Page 37 – DataToBiz

Yashica Chopra
September 14, 2021
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How to Use Power BI to Extract the True Value of Your Data?

Processing data sets to identify patterns and trends to draw meaningful conclusions is called data analytics. It helps enterprises use real-time and historical data to derive insights that help in decision-making. Data analytics requires the use of modern technology and tools. Business intelligence, reporting, and data visualization are components of data analytics. These together help organizations adopt the data-driven model and streamline the business processes. One of the notable business intelligence tools in the market is Power BI by Microsoft. Several SMEs and large enterprises worldwide use Power BI to connect, model, visualize and share data across the business. You can either hire Power BI professionals or rely on offshore Power BI services provided by consulting companies. Microsoft calls Power BI a powerful to bridge the gap between data and decision-making. It has several features that help you extract the true value of data and understand it to make the apt decisions for your business. Types of Power BI Power BI is a package of several products and services. These can be used individually and together to attain the expected results for the organization. You can understand how Power BI works by reading the beginner guides. Power BI Desktop This is a free self-servicing tool that can connect to 70+ in-house and cloud data sources. It helps analyze data and generate reports. Power BI Service This service is a cloud-based service hosted on Microsoft’s cloud platform to allow employees to access dashboards and reports through different devices (browser, mobile, etc.). Power BI Pro This is a paid cloud-based service with additional features that facilitate collaboration between teams. It allows easy sharing of data reports and can embed the data visuals into Power BI apps. Power BI Premium This is not an application. In fact, it is a service that allows you to share the reports in the cloud with people from outside the enterprise. It brings flexibility and scalability to the app, making it possible for an organization of any volume to adopt Power BI with ease. Power BI Mobile This can be used on smartphones with Android, iOS, and Windows 10 operating systems. Power BI Report Server This is used to access the reports created using the desktop or the Pro version. It is an on-premises report server that allows your employees to access reports from any device and location. It is beneficial if you have limited storage capacity on the cloud or external data centers. Power BI Embedded This is for developers to embed Power BI to other software and application used within the enterprise. Advantages of Using Power BI Power BI is undoubtedly one of the best tools to make the most of your business intelligence capabilities and gain an edge over competitors in the industry. The following are some of the advantages of using Power BI- Features Power BI has rich and user-friendly features that help the process, analyze, and present data in attractive and easy-to-understand formats. Data visuals can be generated independently or in collaborative environments. System Integrations Power BI can be integrated into different apps to produce and share reports throughout the enterprise. The features can be customized to suit the varying needs of each business. Easy Accessibility Since Microsoft Power BI can be used on multiple operating systems and devices, it increases accessibility to data and data analytics. The apps are user-friendly and can be used by employees with little or no training in Power BI. That said, this aspect is limited only to the end-user features. Regular Updates From Microsoft Microsoft doesn’t just release products into the market and leave them. The company constantly improves the features, adds new ones, and makes Power BI even more powerful. Multiple Data Sources Collecting real-time data is now easy with Power BI. Whether it is IoT sensors, social media, media, or any other source, collecting raw data in real-time and using it for analytics is a quick and easy task. The changes in data will be automatically reflected in the reports and on the dashboard. Seamless Integration Power BI consultants can integrate the tool into your business environment with apparent ease. Whether you want to use it through cloud services or on-premises servers, experts will ensure that the process is streamlined and simplified. This allows end-users (employees from different departments) to use Power BI for data and report sharing with any team member. Personalized Dashboards The dashboard is one of the greatest features of Power BI. Apart from allowing employees to get real-time reports and edit them using several features like drag-and-drop, the dashboard can be personalized by each employee. The sales team will need reports of one kind while the accounts team needs another. The elements in the dashboard can be customized (added and removed) to suit the team’s requirements. Secure Data Processing and Reporting Microsoft knows the importance of data security and has provided enough options to set up security levels for data analytics and sharing reports. Employees who generate the reports can set up Row-Level Access filters that allow only certain teams to edit or modify a section of the report. This brings a balance between data sharing and data security. Employees can view the reports, but only those with permission can edit them. Ease of Setup and Use Setting up Power BI is as easy as it can get. There is no need to put the team under extensive training to use the tool. Experts from Power BI consulting companies will handle the initial process, and employees can use the dashboards to use the reports in their day-to-day work. Customization of Power BI App The app can also be customized to make navigation simple, fast, and effective. If your employee has to spend precious time searching for the required data, it doesn’t make sense to use such a powerful tool, right? By customizing the navigation, employees can find data and reports in less time and complete their work. Breakdown of Information The main aim of using Power BI is to understand data. If the data visuals are complex and confusing, how

Yashica Chopra
September 03, 2021
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How to Get Started with the Modern Data Management Approach

