing to outsource their requirements to a tech company rather than managing on their own. Conclusion Outsourcing to AI companies help build customized solution and they bring a lot of advantages for businesses who want to resolve their challenges in the most cost effective manner. When you analyze and find out that even top giants like Amazon and Apple are willing to outsource their specific process to AI companies, it wouldn’t be wrong to conclude that outsourcing looks much more feasible option for most companies these days. We at DataToBiz help our partners in their initial phases of R&D involving Artificial technologies. Contact for further details

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Data Warehousing for Business Intelligence: Full Guide

Some would say data’s value is like that of petroleum or water, but in actuality, data is much more precious than water or petroleum because these are depleting or depreciating assets; on the contrary, data will be an appreciating resource continuously adding to its value in the near future. So, if the tech pundits and think tanks from the IT domain say that Big Data is going to get bigger and better with time, they surely have a reason to put up such a bold statement. Those premonitions or rumors that you have heard that data warehousing is going to be dead must kiss the dust as such is not what is going to happen even 30 to 40 years down the line. But just statements don’t make people believe in the larger scheme of things, so you need to read further to know whether DW-a-a-S or Data Warehousing-As-a-Service will see some bright days ahead or it will just settle for no good at all. Future of Data Warehousing for Business Intelligence Data warehousing will turn redundant when the old BI or Business Intelligence techniques cannot easily create valuable queries for a large pool of data, but the pressing question is that is it even possible. Numerous use cases when referred to do not give up a concrete viable solution using the old BI technique. To further demonstrate this claim, we need to look at one of the cases that happened with an employee who joined a Tier 1 Investment Bank in London as a Data Warehouse Architect. His job was to process the Query on a multi-terabyte Oracle Warehouse system querying micro-batch data loading and the end-user performance. But doing this using the old BI systems made life tough. Let’s look at the challenges he faced while using the old BI techniques. Key Challenges Querying Large Chunks of Data Using Old Business Intelligence Dashboard Maximizing Query Performance Data miners or analysts need a solution that can minimize the latency and maximize the query per second. With the old BI systems, the end-user query performance does not perform maximum outcomes. As a result, the analytical query demands have been turning high and unaccounted for. Maximization of Throughput ETL or Extraction of Data must be done in a faster and much more rigorous manner. Such a demand would maximize and utilize the complete potential of the machine. All these things would require high maintenance of the CPU and faster technology solution that can instantly capture queries and deliver efficient query optimization plans. It is quite unlike for an old BI system to perform up to the true potential of the requirement. Hence, the need for a much more scalable