Page 35 – DataToBiz

Isha
April 08, 2021
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What Natural Language Processing Can Do For Your Business?

“Google, call Mom” – how much are you into a habit of asking your phone or another gadget to do something for you, in plain language? If you answered every time or very often, you’d totally understand the importance of Natural Language Processing technology in our lives. The rise in demand for better, advanced means to perform is one of the primary causes for technology to evolve at such a pace. So much so that computers can now understand what humans speak in their native language! Of course, this sort of technology wasn’t achieved overnight. The demand for human-to-machine communication got programmers, coders, and a whole lot of tech specialists to bring out their best. As humans, we may be able to speak and write English or any other plain language, but for a computer these languages are alien. The machine language or code it understands is largely incomprehensible to most people. NLP or Natural Language Processing is a branch of artificial intelligence that deals with the interaction between computers and humans using natural language. The goal of NLP is to read, decipher, analyze, and make sense of the human language in a valuable manner. Almost any industry one can think of has implemented NLP in its operations, so much so that the common people are used to taking its help in their daily lives. The effect of NLP can be easily noted with the rise in demand for NLP consulting firms or those organizations that provide end-to-end NLP services. Why is Natural Language Processing important? Everything we express, through any medium of communication be it verbal or written, carries an enormous amount of information. The way we talk, tone of the conversation, selection of words, or anything that compiles our speech, adds a type of information that can be interpreted and its value, extracted. NLP helps computers communicate with humans in their native language. It also makes it possible for computers to read a text, hear speech and interpret while determining which parts of the speech are important. Moreover, as machines, they have the ability to analyze more language-based data than humans in a consistent manner, without getting fatigued, and in an unbiased way. Considering the staggering amount of data that are produced every day be it in the medical industry or social media, automation of language processing will always be critical to analyze speech and data efficiently. Which techniques are implemented for Natural Language Processing? Haven’t all of us come across that moment when Alexa or Google replies about not being able to understand what we communicated? Sometimes the computer or device may fail to understand well leading to obscure results. In order to minimize the frequency of such results, there are two main techniques used to accomplish NLP tasks. This refers to how words are arranged in a sentence to make the best grammatical sense. The Process of NLP uses syntactic analysis to assess how the natural language assigns with grammatical rules. A few syntactic techniques that are used are… – Morphological segmentation: Divides words into individual units called morphemes. – Lemmatization: Works at reducing a word to its original form and grouping all the different forms of the word, together. – Word segmentation: This involves dividing a large piece of continuous text into equal, and distinct units. – POS tagging: Identifies the part of speech for every word. – Sentence breaks: Places sentence boundaries on a large piece of text. – Stemming: Involves striking off an inflected word to its root form. – Coreference resolution: The task of finding all expressions that refer to the same entity in a text. Coreference resolution is a very important aspect of NLP when it comes to natural language understanding tasks such as document summarization, question answering, and information extraction. – Stopwords removal: Stopwords are the most commonly used words in any language. When analyzing text data and building NLP models, these stopwords do not add much value to the meaning of the document, like, ‘a’, ‘the’, ‘is’, ‘on’ etc. NLP helps in stopwords removal for a text classification task so that more focus can be given to other words. Semantics basically involves the meaning that is conveyed by a text. It is one of those problematic aspects of NLP that hasn’t been resolved yet. It requires computer algorithms to understand the meaning and interpretation of words while structuring the sentences. Here’s what helps in a semantic analysis… – NER: Named entity recognition is where parts of a text are determined, identified, and classified as pre-set groups. Examples of such groups are names of people, events, locations, and so on. – Word sense disambiguation: Involves giving meaning to a word based on context. – Natural language generation: uses databases to derive semantic intentions and translate them into human or native language. Firms are using NLP for business benefits in multiple ways, some of which are… How is Natural Language Processing used in different industries? Typically, Natural Language Processing works in a particular way. A human talks to the machine through the voice input, the machine captures the audio input, audio to text conversion happens, the text data is processed by the AI, data to audio is converted and the machine responds to the user by playing the audio file. While NLP is considered one of the most difficult things in computer science and engineering, it’s not the work, but the nature of human language that makes it difficult. NLP makes use of algorithms to identify and extract the natural language rules in such a way that the unstructured language data is translated into a form for the computers to understand. And while this technology has been around for some time, it’s a fascinating extension of AI and has enormously changed how we live in this age of digital transformation. Here are some of the areas which have been widely using natural language processing in their operations… A significant challenge for healthcare systems is to utilize their data to its full

Isha
March 31, 2021
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Which is better: Tableau vs Power BI vs Python?