Data is priceless in today’s world. Never before was it considered as essential to a business as it is in recent times. Whether it is easy to access real-time data or the availability of advanced technology to process and analyze this data, enterprises are trying to make the most of this precious raw material. Data management in today’s world needs a modern approach to effectively handle the complex problems of dealing with multiple data formats. Data in its original form is raw, unstructured, and comes in multiple formats. Data management is the process of collecting, sorting, arranging, and managing vast amounts of data to help businesses derive valuable insights from it. From data governance to data architecture and security, modern data management is a multifaceted discipline. But following the modern data management approach is not easy, especially for enterprises that are used to traditional business methods. That’s where consulting companies take care of data management services and help SMEs unlock the full potential of data on their own. In this blog, we’ll see how to adopt the modern data management approach in an enterprise. But before that, let’s read a little about data management, why it is necessary for every business, and what benefits an enterprise can get by investing in modern data management. What is Data Management? Data management is a combination of various functionalities that help enterprises use accurate and reliable data to derive insights that help in decision-making. An enterprise needs a comprehensive data management strategy if it wants to adopt the data-driven approach and rely on business intelligence tools to generate valuable reports. The enterprise needs to establish policies and best practices to ensure that the data management strategy is aligned with the goals. The data management system includes a wide range of processes like- Types of Data Management Data management can be classified into the following types- Master Data Management (MDM) This is a process where all data sources lead to the same data center. A master database is updated and used to make all business-related decisions. A single, latest version of the database is used as a reliable source of data. Data Security Security filters and applications are used at every level to ensure that the data in the database cannot be easily accessed or hacked by people without permission. It also safeguards the data from being deleted or modified by mistake. Data Quality Management Identifying and removing duplicates, eliminating inconsistencies, etc., are a part of data quality management. This is done when data is collected from all sources and stored in the database. Data Stewardship A data steward is a tool that is used to supervise and implement data policies across the enterprise. A data steward is also a person responsible for the same. Data Governance These are the set of laws framed by the top management to ensure proper use of data. It is a framework of rules and regulations which the data stewards implement in the business. A data governor is a person who supervises the data steward. Data Warehousing The process of storing vast amounts of data and using it for analytics is known as data warehousing. It can either be managed on-premises or on the cloud. Choosing the right data warehouse is crucial for a business to successfully utilize its data. Big Data Management Collecting, cleaning, and processing huge amounts of data is known as big data. Managing raw, semi-structured, and structured data collected from multiple sources is called big data management. Traditional systems are not enough for big data management. What is Modern Data Management? So how is modern data management different? It is nothing but an advanced approach where data is connected with the latest technology like artificial intelligence to get predictive insights, identify challenges and opportunities in the market, and help SMEs make faster and better decisions. Why Do You Need Data Management? There are quite a few reasons for a business to invest in data management and ensure there are no glitches in the system. Something as basic as feeding wrong information can lead to disastrous results for the business. Enterprises hire big data consulting services to streamline their business processes and migrate from the traditional approach to the modern one. It makes the transitions easier and amplifies the results. In short, the success of a modern enterprise depends on how effectively it can manage the huge amounts of data in this system. How to Build a Modern Data Architecture? Following a comprehensive step-by-step is essential if you want to build a modern data architecture in your business. Benefits of Modern Data Management Whether it is cloud data analytics or on-premises services, the following are some of the benefits of following the modern data management approach in an enterprise. Data Security and Data Privacy Compliance The increasing cyberattacks have resulted in new and stringent data security policies. Unless businesses adhere to these regulations, they cannot be eligible to bid for government projects. Modern data management services include high-level data security and enable enterprises to use automated tools for data protection. Speeding up Modernization Digital transformation is a continuous process where the systems, tools, employees, and work environment has to be aligned to achieve the common goals. Modern data management makes it simpler for the management and employees to accept and adapt to the changes. This increases business efficiency and productivity. Optimization of Resources There is no point if data scientists spend more than half of their time collecting and cleaning data. When will this data be analyzed? What about real-time data? Data strategy consulting services are the answer. Automating recurring tasks, using AI-based technology, and investing in advanced software for data analytics will help enterprises optimize resources and increase returns. Enhancing Decision-Making Abilities Isn’t this what every enterprise wants? Imagine getting in-depth reports that are easy to understand. It will help employees and management choose the best way forward for the business. Being armed with accurate insights can make an enterprise a leader in the industry. Modern Data Management Approach: Things to

Prasansa Singh
August 27, 2021
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How Can Big Data Help in Augmenting Cybersecurity?

