Page 29 – DataToBiz

Yashica Chopra
July 28, 2022
5 Comments

How Can Businesses Adopt AI Technology to Achieve Their Goals

Artificial intelligence is a dynamic force that keeps the industry moving forward to conquer more technologies. From manufacturing to hospitality to retail and aerospace, AI is being adopted by several organizations across all industries. However, businesses are still in varying stages of adopting AI in their enterprises. While the top companies have added AI technology as an integral part of their systems, SMEs still use AI to develop pilot projects for certain departments like sales, marketing, etc. Let’s learn about the adoption of AI in certain industries and how they leverage it to achieve their goals. Why Artificial Intelligence Is Important The social applications of AI, such as home assistants, smartphone applications, etc., are different from enterprise-level applications. The latter is on a much complex and wider scale, encompassing various systems and processes in the business. Artificial Intelligence Types Unlike, in theory, AI is not a one-size-fits-all solution. It is vital to understand what the enterprise needs before investing in AI solutions. While pre-designed software is useful, it doesn’t deliver results like customized AI systems. But to understand this difference, we need to know more about artificial intelligence, the types, and what AI aims to achieve as an end goal. AI is classified as follows- Narrow AI It is used for goal-oriented purposes such as facial recognition, speech recognition, etc., where machine learning algorithms complete the task based on a set of narrow constraints. General/ Strong AI It mimics human intelligence and behavior so that it can solve any problem and not be limited to specified tasks. We still haven’t successfully created AI machines with cognitive abilities. Artificial Superintelligence This is currently in the theoretical stages. ASI machines are supposed to not just mimic humans but become one of them by being self-aware. Artificial Intelligence vs. Machine Learning In simple terms, AI is an umbrella concept where machine learning and deep learning, understand data and make decisions based on it. ML algorithms are used to quickly process vast amounts of data and deliver valuable insights. What is the End Goal of AI? AI sounds fascinating, and we can see that large-scale enterprises and global organizations are already getting results from AI. But questions are still aplenty for new businesses that want to hire AI services to revamp their systems. The ultimate aim of using AI is to get measurable results to prove that the enterprise is better than its competitors. AI works the best with a centralized system where data is collected from several sources and is stored in a data warehouse or a data lake for further cleaning and processing. Artificial intelligence drives ROI for businesses and delivers long-term results when the technology used aligns with the systems in the enterprise. AI is not limited to scaling but also to measure the results and get the most out of data. Why Should Companies Adopt AI? What makes AI so important that companies adopt and integrate it as a part of their business processes? How does AI help organizations overcome challenges and become industry leaders? The following are some uses of artificial intelligence that empower businesses to perform better in the competitive market and grab the opportunities that come their way. 1. Artificial intelligence enables better customer service and support. AI is used to create and deploy chatbots, provide smart suggestions in emails, develop human-like interactive voice response systems, automate ticket generation and routing complaints, and make it easy for customer service agents to solve an issue in less time. The live chat option on many websites has customized chatbots to respond to users’ queries. 2. AI helps with target marketing and generating more leads. Artificial intelligence is used to create content for promotional emails, develop ads for each customer segment, provide product recommendations based on customers’ purchase and search history, speech recognition, etc. Amazon’s website is the best example of target marketing using AI. 3. Use AI in the HR department to hire eligible candidates and create customized training programs for employees. HireVue is a well-known AI-powered platform that helps more than 700 leading brands to hire some of the best talents in the market. Vodafone, Unilever, and Oracle are some companies that use HireVue for recruiting purposes. Similarly, artificial intelligence helps create different training modules based on employees’ skills. 4. AI streamlines the supply chain and shortens the delivery cycle. Amazon has the best warehouse management system powered by AI. Artificial intelligence helps in managing inventory, increasing safety for warehouse employees, planning the travel route for deliveries, finding alternate delivery methods, and so on. 5. AI makes the business more secure by enhancing cybersecurity. Many of the latest antivirus software and malware are developed using artificial intelligence. The software is trained to identify phishing attacks and malicious links before employees fall prey to cyberattacks. The FaceID access feature of iPhones is a small yet everyday example of how AI is used for cybersecurity. 6. AI technology is used to detect fraudulent transactions and minimize losses. AI processes vast amounts of data in a few milliseconds and identifies patterns in transactions. Many banking and financial institutions have started to use AI and ML-based fraud detection software to identify unscrupulous customers and cybercriminals with stolen identities. Teradata provides AI-powered fraud detection software to banks. 7. Data is put to good use through AI applications. Data collected from social media and other sources is processed in real-time to extract meaningful and actionable insights. AI tools are used for predictive analytics and data visualization. Tableau is an example of AI-based data visualization software. 8. AI makes the workplace safer for employees, especially in industries like oils and mining. Artificial intelligence is used to monitor the work environment using drones, surveillance cameras, etc., and determine that the workers are not at risk. AI is used with IoT (Internet of Things) to collect data and process it to identify any signs of accidents or risk. AI helps the management create a safer working environment in factories. 9. AI optimizes the use of resources and helps the enterprise and employees

Vikas Ojha
July 27, 2022
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Optimizing the T5 Model for Fast Inference