and agile data analytics system arises that can instantly resolve this problem. Maximum Utilization of Machine When you have to analyze and process a large chunk of data, it should begin with analyzing and processing 100% of the CPU capacity. The old BI systems do fail to utilize 100% of the machine performance. But the new systems are equipped to utilize the machine at its full potential. Therefore, you tend to get the full volume or true potential of the machine. ETL Process The old BI systems completely overrun the true potential of the machine. When the machines are forced to perform beyond their processing levels, they either give botched or inefficient results or at times, they completely heat up and fail to deliver any results at all. At such times, the need for a fully functional data warehousing architecture is required that can cope with the existing tech infrastructure and deliver the results that are expected of it. How to Overcome These Challenges? When experts and IT think tanks raise questions on the existence and sustainability of DWaaS (Data Warehousing as a Service) or Data Warehousing in the near future, here are a few key arguments to support that it will go strong without any signs of giving up anytime soon. As the Business Intelligence is transcending with an advanced time loop for managing key data analytics, the need for agile and advanced warehousing solutions has been felt more than ever. DWaaS or maintaining a Data warehousing architecture is so essential and it will remain that way 10 to 20 years down the line because; Agility is The Future Trend Agility will be the new language that most enterprises would love to speak in the upcoming feature and DW-a-a-S will empower businesses by helping them take a collaborative approach to problem-solving. With advanced DWaaS solutions, enterprises need not have to maintain separate departments, teams and setups for data mining and analysis. When the new data warehousing architecture will help adopt a new model that helps in cross functioning of different teams to support the continuous evolution and improvement, enterprises can better deal with data extraction in a much more fascinated and smart manner. The Dawn of Cloud Systems The needs of the enterprises will change from MYOS or Maintain-Your-Own-Server to cloud-based movement or shift. Cloud-based DW-a-a-S improve the sources from where the data can be gathered and analyzed for future business intelligence. There will be fewer chances of data duplicity when massive data movement is involved using the DWaaS. These trends completely paint a rosy picture of DWaaS as the game-changer in the near future when enterprises and businesses are in need of the right BI dashboard that can perform multiple business operations. Do We Still Need Data Warehouse? One question many people in the industry ask is where we still need a data warehouse. Is it relevant in today’s world?  The short answer is yes. Though data warehouses are becoming older models, and some enterprises are replacing them with data lakes, the data warehouse is still a part of the business intelligence infrastructure. There are many reasons for this: Data warehouses can integrate data from multiple sources. A data lake can store them in one place but not integrate structured, unstructured, and semi-structured data the way a data warehouse