With the ever-evolving cybercrimes, data handling, and security challenges, users must remain informed with ways to tackle them. The good news is that various enterprises are coming up with multiple solutions to combat the nuanced tools and mechanisms being used for data intrusions. The most prominent of them being Big Data Services. Big Data analytics in cybersecurity industry has various applications if enforced properly. If we’re to believe the reports, the incidents of data breaches continue to rise every single year which is why we must make cybersecurity an absolute priority. The world faces more than 10,000 malicious files and 100,000 malicious websites on a daily basis. Phishing attacks account for over 80% of the reported security incidents. Since the pandemic outbreak, remote workers have also been the target of alarming cyberattacks. Despite knowing about the risks related to cyberthreats, most of the companies were still not cautious and ended up being a victim to hackers and cybercriminals. What is Cybersecurity? Cybersecurity can basically be defined as a mechanism meant to protect your devices and the data contained in them. Its application also expands to networks and software. It’s not just a department that’s responsible for protecting your data, rather every step you take towards creating a safer environment for your system contributes to an overall secure user experience. A cyber threat comprises any malicious attempt at unauthorized access to a network for stealing private data or making changes to it. Phishing attacks, ransomware, malware, MITM, SQL Injection, Emotet, etc are just a few types of cyber threats. Why is Cybersecurity Important? Due to various vulnerabilities in the system, a company may end up becoming a victim of a cyberattack. There have been multiple instances of really dangerous cyberattacks in the past, for e.g the SamSam ransomware attack that took place in Atlanta, the Stuxnet worm that attacked the nuclear facilities in Iran in 2010, and one of the most recent and global data theft account- Pegasus, the spyware. Overall cyberattacks cost the world economy more than $1 trillion according to McAfee- a leading cybersecurity firm. Apart from the financial point of view, there are many other benefits to cybersecurity like- managing and protecting big data thereby improving confidence in a company’s reputation and hence customer loyalty with a company. There are various global laws like HIPAA, GDPR, DCC, etc that are enforced globally to protect citizen data and to regulate security standards. Application of Cybersecurity Some steps that can be taken for securing your browser and system experience: Apart from these small steps, there are dedicated cybersecurity professionals and companies like Intruder, Cipher CIS, Symantec, Cisco, etc who are responsible for defending and even predicting data breaches on a much larger scale. Their services include vulnerability assessment, external network security audit, cloud security, network security, endpoint security, etc. There are a few added benefits to cybersecurity like productivity enhancement, gaining client confidence in terms of data security, and improved system performance. What is Big Data? Big Data comprises a huge amount of data, structured or semi-structured. Data is generated in a high volume, at a great velocity, and in a huge variety. Did you know that Social Media is the highest contributor towards the generation of data? Important tools like Apache Hadoop, HPCC, Statwing, Qubole, etc can be used to analyze and interpret a huge amount of data accurately and efficiently. The analysis and storage involve Artificial Intelligence and Machine learning as well. Big Data can in fact be converted into Actionable Intelligence to get an added advantage over one’s competitor. Data Analytics can also act as a catalyst to achieve an organization’s goals if applied properly. Big Data is creating a revolution due to its multifold usage throughout the world. The retail industry benefits from an accurate representation of customer behaviors and patterns thereby contributing towards achieving higher sales. And the healthcare industry benefits by harnessing data pertaining to past records and thereby providing more personalized medications and even developing more effective treatments. Cybersecurity companies have been applying big data technologies for cybersecurity in recent times and the results have been promising. Big Data’s role in mitigating cyber-threats Considering the rate of both data production and cyberattacks, it’s not surprising that Big Data seems like the perfect solution to fight and prevent cybercrimes. Cyber professionals can comb through the huge amount of data in a time-efficient manner and thus detect and resolve the issues, enabling them to even prevent and predict any future possibilities of data invasion and data theft. Big Data analytics insights are crucial for the detection and prevention of cyber threats like ransomware, malware, phishing attacks, etc. Using Big Data Analytics in cybersecurity comprises analytical tools, data management, and examination techniques that integrate data from various hosts, devices, and networks and then providing the information to sensors installed within, to detect and prevent any upcoming attacks. A Few Big Data Technologies for Cybersecurity Predictive Nature: Big Data analytics make use of past records, algorithms, and statistics to look out for certain patterns which might’ve caused an issue in the past. Thereby, predicting future attacks while also providing information about previous attacks that might have occurred in a system. Constant Monitoring: Data validates access to the systems while tracking logged-in activities thereby preventing a breach as well as managing passwords efficiently. Complete Automation: Professional Big Data analysts can actually automate the entire detection and prevention of threats process, saving time and human efforts. Actionable Intelligence-Driven Risk-Management: With constant monitoring and processing of datasets, a foundation for actionable intelligence is formed which further plays a crucial role in detecting and predicting any cyberthreats Now, Big Data may apply any past or current insights pertaining to cybersecurity, collected from various sources to solve any issue. It, therefore, becomes rather convenient for the analysts to address and resolve any threats really fast. Threat Forecast: With the help of AI algorithms, companies can even predict any upcoming threats based on past and present records and identification of any patterns after

Yashica Chopra
August 26, 2021
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How Demand Forecasting Is Helping the Retail Industry?