Model deployment is the most important aspect of developing a machine learning product. The development of every product starts with an idea and then we check if it’s feasible or not. Once we are sure that it is feasible we gather the data and start developing a model for it. During the model development, we also keep in mind the accuracy of the model, as well as its size and inference time as all of these factors, are very important for deploying the model but despite doing all these things sometimes we are settled with a model that has good accuracy but its inference time is not so good which might affect both the cost and user experience of the product hence such models needs to be optimized for inference as much as possible so that we can minimize the inference time to as low as possible. In this blog, we will explore the way by which we can optimize a T5 model for fast inference on a GPU. Motivation Behind T5 Optimization We were using the T5 model in our product PrepAI which is a question generation platform where users can upload different kinds of documents, videos, or copy and paste texts and the platform automatically generates different kinds of questions with the provided content. Hence we decided to work on optimizing the T5 model. In this tutorial, we will consider the T5 model is trained for translation tasks and will do the comparison on translation tasks only. Optimization Using TensorRT As a developer whenever we think of optimizing a model for the Nvidia GPU, TensorRT is the obvious choice that comes to our mind because it transforms the model’s graph into a form that can benefit from the architectural structure of the GPU. It achieves this by replacing certain operations in a graph with other operations which are equivalent to this operation but have less computational overhead. It also fuses certain operations together into one and reduces the computational overhead this process is known as graph fusion. We thought of trying the TensorRT for optimizing our T5 model. For this, we used the same codes in the official Nvidia repo to implement the optimization as we just wanted to see how much performance can be achieved by using the implementation. We found out that the model was around 3-4x faster for smaller sequences like <100 tokens but after that, the model starts to slow down, and at 512 tokens long sequences it becomes slower than the original torch model. At that time according to certain developers, there was some bug with TensorRT which later got fixed later on when we switched to the next phase in optimization involving ONNX. Later on, we also found that the implementation also excludes the past key values caching which is essential for speedup on longer sequences. However, this can change in the future and TensorRT might beat other implementations but at the time of writing this blog, it hasn’t been implemented yet. Optimization using ONNX runtime After trying out the TensorRT we decided to optimize the model with ONNX runtime. Converting any model to ONNX and applying little optimization automatically speed up the model by a small bit. During optimizing the model ONNX does basic operations like removing unused nodes, conversion of variables to constant, etc. For models like BERT, BART, GPT-2, Roberta, etc the ONNX also implements graph fusion which fuses the graphs of these models similar to the way TensorRT does. But unfortunately, the T5 model is not available yet. Since graph optimization was out of the plate the obvious method seemed to us was the conversion of the model into float16. Converting Encoder Into float16 The T5 model is an encoder-decoder model hence we tried to optimize the encoder first and then the decoder next. For doing this we utilized the ONNX runtime transformer optimization package. We first all the nodes of the ONNX encoder graph to float 16 and tried to evaluate the speed and accuracy of the model. We observed that converting all the nodes in the encoder destabilizes the encoder and hence the encoder only produces NAN values. The reason for this is that the encoder has a lot of operations which doesn’t work well in float16. Also, the T5 was never designed to be fully compatible with float16 but with bfloat16 and float32 data types. Hence we decided to identify those unstable nodes and keep them in float32 only doing that kept our model stable as well as improved the speed of the model. Conversion of Decoder Into float16 The next part is to optimize the decoder part of the model. Optimizing the decoder is more important than optimizing the encoder as in each generation the number of times an encoder run is for 1 time while the decoder runs for n times where n is the length of the target sentence. Similar to the encoder we started by converting all the nodes in the decoder part of the model into float16 and then evaluating to see how this has affected the accuracy and speed of the model we noticed that the model was not so fast for smaller sequences and it became slower for longer sequences. Digging deep into the ONNX runtime and some open source libraries we came to know about the cause behind this slowing. It was related to the data movement between the GPU memory and the RAM. We know that the decoder accepts the decoder_input_ids and the encoder_ouputs for generating the next token. Each time a prediction is made the input is transferred from RAM to GPU memory and after the calculation when the output which is in ORT format needs to be converted into NumPy which can only be done after moving the data back to CPU. The sizes of these tensors would be large. Moving these tensors consumes a lot of time and hence our model becomes slow as the size of the target output increases as

Kavika Roy
July 20, 2022
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Most Promising Business Intelligence Company in India

Business Intelligence (BI) refers to software that helps companies manage data. It includes tools for analyzing information, making decisions based on those analyses, and reporting results. We have been providing business intelligence solutions to our customers across the globe. We offer a wide range of services including data warehousing, reporting, analytics, dashboards, and mobile apps. Our team of experts has years of experience in developing custom BI solutions for various industries. We use the latest technologies and methodologies to deliver high-quality BI solutions.

Yashica Chopra
July 11, 2022
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9 Advantages of Partnering with a Power BI Consultant