Data visualization has gained massive popularity in recent years owing to the demand for data. In a business setup, these business intelligence tools can help in analyzing all the data and monitoring performance to enhance growth for the firm, and productivity for the employees. With the world switching to digital means altogether in the year that went by, data is now considered fuel for every small, medium, or big firm. In such a scenario, what sounds better- a spreadsheet that mentions the date, time, sales, and profit OR a colorful, descriptive bar chart interactively explaining all the details? Our vote goes to the latter. Tableau vs Power BI, it’s the clash. But which tool would be the best to create such visually appealing charts? And, which one would provide the required versatility to the user? These are the questions that would cross anyone’s mind when planning to implement business intelligence in the system. It is well analyzed from the comparison of Tableau vs Power BI vs Python. Let’s find out which of the top data visualization tools will be best suited to your business requirements. What is a Data Visualization Tool? An essential part of any business strategy, data visualization is the process of collecting data and transforming it into a meaningful visualization to support decision-making. These visualizations could be in the form of bar charts, maps, or anything that is visually appealing and interactive. They convey the information to the viewer by simply looking at them, whereas normally one needs to read spreadsheets or text reports to understand the data. Talking of the best data visualization tools used by analysts in various industries according to their specifications and applications, they comprise Tableau, Power BI, and Python. All these software programs help businesses make decisions questions faster. Brief Overview: Tableau vs Power BI vs Python Tableau A tool that is used by data analysts, scientists, statisticians, and academicians to visualize data and get a clear opinion based on data analysis. It’s regarded as the best solution to transform the unprocessed set of data into an interactive format and doesn’t require the user to excel in any technical skills or coding. As soon as Tableau is launched, one can make use of the built-in data connectors to connect to any database. The data can be easily extracted in its raw form and converted into a comprehensive representation to transform the way people use data for problem-solving. Tableau services have been high in demand ever since its effect has been noticed on the growth of firms. For those firms that do not have an in-house tableau developer, the requirement is outsourced to a data science consulting firm. Power BI Power BI from Microsoft is an exclusive collection of software services, apps, and connectors that are used to convert raw data into visually compelling insights. The tool allows connecting to a wide range of data resources from a basic Excel sheet to databases, and both cloud-based and on-premise apps. Power BI services are mostly used by business analysts and data scientists, but at the same time allow the user base to vary from a beginner to a pro in handling it. Power BI tool is widely recommended by industries where the creation of data models and reports for analysis is mandatory. The airline, healthcare, hospitality, and retail are a few to name. Python Python is a dynamic, interpretive script programming language. Developed sometime during the early 1990s, this language supports major paradigms of today’s software development methods such as structured programming, OOP (object-oriented programming), and AOP (aspect-oriented programming). Due to the availability of powerful value-added packages for a chosen few applications, one can develop high-performance apps through Python. Python program code is transparently translated by an interpreter into an intermediate code, the so-called byte code. Python interpreter is available for all common operating systems such as Mac OS, Windows, Linux, and others. Top tech companies including Google opt for Python as their programming language as their in-house scripting language for the development of web applications. As far as using it as a data visualization tool is concerned, Python offers multiple libraries in graphics that are packed with different features. Some of its top-notch graphing libraries that help in creating live and highly customized plots are Matplotlib, Ggplot, Seaborn, Plotly, and Pandas Visualization. Python is preferred for data analysis of the highest levels, which is why it is also the most-sought programming language when developing data visualization software. Comparison Parameters 1. Data Sources Both Power BI and Tableau often use Excel files as a source for raw data. While Tableau offers support for multiple data connectors including cloud platforms, online analytical processing, and big data, Power BI is capable of connecting to external sources including MySQL, third-party databases, Microsoft Azure, and online services like Salesforce and Google Analytics. As far as Python is concerned, it’s a bit difficult to make visuals in a rapid manner as compared to the other two. However, if you are to deal with streaming data there’s nothing better than Python. 2. Data Models and Adaptability Tableau allows the creation of simple data models such as a single table or multiple tables with different combinations. It is mostly suited for the quick and easy representation of big data which helps in analyzing and resolving issues. Power BI, on the other hand, has its data models focused on ingestion and building relatively complex models. Python is the best when it comes to handling streaming data. With its big user data, Python can easily help you find a package to parse the data collected by the user, even if it’s an obscure type. 3. Data Discovery The process of data discovery involves detecting patterns and oddities in data by visually navigating or applying guided advanced analytics. Both Tableau and Power BI allow the user to freely explore data without knowing the answer. These business intelligence tools give the user the freedom to spot correlations and trends while digging down to understand

Isha
March 25, 2021
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19 Quick Questions to Ask from Power BI Consultants Before Hiring Them

In an age where data is the determining factor to make decisions, data analytics is crucial for firms to grow. Data analysis involves discovering, interpreting, and processing data to reach a conclusion. Until a few years back, business and data analysts had not many tools to choose from to organize and interpret their data, thereby aiding in decision making. But then Microsoft launched its business intelligence tool in 2013, which combined new features with several add ons of Excel to form a completely new platform with interactive data visuals and interactive reports shared through a personalized dashboard The demand for Power BI developers has increased by many folds in recent years owing to the rate at which firms are adopting business intelligence. This also led to an increase in the number of Power BI service providers who offer remote consulting services. For those industries which do not find the need to apply BI in their daily operations, outsourcing is one of the options. But how would you decide if the outsourced firm offering Power BI consulting services or individuals is the right way choice to get your job done? Microsoft Power BI consultants have different levels of experience which is valuable to firms in respective ways. You need to be familiar with the way they work and their experience before you commit to any finances. To help you get access to the best Power BI services, we have compiled some important questions and expected answers from a Microsoft Power BI consultant. Take a look… Basic: Consultants with 2-3 yrs experience. 1. Why should I use Power BI? As a business intelligence tool, Power BI helps you convert raw data, like that from an Excel sheet, into interactive insights. Companies offering Power BI consulting services for data analytics help you adopt the data-driven model in the enterprise to enable faster and efficient decision-making. The primary reasons for using the Power BI tool are… 2. What would suit my requirements better – an in-house Power BI resource or outsourcing it to a Power BI consulting team? Recruiting an in-house Power BI consultant has its own benefits and disadvantages. To talk of benefits, I would say the response time of the team is better, they can be involved in internal and external projects, and are flexible to adapt to your corporate culture. However, more than anything, when you are hiring a Power BI specialist, the candidate must have specific skills, to be efficient in working with the data visualization tool, and such professionals don’t come for a small salary. Secondly, there will be a time when your business expands. In such a situation you might have to re-engage in recruiting or expanding your team to handle the requirements of the entire organization. Opting for consulting services would be a smart choice in such instances. On the other hand, outsourcing it to a Power BI consulting team that specializes in data science tools and services gives you access to a fully equipped team with experts in every field. This also helps in saving a lot of money and efforts you’d put in otherwise. You can get high-quality results for the lowest cost. The other alternative is outsourcing it to a Power BI services firm. However, it is observed that following an agile methodology works best when you’re outsourcing service for this helps in avoiding delays and disruption of deadlines. If you want to be one of the industry leaders, you’ve got to hire only the best Power BI consulting services. 3. What are the essential applications of Power BI? Power BI is mainly used by industries where data is considered as fuel for daily operations, like Airline, Hospitality, Retail, Healthcare, and so on. It is widely used by data and business analysts to monitor performance, and drive growth in an organization. To summarize some of the top roles that make use of Power BI are… 4. What are the drawbacks of using Power BI for a small or medium firm? One of the major disadvantages of Power BI is that even though it’s easy to use for simply importing data and creating reports, it is complex to master for other functions. Power BI helps increase operational efficiency by allowing you to make data-driven decisions based on custom visuals generated using the personalized dashboard. When the purpose of using this business intelligence tool is much more than creating interactive visualizations from raw data, Power BI is an entire suite with a lot of interrelated functions like Gateways, Report server, Power BI services, etc, which you’ll need to learn. The power platform has a lot to offer if you know how to use it the right way. It can make you one of the industry leaders, given you have the necessary systems to work on data analytics. 5. Name some important components of the Power BI toolkit along with the function they perform. Power Query: This allows you to discover, access, and interpret information from different sources.Power Pivot: A data modeling component.Power View: A presentation tool to create charts, tables, and other compelling visualizations.Power Map: Helps in adding geolocation to your data.Power Q&A: This allows the user to ask questions in plain language and get answers accordingly. 6. What are the types of data Power BI is compatible with? Some of the major data sources used in Power BI Desktop are… Mid-level: Consultants with 4-5 yrs of experience 7. What is grouping? How can my firm benefit from it? A component of Power BI, Power BI Desktop allows you to collect data and organize them into smaller groups. In a firm that has cross-functional teams, grouping can be a way for both teams to view and extract data crucial for either. Just use Ctrl+click to select multiple elements in the visual and right-click to see which of them appear on the group’s window. 8. What is the purpose of the ‘Get data’ icon in Power BI? When users click on the Get-data icon in Power