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The Past, Present and the Future of Natural Language Processing?

Making our machines understand the language has made significant changes in the field of machine learning and has improvised the various Natural Language Processing models. But on the contrary, it was quite difficult for machines to understand the underlying meaning of a sentence and how it has its importance in a bunch of sentences, until Google published BERT. Let’s consider the following statements: Sushant was my friend. He was a good coder but lacked the idea of optimized code. When in need, he has always helped me. Humanly, this sentence has a clear meaning but quite difficult to understand for a computer. Natural Language Processing (NLP) has been a major player for training machines to understand and evaluate the meaning. But every Natural language processing (NLP) module, at some point lacked the ability to completely comprehend the underlying meaning of the sentences. In the above sample statement, every highlighted word points towards the person “Sushant”, for a model trained to find and evaluate the specific keywords in a sentence would fail to connect the dots here. Models are particularly trained to understand and evaluate the meaning of the words in one-after-one manner, which made the above mentioned sample quite out of scope. Now the need was, of something, that did not just understand the later part of the word but also the prior. Not just to connect the meaning with next word but to compare the meaning with last word too. Transformer by Google: The Transformer by Google, based on Novel Neural Network Architecture follows a self-attention mechanism and did surpassed recurrent and convolutional models for English language. Along with translating English to German and English to French, Transformer requires competitively less computation. Transformer performs small tasks over a sequence and applies self-attention method, which establishes a relationship between differently positioned words in a sentence. In the sample statement about Sushant, it is important to understand the normal word ‘he’ refers to Sushant himself and this establishes the ‘he-him-his’ relationship to the mentioned person in a single step. And then Google Introduces BERT: Until BERT by Google came in to picture, understanding the conversational queries was quite difficult. BERT stands for Bidirectional Encoder Representations and is a big leap in the field of Language Understanding. The word Bidirectional itself means functioning in two directions. It was amazing to see BERT exceed all previous models and become the unsupervised pre-training natural language processing. In practice, BERT was fed with word sequences with 15% of words masked, kept hidden. The aim was to train the model to predict, value of the masked words based on the words provided in the sequence, unmasked words. This method, known as Masked Language Modelling performs to anticipate the masked, hidden words out of sentence, based on context. One of the finest application of such improvised models are seen with search engines, to find particular meaning of the sentence and to provide matching results, greatly helps in filtering the required information. There was time when Google used to rely on keywords, specifically added in blog post or website content, but with BERT, Google steps ahead and will now interpret words, NOT JUST KEYWORDS. Google search has been implementing BERT, as improvised software for better user experience. But with advanced software we need to implement hardware with similar capacities and this is where latest Cloud TPU, Tensor Processing Unit by Google, comes in picture. While enhancing the user experience, Google’s BERT will affect your SEO content too. Currently, these changes are being made with English Language Search for Google U.S. But with aim to provide better result over the globe, Google will be implementing teachings of One Language to others, from English to rest. Consider the following sentences: That flower is a rose. That noise made him rose from his seat. If the machine is trained to understand and interpret the meaning of the sentence with one-by-one method, the word “rose” would be a point of conflict. On the contrary, with latest developments and thanks to google for open sourcing the BERT, the meaning of the word rose will now vary according to the context. The aim is not to interpret, how the flower is ‘rising’ or how the noise is making him into a ‘rose’, a flower. XLNET and ERNIE: Similar to Generative Pre-trained Transformer aka GPT and GPT-2, XLNET is BERT like Autoregressive language model, which predicts to next word based on context word’s backward and forward intent. Outperforming BERT and XLNET, Baidu has open sourced ERNIE Another Pre-Training Optimized Method for NLP by Facebook: Improvising what Google’s BERT offered, Facebook advanced with RoBERTa and DeBERTa NLP models. Using Bert’s Language Masking Strategy and structure, Facebook’s RoBERTa offered an improvised understanding for systems to anticipate the portion of text which was deliberately kept under surface. Implemented using PyTorch, FB’s RoBERTa focuses on improving a few key hyperparameters in BERT. Various Public News articles along with unannotated Natural language processing data sets were used in training RoBERTa. DeBERTa, Decoder-based Pre-Training is a dynamic NLP model based on BERT structure. It utilizes a dynamic masking pattern of pre-training and a larger model size than BERT and RoBERTa which allows it to better apprehend the context and meaning of the text. Overall, it ensures a more robust representation of input content. GPT 3 GPT3 or Generative Pretrained Transformer is a trending NLP model developed by OpenAI is an advanced language model for natural language processing. It functions upon the massive amount of text data containing 175 billion parameters trained upon the Common Crawl dataset. It performs a wide array of tasks such as language translation, question answering, summarization, and very viable human-like text generation as well. And then Microsoft Jumped in: Moving ahead, Microsoft’s MT-DNN, which stands for Multi-Task Deep Neural Network, transcends the BERT by Google. Microsoft’s NLP model is built on 2015’s proposed model but implements BERT’s Network architecture. Implementing Multitask Learning (MTL) along with Language Model Pretrainig of BERT, Microsoft has exceeded previous records. Achieving