With organizations investing in business intelligence, there is an increase in demand of Power BI experts. Partnering with a consultant is a popular choice in the global market. We’ll discuss the advantages of hiring a Power BI implementation partner for a business. Data is a vital part of the current global industry. So much that 2.5 quintillion bytes are generated every day by internet users. A research study says that daily data generation will reach 165 zettabytes per year by 2025. Statista shows that the global big data market will reach $103 billion by 2027. Data delivers valuable insights and predictions to an organization to make decisions faster and better. The insights are derived through data analytics and business intelligence technologies. A majority of businesses around the world are adopting data analytics and advanced analytics to make the most of the available data. Facts and Factors has reported that the global predictive analytics market will reach $22.1 billion by 2026. On-premises, cloud and self-servicing analytical and business intelligence tools like Power BI have a crucial role in collecting, cleaning, processing, and analyzing data to deliver actionable insights. But how do organizations integrate and use BI tools? There are two ways to do so. One is to build an in-house team with data scientists and Power BI experts. Another method (the most preferred) is to hire offshore Power BI consultants to take care of the process. Various BI consulting companies offer services to help small, medium, and large enterprises adopt the data-driven model and make use of the insights derived by Power BI. Let’s read to find out how Power BI can help your business and why partnering with a consultant is a worthy choice. What are the Benefits of Microsoft Power BI? Power BI is a suite of business intelligence applications and services offered by Microsoft. It comes in various versions (desktop, web, & mobile) and can be used by any business organization. It is a data analytics and visualization/ reporting tool that shares micro and macro insights in real time. Power BI is an internationally famous BI tool for the range of features and services it offers. Here are some benefits of using Microsoft Power BI in your enterprise. Connect Data Sources Data is indeed found in abundance. But how do we collect this data from different sources? You can use Power BI to set up a data processing model where data from multiple sources is collected at a central location. This central database can be a data warehouse or data lake (on-premises or cloud). Power BI is connected to the central database, which has access to data from several sources. Data Integration Data integration is the next step after data collection. It’s where data from different sources is cleaned and transformed to provide a unified view. Power BI is known for its data integration abilities that allow a user to create a common data model after aggregating data from a range of sources. The sources can be online and offline. Power Bi’s ETL (Extract, Transform, & Load) tool is known as Power Query Editor. Turning Data to Insights The biggest advantage of Power BI is to turn raw data into actionable insights. The insights make it easy to understand the patterns, trends, etc., in large datasets. Power BI works with historical and real-time data to deliver insights for decision-making. Employees at different levels in the enterprise can use the insights to complete day-to-day work. Data Security Microsoft has built Power BI to ensure adequate data security and protection. Sensitivity labels, data compliance, defending cloud apps, Microsoft 365 data loss prevention, etc., are some examples of the data security methods adopted by Microsoft to keep your business data safe from unauthorized access and cyber-attacks. Data Visualization Power BI is also called a data visualization tool because of its ease to create reports and view them in real-time. Data visualization is a way of visualizing data insights in easy-to-understand formats. Line and bar graphs, pie charts, heat maps, tree maps, etc., are a few ways to represent data in graphical/ pictorial format. This allows users to understand patterns easily and quickly. User-Friendly Dashboard Power BI offers a customizable and interactive dashboard with a drag-and-drop feature. Users can add filters, KPIs, identifiers, etc., to the dashboard to analyze the same datasets based on various factors. The necessary information can be directly accessed from the dashboard with a couple of clicks. Since the Power BI dashboard is connected to the central system within the business, the data will be up to date at all times. Multi-Platform Usage Power BI comes in different versions. It can be used as a desktop app, web service from the cloud, and mobile app. It can be used as a free or paid service. Microsoft currently offers three versions of Power BI- Free, Pro, and Premium. The consultant will help you choose the right version for your organization based on the volume of business. You can upgrade or downgrade the plans as required. What Does a Power BI Consultant do? A Power BI consultant has an extensive role to play in an organization. The consultant has to provide end-to-end services: The consultant will be responsible for Power BI development, implementation, management, and maintenance in an enterprise. The expert can work with a business from any industry and with varied business requirements. The Power BI consultant will provide tailor-made solutions for each enterprise based on the niche, business volume, market conditions, and long-term goals. Advantages of Partnering with a Power BI Consultant Why do more than half the enterprises adopting the data-driven model hire offshore consultants? There are several advantages of partnering with a Power BI consultant than building an in-house team from scratch and having employees on the payroll. 1. Bridge Talent Gap You need employees with Power BI certification and expertise to build an in-house team. You either have to hire experts or train your existing employees. Recruitment and training are expensive and time-consuming for any business. An easier solution

Mayank Wadhwa
July 07, 2022
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AI in Stock Market: Predicting the Ups and Downs