Forecasting is the process of analyzing existing data to determine future events. In the retail sector, forecasting is used to understand the customers’ purchase behavior. Actual data that is in existence and its related market research might differ by the kinds of products a retailer usually intends to trade for, but the basic denotation of forecasting in retail accompanies similar patterns, even across different product lines. Demand forecasting is one of the major aspects of running a successful retail business. A retailer cannot accurately predict and prepare for the changing market conditions and customers’ requirements without demand forecasting. And one such indispensable component is demand forecasting. Put separately, demand describes the eagerness of consumers to purchase a specific product, whereas, forecasting predicts future performances by utilizing statistics of the pre-existing data. Hence, demand forecasting in retail is the insightful prediction of consumers’ purchase actions regarding a specific product, at a specific price and in a specific time area. Simply, it is the demand forecasting of future bids that lead the businesses to create a win-win situation for consumers and themselves. Many small and big companies implement demand forecasting for supply chain control, financial planning, and customer success management. This strategy allows businesses to prevent any sort of loss and ensures client retention while making any necessary and sudden changes. Without proper implementation of demand forecasting techniques, businesses might find it hard to possess an adequate amount of stocks in hand. Especially, in the COVID times where physical interaction has come to a halt and companies are making rapid changes to their working models. It plays a significant role in the growth and survival of the retail market. Importance of Demand Forecasting in Retail Here are the two major points that define the importance of demand forecasting: 1) Cost-efficient One of the simplest ways to maximize profit in the retail business is to cut down costs. Initially, you can diminish the amount of money you have invested in unnecessary inventory. That will result in lower carrying costs since you have lesser stock in hand. Plus, this also ensures that you don’t go out-of-stock and capitalize on every sale you make. While the above ways do work, a systematic approach towards the need for demand forecasting ensures to beef up the profits and business structure. Once you forecast the demand, you can easily have a look over the time period and check if you are close to hitting the anticipated sales. If you were left behind on your goals, you can always use marketing and advertising techniques. And, in case you underestimated, you can reorder the stuff and cross-promote a similar product. 2) Customer Experience Suppose you visited an e-commerce site to purchase groceries and essentials for your home. Unfortunately, the brand you prefer is currently out of stock. What will be your next move? Will you wait for the brand to restock them and then proceed to make a purchase? Or, will you look for another brand that offers similar items? The latter, right? Enhancing the customer experience is the quickest way to improve profits. Neglecting the “out-of-stock” from your business can retain your consumers and will stop them to entertain your competitors. Plus, aiming towards the end-users might lead to more referrals and loyalty, rather than focusing on raising the prices, altogether. What Are the Advantages of Demand Forecasting in Retail? It is necessary to perform demand forecasting in a proper way to stay in the retail industry game. Forecasting may not be easier for both brick and mortar and giant companies, however, the results-driven by it can make a huge impact. Here are the advantages of demand forecasting: 1) Better Financial Planning The demand forecasting formula is used to help you achieve better and smarter financial planning for your retail business. Detailed and accurate information obtained from the forecasting lets retail companies build better financial plans. Accurate predictions can easily highlight the peak intervals, demand trends followed by months, dates, or even a specific time of the day, and seasonality. Hence, making it easier for companies to look upon the cash flow and safeguard themselves from peaks and unexpected expenses. 2) Eliminates Staffing Problems A well-structured demand forecasting process can result in preventing staffing problems in companies. These issues generally arise during the peak season. It can be easily avoided by anticipating the hike and fall in the demand during different time intervals or days. This way, companies can eliminate the guessing and come up with a more structured schedule list. Moreover, managers can also plan the shifts accordingly for the workers. 3) Enriches the Marketing Plans Forecasting also plays a vital role in developing effective marketing campaigns. For instance, if a company experiences a loss in sales, it is advisable to put effort into its marketing strategies. With proper demand forecasting solutions, marketing executives can develop properly tailored marketing promotions that can take on the loss. Moreover, making modifications to the pricing index according to the customers can also make an impression. 4) Advanced Inventory Management Not only the inventory but forecasting also helps the companies willing to enhance their production management. Companies strive for inventory overflow whenever their product goes out of stock or sales fall down in peak times. Precise forecasting allows them to anticipate and adapt the situation to meet the required demands accordingly. In addition, it also ensures that the company holds the required material, shipping, and labor. What Are the Reasons to Use Demand Forecasting in Retail? The biggest benefit reason to use demand forecasting is to reduce uncertainty in retail operations. Demand forecasting kills uncertainty, substantially, with its predicted calculations and thus, allows retailers to order, allocate, and refill accordingly. Additionally, it is also beneficial in terms of workforce scheduling and optimizing capacity management systems. Benefits of Demand Forecasting Here is the list of benefits driven by demand forecasting: How Is Demand Forecasting Performed? Demand forecasting relies on 3 main models that are used in the retail industry. However, each model

Ankush Sharma
August 25, 2021
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8 Tips for Using AI to Improve Customer Insight Strategy

Artificial Intelligence or AI applied appropriately, can help deliver customer insights that deliver an in-depth understanding of purchase behavior, needs, likes, dislikes, and preferences. Do you know your customers? Do you understand what makes them buy your products or avail of your services? Do you know how many customers are unhappy with your business and looking at competitors? How do you plan to retain them? To successfully answer these questions requires customer insight. Customers are the heart of every business. In order to succeed, you need a loyal customer base to buy, promote, and recommend your brand. The demand and supply ratio in the modern market is constantly changing. People today have more options to choose from. While this is wonderful for consumers, it puts responsibility and pressure on businesses to ensure that their customers are satisfied and keep them happy. Whether it is product quality, variety, price, or post-purchase services, several factors play a role in influencing a customer. Sometimes, all it takes is one special offer by a competitor to lose a customer. In such instances, having a comprehensive customer insight strategy can help you retain your customers and attract new ones. However, traditional processes aren’t enough anymore. You need artificial intelligence solutions to use your customer insight strategy and obtain the expected results. In this blog, we’ll learn more about customer insight strategy, why it is necessary, and how AI can effectively improve the strategy and your business. What is the customer insight strategy? Customer insight means having an in-depth understanding of customers’ purchase behavior, needs, likes, dislikes, and preferences. It is to know whether a product will be loved by your customers or not. The insights are a combination of multiple factors like inquiries, feedback, customer analytics, market intelligence, research, segmentation, loyalty analysis, and more. Consumer insights play a critical role in marketing strategy development for your business. You need to use those insights to create a strategy that delivers results and puts your brand at the top. To survive the competitive market and set your business apart from your peers, you should compulsorily gather customer insights and create a strategy to achieve your goals. Ways to gather customer insights Customer insights aren’t generated of their own accord. You need to collect them using different methods. Each method has its pros and cons and focuses on revealing a new aspect about the customers. The following are customer insight techniques used by businesses around the world- Trend analysis This is classified into three types based on geographic location, time frame, and behavioral patterns. The current trends are analyzed to predict the future ones. Custom surveys These are conducted to get a general idea of how satisfied/ dissatisfied customers are with your brand. Market analysis It is a quantitative and qualitative assessment of the market to understand customer segmentation and purchase patterns. Interviews These are used to gather the voice of the customer to see if a particular product/ service is worth pursuing. Online research Using the internet to conduct surveys, polls, share questionnaires, and gather feedback is known as online research. Social listening This is a consumer AI technique where you monitor social media for mentions, tags, etc. You track your business as well as your competitors. Predictive analytics Using ML algorithms and AI to predict future trends based on historical and real-time data is known as predictive analytics. Focus groups You collect data by interacting with a selected group of customers/ target audiences. The need for customer insight strategy for businesses Before we talk more about using AI services for customer insights strategy, let’s see why you need to consider customer insights for your business. Customer insights and product strategy: Customer insight strategy provides you with relevant information about how customers will receive a product from your brand. You might be sure that customers will like a product you plan to release in the coming days. But what if they don’t like it? How will you know with certainty that the new product will be successful in the market? Customer insight strategy is the answer. You can collect firsthand information from the target audiences and use that to develop a proper product strategy. Customer insights and marketing: Customer insights analytics are used to help in planning your marketing strategy for existing and new products. How to take a product into the market and attract people? You use customer feedback to create a strategy that will highlight the products and the brand to show customers that you can provide them with what they are looking for. It also helps in segmenting customers into exact categories and creating targeted promotional campaigns for each segment. Customer feedback and customer service: Customer feedback and service go hand in hand. It is a part of the customer insight strategy to gather feedback from customers and make the necessary changes to the processes so that customers are happy with your business. You can collect feedback immediately after providing a service, contact them after a while, or wait for them to review your business. Find answers to the following questions If you want to use artificial intelligence to improve the customer insight strategy for your business, you need to first answer a few questions. These will help in providing you with a clear picture of how to implement AI in the business. What do you want for business? Unless you have a concrete goal, you cannot define the parameters to collect customer insights and use them to develop a strategy. You need to consider the short-term, mid-term, and long-term visions of your brand. Where and how to gather data? Which customer insight tools should you use to gather data? How do you plan to approach your customers? We’ve already mentioned different methods to collect customer insights. Besides the above, you can also use Google Analytics, social media analytics, social mentions, Google Trends, and other data collection tools to gather insights. Which data storage and data analytics tools do you need? The collected data requires to be stored and processed to derive insights. Do you want to