Yashica Chopra
March 17, 2021
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How is Vision Analytics Retransforming Modern Industries?

Vision analytics has always been considered a game-changer in the industry. It was expected to revolutionize the way security tasks were performed. Improving operational efficiency was another aim of vision analytics. Both the public and private entities are leaning towards computer vision analytics to revamp their business processes and gain the top position in the markets. Artificial intelligence, machine learning, deep learning, 3D imaging, etc., are some terms we often hear when people talk about vision analytics. We often read about vision analytics retransforming modern enterprises and SMEs. Before we see more about what these mean, let’s understand what computer vision analytics is. The process of analyzing digital image/video signals to understand the visual world using the latest technologies in place of the human eye is known as vision analytics. Identifying intruders & impostors, recognising & tracking objects, identifying behavioral patterns etc.. are some examples of vision analytics. The global computer vision market anticipates having a CAGR (compound annual growth rate) of 7.6% from 2020 to 2027. There has been a significant escalation in the demand for computer vision services during the last year due to the COVID-19 pandemic. Taking the increasing adoption of vision analytics into account, we can say that the following trends are going to rule the industry in the coming days. Latest Trends in the Vision Analytics Industry Artificial Intelligence AI has made it possible to analyze vast amounts of data in less time. Data can be in any form- text, images, or videos. Artificial intelligence in vision analytics is used to examine videos and detect patterns. It helps to identify and predict events based on existing data. The systems can communicate with each other and alert the user about a potential change in the pattern. For example, AI in the security department is used to analyze videos and identify suspicious activity such as trespassing, sneaking, breaking in, etc. Vision analytics can help detect the change before the actual event takes place and alert the concerned authorities. In the retail sector, AI in vision analytics is used to identify customer behavior patterns and purchasing trends. Deep Learning and Machine Vision Even though machine vision and deep learning are two independent elements, they complement each other and have abilities that overlap. Deep learning has given machine vision a new dimension. Neural networks are an example of deep learning that works well with machine vision. It helps identify the presence in an image/ video frame. It determines if the presence is good news or bad news. We can call them image-classifiers. Deep learning also helps in increasing the speed of a business process by improving operational efficiency. Many machine vision consulting services include artificial neural networks (ANNs) to provide a comprehensive system for automation in the manufacturing industry. Thermal Imaging Thermal imaging is the process that uses infrared and heat radiation to detect objects in the dark. The thermal cameras can distinguish the difference in temperatures so that we can detect the warmer objects/ beings. It becomes easy to identify the presence of a person or an animal against the cold and dark background. When terminal imaging is used with vision analytics, it sends alerts only for a fixed range of temperature levels. For example, the movements of trees, winds, vehicles, etc., are usually false positives when you want to find a human presence. This is especially useful for security purposes. The percentage of false security alerts can be reduced, thereby improving the efficiency of the security system. 3D Imaging Do you know that the 3D vision market is estimated to have a CAGR of 9.4% from 2020 to 2025? It is the next big thing in the market as the demand for quality inspection of the end products is touching the skies. With SMEs and large-scale enterprises wanting to automate their business, they are turning to 3D vision analytics for high-speed imaging, vision-guided robotic systems, and surface profiling. 3D imaging and vision analytics are also important as the industry is shifting from standard products to personalized products based on customer requirements. 3D smart cameras are said to rule the industries in the coming years. 3D imaging also helps in logistics for autonomous navigation via object detection, self-localization, etc. Use of Liquid Lenses for Vision Analytics Liquid lenses are single optical elements but with an optical liquid material that is capable of changing its shape as and when required. They are used in smart cameras and smart sensors though now we can find them being used in various fields such as biometric recognition and data capturing, reading barcodes, digital photography, and more. Heavy industries are investing more in liquid lenses to help with various manufacturing applications. The lenses have great focus and adjust to the changes in the voltage and current automatically. Apart from industries, public spaces are also going to be monitored using liquid lenses to track if people are following the safety norms or not. Embedded Vision In simple terms, embedded vision is the integration of a camera and a processing board. Instead of having more than one device to stay connected and deliver us the results, embedded vision systems directly work with algorithms. When an embedded system (a microprocessor-based unit) is combined with computer vision technology to digitally process the images/ videos and use machine learning algorithms to share the information with other cameras and systems in the network, it is known as embedded vision. The main reasons for embedded vision systems to become popular are low cost, lesser energy consumption, smaller in size, and lightweight. Embedded computer vision consulting services are used for robotics in the manufacturing industry (for factory automation), the healthcare sector (for medical diagnosis), gesture recognition (for transportation and logistics), the famous facial recognition systems and many more. Several multinational organizations and public sector industries have adopted vision analytics to retransform their operational processes. Vision Analytics and Retransformation of Modern Industries Below are some ways to see vision analytics retransforming modern industries in the global market. Public and Workplace Safety