If you think AI still has a long way to go before it enters the stock market, you’re mistaken. Because hundreds, probably more companies have already started hitting the plans to use AI for stock market trading. For instance, Goldman Sachs, a renowned bank in the USA, led a $72.5 million funding round in an AI and ML specialist (H20.ai) to adopt AI models within the equities trading business. And this was in 2019. You can only wonder how many more companies in the finance sector have adopted AI to benefit from the stock market. But the question is, does AI in stock market work? This blog talks about everything from what AI is and how it is impacting the stock market to AI that can predict the stock market and companies that use AI in different ways to profit from the stock market. You’ll also find some popular tools that you can use for stock market analysis and so much more. So, read in full. Before we move to our main topic, let’s briefly understand what AI is. What is AI? AI or Artificial Intelligence is a technology that lays stress on developing intelligent machines that can work like humans. Common examples of AI in real life are speech recognition, facial ID, weather prediction, etc. Machine learning, an application (or subpart) of Artificial Intelligence, enables machines to learn with experience just like humans. With machine learning, machines are fed with different sources of data which they use to learn, evolve and become better on their own. And this technology has a major impact on the stock market. How? Find out below. How is AI Impacting the Stock Market? AI Reduces Research Time for Finding Stocks When it comes to finding the right stock, a lot of research goes into it. For instance, you have to analyze financial statements, revenue, P/E ratio, dividends paid, profit, loss, and whatnot. And this requires an immense amount of time. However, AI in the stock market solves this problem. By using AI analytics (that uses machine learning), you can crunch all types of data from different sources within seconds. And this way, finding the right stock to invest in becomes a piece of cake. How is this impacting the stock market? AI has reduced both the time and effort required to invest in a stock. People, especially in India, are crazily opening dematerialized (demat) accounts as they can easily choose which one to invest in. For instance, according to Business Standard (published on 15th April 2022), the number of demat accounts in India jumped 63% to a whopping 89.7 million in the financial year 2021-2022. AI Is Completely Free from Emotional Influence Emotional investing is one of the biggest enemies of retail investors. It’s basically a phenomenon wherein individuals base their investment decisions on emotions such as greed or fear. And such financial decisions based on emotions are usually less intelligent. For instance, if you fear that a stock may go down and sell all your holdings without solid research, what you’re doing is an emotional investment. And it’s highly likely that you’ll lose money. Fortunately, AI in the stock market can help. Artificial intelligence knows no emotion. All it knows is algorithms, logic, and the data it’s fed. This way, you can refrain from making a hasty decision based on emotions and thus save money. How is this impacting the stock market? As AI is pushing emotional investing out of the picture. It’s making investors smart. People are now able to make decisions that are based on data and logic. This way, they’re becoming smarter and losing less money. AI Helps with Prediction Prediction is an integral part of the stock market in general and stock trading in specific. An investor performs technical analysis by understanding the historical data, noticing patterns and then analyzing the fundamentals to determine what will happen to the stock. And you can call this phenomenon prediction, based on which, buying/selling decisions are made. While some people are remarkably good at predicting the stock market, the risk of losing always prevails. Because missing out on even the tiniest bit of information can lead to horribly wrong outcomes. However, AI can help. AI in the stock market can help you perform technical, fundamental analysis and recognize patterns better than humans. What’s more, AI can process the latest news, blogs, and interviews to understand and predict how the stock will perform in a more accurate manner. And this improves the overall chances of you making the right prediction. How is this impacting the stock market? While you can call prediction a calculated guess, several people rely on AI for the same. And with AI in the picture, predictions are becoming more reliable, which is motivating more people to invest in the stock market. Also, it’s fueling startups operating in this domain to work harder and produce better systems. AI Helps Automate the Sale and Purchase of Stocks Don’t have time to stay in front of your TV/laptop/phone screen for stock updates? Well, AI platforms make this possible. With the help of AI tools such as eToro Copy Trading, you can automate trades and set your entire or some of your portfolios on autopilot. The platform will charge you a fee and will use AI to make buying or selling decisions. Sounds lucrative, right? Please Note. No matter how lucrative it may sound, you should always have control over the decisions you make. Completely relying on AI for anything, let alone the stock market isn’t recommended. How is this impacting the stock market? With the ability to automate trades, AI is making the stock market more accessible. More people, regardless of their profession, are entering the industry relying on AI-based automated trading tools. AI Helps Reduce the Overall Cost of Trading Hiring a stockbroker is quite costly. You need to pay a commission for every trade the stockbroker makes on your behalf. However, with AI in the stock market, you can reduce these costs to

Yashica Chopra
June 29, 2022
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12 Best Tech Companies to Work For