Abhishek Singh
August 23, 2021
2 Comments

PrepAI – Most Advanced Question Generator

PrepAI is the smartest AI-Powered question generator and a solution to create question-answer pairs for exams and online test papers. It can generate questions from text in minutes. It turns up as a solution in the form of a question answer generator that helps teachers, exam administrators and content leaders save time and reduce human errors. PrepAI is the world’s most powerful solution for question generation using NLP. Watch the video to learn how to generate questions from text online using PrepAI – PrepAI Question Generator Demo PrepAI leverages the power of Artificial Intelligence and Machine Learning algorithms to create questions from text of varying difficulty. Besides being a smart and random question generator for test authors, it also serves as a one-stop solution for all content needs of an organization. Developed by EdTech professionals and innovative engineers, it uses Natural Language Processing technique for text analysis so that you can generate questions out of any text you feed, PDF/Docx file, Video and Wiki search. A Quick Walkthrough to Get You Started 1. First, add your content… Login and enter the title of the question paper. Select ‘Type or Paste Text’ as Content Source for now. Copy and paste the content for which you want to generate questions. When you are done, click on the ‘Generate Questions’ button. 💡 There are other Content Source options available, such as: Enter Topic Name, Video and Upload PDF/Docx. 2. Check the generated questions and options PrepAI takes a few moments to process and generate questions and answers. Go through the results. If needed, you can edit the questions or options too. Select the questions you want to include in the question paper. Click on ‘Preview Question Paper’. 3. Review the question paper and download Check the question paper review. If you are satisfied with it, then click on ‘Finalize Questions’. You have an option to download the question paper in PDF, DOC or EXCEL. Hope that helps! Resources:

Kavika Roy
August 20, 2021
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Importance of Building AI and Data Science Capability For Strategic Growth