Yashica Chopra
March 17, 2021
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14 Powerful Reasons to Invest in Tableau Consulting

What are the two things that are crucial for every business? Technology and data. In a world where customization, user preference, targeted campaigns, and tailor-made solutions are the buzzwords, a business cannot survive without using the latest technology to process data and gain in-depth insights. Data analytics and business intelligence are an integral part of every business. And enterprises can no longer afford to rely on age-old methods to process and analyze data. Spending weeks and months on a small amount of data analysis will only result in losses. Enterprises need advanced technology that deals with real-time data and delivers accurate reports in less time. Tableau consulting services is a popular choice that has been helping businesses manage their data effectively and provide automated analytics solutions for their complicated data. Tableau software is a prime business intelligence and data visualization software. It has been an industry leader for more than 7 years. Though there are many other BI software tools, Tableau continues to be a powerful, efficient, and highly useful platform. While it is a fairly easy software, availing of the services of a specialist will help enterprises in managing data analytics effectively and make the most of the platform. But before enterprises invest in Tableau, it is important to understand the need for such software and how it can change the business processes for the better. Reasons to Invest in Tableau Consulting In this post, we are going to read more about the numerous powerful reasons for an enterprise to invest in Tableau consulting services. 1. Work with a Vast Amount of Data A great amount of data is generated every day. And add to the data on the internet, social media platforms, and whatnot. We are in a scenario where we cannot ignore every tiny piece of information. So, where and how can we store all this data? Tableau server offers a centralized database that can hold abundant data. Analysts don’t have to spend hours and days collecting data from various platforms. It’s done by the system and stored in one location. Tableau software doesn’t have any row limit as MS Excel does. We can continue adding data and processing it to generate reports. It is convenient and saves many precious business hours. 2. Interactive Visuals Data visualization is one of the best Tableau consulting services an SME can get. Looking at a never-ending sheet of numbers and codes can drive employees mad. That’s the last thing an enterprise would want, isn’t it? Tableau presents the data in interesting and attractive formats, helping in better visual analytics. Colorful graphs, bars, charts, pies- name it, and we’ll find it in Tableau providing unmatched visual analytics. We can drag and drop the factors we want to consider when creating a report. It provides This visual representation of data makes it easy for the analysts to understand the insights and generate reports for the top management. Making decisions becomes a stress-free event as the top management can clearly see what should be done. 3. User-friendly Interface Many employees are wary of data analytics and business intelligence tools. The main reason is that the interfaces of these platforms are complex, confusing, and outright terrifying for beginners. Tableau differs from other BI tools in this aspect. The software is extremely simple to understand and use. Even beginners with little experience in the field can work on Tableau and generate reports based on their requirements. Specialists, of course, find it child’s play to work with this platform. Enterprises can either use it for simple forecasting purposes or for in-depth analytics. And since it can be integrated with other platforms, Tableau is considered a highly desired BI tool. 4. Mobile-friendly and Automated Tableau Dashboards With more enterprises encouraging employees to bring their devices to work, we have seen employees using multiple devices to manage their work. They work on laptops, tablets, and even mobile phones. But how many BI tools can be accessed from mobile phones? Not too many, right? Tableau software developer loves that it can be easily accessed from smartphones as well. It doesn’t matter where the employee is. There is no more waiting for the analyst to open the laptop and check the dashboard. It can be done from a mobile phone as well. Not only this, Tableau servers automatically update the data on the dashboards. 5. Integrate with Development Languages Won’t it be great to have software that can also manage complex calculations? Why should employees alter between multiple screens and tools to process data? Tableau provides a solution to this as well. The platform can be integrated with development and scripting languages like Python and R. Employees can generate all kinds of reports for the data stored in the database. They can get more insights from raw data by using different data analysis tools and combining them with Tableau for a better output. Remember, we talked about visual impact in the previous points? It’s easy to present even the most complicated reports in a simple and interactive format. 6. Support for Advanced Analytics Similar to languages, Tableau can also be integrated with other analytical tools. This feature is used by a professional Tableau consultant to get scientific data pointers for various aspects of the business. For example, integrating Tableau with marketing tools will help the sales teams to create comprehensive lead-capturing strategies based on the data insights provided by the software. When the sales team reaches out to prospective leads who are more likely to become customers of the business, the team can successfully complete the campaign in less time. Advanced analytics also help enterprises accurately forecast the growth curve of the business and take the necessary steps to keep the process aligned with the goals. 7. Data Segmentation and Reports Data on its own is jumbled and doesn’t make sense. Isn’t that why enterprises spend so much money on data analytics? Now, segmenting data using various filters is a part of the process. How many customers like a

