Page 39 – DataToBiz

Kavika Roy
June 01, 2021
3 Comments

Role of Machine Learning in the Medical Industry

Technology in healthcare helps provide better treatment and save lives. But do you think it is as simple as it sounds? Quality of services, valuing human life, and delivering better outcomes are the primary purposes of using advanced technology like Machine Learning or ML in medicine. Businesses and SMEs are extensively adopting artificial intelligence and machine learning (a subset of AI) in various industries. The medical sector is no different. Many research centers and healthcare organizations have recognized the potential of machine learning and are actively improving their patient care services and administrative processes and systems. Machine learning algorithms become more accurate as they gather and process data over time. As a result, it increases accuracy and efficiency. From screening a patient to prescribing the right medicines, AL can empower the healthcare providers to understand the patients’ condition down to the minute details. Machine Learning and Healthcare ML services help in automating recurring tasks. It saves time for the nurses and allows them to focus on the patient rather than spend their energy on filing records, processing claims. Patient management is one of the most challenging things to handle for healthcare centers. Artificial intelligence and machine learning can help streamline the processes to provide high-quality treatment and services to patients. Chatbots are not the only way to use machine learning. Machine learning is fast becoming a part of the pathology, oncology, and other such departments. ANN (artificial neural networks) helps with image modeling, disease diagnosis, identifying harmful cells (cancerous cells) in the early stages, and so on. Of course, this is only limited to direct patient care. Machine learning algorithms are just as valuable for medical research, where labs run clinical trials, discover and develop new drug combinations, and process a vast amount of historical and real-time data to study and control the spread of an outbreak or an epidemic. It is one of the main reasons why the demand for artificial intelligence in the medical industry has doubled during the last year. The Covid-19 pandemic resulted in various researchers and healthcare centers relying on technology to understand the spread of the virus and look for ways to stop it. Role of ML in Medicine Several organizations hire offshore machine learning consulting companies to implement AI technology in their processes. ML has various roles to play in the medical industry, and here are some of the most important ones. 1. Improving Health Records Data entry might have become more accessible during the last few years, but maintaining the health records up to date is still a labor-intensive job. Nurses and the non-medical staff spend a lot of time updating the records. If this were to be handled by machine learning, wouldn’t it save time, money, and other resources? AI can help build and maintain smart health records for every patient: – Whether it is about storing the records on the cloud and making them easily accessible to the medical staff or – Using ML-based handwriting technology to understand and convert written files into other formats. When the necessary patient details are already available on the file, the doctors will have more data to understand the patient’s medical condition. Hence, it contributes to a higher quality of treatment. 2. Diagnosing Diseases It would seem quite natural that machine learning is very good at diagnosing diseases. This is one of the prominent areas where machine learning is highly effective. Machine learning algorithms can quickly identify diseases like cancer that are hard to diagnose early (especially skin cancer). IBM Watson Genomics by IBM Watson Health in partnership with Quest Diagnostics is a prime example. Using genome-based tumor sequencing with cognitive computing, harmful cells were detected faster and with greater accuracy. Artificial Intelligence consulting services are used by healthcare centers for predictive analytics to diagnose brain diseases like depression. Furthermore, it helps plan a proper treatment chart for the patient before getting too late. 3. Manufacturing Drugs Research and development (R&D) is an inherent part of the medical industry. By using machine learning during the early stages of discovering and developing drugs, researchers can know the possible outcomes and the success of using the medicine to cure a disease. Researchers can identify the potential side-effects of using the drug and find alternate components to reduce the side-effects while increasing efficiency. Next-generation sequencing and precision medicine are two AI technologies used in discovering and manufacturing drugs. Precision medicine helps in identifying multifactorial diseases and finding alternative therapies. Project Hanover (by Microsoft) used ML technology to treat cancer and helps in personalizing the drug combination for patients suffering from Acute Myeloid Leukemia. 4. Personalized Treatment and Medicine Personalization is seen everywhere, and the medical industry is no exception. It helps to provide better and accurate treatment to patients based on their health conditions rather than based solely on the disease troubling them. In a way, this point is an extension of the previous one. Though doctors can only choose from a limited set of data, ML makes giant leaps in this region. IBM Watson Oncology is the first thing that comes to mind when you talk of using the patients’ history to predict their health and make a list of treatment methods that are best suited for their conditions. 5. Diabetes Prediction Diabetes is both common and dangerous. It leads to more health complications by damaging other vital organs in the body such as the heart, kidneys, and even the nervous system. While type-2 diabetes is something many people are familiar with, type-1 diabetes (also called juvenile diabetes) is still not known by many. Do you know that WHO estimated around 1.5 million deaths in 2019 due to diabetes? Using Artificial Intelligence in Healthcare to predict diabetes can help physicians detect the disease in its early stages and help patients find the correct method to control their blood sugar levels. Predicting diabetes can save lives and allow the patients to lead a quality life. 6. Liver Disease Prediction Fatty liver or liver cirrhosis is a liver disease caused by alcohol abuse. The simplest