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10 Amazing Advantages of Machine Learning You Should Be Aware Of!

Machine learning (ML) extracts concrete lessons from raw data to solve complex, data-rich business problems fast. ML algorithms iteratively learn from the data and enable computers to discover various types of deep insights without being specially trained to do so. ML develops at such a rapid rate and is driven primarily by emerging computational technology. Machine learning in business helps improve business scalability and business operations for companies around the globe. In the business analytics community, artificial intelligence tools and numerous ML algorithms have gained tremendous popularity. Factors including rising quantities, convenient data access, simpler and quicker computer capacity, and inexpensive data storage have led to a massive boom in machine learning. Organizations can, therefore, profit from knowing how businesses can use machine learning to apply the same in their processes. Machine learning (ML) and Artificial Intelligence in the business sector have created a lot of hype. Marketers and business analysts are curious to learn about the advantages of machine learning in the industry and its implementations. For ML architectures and Artificial Intelligence, several people have heard for. But they’re not entirely conscious of it and its implementations. You must be mindful of the business problems it can address to use the ML in the market. Machine learning collects useful raw data knowledge and offers detailed tests. And that knowledge helps to solve dynamic and data-rich issues. Machine learning algorithms, too, learn and process from the input. The methodology is used without needing to be trained to find different perspectives. The ML is rapidly evolving and being powered by new technologies. It also allows the company to boost regional organization scalability and business operations. Recently, in their company, several top-ranking businesses such as Google, Amazon, and Microsoft have embraced machine learning. And they’ve introduced tools for online machine learning. Why Machine Learning Is Important?  Machine learning is important because it primarily works with a huge variety of data. Processing big data is cheaper when you use an algorithm to automate the process rather than rely on manual processes done by humans.   A machine learning algorithm can be quickly trained to analyze datasets and detect patterns that are not easily identifiable otherwise. ML makes automation possible, which, in turn, saves time, money, and resources for an enterprise. When you can get better and more accurate results for a fraction of a cost and in a handful of minutes, why not invest in machine learning models? Here’s why machine learning is important in today’s world: Voice assistants use Natural Language Processing (NLP) to recognize speech and convert it into numbers using machine learning. The voice assistant then responds appropriately. While Google Assistant, Siri, etc., are used in domestic life, organizations are using similar voice assistants at the workplace to help employees interact with machines using their voices. It promotes self-service and allows employees to rely on technology instead of their colleagues to finish a task. Companies in the transportation industry (like Ola, Uber, etc.) use machine learning to optimize their transportation services. Planning the best route, setting up dynamic pricing based on the traffic conditions, and other such aspects are managed using machine learning software. ML also helps create better physical security systems to detect intruders, prevent fake alarms, and manage human screening in large gatherings Machine learning helps improve the quality of output by minimizing/ preventing bottlenecks. Be it the production lifecycle, cyber security, fraud detection, risk mitigation, or data analytics, ML technology offers valuable insights in real-time and gives businesses an edge over the competitors.  Some Basic Advantages of Machine Learning Here are some of the major benefits of machine learning that every businessman must be aware of. Each business organization relies on the information received through data analysis. Big data is on the businesses. But it’s difficult to extract the right information and make a decision from the results. Machine learning takes advantage of ML algorithms. It also learns from data already in use. The findings help to make the right decision for the businesses. It allows companies to turn data into knowledge and intelligence that can be used. The experience will work into daily business processes. These processes then deal with changes in market requirements and business circumstances. Business organizations should use machine learning in this way. It holds them on top of the rivals. Top Advantages Of Machine Learning ML aims to derive meaningful information from an immense amount of raw data. If implemented correctly, ML can act as a remedy to a variety of problems of market challenges and anticipate complicated consumer behaviors. We’ve already seen some of the significant technology companies coming up with their Cloud Machine Learning solutions, such as Google, Amazon, Microsoft, etc. Here are some of the critical ways ML can support your company: 1. Customer Lifetime Value Prediction Prediction of consumer lifetime value and segmentation of consumers are some of the significant challenges the advertisers face today. The business has exposure to vast amounts of data, which can be used easily to provide insightful insights into the Market. ML and data mining will help companies to forecast consumer habits, purchasing trends, and improve individual customers to submit best-possible deals based on their surfing and purchase experience. 2. Predictive Maintenance Manufacturing companies regularly follow patterns of preventive and corrective repair, which are often costly and ineffective. With the emergence of ML, though, businesses in this field will make use of ML to uncover valuable observations and trends embedded in their data on their factories. It is recognized as predictive maintenance, which helps reduce the risks of unforeseen problems and reduces needless expenditures. Historical data, workflow visualization tools, flexible analytical environments, and feedback loops can be used to build ML architecture. 3. Eliminates Manual Data Entry Duplicate and unreliable records are among the most significant problems. The businesses are facing today. Machine Learning algorithms and predictive models will significantly prevent any errors induced by manual data entry. ML programs use the discovered data to make these processes better. The employees can, therefore,

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Data Warehousing and Data Visualization for Massive Growth!