Businesses across the globe have started adopting artificial intelligence and data analytics to streamline their operations. Investing in the latest technology helps enterprises optimize resources, increase productivity, improve quality and get a faster return on investment. From following the traditional approach of decision-making, SMEs are slowly turning into data-driven businesses. Big data is being stored, processed, and analyzed to gain actionable insights. These insights are gathered from historical and real-time data to help the management make faster and better decisions. However, doubts and confusion about AI, ML, and data science still persist. The concepts overlap and coexist. That makes things a little hard to understand. Artificial intelligence consulting companies have been helping SMEs understand how AI, ML, and data science are different from each other. The companies provide continuous assistance to revamp the business processes and build an efficient operational system in the enterprise. The aim is to strengthen the business in all aspects to achieve its long-term goals. What is Artificial Intelligence? One thing most of us do know is the generic definition of artificial intelligence. Minsky and McCarthy, in the 1950s, defined AI as a task performed by machines that would usually be handled by humans. But artificial intelligence is much more than a machine taking over the tasks of a human. AI is a collection of algorithms that help computers understand the relationship between various entities, gather meaningful insights, or plan a course of action for the future. AI is all about actions. Machine learning and deep learning are a part of artificial intelligence. The machine-learning algorithm can learn and improve itself without the intervention of humans. Have you read about AI solutions like Chatbots and virtual assistants providing personalized assistance to employees depending on their responses to the questions? It means that the machine learning algorithms use data in the system and the response of the employees. The algorithms learn from the feedback and deliver better accurate services. Types of AI Artificial intelligence is classified into different types based on functionality and technology. Based on Functionality: Reactive Machines These are the oldest AI type of AI systems with limited capabilities. They do not execute memory-based functions but are used to respond quickly to typical input datasets. Theory of Mind These AI systems are supposed to understand the human mind, emotions, feelings, thoughts, etc., and identify the factors that influence the human thinking process. Limited Memory These AI systems learn from previous data and are the ones we commonly see in today’s world. A vast amount of data is fed to train these systems. Self-Aware These AI systems are yet to be fully used in the market. Some of these are still in the developing stages. These systems are meant to have the self-awareness and consciousness of a normal human being. Based on Technology: Artificial Narrow Intelligence (ANI) Also known as weak AI, this is the common type of AI technology we see in the industry. The systems use a predefined set of constraints to process data and deliver results. Artificial General Intelligence (AGI) This technology is connected to the Theory of Mind and has been fully developed as yet. The aim of this technology is to develop machines that create independent connections across various domains. Artificial Super Intelligence (ASI) This technology is still in the early stages of development and can be linked to Self-aware AI systems. Why Use AI for Future Growth According to Fortune Business Insights, the global AI market value is expected to touch $267 billion by 2027. AI-powered robots and machines are going to become an integral part of most industries, be it the manufacturing or service and hospitality sectors. Investing in AI at this stage will ensure that the organization is ready to adapt to the changes in the future market and grab more opportunities. How Do You Develop an AI Strategy? (AI Strategy Framework) Developing an AI strategy is a task, right? Why not take the help of our AI services for easy implementation and customized solution for your business problems? Our AI developers have the skill and expertise required to transform your organization into a dominant force in your niche. What is Data Science? Data science is a broad field of science that deals with collecting, cleaning, storing, processing, and analyzing data to derive meaningful insights. The techniques of mathematics and statistics are used along with advanced technology and tools to derive useful information and knowledge from vast amounts of data. Data analytics, data mining, and data visualization are a part of data science. AI and ML are used in the data science field to produce insights that help in better decision-making. Hiring the leading data analytics company in India provides the necessary skills and technology required to gather raw data and process it to detect hidden patterns, identify market trends, and predict future outcomes for the enterprise. Data scientists and analysts use AI and ML tools to work on chunks of data (historical and real-time) to draw inferences and generate reports to explain the insights in a simple and easy-to-understand format. How Do You Develop a Data Science Strategy? (Data Science Strategy Framework) Similar to the AI strategy framework, the data science framework also needs to follow a systematic process to yield the expected results. Relationship Between Data Science and Artificial Intelligence Human intervention is required in data science to process and analyze huge amounts of data and derive actionable insights. Data scientists build ML models that can be implemented in the business to make the most of the data that’s available in the enterprise to forecast business. The following are the two popular ways in which consulting companies use data science in business development. Predictive Analytics: Data from the past and present are used to analyze trends and patterns to predict the future outcomes for the business and in the market. This helps enterprises be ready to grab new opportunities in the market and make the most of the latest trends. Organizations can also be better prepared to deal with market fluctuations. Predictive analytics is

Kavika Roy
August 09, 2021
1 Comment

10 Steps for Turning Enterprise ML into Success

Artificial intelligence and machine learning (ML) are fast becoming a part of our lives, professional or domestic. Virtual assistants, chatbots, data-driven models for better decision-making, data analytics, and much more result from implementing AI and enterprise ML in the business. AI-powered tools streamline the processes and help enterprises improve quality, increase productivity, enhance customer satisfaction, and get more returns. SMEs can either build an internal team to integrate AI into their business systems or hire AI services offered by the leading consulting companies. Though AI and ML improve the performance of an enterprise, things can quickly go wrong if you don’t have a proper plan to integrate AI into your business. In this blog, we’ll read how to implement ML models in your organization successfully. We know about artificial intelligence and are aware that machine learning is a part of it. But what exactly does a machine learning algorithm do? A machine learning algorithm is software that uses historical and real-time data to help enterprises trace patterns, predict trends, detect fraud, analyze customer behavior, and provide personalized suggestions. The algorithm is written in a way that uses the feedback in the system to improve and deliver better results. Over time, enterprise ML algorithms can perfect themselves and provide accurate predictions. Of course, ML is much more than a simplified explanation. There are different types of ML based on the kind of algorithm used (or how the software approaches learning). Types of Machine Learning Data is a common factor for any machine learning. Reasons to Invest in Machine Learning With AI and ML making great headway in the market, it has become essential for several enterprises to invest in the technology. The following are some reasons you should invest in enterprise machine learning and how it can help achieve your business’s short-term and long-term goals. Inventory Maintenance Inventory management and maintenance is a labor-intensive and time-consuming process. It is even harder for large-scale enterprises with vast production. Machine learning simplifies the process by automating inventory maintenance to minimize the need for human intervention. Reduce Work Pressure When repetitive tasks are automated, employees do not have to spend most of their time doing the same thing repeatedly. Therefore, employees have more time to devote to their projects and less stress completing them on time. Also, with virtual assistants and chatbots enabling self-servicing within the Enterprise, employees can be empowered to become more productive without feeling the pressure. Market Analysis Machine learning algorithms can process data in real-time and detect the latest trends in the market. Suggestions about changing the product’s price, reaching out to a new target audience, managing demand with supply, etc., are possible. Data Sorting and Analytics AI and ML models are an inherent part of data analytics. From collecting data to cleaning and sorting it and data labeling, machine learning can make things easier and complete the task in less time. The data science teams can use enterprise ML models to analyze data faster than before. Decision Making AI and ML software provide accurate insights and predictions that help make the right decisions for the Enterprise. The reports produced are easy to read and can be presented in any format (graphical, tabular, textual, etc.) so that you can understand the insights and know where things stand. Data and System Security Data forms a vital part of every business and has to be protected from external forces. The machine learning algorithm can help enterprises enhance the overall security system in the business. The latest antivirus and spamware software is built using AI and machine learning to identify and prevent cybercrime before affecting the business. Fraud Detection and Prevention When discussing cybercrime, we should also mention fraudulent transactions commonly seen in the eCommerce, insurance, and banking industries. The machine learning algorithm can detect such transactions and alert employees. Many insurance companies and banking institutions have invested in ML-based fraud detection tools. Retailers and eCommerce business owners also integrate AI solutions with their business systems to prevent being duped by fake transactions. How to Ensure Enterprise Machine Learning Success Let us look at the steps for a successful enterprise ML implementation and getting the expected results for your business. 1. Understanding AI and ML and Becoming Familiar with Them The first step to implementing anything would be to know what it is. Unless you and your employees understand how artificial intelligence and machine learning can help you improve, adopting new technology will not help much. Several ML consulting companies assist right from the start and continue to offer support even afterward. They help in training your employees to work on the ML models and increase their work efficiency. Read the use cases shared by the consulting companies and download the whitepapers and understand them. Join online crash courses or training programs to gain a comprehensive idea about AI and ML. 2. Knowing Why You Need ML- Identify the Problems ML Will Solve in the Enterprise Why do you want to invest in Enterprise AI? Which issues do you want to tackle by integrating AI software into the business systems? Saying AI and enterprise ML will simplify the workflow is not enough. You must know where and how to use machine learning algorithms. Start by making a list of problems and gaps in your business system that can be solved using ML tools. Which problems can be solved using NLP, ML, DL, computer vision, etc., and what kind of AI tools will you need to integrate into the business? 3. Prioritizing What You Want from ML and Acknowledging the Talent Gap Investing in ML is a costly affair, whether you own a startup or a large-scale enterprise. You have to prioritize the areas where ML will first be implemented to scale it throughout the organization later. It would help if you also considered your existing talent pool. Can your employees adapt to the changing systems? How many will have to be trained in the first batch? How many new employees should you hire? Even if you