Kavika Roy
May 25, 2021
3 Comments

How is AI Contributing to the Education Sector?

Artificial intelligence has entered every industry, and the educational sector is no exception. The administrative staff, management, teachers, and students are all using AI in different ways to achieve similar goals. During the last few years, AI has spread its roots much wider and deeper in this sector. Though we haven’t seen humanoids replacing teachers, we sure are seeing AI systems taking over teaching and guiding students during their online courses. Whether it is automating tasks or personalized training courses based on students’ capabilities, AI is changing the way people look at education. Before we say the various ways in which artificial intelligence is contributing to the education sector, let us look at how an educational institution should implement AI in its establishment. Implementation of AI in Education Sector Do you want to implement artificial intelligence solutions in your educational institution? You will first need to define the long-term goals and understand the limitations of the existing system. Without knowing what you want to achieve, investing in AI will not be beneficial. Then, you will either have to hire AI experts to bridge the talent gap or outsource the responsibility to a machine learning consulting company. If you want to hire a third-party company, it is recommended to do so in the beginning. This will ensure that your institutional goals and the company’s services are aligned and in sync with each other. Well-known institutions such as Nuance (Massachusetts), Querium (Texas), Kidsense (California), and many others have successfully adopted AI models in their educational systems. Identify Needs: What do you expect AI to do in your establishment? Which processes do you want to automate? Which training areas do you want to focus on? Determine Objectives: Are you aware of how AI works? Do you know the drawbacks of using AI systems? How do you plan to overcome the challenges in adopting artificial intelligence? Align Processes: The AI solutions you choose should create the right environment to align your establishment’s talent, technology, and work culture. You need to convert your institution into an analytical-based model that focuses on actionable insights to make better decisions. Control the Environment: The new work environment should accommodate and allow humans and AI systems to work in tandem. The processes should be transparent, efficient, effective, and secure. Role of Artificial Intelligence in Education Automating Administrative Tasks Is there an establishment that can function without backend work by the administrative department and the teaching faculty? Whether it is filing paperwork, sending emails and messages, contacting students and their parents, or making periodic reports, the administrative staff constantly works from morning to evening. Similarly, the teachers and professors have to mark the homework, test papers, create a teaching schedule, prepare for the next classes, etc. Do you know that a teacher spends less than half their time actually teaching the students? According to an article posted by The Telegraph, teachers spend only 43% of their time on teaching. The remaining time is allotted to marking test sheets, planning future lessons, and completing the administrative work. The artificial intelligence consultant will help you create an AI system that will allow the staff and the teachers to be more productive at work. Teachers can use smart question generators like PrepAI that help them set better question papers for the tests and save time in the process. Personalizing Learning for Students Personalized learning is pretty much similar to personalized recommendations offered by online marketplaces or Netflix. The courses and subjects are recommended based on the interests and preferences of the students. AI for online learning doesn’t replace teachers. Instead, it helps teachers understand the abilities and limitations of each student. It makes things easier for the students and teachers to create an exclusive course plan for each student. Smart tutoring systems also offer instant personalized feedback to help students understand how they can do better. Teachers can convert their lessons into interactive sessions and flashcards so that students will take more interest in learning the subjects. Creating Smart Content Smart content is a popular topic in the education sector. It is nothing but creating virtual content by digitizing textbooks, notes, creating video lessons and lectures, developing interactive training sessions, and more. AI in education helps create customizable interfaces for the students to learn easily and understand better. Be it the elementary level or the postgraduate level, smart content can be developed for any subject and any class. Microlearning and skills mapping are two concepts that belong to this category. Skills mapping is the process of creating a visual representation of the skills needed by students to perform well in their studies. Microlearning is a skill-based learning module that deals with specific areas to achieve expertise in the field. Breaching the Geographical Barriers Online courses have made it possible for students from any region to join a course from any other country. The physical barriers between countries have been eliminated in terms of online education. Technology made it possible to acquire specialized skills from the best teachers, no matter where they are located. Now, students have even better chances of updating their skills through AI-powered training sessions. Information Technology is one area where numerous courses are using AI. That shouldn’t be surprising since artificial intelligence is an inherent part of the industry. Students and professionals who wish to become AI and ML experts are enrolling for these courses. Education Made Easy for People with Special Needs It has always been a challenging task to create a learning program for people with special needs. Though there are exclusive schools for students with hearing, speech, and visual impairment, the teaching process could have been more effective. Also, students with ADHD, dyslexia, autism, etc., have to be enrolled in special schools to help them deal better. But why can’t these students continue to be a part of regular schools? Is there a way teachers can understand and pay more attention to these kids? Artificial intelligence is making it possible for kids with special needs to study most comfortably. SpeakIt!, Widex’s Evoke, and Empower Me are a few examples of AI in education that