Yashica Chopra
March 12, 2021
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10 AI & ML Secrets Your Competitors Use to Win in the Market

Artificial intelligence has been seeing rapid growth during the last few years. More and more organizations from across the world are investing in AI and machine learning technologies. As per a report, the global market value of artificial intelligence is estimated to be $126 billion by 2025. Be it marketing and sales, business intelligence, customer care, logistics, or the banking and financial sector, AI and Machine Learning hacks play a vital role in streamlining the business processes. Artificial intelligence is said to reach $22.6 billion in the fintech market by 2025, and it is said to touch $40.09 billion in the marketing market by the same year. Many large-scale enterprises and smaller businesses alike are looking at AI and ML with anticipation. But not all of them have the necessary talent pool to implement and work with the updated systems. That’s where artificial intelligence consulting firms are stepping into the picture. By providing customized services to these companies, the AI firms help the top management integrate the latest technology into their systems and train employees to work with AI tools. At the same time, some enterprises have failed to become successful by investing in artificial intelligence. And we know that implementing AI and machine learning includes facing challenges related to organizational culture, skill gap, employee psychology, financial limitations, and data management, among other things. Also, the top management has to think of the existing business challenges such as reduced productivity, lengthy product cycles, delayed transportation, unhappy customers, fraudulent transactions, and much more. So how are the leading multinational companies able to overcome so many challenges using AI? What kind of AI Solutions are they using to become successful? Let’s unveil a few Machine Learning secrets your competitors are using to solve their business challenges and succeed in the market. Machine learning algorithms are dynamic in nature and capable of continuous improvement. Across various industries in the market, machine learning is being used predominantly in these ways to overcome business challenges. 10 AI and Machine Learning Hacks Used By Successful Companies 1. Data Analytics – The Importance of Clean Data Data Analytics is the process of collecting, sorting, and analyzing a vast amount of data to derive valuable insights. There is a lot of raw data scattered throughout the enterprise, and, not to mention, the real-time data that’s always available on the internet. Continuously increasing data these days led to a new process called Data Cleaning. The AI solutions company now focuses on clean data along with big data. Data from the past may not always be relevant in today’s world. Using it for analysis and predictions for the future doesn’t make sense, right? For example, businesses that use mobile eCommerce do not need data from the era where mobile phones were not used for shopping. It further takes more time, money, and effort to sort and process unstructured data, arrive at what is essential, and then use it to generate predictive reports. AI can help you identify which data is relevant and which is not so that your team can work only on new and clean data to get better and accurate predictions. 2. Continuous Improvisation of Customer Segmentation Customer segmentation is the technique of classifying customers and target audiences into different sections based on similarities in their purchase behavior, product requirements, etc. Traditional procedures are time-consuming, and the margin for error is also high. Machine learning consulting company uses data mining and ML algorithms to process data and segment customers into different categories. Instead of guessing or going by instinct, use data-driven marketing procedures to understand customers and target audiences. Data is already available in abundance in the form of email newsletters, website visitors, social media posts, and lead capturing information. It will help to identify profitable customer segments and focus on catering to individual customer needs. By doing this, you can increase sales and customer satisfaction at the same time. However, you need to ensure that you have a proper business case before implementing ML for customer segmentation and customer lifetime value (LTV) prediction. 3. An Additional Approach to Demand Forecasting Demand forecasting is a crucial factor in the manufacturing industry. Producing more when the demand is less and producing less when the demand is more will result in losses for the enterprise. Industries have been following traditional approaches to predicting how much they need to manufacture, how much stock has to be stored in the warehouses, and when it has to be moved to wholesalers and distributors, etc. so that the products will be available in the market for customers’ consumption at the right time. But the forecasts have not always been accurate enough, isn’t it? Wouldn’t you want software that gives more than 90% accurate forecasts? An artificial intelligence consultant can create a robust demand forecasting system that analyses more data in less time. It can find the hidden patterns which the age-old methods ignore. And when data prediction is accurate, the decisions made based on the predictions will also be beneficial. Right? 4. Improved Spam Identification Tools for Enhanced Data Security Spam identification may not seem like a big deal when you say it. But when it comes to cybersecurity, this is one of the most important factors. Machine learning came into existence with spam filters in emails. The algorithm would detect emails that seemed dubious, suspicious, and fake. While this is great for personal use, how does it help businesses? Proofpoint said that 88% of the firms from around the world experienced spear phishing in 2019. According to a report by IBM, it took around 207 days to identify a data breach in 2020. AI services include creating a comprehensive security system that prevents cybercriminals from breaching the security walls and compromising confidential data. Some of the leading antivirus software solutions use machine learning algorithms to identify different types of cybercrimes and protect employees from becoming victims. AI firms are also developing data security protocols to help SEMs and institutions add more security

Yashica Chopra
March 02, 2021
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12 Reasons Why Business Intelligence Is Vital for Your Business