Kavika Roy
August 05, 2021
1 Comment

How to Use AI to Move IoT to Next Level

Enterprises around the world are widely adopting artificial intelligence. The Internet of Things (IoT) is not far behind either. In fact, with IoT making it easy to collect and share data across a network of devices, it has become a must-have for many industries. However, you need advanced tools and technology to process and analyze the data collected by IoT. That’s why large enterprises have been investing in artificial intelligence to streamline and automate business processes. As a result, Gartner has predicted that around 65% of SMEs and large-scale enterprises will be using IoT by 2024. Combing AI and IoT will take things to the next level by helping businesses use data in a way that’s never been done before. The combination of AI and IoT is a hit because IoT helps digitalize the physical world, while AI solutions make it easy to process and analyze a huge volume of data in less time. What is the Difference Between AI and IoT? Artificial intelligence is a mix of rules and intelligence. Rules are the part where programs are written. Intelligence is the use of machine learning and deep learning to understand data and make sense of it. Internet of Things is a network of several devices that are interconnected and constantly send information to each other. This makes the devices smart and allows you to control them from remote locations. Voice assistants and smart devices at home (smart locks, smart lights, etc.) are examples of IoT in domestic life. The main AI and IoT difference is that the Internet of Things is essentially hardware and network-based. Sensors, data collectors, computers, etc., are connected through a wireless network and send data to a central cloud server. Artificial intelligence usually works with data. Be it data gathering, cleaning, structuring, formatting, analyzing, or visualizing, AI is used to understand data collected by IoT. Internet of Things is less expensive compared to AI but has a higher success rate. The only way IoT can fail would be when the devices and components in the network are damaged or don’t work. However, AI has more chances of failure due to the wrong AI tool, incorrect data processing, or even inaccurate data. As long as you hire reputed AI strategy consulting companies to assist you, you can be assured that your investment will deliver the expected results. Reasons for the Surge in Demand for AI and IoT A survey by SADA Systems in 2018 showed that AI and IoT were the leading technologies that the enterprises adopted. The trend is probable to continue for the foreseeable future. So what resulted in this increase in demand for AI and IoT? AI as a service is helping streamline business processes and adopt the data-driven model to make better decisions in less time. From automating recurring tasks to providing virtual assistants for employees, AI increases productivity, quality and reduces costs. Internet of Things has made it feasible to develop new products and find new ways of distribution. Since devices and cloud computing technology are already available, IoT is very much a reality that’s delivering great results to industries. It is helping solve real-world problems ranging from traffic issues in cities to data sharing in remotely located factories. AI and IoT are providing enterprises with a definite edge over competitors and helping them become industry leaders. Adopting AI has become necessary to survive in this competitive world. Several offshore companies offer customized artificial intelligence solutions to assist SMEs in unlocking the power of data and increase returns. Using AI to Move IoT to the Next Level Let’s see the role of AI in IoT and how artificial intelligence can move the Internet of Things to the next level. Risk is a part of every business. Be it the safety of the workers, maintenance of heavy machinery, unexpected market conditions, or fraudulent transactions, IoT and AI help businesses understand and predict different risky scenarios in the enterprise. This makes them aware of what all could go wrong and how they can prevent it. Data is collected from sensors in the factory, devices worn by workers, transportation vehicles, computers, etc. This data is analyzed using AI to alert the management in time and prevent an accident from happening. Automation has changed the way work is done in enterprises. Instead of putting excess pressure on employees to complete the recurring tasks, the process is automated using artificial intelligence. As a result, employees have more time on their hands to take up complex tasks and be productive without getting stressed out. AI and IoT together assist employees in identifying tasks that can be automated and fine-tuned to increase efficiency. In addition, understanding patterns and processing data in real-time facilitates management to make faster decisions. Artificial intelligence can make IoT scalable in an enterprise of any size. A range of the device can be used as a part of the IoT network. Whether it is a mobile phone or a high-end computer, it serves the purpose when powered by AI. In fact, many organizations use low-end sensors for data collection to manage costs. AI reduces the large volume of data to a manageable level and allows you to add as many devices as necessary to the IoT network. AI and ML solutions use data from IoT to identify patterns and trends in customer behavior. This helps you in developing new products/ services that are useful for customers. It also helps in making changes to the existing products/ services to attract more sales. We can say that using AI, especially NLP (Natural Language Processing), to understand customer behavior will help manufacturers streamline their R&D. Rolls Royce, the British luxury automobile manufacturer, has been using IoT and AI to develop new aero engines. Broken and damaged machinery causes a lot of loss for manufacturers. The only way to prevent unexpected machine breakdowns is by implementing preventive maintenance. But how does the management keep track of so many machines and their maintenance? That’s where IoT has been