The best tech company is the one that allows a candidate to achieve their dreams, follow their passion and ultimately become successful in the IT industry. Let’s look at the top 12 tech companies to work for and know more about the job opportunities they offer. Technology is a part of our lives. Many of us work with technology for a living. Even non-technical jobs require technology in today’s world. And the tech industry is a world of its own. Working for tech companies is a big deal in many ways. While the brand name, pay package, and popularity are one side of the story, the other side deals with the passion of being an IT employee. Whether you are a developer, programmer, software engineer, data scientist, or ML engineer, the tech industry is your home and dreamland. It’s where you explore your strengths and work on your weaknesses to become better at your profession. You gain domain experience and move forward in your chosen career path. All of this is possible when you choose the right tech company to work for, and the right choice depends on what you want for your career. While you figure it out, let’s look at the best tech companies to work for. We have fast-growing service providers and established market leaders on our list so that you don’t miss out on the chance to make it big. And that’s not the only factor we considered. We understand the need for work-life balance, an inclusive and positive work culture that allows employees to express their ideas without doubt and provide them a platform to take risks. Having leaders who mentor the team members and encourage them to do better is something every candidate wants at work. A leader who is not prone to partiality and can provide constructive feedback and assessments nurtures the entire team. The companies on our list offer all of this and more. Remember that the tech companies also have HR, marketing, finance, and other departments. 1. DataToBiz DataToBiz is a company that believes in teamwork and has a friendly work environment. With flexible working hours, a casual dress code, and a flat job hierarchy, the company knows how to keep employees happy and stress-free to increase productivity. As one of the top tech companies to work for, DataToBiz ensures extensive training and ample rewards for its employees. The company believes in diversity and inclusion. There are openings for various positions in the company, two of which belong to the non-IT departments (marketing and HR). The company is always looking to expand the team and acquire more talent. DataToBiz is one of the fastest-growing solution providers in the IT industry with industry-leading solutions such as PrepAI (an AI-powered question generation platform) and HireLakeAI (an all-in-one smart recruitment solution). The company is located in India and has clients from the Middle East and the West and other parts of the world. It works with various domains like data science, artificial intelligence, machine learning, and business intelligence. The company develops new ML models and customizes the existing ones to assist its clients with digital transformation. 2. Google Just about everyone has heard of Google. While the search engine is a part of most lives, the company is home to people responsible for offering a vast array of services around the world. Google, without a doubt, is among the world leaders in the IT industry. This multinational company (with headquarters in the US) is known for having a relaxed and productive workplace. Google has in-house and remote teams collaborating on various projects. From cloud engineering to data center responsibilities to hardware and software development, Google has openings in different job positions. The company is also looking for executive leaders and department heads at different locations. Students and aspiring graduates can check out the openings for interns, part-time, and temporary positions. 3. Microsoft Microsoft is a well-known name in the global market. This American company was established in 1975 and had been a pioneer in developing some of the best technologies over the years. Microsoft is a multinational company and has employees from different countries across the globe. The company prides itself on being diverse and inclusive (having employees with disabilities showcase their skills). Microsoft has various job opportunities for students, recent graduates, and experienced IT professionals. From cloud to artificial intelligence to software development, the company works with different domains in the tech industry. Autonomy, passion, and empowerment are the keywords at Microsoft. 4. TCS Tata Consultancy Services (TCS) belongs to the much-acclaimed Tata Group and is one of the best tech companies to work for as a beginner. The Indian multinational company was founded in 1968 and offers IT services in 46 countries around the globe. The company has ranked the first spot on LinkedIn among the top companies operating currently. TCS has job openings for fresh graduates, experienced professionals, and women professionals looking to rebuild their careers after a break. The inclusive and diverse workforce is also a pioneer in research, development, and innovation. From automation to analytics to blockchain, AI, cybersecurity, IT consulting, and sustainability, TCS has various domains and is a part of several industries in the market. 5. IBM IBM (International Business Machines) was established in 1911 and has been dedicated to providing computer-based services to different organizations from across the globe. The company has a global presence and has played a crucial part in promoting digital transformation. This US-based company has gained a reputation for developing cost-effective hardware and software solutions for domestic, commercial, and industrial use. Inclusivity is the highlight of IBM’s workplace policy. The company believes in applying intelligence, logic, reasoning, and science to improve the social and human conditions in the world. IBM creates a work environment that promotes learning, collaboration, and trust. The company also takes its social responsibility seriously and is eco-conscious. The flexible work environment encourages work-life balance. 6. Tesla Tesla was started in 2003 as a manufacturer of electric cars to

Anshul Grover
June 22, 2022
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Getting Started with Dataflow in Power BI: Creating Your First Dataflow

Dataflow is a comprehensive way to deal with large datasets and reduce the load on data analytical tools/software like Power BI. We’ll discuss the need for dataflows, ways to create them, and the uses of dataflows for a business. Power BI is a popular data analytics and data visualization software developed by Microsoft. It is a collection of apps, software services, and connectors that collect, process, store, and analyze data to deliver reports in real-time. There is much more to Power BI than its definition. That’s because Power BI deals with the continuous inflow of data from multiple sources. The accuracy of the reports generated by the software depends on the quality of the input data. Cleaning, sorting, formatting, and streamlining data within the system is essential to get actionable insights. This gets harder when the business has to deal with large datasets. When you add large volumes of data to a system, you need to take extra care to maintain the overall quality. Setting up dataflow in Power BI is a smart solution to manage input data and ensure accurate reports. In this blog, we’ll read more about the problems caused by large datasets and how dataflow solves the problem. Issues with Large Datasets in Power BI Dirty data or unclean data is a real problem in today’s world. We have access to countless information sources. But how good is the data from each source? Errors, redundancy, unwanted details, etc., need to be identified and cleaned before the data is used for analytics. Big Data Data with greater velocity, variety, volume, etc., that cannot be processed by traditional systems is known as big data. Processing unclean big data requires a higher computing and statistical power that can increase the expenses for a business. Spellings and Missing Values Misspelled words or missing characters/ values can change the context of data and lead to the wrong analysis. Identifying these errors in large datasets is time-consuming and effort-intensive. Lexical Errors The difference in data structure between two or more data sources can create confusion when formatting the data into a single structure. Imagine what would happen if one field was attributed to another. Mismatches and Contradictions Data from two sources might contradict each other based on the parameters used. Common abbreviations have multiple meanings, and each source might refer to a different one. Money could be measured in different currencies. Changing the values and correcting them in a large dataset can be a never-ending task. What is Dataflow? Dataflow is a way to prevent issues with large datasets in Power BI. But what is dataflow? The term dataflow has quite a few meanings. Microsoft defines dataflow as a collection of tables that are created in the Power BI workspace. Any number of tables can be added to the dataflow. The existing ones can be edited to correct and update the information. According to another definition, dataflow is a process running in the cloud and not related to any particular Power BI report. The dataflow can be used for numerous reports simultaneously. That means five or ten employees can send a query to the same dataflow at the same time and get the information they require. Since dataflow runs on the cloud, any changes required will not have to be made to all the reports but only to the data in the dataflow. Another explanation of dataflow is comparing it to a typical river or a water body. Just like a river has different sources and stops but ends at a single destination, data in the system also comes from different sources but gets stored and used in the data warehouse/ data lake for analytics. By releasing data from silos and removing barriers, it will create a seamless data flow within the enterprise. When this data is used for querying in Power BI, it will provide better and more accurate insights. Why are Dataflows Important? We now know what dataflow is. But why is it so important for a business to create dataflow in Power BI? What changes does it bring to the business processes? Let’s take a look. Reusability The biggest advantage of creating dataflows is to reuse them multiple times. You don’t have to create a new dataflow for each report. You also don’t need to remove/ delete old dataflow and create a fresh one because of outdated information. One more advantage is that you don’t have to create new data connections each time (both on the cloud and on-premises). Seamless Integration Dataflows can be integrated with existing systems and tools in the business. Dataflows work seamlessly with Power BI as you only have to set up the connections and run queries. Cost-Effectiveness Your Power BI premium subscription is enough to create and access dataflows in data lakes. If you don’t use Microsoft Azure, there’s no need to start using it only for dataflows. There won’t be any additional expense to pay for licenses. Scheduling Data Updates Keeping data up to date is necessary to generate real-time reports. You can track the updates and changes made to dataflow and schedule the refreshing of the tables. Furthermore, you can build different processes to manage dataflows and save them in different places. Short-Term Data Storage A dataflow also serves as a temporary data storage center. Processing a large data file/ database doesn’t require extra time. The data can be stored in dataflow for the time being to speed up the analytics and deliver timely reports. How to Create Dataflow? Here’s how to create dataflow with new tables that are hosted on OneDrive Business: Results of Using Dataflow in Power BI Dataflow reduces the load on Power BI by taking over the transformation layer. Since the tables in dataflow can be edited and reused multiple times, dataflow can be used with many applications within the enterprise. The dataflows can be connected to other Microsoft Power Platform technologies Power Query, MS Dynamics 365, Power Automate, Power Apps, and so on. What are the Uses