Business intelligence is the process of using technology and strategies to analyze large volumes of data and come up with data-driven insights to make better business decisions. Historical data, real-time data, and predictive data are combined to find patterns in market trends and customer preferences. This helps enterprises in staying ahead of competitors and capture markets to improve sales and profits. Business Intelligence in 2023 is no different. In a world where technology is an inherent part of our lives, not investing in the latest software solutions such as artificial intelligence and machine learning would be a disadvantage for enterprises. BI can either be in-house or outsourced. However, taking the assistance of a business intelligence services company will provide a broader and more accurate insight into their own business processes and improve their decision-making skills. Make the most of BI Consulting services by adopting business intelligence strategies in a smarter way to enhance business processes and gain a competitive edge over others in the global market. What are the Stages of Business Intelligence? Business intelligence has four stages, which together are called BI processes and are used to manage data in an enterprise. Gathering Data The first stage of business intelligence is to collect the required data from various sources and store it in a centralized location/ database. Existing data and new information from internal and external sources are gathered for cleaning, formatting, and processing. Data can be structured, semi-structured, or unstructured. Information from social media platforms, emails, testimonials, competitors’ websites, feedback surveys, polls, etc., is used for business intelligence purposes. Data Analysis Data analysis is the second and vital stage of BI. The collected data is processed and then converted into actionable insights and predictions in this stage. Raw data is of little use if it cannot be analyzed to help enterprises with decision-making. Data analysis is generally categorized as follows: Spreadsheet Analysis: It uses spreadsheets like MS Excel and Power BI to analyze data. Software-based Analysis: AI-based software like SAP BI, QlikView, etc., are used to collect data automatically and process it to derive insights. Data Visualization: Tools like Tableau are used to process big data and present the insights in graphical and pictorial formats. Generating Reports The information derived from analyzing data should be converted to reports that the employees can easily understand. Though reporting is the third stage of BI, it is considered an extension of data analysis. Data visualization software like Power BI and Tableau are commonly used to generate reports. Monitoring Business intelligence is a continuous process. Monitoring is the last stage of BI, where the entire process is monitored in real-time. This helps identify KPIs and measure results to determine if you are meeting your goals or not. Reasons Why Business Intelligence is Vital for Your Business • Data is all around us – Use it Business intelligence is majorly about how enterprises manage their data to get accurate insights from it. But where does this data come from? It is everywhere around us. However, knowing which data to consider and how to process it is crucial if enterprises want to effectively improve their business processes. From accounts to sales to inventory to customer relationship management and real-time data on the internet and social media, every piece of information has to be collected and moved to the Data Warehouse for processing and analytics. The more data an enterprise can add to the warehouse, the more chances to gain in-depth insights. A predictive report that considers various factors can help the management in making the right decision and grab market opportunities before others can. • Tracking KPIs More Often Every enterprise has a set of key performance indicators against which it measures how the business is doing. The analysis was usually done once every quarter to make the required changes to the business strategy and realign the processes with the goals. But is that enough in the current scenario? Maybe not. Survival has become rather important for many businesses due to the pandemic. Enterprises need to compare and analyze the business performance with the KPIs on a weekly or a daily basis. A business intelligence consultant can help enterprises achieve sync between what’s required and what’s being delivered. The margin for error needs to be close to zero. Investing in advanced tools such as AI-based software and machine learning algorithms will make it easy for enterprises to run the comparative analysis in less time and adjust the business strategies to the minute level. • Intuitive Data Designing Business intelligence is a time-consuming and effort-intensive process. Identifying and processing data hidden in various folders and documents is not as easy as it sounds. That said, it is possible to create an interactive dashboard where the employees can find everything they are looking for. A smarter way to make the most of business intelligence would be to have processed data and insights at your fingertips. One way to make this come out is by having everything on the dashboard. Instead of asking the employees to gather insights from various systems and software tools, bring it all together on one screen. Arrange this data in a neat and user-friendly layout. Let the changes be updated in real-time so that employees will not end up working with outdated data. In today’s world, data from the morning gets outdated by noon. • Having a Smart Strategy in Place This might seem like a no-brainer. After all, which enterprise would be stupid enough to not have a proper strategy? That’s where quite a lot of businesses go wrong. The strategies to run a business and the strategy for business intelligence are not the same. We will need to know why we are investing in BI and what you want from it. Unless we know what kind of reports we want the software to generate, how will we work on data analytics? Imagine spending days processing data and creating reports. And if the top management were to say that the reports are of little or

Isha
February 26, 2021
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Ultimate Guide to Tableau: 5 Mind-boggling Advantages!