Isha
July 26, 2021
6 Comments

A Definitive Guide to Power BI for Beginners

You want to gain competitive advantage from your data analytics but don’t know where to start. Or have you started using Power BI but don’t really know how to optimize its usage in your organization? If yes, then this post is exactly what you’ve been searching for. In this article, I’ll reveal everything you need to know about Power BI. You’ll also get some tips to boost your productivity using Microsoft’s cloud solution. Power BI is one of the newest tools offered by Microsoft, offering several interesting features such as dashboards, reports, analysis etc… With this tool, it’s now possible to manage large amounts of data easily thanks to the ability to visualize them in attractive ways. You can create your very own dashboard or even build a custom report. But before you jump into building those awesome visualizations, there are a few things you should know first. Let’s go through each one of these aspects carefully. What is BI or Business Intelligence? Before digging into what is Power BI and the benefits of using it as a tool, it is important to understand the basics of BI. Business Intelligence or BI is a process that analyses and converts raw data into coherent and actionable information for use in analysis of business operations. Firms that have incorporated BI for making decisions have experienced better performance and productivity. Teams are observed to be delivering up to a ten-fold increase in ROI. If stats are to be believed, the absence of BI in a firm has shown a high percentage of raw data, as much as 99 percent, left unutilized. To sum it up, business intelligence uses tools to take raw data, and then converts it into smart plans, aiding in right decision-making. BI helps organizations and businesses adapt an object-oriented approach by defining KPIs and setting benchmarks to have an aligned direction for achieving goals. Moreover, BI makes it possible to organize and manage the otherwise unmanageable data into controllable streams, while putting it into respective uses. What is Power BI? Analytics is a major concern today considering we live in a data-driven age. It deals with discovering, interpreting, and processing data to reach a conclusion. Albeit data and business analysts have multiple tools to choose from, they always go for something that is convenient to use and provides easy access. Power BI tool from Microsoft is a collection of software services, apps, and connectors that work together to utilize all sources of raw data into coherent, easy-to-process, visually compelling, and interactive insights. Curated by the tech-giant Microsoft, Power BI allows the user the access to resources they need to aggregate, visualize, analyse, and share data. Connecting to a wide range of data resources from a basic Excel sheet to databases, and both cloud-based and on-premise apps, This business intelligence tool gives the user the convenience of sharing intelligible insights of what’s happening in their business. Often used as an umbrella term, Power BI can also refer to either a Windows Desktop application called Power BI Desktop, or mobile Power BI apps on a Windows OS device, as well as for iOS and Android devices. Who can use Power BI? Often it is observed that business intelligence products tend to fall in the user-friendly zone for profiles like business analysts and data scientists. But thanks to its user-friendly and easy-to-process nature, Power BI can be used by a wide range of people in an organization. In fact, one of the major reasons for Power BI to be so widely popular is the point that users irrespective of their technical expertise level or background find it convenient for use. Because of its versatility and useful features, professionals consider Power BI in healthcare industry as a must-have tool. Organizations who do not have an in-house Power BI service, usually outsource it to another firm that has rich experience in data science and related tools. Such is its effect on users and growth of organizations, that there has been a spike in demand for Power BI developers and services in almost all the industries. Businesses wherein creation of data models and reports for analysis is mandatory, may or may not have the skills to operate all the BI tools. In such cases data science consulting firms such as Datatobiz turn out to be extremely supportive, as Power BI is an essential tool for data scientists and analysts to work on. Cumulatively, Power BI works with data in whatever way it is asked to do, so you can use it in any way. A couple of industries that use Power BI regularly are airline, hospitality, healthcare, and retail. Departments irrespective of the industry the firm belongs to, that use this BI tool include finance, marketing, human resources, and IT. Why are organizations or individuals using Power BI? Power BI facilitates intelligent decision-making, eventually reducing the likelihood of failure and mitigating inherent risks. The data that BI processes are available for viewing and interacting 24*7 from anywhere, as long as you have mobile access. The mobility that it provides is considered a star feature against other BI tools where access to viewing is restricted to a particular location only. This convenient feature allows the users to pull, process, and share real-time data anywhere anytime, making Power BI services rise in demand. Data security is a major concern for any organization irrespective of the industry. Power BI lets the user track and control data usage, granting access only to those who are supposed to have it, without having to bother tech support or IT security elements. Power BI also offers the flexibility to save data on cloud storage, thus preventing exposure of sensitive data by blocking risky user activities in real-time. That apart, Power BI is scalable, so it can fit under any organization and is capable of handling changes in size, be it a rapid business expansion or an unexpected downsizing. The beauty of Power BI is its ease of use, even by people who