Yashica Chopra
June 16, 2022
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Machine Learning for Trading – Can It Predict the Trend?

Machine learning allows machines to trace patterns and identify trends by analyzing huge datasets. Data scientists are working on building ML models to assist traders in day-to-day trades in the stock market. Let’s discuss the role of machine learning in the trading industry. Machine learning is a field of artificial intelligence that deals with how computers process large datasets and learn from them to make decisions and predictions. Machine learning is a vast concept and is classified into supervised and unsupervised-based learning. There are several algorithms designed to train machines on available data. A few of them are Simple & Multi Linear Regression, Logistic Regression, Decision Tree Classification, XgBoost, and many more. The algorithm is trained in different ways depending on the type of datasets fed into the system. Machine learning is almost everywhere in today’s world. From Netflix’s search engine to sales forecasting, supply chain management, manufacturing, healthcare, and more, machine learning is already a part of every industry in multiple verticals. The financial and trading industry also uses machine learning algorithms in different ways. FinTech is changing the way we look at banking and insurance. The crypto market has various AI-based apps that allow users to trade in cryptocurrency with the assistance of advanced algorithms. AI and ML have a role to play in the trading market too. There is no 100% automation yet; ML algorithms assist stockbrokers to offer better services to investors. AI and ML consulting companies offer consulting services to traders and trading establishments to build, customize, and fine-tune the algorithms and automate statistical work. Let’s explore the role of machine learning in trading and see how it can help stockbrokers, traders, and investors. How is Machine Learning Used In Trading? Patterns and trends are an integral part of the trading industry. Machine learning algorithms are good at processing large amounts of data to identify patterns that cannot be easily spotted by humans. The stock market is volatile and prone to be affected by several factors (social, political, economic, demographic, etc.). Being able to correctly predict the trends in advance will help traders minimize the risk of market crashes and increase returns. This makes machine learning a valuable resource in trading. ML algorithms can also be used for: Sentiment Analysis Analyzing the sentiment in the market might help traders determine whether the stock prices for a brand will increase or decrease. Data is collected from multiple sources like social media, websites, forums, news platforms, and so on. Natural Language Processing (NLP) is used to understand the context of the data to determine the market mood. Traders can use this knowledge to adjust their investments and decide if they should buy more stock, sell what they own, or wait for the patterns to establish more clearly. Pattern Detection A majority of the stock market predictions come from hours of manual processing combined with years of experience. Machine learning in stock trading reduces the need for hard labor by automating the analysis. However, human expertise is still vital to deriving insights. The trader should know where to look to identify the patterns. Whether it is designing an automated trading machine or using ML to detect patterns, human experience and intuition are very much necessary for the algorithm to deliver accurate results. ML cuts through the time-consuming tasks of collecting and processing information, but it’s ultimately people who use the insights to make decisions. Real-Time Data Forecasting The machine learning algorithms can be fed more data in real-time. The algorithms are trained to learn and fine-tune the predictions to increase accuracy. For example, aspects like the global weather conditions, political unrest, climate change and its impact on renewable energy, etc., are real-world issues that have a direct impact on the trading industry. The ML algorithms can predict the results of the world’s issues and thus provide a basis for what could happen in the stock market subsequently. Using a combination of algorithms and forecasts will provide better results as multiple factors influence each other. High-Frequency Trading Machine A high-frequency trading machine runs on artificial intelligence. It performs thousands of transactions per day by taking advantage of the subtle changes in the stock market. These changes are almost impossible for humans to track as they happen in a few seconds or minutes (at the most). However, a properly trained trading machine will be fast enough to see the change and use it. That said, similar to pattern detection, this too requires human expertise. The algorithm has to be developed to detect minute changes. Regular updates are necessary to tune the algorithm and minimize errors. The tuning can also be automated to avoid statistical work but still requires human supervision. Chatbots in Trading Another way to use Machine Learning in trading is by developing chatbots for communication. Chatbots in any industry have the same roles and responsibilities. Chatbots communicate with traders and provide the information they ask for (past deals, financial statements, investment records, etc.). The chatbots can also compile a list of trading offers, potential shares to buy, the latest prices, and much more. AI-powered chatbots are faster and more effective than human support teams. A single chatbot can handle multiple conversations at the same time and provide each trader with the required data without feeling tired, getting confused, or making mistakes. The algorithms are designed to take feedback and learn from the previous mistakes to provide better support to the traders. Is Machine Learning Suitable for Day Trading? Day trading is where a trader purchases and sells equities on the same day. Traders buy the stocks of a company in the morning and sell them by the end of the day, preferably to make a profit. Day trading requires a lot of experience and knowledge about the stock market. A minor mistake could lead to losses of thousands and millions. Machine Learning for trading is relatively a new concept, with ML engineers working on developing algorithms that can accurately offer predictions and insights. Different types of algorithms need to be used to develop