Beatrice Johnson
May 18, 2021
1 Comment

4 New Implementations of Computer Vision in Marketing

For many in the business and marketing world, computer vision is still a new and somewhat obscure concept. However, it is also one that is rapidly becoming more relevant, particularly with regard to the acquisition, service, and retention of customers. Leaders and professionals implement Computer Vision in Marketing, Operations, Sales, Retail, Security, and many other areas. To recap the core concept quickly, we’ll turn to a simple definition from Towards Data Science, which characterizes computer vision as a field of computer science that enables computers to identify and process objects in images and videos the same way that humans do. We would also add that the improvement of augmented reality technology is in some respects extending computer vision into the world — such that computer systems can also recognize real-world objects and images through their own cameras. It’s all extraordinarily impressive technology, and it can be used for a wide range of purposes. In this piece, though, we’re going to look specifically at some of the ways in which computer vision can help businesses in marketing. Visual Search Assistance Monitoring Store Traffic Customer Personalization Searchable Images Visual Search Assistance Nowadays, marketers are assisted by certain automated features that help to make recommendations and narrow down selections for online shoppers. The process can work in different ways, but typically a customer’s search activity produces unseen tags that reflect apparent interest. Those tags can then be used to filter through additional store offerings so that customers can be presented with suggestions they’ll be likely to appreciate. It is a simple, automated means of improving direct customer engagement. Now, however, computer vision in marketing is refining this same general concept. Through this technology, a company’s system can actually recognize — look at, in a sense — what customers are observing. Rather than relying on tags, which can be somewhat vague, a computer can identify customers’ selected items and actually look for similar items or appropriate accompaniments. The potential is there to improve customer engagement with even greater accuracy. Monitoring Store Traffic Some time ago, The Atlantic posted a thorough, interesting article on what stores do to “follow every step you take.” The idea is to track customers within stores in order to gather data that can effectively shape in-store marketing strategies. By tracking customers — through Bluetooth and Wi-Fi signals, the customers’ own smartphones, etc. — companies can gain insight into which products are being favored, how the store layout might be made more effective, and so on. And now, computer vision is essentially simplifying this process for marketers and shows how computer vision in marketing is beneficial. Customer Personalization Customer personalization is something we typically think of as having to do with content marketing and data analytics. In a broader sense, today’s businesses go to great lengths to make sure that their written and shared content is tailored to specific audiences. Ayima Kickstart examines this as an aspect of content SEO — explaining that companies employ “expert writers” to research target audiences and construct content according to that research. Beyond this, on more of a customer-to-customer basis, a lot of modern businesses are also using various analytical methods to track activity and tailor follow-up recommendations as needed. Through those practices, consumers are effectively guided toward conversions: They’re found and spoken to strategically within broader audiences and then tracked and catered to via tracking as they browse or otherwise engage with the business. It’s an effective process, but we’re now beginning to see computer vision in marketing simplifying it. Searchable Images The last benefit of computer vision in marketing that we’ll discuss here — and perhaps the simplest — is its impact on consumer searches. With computer systems better able to recognize and interpret images, consumers now have the option of plugging images into search mechanisms. This means that if a consumer should come across a photo of an intriguing product — or even take that photo personally — it can be used to search for further information. As this practice becomes more common, it will naturally produce benefits for businesses. However, it also gives marketing departments a whole new way to think about image-driven product marketing and social media outreach. Conclusion All of these represent significant changes and advancements. And yet they’re also only the beginning! In our article ‘How is Vision Analytics Retransforming Modern Industries?’ We pointed out that the global computer vision market anticipates a 7.6% CAGR between 2020 and 2027. Due to sharing of visuals among the customers, online marketing using visual datasets has become crucial for marketers. With the help of computer vision, they can gain customer insights, improve the campaigns and ultimately improve their buying behavior. As Computer Vision is maturing with each passing year, it holds many new opportunities for marketers. That amounts to a prediction of significant growth, which means that computer vision in marketing is going to become even more sophisticated — and produce even more beneficial concepts — over time.

Yashica Chopra
May 12, 2021
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11 Advantages of Having Predictive Analytics in Healthcare Industry