For a modern-day business to flourish, data is proving to be an indispensable factor. It is now considered the new fuel for a company’s growth, making data scientists indispensable. In an article published about a year ago, Forbes magazine talked about how data scientists ended up spending eighty percent of their time just preparing data. After a schedule breakdown, it was concluded that the data scientist spent only one day a week on average, participating in the activities that truly improve profitability. Not anymore though. Firms that have been using business intelligence for data handling and analysis have seen a constant growth of the firm while also observing better productivity for data analysts. How have firms improvised on data handling for growth? Tableau, one of the widely used BI tools has helped firms to gain efficiency and access to better insights for business. Considering the primary motive of any report is to enable stakeholders to make timely and accurate decisions, they must place their trust in the report creator. If data is required to be extracted, prepped, and combined manually, it is bound to consume more time with enough room for manual error. Tableau’s built-in features and preparation tools enable the user to collaborate and blend data while analyzing it in real-time. Because it also requires creating data that can be understood by professionals with or without any technical knowledge, independent tableau services are bespoke in nature, for now. But before we talk about Tableau and its use in 2022, let’s take a look at all you need to know about Tableau… What is Tableau? Tableau is a fast-growing data visualization tool that helps firms in making better decisions. It helps in transforming raw data into an easily understandable format that can be understood by professionals of any level in the organization. Users who are not very technologically sound can also create customized dashboards. This BI tool has gathered interest from users belonging to various sectors such as businesses, researchers, and academicians. Why do firms use Tableau? The major task of Tableau software is to connect and extract the data stored in various places. This data is used in creating interactive visuals. Data visualization helps in better decision-making and the growth of the business. In a situation where data needs to be prepared and combined, it will require a lot of effort if done manually. Tableau’s built-in data connections and tools enable a user to save up on time while boosting productivity. This allows the analyst to focus on what their core objective is – analyze data and formulate insights. To add to the benefits of using Tableau is the fact that it can easily handle a large amount of data. Unlike Excel or other data-handling software, Tableau has no row limit and allows the user to bring in and analyze as many rows of data as needed. Because Tableau is designed for scaling, overly-long processing times can be easily managed, giving the users a platform for more comprehensive visibility without consuming resource time. This is also one of the reasons why tableau consultants suggest bigger firms use the tool, to save up on time, effort, and money. When talking about a report, stakeholders may always go for questions like ‘what does this look like for the customer segment’ or if the trends in the past can be viewed to come to a decision. Data analytics consulting companies who are required to provide tableau services to firms often come across such customers. The reason for them to use Tableau is that it allows stakeholders to interact with and customize the dashboard within the parameters created by the user, in this case- Tableau consulting services. Because of its popularity among a wide number of users, the Tableau community is full of passionate and knowledgeable people dedicated to making the most out of this BI tool. While the competition between Tableau and other BI tools seems fierce, Tableau seems to have garnered the likes of the BI community altogether, for seven years in a row now. How is Tableau Different from Excel? Microsoft Excel is a spreadsheet and database tool that offers limited data analytics. Tableau is a proper business intelligence tool used for online analytics, querying, and reporting. It’s a data visualization tool where data analysts can present data in varying formats. The dashboards add/ edit data in real-time to provide the latest information to the analysts. There are a lot more differences between MS Excel and Tableau. Let’s look at them in brief: Excel is used for calculations and manipulating data using formulas. It was first released in 1987 and has various outdated and in-use versions available as a part of the Microsoft office package. Despite the latest updates, Excel is still limited in its usage when working with external data. It requires plug-ins to accept external data. Tableau was released in 2003 and is data visualization software that focuses on the graphical and pictorial representation of data and analytics. Working with data from external sources is super easy with Tableau. MS Excel requires at least a basic understanding of spreadsheets. The users should have technical knowledge and skills to work on advanced features and write custom formulas to analyze data. Tableau is an easy-to-use software that requires little or no knowledge of complex formulas, etc. It is user-friendly and allows users to drag and drop the elements to the dashboard to make changes to the reports/ visualizations. Excel cannot process big data. It is a straightforward tool limited to rows and columns. Tableau was created to overcome this challenge. It works with big data and can present the data in various formats. While Excel is perfect for short-term use and limited data, Tableau is preferred to analyze big data and create visualizations in attractive formats. Tableau’s Product Suite There are five major elements that make Tableau what it is. Tableau’s products always operate in a virtual environment when they are configured with proper underlying operating system and hardware.

Isha
February 19, 2021
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10 Greatest Reasons to Use Business Intelligence for Your Firm

It comes as no surprise that customer experience is one of the leading factors for companies to compete. No matter which era of customers we talk about- the radio, computer, cell phone, or the ones belonging to the digital era, they form the core of how businesses operate. A good number of modern businesses have been using tools from the digital age to enhance the growth of their business. Amongst those which have not yet, either lack awareness of the technology or the resources to be able to use the right tool. In order to provide a better experience to the end-users, a firm needs to make better decisions. While leaders may want to implement solutions they consider necessary for the firm, using a huge range of data to do so can be time-consuming and draining. This may lead to average decisions or confused teams, especially when the CX (customer experience) solutions don’t seem to work. What is BI? Business Intelligence or BI combines business analytics, data mining, data visualization, data tools, and infrastructure to help organizations make data-driven decisions. To cut it short, you equip your business with a comprehensive view of the organization’s data to use for making decisions. These decisions help the business grow by eliminating inefficiencies and quickly adapting to industry trends. How has BI evolved to help firms grow? Traditionally, business intelligence emerged in the 1960s as a then-modern way of sharing information in a cross-functional team set up, or across organizations. It further developed two decades later alongside computer models for decision-making and turning data into insights. In the mid-2000s, data segregation, using it to create visualizations, and eventually, decision-making became a special offering from IT-reliant service solutions. Modern-day BI solutions prioritize flexible self-service analysis empowering firms with accessibility and speed to aid their decision-making. Hence nowadays, opting for business intelligence services for their organizations is a common sight. Over the past few years, more processes and activities have been included for BI to evolve and provide better performance. Some of these processes include… – Data preparation: Extracting data from various resources, organizing them in a structured format, identifying dimensions and measures to prepare for data analysis. – Data mining: Using databases, statistics, and machine learning to discover trends in large datasets. – Querying: Asking data-specific questions in normal language and pulling answers from datasets. – Track analytics: Using data analysis to find out what happened in the past. – Diagnostic Analysis: Extracting results from tracked analytics, and exploring what caused the trend. – Monitoring performance: Analyzing data from current performance to historical data, to benchmark goals and increase productivity. – Creating and sharing reports: Sharing data analysis with those concerned so they can draw conclusions and decide. – Data visualization: Converting reports into visual representations that are interactive, such as charts, graphs, and histograms. – Visual Analysis: Exploring data through visual storytelling to communicate insights. For the firms or industries which have trouble understanding the way BI works, business intelligence consultants come to the rescue. How have firms accepted BI as an integral part of their business? Making use of modern-day technology, many disparate industries have adopted BI ahead of their curve; healthcare, information technology, and education. All organizations can use data to enhance operations and promote growth. In a case where businesses do not have a high-priority tech environment such as the airline, education or retail industry, business intelligence consulting services are utilized to help them in daily operations. Why Use Business Intelligence – 10 Vital Reasons 1. ROI In a world where digital transformation is driving the course of businesses, social media campaigns and consecutive analytics like PPC campaigns are an essential part of marketing. Business Intelligence can translate analytics reports where businesses can base decisions on concrete research, data, and facts rather than intuitions or assumptions. To have evidence on what’s working and what’s not in your favor is a good way to analyze the return on investment for any marketing activity. Hence, opting for business intelligence consulting services can help increase your ROI. 2. Get to know your customers better When you get accustomed to working with BI solutions, collecting and analyzing customer-experience data becomes easier. You get a knack for customer behavior by spotting patterns that wouldn’t have been possible earlier. It is imperative that businesses work hard to understand their customers better because you never know what your competition is using to get to know the customers. The buying experience today is way different from what it was a decade ago. Consumers are more resistant to being sold to since they can access all the information available online for a specific product. To be able to know who is your end-user, what drives their decisions, and the reason they go for some other product instead of yours is always an advantage. That is why understanding what drives revenue for your business is important, and BI helps you achieve that without much hassle. 3. Knowledgeable data More than half the data available online today was created in the past three years alone. In the coming years, this rate of data creation is only expected to increase. One of the major contributing factors for that is the exponential rise of social media channels and an increasing number of users. In order to derive meaningful insights from an iota of data, businesses have begun to implement business intelligence to handle and organize all that data floating around. This also helps in improving performance and building long-term relationships. For those companies which do not have an in-house BI expert to avail of this benefit, outsourcing it to a BI services firm is recommended. 4. Personalize sales strategies When it comes to sales, business intelligence understands more about the other business you may be trying to work with. Sales teams are often observed to use this information for research and get prepared for related objections specific to a company’s situation. It is particularly useful to know whether the firm you are