Data is not just any simple random collection of information anymore, rather, an influential insight that can convert into a monetary form if used properly. As a result of such an influence, many corporations have been choosing and managing data warehouses powered by DW-a-a-S (Data Warehouse As a Service)  to keep important information coming from multiple sources in one common region termed as a data warehouse. These relevant chunks of information are refined and processed for better use and application. As data delivers value to the organization through data visualization, it significantly influences; But the role further transcends when data visualization fuses with data warehousing to provide real-time leverage and value to the enterprises. But before we delve deeper and explain the correlation between data warehousing and the data visualization process, the readers must first know these terms.  What is Data Warehousing or DWaaS and Data Visualization? Data warehousing is a specific process that collects data from multiple sources and stores it in one place for use. Data warehousing services include data cleaning, data integration, and data consolidation. On the other hand, data visualization is a technique representing data in a visual form for a better understanding of underlying data. The role of visualization is essential to clearly convey what the data means and how it can significantly influence decision-making through such representation. Data Warehousing for Business Intelligence The older data-driven models included decision support applications that worked with transactional databases rather than data warehouses. It is similar to accessing a data lake but without the benefits of using big data. The lack of a data warehouse led to certain challenges:  Data warehouses arrived as a solution to the above challenges by creating a vast centralized database with historical and real-time information collected from several sources. From managing transactions to organizing and understanding data, data warehouses allow enterprises to use data efficiently irrespective of the nature and volume of the business.  Data warehouses have become an integral part of data pipelines and business intelligence systems. Business intelligence tools are connected to data warehouses to run analytics and generate data visualization reports in real time.   Why use Data Warehouses of OLAP?   Data warehouses approach data using a process called OLAP (Online Analytical Processing). OLAP is used by enterprises to run complex queries for day-to-day operations. The retail, sales, and financial systems are some examples of OLAP. OLAP requires data from a centralized database. Data has to be ETL (Extract, Transform, Load) processed before it can be used for OLAP. Data warehouses provide the necessary infrastructure for both purposes. The combination of OLAP and data warehousing makes it easy to run business intelligence analytics and derive actionable insights. The insights are presented as data visualization reports using BI tools. These reports are used by employees and management to make faster and better decisions.  How Business Intelligence Relies on Data Warehousing & How Data Visualization Adds Value to It? Business intelligence is important for analyzing and influencing the stored data in a much more refined manner for better insights. When data is stored in a proper & sequential format, it speeds up the decision-making process. The business intelligence tools showcase important information from the data and portray the real problems which significantly slow down the business. Such information is stored in the data archives or warehouses and DWaaS providers help in the meaning extraction of such data and provide a true shape to it for information gathering and analysis.  When data warehouse fuses with Business Intelligence, better availability of historical data, data analysis from heterogeneous sources, reporting of queries from multiple sources, and availability of data in the required format happen. But everything remains incomplete until and unless, data visualization comes into the picture. Data warehousing collects all the data and stores it in one place; whereas, data visualization significantly pinpoints the key areas that need focused attention.  With the help of data visualization services, an additional value is added to the already existing collaborative and consolidated data. Such insight can help in predicting sales, anticipating trends, and even manipulating prices as per the changing market dynamics. To understand how data warehousing is adding value to the Data Visualization Process, you need to understand the use case that simplified flight analytics and improved the business of the airlines.  Use Case to Demonstrate How Data Warehousing is Adding Value to  Data Visualization Process Data visualization cannot act on its own until and unless there is a large chunk of processed, cleansed, integrated and consolidated data available from which the trends and patterns can be sorted or meticulously picked. To better explain this, you need to take the example of air flight travels and how DW-a-a-S along with the Data Visualization Process can significantly change the overall scenario of flight delays and other problems that lead to a significant loss in the revenue of the airlines and discomfort for the passengers.  Everyone flies once maybe in their lifetime and the worst experience for such fliers would be delayed or canceled flights. The situation might be bearable for someone who flies occasionally like 3 to 4 times a year. But what if someone is flying maybe 5 to 10 times a month? The delay might be unbearable for them. Not just for the passengers but even for the airport and the airline company. So, these airline companies are looking for a smart solution that can anticipate delays in flights through the use of technology.  In this pursuit, representation of data that must be presented in a clear and concise manner will make the difference, hence Data Warehousing or DW-a-a-S looks like an amicable solution for the same. But without bringing data visualization into the process, it will be very hard to pinpoint the key problem areas that need the right approach for an amicable solution. Data warehousing or DW-a-a-S can add value to the Data Visualization Process by working on the following areas;  Specifying the Reasons for Flight Delays   Let’s take an airline industry problem

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How Can Data Science Help Grow Your Business? The Advantages of Data Science!