Rohit Narain
June 10, 2022
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Analyzing the Brain Waves Data Using Python

The brain waves play a crucial role in sending signals to different parts of the body. Analyzing this data helps scientists uncover the intricacies and complexities of the human brain and provide solutions to help people with brain-related disorders. We’ll discuss brain waves and ways to use Python to analyze this data. The human brain is a key organ to keep us functioning and active throughout our lives. We know that the concepts of artificial intelligence, deep learning, and artificial neural networks are derived from the working of the human brain. ANNs replicate the patterns and designs of the neural networks in the brain to allow the machines to perceive this and analyze data as the human brain does. This, in turn, helps doctors and scientists use advanced technology to understand the complexities of the human brain and identify activity related to brain diseases. We’ll deal with one such method to study the functioning of the brain and analyze the signals it sends to other parts of the body. The brain constantly generates waves of electrical activity. The pattern of the waves changes based on the emotion a person experiences at that point. Devices like EEG kits are used to detect and record wave patterns. Computer programming languages like Python can be used to analyze this data. It helps determine how alert or focused a person is. By processing large datasets with such information, scientists can identify the causes/ reasons for brain diseases and find ways to cure them. Let’s start by reading more about brain waves and the types before learning how Python helps analyze the brain wave data. What are Brain Waves? Brain waves are the electrical pulses used by neurons to communicate with each other. The neurons use electrical impulses to send signals about different human emotions and behaviors. The frequency of each brain wave is different, depending on the emotion felt and displayed by the person. Measured in hertz (Hz) or cycles per second, we have slow and fast brain waves released by the neurons. The brain waves are given individual names to differentiate one from another based on frequency. Types of Brain Waves There are five types of brain waves, with delta being the slowest and gamma being the fastest. The level of human awareness is determined by the frequency/ speed of the brain waves. Delta Brain Waves As the slowest of all, these high-amplitude brain waves have a frequency of 1 to 3 Hz and are experienced by humans when they are asleep. Theta Brain Waves The Theta waves have a frequency range of 4 to 7 Hz and are found when a person is in a dreamy state. When the waves are close to the lower end, they represent the state when a person hovers between sleep and consciousness. It’s also known as the twilight state. Theta waves, in general, signify that mental inefficiency or that the person is either too relaxed or blanked out (zoned out) at that moment. Alpha Brain Waves The alpha brain waves have a frequency range of 8 to 12 Hz. These are larger and slower, representing a relaxed or calm state of mind for a person ready to get into action if the need arises. The alpha brain waves are generated when someone feels peaceful after closing their eyes and picturing something they like. Beta Brain Waves Beta brain waves are faster and smaller, with a frequency range of 13 to 38 Hz. These waves imply that the person is focused on something. They signify alertness, where the person is in their senses and displays all signs of concentration and mental activity. Gamma Brain Waves Gamma brain waves are the fastest ones, with a frequency range of 38 to 42 Hz. These are subtle compared to the other brain waves and work on the consciousness and perception of the person. The waves occur when a person is highly alert and can feel every minute change in their surroundings. Waveforms of Different Brain Waves: Capturing Brain Waves EEG (Electroencephalography) is a popular and most used method to capture brain waves and record the electrogram of the electrical activity on the scalp. It represents the macroscopic activity of the brain waves inside the brain. The electrodes are placed on the scalp to record the activity. EEG is typically a non-invasive process. However, Electrocorticography (Intracranial EEG) is an invasive process. The method measures the fluctuations in the voltage of the ionic current released within the neurons. In clinical terms, EEG is the recording of spontaneous electrical activity in the brain over a period. This data is collected through the numerous electrodes placed on the scalp of a person. The focus of the diagnosis is either on spectral content or the event-related potentials, during a particular duration or for a particular event. Spectral content, on the other hand, analyzes the type of neural oscillations or the brain waves. Applications Used to Analyze Brain Waves Two major applications analyze the brain waves, where each application focuses on a different aspect of analysis. Emotion Analysis Human emotions are determined by the brain. The brain waves carry messages with emotions that make the person feel something. This ‘emotion’ can be understood by analyzing brain waves. However, the concept of emotion and what it represents varies from one person to another based on cultural and environmental backgrounds. A classification algorithm is vital to accurately analyze the emotional aspect of brain waves. Brain-Computer Interface (BCI) The function of BCIs is to collect the brain waves, analyze the messages, translate the messages to commands and relay them to the output devices. BCIs don’t use neuromuscular pathways for this process. That’s because the purpose of using BCIs is to restore the functioning of the neuromuscular pathways in people suffering from brain diseases. For example, cerebral palsy, stroke, amyotrophic lateral sclerosis, or spinal cord injury can damage the pathways and affect the transmission of brain waves. BCIs aim to restore the damage done so that the person can