The healthcare industry is almost always under pressure to deliver better than before. Doctors, nurses, staff, etc., need to be accurate every single time. They are expected to make no mistakes but we also know that this is not possible. Knowledge and experience have their limitations. But technology using artificial intelligence and machine learning can support and help the healthcare industry to be one step ahead at all times. Healthcare predictive analytics is one such technology that is assisting doctors in offering better treatment to patients. Hospitals can streamline business operations and manage staff effectively. Predictive analytics in healthcare is helping doctors be proactive instead of reacting when a crisis occurs. The aim here is to avoid and avert the crisis rather than minimize the damage once it happens. What is Predictive Analytics? It sounds fascinating to know that artificial intelligence, machine learning, and the Internet of Things (IoT) can improve medical care and empower the medical teams to deliver exceptional performance. But what exactly is predictive analytics? As the name suggests, predictive analytics is a branch of advanced analytics that predicts future events by analyzing historical data. AI services, deep learning, machine learning algorithms, data mining, and statistical modeling are used to analyze this historical data and come up with insights for the future. Unstructured data is arranged in an easy-to-understand format for data processing and extraction. Predictive analytics in healthcare is used to identify at-risk patients in their homes so that timely treatment can be provided to prevent their re-admission. Similarly, it is also used to track the recovery progress of patients in ICU to detect if there are any signs of relapse or health deterioration. How is Healthcare Predictive Analytics used? Predictive analytics in healthcare includes processing historical and real-time data. It helps detect trends and find ways to contain the spread of diseases. Using predictive analytics in healthcare can improve the quality of healthcare, collect more clinical data for personalized treatment, and successfully diagnose the medical condition of individual patient. It also helps in keeping a tab on population health management. Why is Predictive Analytics in Healthcare Industry Important? A data-driven system is efficient in delivering quality care for patients. Whether it is reducing the waiting time or bringing down the percentage of readmission, predictive models in healthcare can help understand a patient data and provide accurate treatment. Advantages of Predictive Analytics in Healthcare There are various advantages of implementing predictive analytics in healthcare using machine learning tools and techniques, be it improving business efficiency or assisting doctors in providing health care services to each patient. 1. Selecting the Right Location to Set up New Clinics and Hospitals Setting up a new clinic or a medical center is no small feat. The first step is to pick the right location for the establishment. If the management makes an erroneous judgment here, it could affect everything else and lead to losses. For a hospital to provide valuable services to the public, it needs to be closer to the target audience, easily reachable, and carve a place or itself among the competitors. Predictive analytics can help the management in evaluating the prospective sites based on various factors. By studying how competitors are doing and analyzing the accessibility of the place (along with other details), predictive analytics in healthcare can give you the pros and cons of setting up the clinic at a particular location. 