In this piece, you will get to know whether you should include Data Science in your business plan or not. Here we are going to define why enterprises are using Data Science and what are the basic advantages of Data Science. Unlike the times before, terms such as Big Data & Data Science are not confined to techies anymore. As digital technologies are outgrowing all the expectations, business owners are equipping themselves with knowledge of the new technologies that can help their business growth. The advancements in technology have led to a new era of growth and success. According to the reports, big data is expected to generate around a 60% increase in the retailers’ operating margins on a global scale. The experts have also said that the European government administration can save around $149 billion in operational efficiency if they keep pacing on the big data technology.  Did you know that an exponentially large number of internet users leave impressions of all their choices, preferences, thoughts in the digital form? But what’s the point? The point here is that every business owner can exploit these impressions using data science to help their business grow. Although it is easy said than done, still more and more businesses are exploring these opportunities to excel in their respective industry.  There are many advantages of Data Science when it comes to Your business.  Before we jump on discussing these advantages, let’s find out what data science actually is. Data is the information that is available in abundance all over the web. The scientific use of this data collected online from various sources can only be done if it is churned and categorized on the basis of the information that a user wants.  This is where Data Scientist comes in. Using different algorithms, they managed to extract the exact information that can be used for your business.  Kevin Murcko, CEO, CoinMetro, said that “Users of big data have typically been large enterprises who can afford to hire data scientists to churn the information. But now, thanks to the democratization of tech and the rise of blockchain, there are tools that can be used by small and medium-sized companies to both gather big data and to use it to make good business decisions – decisions that will help them be competitive and grow.” With that being said, let’s discuss how Data Science Can Help Grow Your Business.  How Data Science Can Help Grow Your Business? The Advantages of Data Science! According to the survey, around 65% of business owners have already included data science in their IT infrastructure, the number is set to increase. Several surveys have also shown that spending on big data is expected to increase to $114 billion in 2018. Here is why and how data science can help your business. Understanding this, companies like DatatoBiz has started to develop the best service set for enterprises. 1. Improved And Educated Business Decision Making Decision making is one of the integral parts of running a business. When it comes to taking a decision, it would be much better if you have some facts to back your decision. With Data Science, you can actually measure & track the response of your present and potential customers towards your service or product and make an educated decision in the future. The study of consumer patterns and behavior can actually bring forth several new opportunities for your business. Many eCommerce business owners have been seen taking advantage of Data Science to improve their sales. If you are not among them, check out our article on kickstarting the data analytics for your eCommerce Business.  2. Better Analysis of Market Trends  With the incorporation of data science in your business, you can actually gather useful information on the present market trends with respect to your industry. A data scientist can actually fetch the information and study it to bring hard facts about consumption trends into the light. Which in turn, can help you modify the products and services for good, helping in business growth.  3. Save Extra Expenses | Benefits of Data Science. There are many advantages of data science, one such benefit of it is the cost-effectiveness. With the help of data science, big data, you can actually study the market and figure out if your business is going to be in the right direction. If the data analysis shows that the product or services you are planning are not that popular, it might be a wrong decision. Thanks to the report, you can now stop right there and then, saving the money you could have lost without the information. Not only this, a great data scientist like we have at DatatoBiz can also use this data science to predict what is going to be big in the coming time, helping you make the decision to make more money.  You might also like to check out how data analytics is changing the Fin-tech industry?  4. Better Idea Testing, Advantages of Data Science. Every business runs on ideas. So, if you have ideas of introducing new products or services to the market, it is definitely good to have data science play out the field first. With the combination of data science, big data and AI, you can actually tell if your idea is worth spending money or not, which can be done by analyzing current trends and making predictions on the data collected.  5. Educated Selection of The Target Market  There are many benefits of Data Science like with the help of data science, you can collect the customer data which may include the entire demographics including their behavior and their consumption patterns towards your product or services. This analysis of their behavior and consumption pattern you can find the right set of customers. This process will help boost your business and increase the sale of your service or product.  We are sure that after reading all these points you must have gathered the insight on how data

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Data Warehousing as a Service (DWaaS): Faster Business Decision Making