Yashica Chopra
June 08, 2022
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Top 10 Benefits of Artificial Intelligence in the Workplace

Artificial intelligence occupies an increasingly important role in the workplace today. From automation to virtual assistance, collaboration, and analytics, AI offers effective solutions to many work-related challenges and creates a positive setup for employees to be more productive. Artificial intelligence is not a new phenomenon. It has been slowly entering different industries and verticals to replace the traditional systems and processes. The need for digital transformation has made artificial intelligence a prime investment option for many enterprises. Forbes has reported that 50% of the businesses intended to invest more in AI and make it a part of the everyday workplace environment. Around 83% of enterprises have increased their budgets for AI and ML since 2019. With each passing year, more organizations are adopting AI tools to automate the processes and stay competitive in the market. The Covid-19 pandemic has further intensified the adoption rate. Remote teams and collaborations have put AI at the front. Digital transformation invariably brings several changes to the workplace. The first factor to consider here is the reaction of employees. What does it mean to the employees when advanced technology is adopted by the business? What changes does AI bring to the work environment? How can the management streamline the business processes while assuring employees about the changes? What is the future of AI in the workplace? Let’s find out. How is Artificial Intelligence Changing the Workplace? Machines (analog and digital) have been a part of the work environment for a long time. AI takes it one step further by making these machines intelligent to reduce the workload on employees and increase productivity. However, it is likely that employees will be wary of artificial intelligence and new technology. The management should first educate and assure employees about the value of using AI in the business. A cohesive workplace where employees and technology go hand in hand will be better equipped to deliver the expected results. Artificial intelligence can be a part of various departments within an enterprise. It can be used by entry-level employees and top management. Data collection, analytics, and reporting are the three major aspects of AI. Streamlining Human Resources Artificial intelligence can help the HR department streamline the recruitment process. Organizations spend millions every year to find the best candidates for different job positions in their business. The entire process often requires a few months only to start again (due to expansion or employee turnover). AI software can automate the initial screening and filter eligible candidates from the large pool of applicants. It can also go through thousands of social media profiles to look for potential candidates. Artificial intelligence can then be used to create and analyze assessments that help the HR team shortlist suitable candidates for the job. Unilever is already using AI to automate the recruitment process. Smart Remote Working Artificial intelligence not only helps in hiring on-site and remote workers but also assists remote workers to do their job effectively. Let’s take maintenance and repairs as an example. Technicians can control semi-automated robots from a distance and use them to make the necessary repairs to the machines. IoT, robotics, and automation make it possible for employees to stay away from the worksite and still get the job done. Robots can be sent to dangerous/ risky workplaces instead of humans. The engineers can stay in safe zones and complete the inspections, repairs, and maintenance works. This makes the workplace safer and allows employees to work at their convenience. Workplace Optimization Using artificial intelligence in the workplace will bring many changes. More sensors and devices will be installed and connected to each other. Data will continuously flow from one end to another. Similar to how smart devices make a smart home, AI devices can create a smart workplace where machines observe, monitor, and analyze humans around them. AI sensors can optimize and personalize the workplace by adjusting the lighting, temperature, etc., of the room based on the employees present at the scene. AI can give management an ideal layout design to optimize space management and create a work environment where employees feel comfortable and productive. Team Collaborations We see two types of collaborations here. One is between humans, and the other is between humans and machines. Accenture reported that the collaboration between humans and machines will substantially increase productivity. Data flow is streamlined within the enterprise so that employees can directly access the necessary information required to complete their job. Data sources can be connected across the enterprise to prevent data loss and keep the knowledge base up to date at all times. AI platforms make it easy for in-house and remote teams to work together, share data, updates, comments, etc., and track the project’s progress with ease. Performance and Productivity Management Artificial intelligence in the corporate environment can help monitor and assess the performance of employees by minimizing human intervention. Instead of team leaders and managers, performance appraisal will be handled by the AI software. This reduces the risk of human bias and personal feelings when measuring the performance of an employee. Similarly, people analytics will give the HR team and the top management with the necessary information about how the workplace is affecting the employees. Are they feeling too stressed? Are the employees not comfortable in the environment? What changes can be made to create a relaxed workplace so that employees become more productive? AI helps provide answers to such questions and more. Self-Servicing, Help Desk, and Chatbots Chatbots are powerful tools that streamline communication internally and externally. For example, chatbots are used within the enterprise to assist employees with day-to-day work-related issues. Employees don’t have to wait for the tech team or HR team to respond to their emails/ messages/ calls and help them with recurring issues. The chatbots and virtual assistants will take care of it. The AI help desk is one of the most popular customer service tools in the market. Many organizations hire AI software development companies to build and customize customer service tools to increase customer satisfaction. Self-servicing empowers employees