Abhishek Singh
December 29, 2020
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Shoplifting – A Big Concern for the Retail Industry

The Big Question ? Have you ever wondered that the CCTV cameras we use in our workplaces, retail stores, jewelry shops, etc. are being underutilized compared to what they are actually capable of? There are many use cases that can be solved by using your CCTV cameras be it any anomalous event happening around. Here in this blog, we will talk about one of the major concerns of the retail industry i.e. Shoplifting. Along with this, we’ll also talk about how we at DataToBiz approached the solution to the problem. These days Computer Vision and Deep Learning are becoming prime choices for automation of daily work at many places. The reason behind their success is that they have an edge over providing security to businesses. But, till now only big enterprises have unleashed the potential of automated systems. This time, we at DataToBiz have come up with a solution that any business, be it small or big can use to prevent their daily business loss. As we all know, most of the shop owners nowadays prefer to install CCTV cameras in their shops. But, on a broader view, they limit their motives to only 2 purposes. First, to keep recordings of previous ‘n’ days. Second, to monitor the CCTV live stream for any anomalies. How can you save your business from daily loss ? Datatobiz has taken a step forward to better utilize your existing CCTV real-time feed and save manpower for your business. We all know that any crime generates significant losses, either human or economics, or both together. One of the major forms of crime in retail shops is Shoplifting – “the action of stealing goods from a shop while pretending to be a customer”. The second motive of every retail shop owner is to monitor such kind of activity. But the way they follow demands extra manpower that ultimately leads to recurring expenditure. Even following this traditional practice doesn’t prove to be an efficient solution. So, this approach needs to be solved in a completely automated way. Motivation behind the Shoplifting Solution According to the 2018 National Retail Security Survey (NRSS) inventory shrink, a loss of inventory related to theft, shoplifting, error, or fraud, had an impact of $46.8 billion in 2017 on the U.S. retail economy. According to a survey released by the shoplifting prevention association, Metropolitan Police Department of Japan, the loss is estimated to be 4615 billion yen per year, which is equal to 12.6 billion yen per day. The stunning figure of 12.6 billion daily loss is equal to buying 126 Tesla model S (Big enough! Right?). And, if we look wisely there is no such manpower that can watch continuously to all such cases daily, and also will not be feasible for any business. Technical approach to the solution Fig.1 – Workflow of the Solution What’s new in our proposed solution ? We have implemented a 3DCNN (3-Dimensional Convolutional Neural Network) to process the CCTV video stream and extract the Spatiotemporal Features out of the frames. Spatiotemporal features are different from traditional 2DCNN models in a way such that it extracts features for an extra segment i.e. Temporal Segment. 3DCNN feature extractor takes a batch of frames as input and out of those frames, it selects some of the frames only for capturing ‘appearance’ features and some of the frames for capturing ‘motion’ related features. Let’s take examples of two different 3DCNN models proposed by Facebook and look at the way these models select frames for feature extraction. C3D vs Slowfast – By Facebook Example 1 – If we look into the working of C3D feature extractor model, it selects the first ‘x’ frames out of total ‘y’ frames of a batch to extract appearance-related features and use remaining all frames for extracting motion related features. Example 2 – But, if we look Slowfast (4×16) model, it takes a total of 64 frames as an input. Then, it selects a total of 4 frames each with an interval of 16 frames for extracting spatial features. Parallelly, it selects a total of 32 frames each with an interval of 2 frames for extracting temporal features. Note:- Complete explanation of 3DCNN models are beyond the scope of this blog. The Final Loop – Getting Results After extracting features, a model is built to perform certain pre-processing to bring down all the features into a fixed shape and then perform regression or classification on the extracted features. Here, whether to do classification or regression depends on your selected feature extraction model and the target use-case. Setting a threshold above which your model will treat the event as anomalous will be different from use-case to use-case because some human activities are comparatively easy to detect (e.g. Running, Eating, etc.) and some are hard (Shoplifting, Shooting, etc.). Once the Shoplifting event is confirmed by the model, a dedicated pipeline has been set up that sends notifications (messages, sound, etc.) to the staff members present there along with that particular event’s screenshot. Conclusion The proposed solution is a fully automated way to solve one of the biggest concerns of Retail Shop Owners, Jewelers, Museums, etc. This solution is capable of saving their manpower along with the loss that they had to bear till date. DataToBiz has its expertise in developing state of the art Computer Vision algorithms and inferencing them on edge devices in real-time. Our highly experienced AI engineers will help you build a vision system customized for your requirements.

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