The role of data is changing significantly with advanced methodologies used for upgrading sales and integrating business intelligence in the organizational schema for better outcomes. But in the absence of proper tools and techniques to manage and organize such a large pool of data, results might appear too far-fetched. So, what is the way out to handle bundles of unstructured data and harness its true potential for catalyzing growth? Data warehousing is the solution that enterprises seek to support in decision making and grabbing potential markets using real-time insights provided by it. Data analytics helps in reporting, analyzing, and creating numerous use cases that revolve around solving your business problems.  Today, several industries around the globe are partnering with data warehousing consultants or data warehousing companies to streamline their business processes. What is Data Warehousing & How It is Helping Enterprises Grow Rapidly Through  DWaaS Data warehouse is an advanced system designed to help enterprises perform data analysis and reporting. The organization has multiple sources to collect data; therefore, it requires an advanced approach that structures all collected data and harnesses the value from it to speeds up the decision-making process and contribute to the faster growth of the organization. But modernizing the use of such a huge chunk of unstructured data requires an innovative approach. Therefore, enterprises trust DW-a-a-S for this purpose where data usage by integrating the organization’s operational system (ERP, Historian, PI System) can be simplified under one hood termed as data warehouse and refining process can work better to fit in the use cases that directly contributes to the growth and market capitalization.  What is the Purpose of Data Warehousing? A data warehouse is a repository of digital data stored by the enterprise. The primary purpose of data warehousing is to allow companies to access the huge amounts of data stored in the centralized database. The data is cleaned, formatted, and analyzed to provide actionable insights.  The right data warehouse is used as a data management system where business intelligence and data analytics can be performed to understand patterns in the data and derive actionable insights for decision-making purposes. The data warehouse is used to run queries to find the information the business wants and can seamlessly deal with large datasets, unlike database systems.  Data warehousing is not a new concept in the industry. With data becoming increasingly available from several sources, enterprises need to find a way to store this data and use it for analytics. A data warehouse is a comprehensive solution as it can store huge volumes of data in its multi-tier structure. It can be a physical storage unit located on-site or a cloud storage platform accessed through the internet.  Depending on the size of your business and the data collected, you can opt for either of the data warehouse architecture:   A data warehouse is important to run business intelligence tools without spending too much money on querying. It helps define the data flow within the enterprise and makes it easy to access and share data among the departments and teams.  Working of Data Warehousing, Data Lake & Benefit They Can Bring For the Enterprise  Data warehouse works like a big data lake of information that is designed to store and preserve a large chunk of information. Business requires real-time insights that support trend analysis by setting up an efficient system that improvises the processing and sorting of details; thus preventing data duplication and creating a better usable data history that yields results. Such an approach or working of data warehouse helps in maintaining a complete data history even if it has been purged from different source transaction systems. When the data gets assembled from a data lake is one place working like dashboards, the resulting outcome is different business applications working efficiently to deliver quality as they have just one data source from where they can extract information and refine the data for different purposes in the organization.  Why Enterprises/Corporations Prefer Data Warehousing as a service (DWaaS) for Business Intelligence? Bettering Business Intelligence  Decisions are important in business and they can be perfect when fuelled by insights from real-time data collected. While framing the business strategy or setting up a specific operational module as a standard operating procedure (SOP), corporations have to rely on data-driven facts that can help them take concrete decisions. Real-time insights from unstructured or parsed data that establish a better data visualization can increase the efficiency in market segmentation, inventory management, financial management and lastly sales. In this way, the business can grow up to become much more competitive and market-ready to withstand any disruptions occurring at the technology, trends, or consumer behavior.  Setting Up High Performance Data Analysis  Data warehouse is not just a godown storing information rather a place that expedites the data retrieval process and organizes data engineering and analysis. When you need to make quicker decisions, the presence of a system that can query the stored data in a structured manner and provide real-time feedback simply makes the process swift.  The high-performance data analysis helps in understanding trends and adapting as per the change so that better decisions can be made in due time that delivers results for the corporations.  Faster Data Access Data warehouses create one single dashboard that stores KPI or Key Performance Indicators. Some of the data indeed are KPIs that influence decision making and when they are available in a flash, it saves a lot of time for the corporation. When one dynamic dashboard works in the organization, the higher management does not have to rely on the IT team for collecting data. When you have access to one single dashboard that performs multiple functions, decision-making becomes faster as you do not have to spend much time on gathering data, rather, you can focus more on the analysis that can influence the outcomes.  Quality Enhancement  Quality enhancement is necessary for creating a frugal data visualization that has the potential to influence decision-making with long-term objectives. In data warehousing,

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Face Recognition: ONNX to TensorRT conversion for Arcface model problem?

Are you also fascinated to get an inference from a face recognition model on jetson nano? I fail to run TensorRT inference on Jetson Nano, due to PReLU activation function not supported for TensorRT 5.1. But, the PReLU channel-wise operator is available for TensorRT 6. In this blogpost, I will explain the steps required in the model conversion of ONNX to TensorRT and the reason why my steps failed to run TensorRT inference on Jetson Nano.  Steps included to run TensorRT inference on Jetson Nano : The first step is to import the model, which includes loading it from a saved file on disk and converting it to a TensorRT network from its native framework or format. Our example loads the model in ONNX format i.e. arcface model of face recognition. Next, an optimized TensorRT engine is built based on the input model, target GPU platform, and other configuration parameters specified. The last step is to provide input data to the TensorRT engine to perform inference. The sample uses input data bundled with model from the ONNX model zoo to perform inference. Sample code: Now let’s convert the downloaded ONNX model into TensorRT arcface_trt.engine. TensorRT module is pre-installed on Jetson Nano. The current release of TensorRT version is 5.1 by NVIDIA JetPack SDK. Firstly, ensure that ONNX is installed on Jetson Nano by running the following command. import ONNX If this command gives an error, then ONNX is not installed on Jetson Nano. Follow the steps to install ONNX on Jetson Nano: sudo apt-get install cmake==3.2 sudo apt-get install protobuf-compiler sudo apt-get install libprotoc-dev pip install –no-binary ONNX ‘ONNX==1.5.0’ Now, ONNX is ready to run on Jetson Nano satisfying all the dependencies. Now, download the ONNX model using the following command: wget https://s3.amazonaws.com/ONNX-model-zoo/arcface/resnet100/resnet100.ONNX Simply run the following script as a next step: We are using Python API for the conversion. import os import tensorrt as trtbatch_size = 1 TRT_LOGGER = trt.Logger() def build_engine_ONNX(model_file): with trt.Builder(TRT_LOGGER) as builder, builder.create_network() as network, trt.ONNXParser(network, TRT_LOGGER) as parser: builder.max_workspace_size = 1 << 30 builder.max_batch_size = batch_size # Load the ONNX model and parse it in order to populate the TensorRT network. with open(model_file, ‘rb’) as model: parser.parse(model.read()) return builder.build_cuda_engine(network) # downloaded the arcface mdoel ONNX_file_path = ‘./resnet100.ONNX’ engine = build_engine_ONNX(ONNX_file_path) engine_file_path = ‘./arcface_trt.engine’ with open(engine_file_path, “wb”) as f: f.write(engine.serialize()) After running the script, we get some error “Segmentation fault core dumped”. After doing a lot of research we have found that there is no issue with the script. There are some other reasons why we are facing this problem. The reasons and explanations are discussed in the following paragraphs. What are the reasons for which model conversion failed? Jetson Nano is a ARM architecture-based device where TensorRT 5.1 is already pre-installed. The image which is written on SD card of NVIDIA Jetpack SDK does not includes TensorRT 6. It is possible to convert other models to TensorRT and run inference on top of it but it’s not possible with arcface. The arcface model cannot be converted because it contains a PRELU activation function which only supports TensorRT 6. Model cannot be converted because we are unable to upgrade the TensorRT version from 5.1 to 6. So, unless and until NVIDIA provides us a Jetpack SDK OS image with the latest version of TensorRT 6 specifically the arcface model cannot be converted. Why can’t we upgrade from TensorRT 5.1 to TensorRT 6? The installation file of TensorRT 6 is only supportable for AMD64 architecture which can’t be run on Jetson Nano because it is an ARM-architecture device. That’s why, the arcface ONNX model conversion is failed. Future Work and Conclusion As soon as, NVIDIA Jetpack SDK releases OS image with TensorRT 6 the arcface ONNX model will get converted to TensorRT and we can run inference on top of it. I am all ears to know your thoughts/ideas to make it happen if NVDIA is taking its time to update jetpack SDK. We at DataToBiz always strive for latest tools & technologies to get ahead from our competitors. Contact for further details About Author: Sushavan is a student of B.Tech in Computer Engg. at Lovely Professional University. He worked as an intern at DataToBiz for 6 months.

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