2. Improving the Business Operations for Seamless Hospital Management Hospital management is probably the hardest of all. Even the smallest of mistakes and miscommunication could lead to life-threatening situations. Everything has to be in sync and streamlined to perfection. But it is easier said than done. However, using advanced technology can make it possible. Especially predictive analytics in healthcare insurance has led to the patients, hospitals, and insurance companies working in tandem to process claims and avoid complications. Delays in processing and approving claims can be reduced to help patients get faster treatment. Healthcare centers can have a stress-free work environment where recurring tasks are automated, allowing the staff to focus on delivering friendly and efficient customer service to the patients. 3. Effectively Managing Staff to Increase Productivity and Patient Satisfaction If you are planning a new hospital, how many staff members do you wish to employ? Which roles and responsibilities are they going to fill? How many specialists should you have onboard, and how many visiting doctors can you hire? And if the healthcare center is already established, there are another set of questions to answer. What are the productivity levels of the existing staff? Can you cut down the number of employees you have? Do you need to hire more? Predictive analytics will help find answers to these questions and more. You can create a work culture that empowers the staff to be more productive. This lowers the risk of error and increases customer satisfaction. Patients who find the process hassle-free will naturally prefer your healthcare center over your competitors. 4. Identifying the Right Target Audiences to Promote the Clinic As we already mentioned, promoting the clinic is just as important as delivering quality services. The primary step here is to identify who the target audiences are. Predictive analytics in healthcare organizations are recreating their marketing strategies to target those families and audiences who are more likely to respond to the ads. For example, a child healthcare center should attract parents with young kids rather than senior citizens. The marketing and advertising strategies should be crafted to reach as many young parents as possible in the region. Instead of creating a blanket campaign to target all kinds of audiences, the healthcare center should create individual strategies to reach out to different types of audiences. Predictive analytics will help in formulating this strategy by showing which one is more likely to deliver the expected results. 5. Understanding Market Opportunities for Growth Real-time predictive analytics in healthcare should not be limited to helping doctors and specialists. For a hospital or a clinic to be

Yashica Chopra
May 05, 2021
1 Comment

10 Best Data Visualization Tools for Massive Business Growth (You Shouldn’t Ignore This!)

Big data, data science, artificial intelligence, etc., are some terms we hear quite often these days. Though they are all different from each other, the common point for all the concepts is data, information, and database. Collecting, processing, and analyzing data is the core aspect. With access to volumes of data every second, enterprises are now under the pressure of excess data and overflowing databases. This earlier led to delay in making decisions when it should have been the opposite. But raw data on its own is of little or no value. We can hardly understand anything from it. One famous method of understanding a vast amount of raw data is known as data visualization. Visualizing complex data becomes crucial when it comes to understanding and detecting patterns that are otherwise not easily found. And then… Data Visualization tools come into play. What is Data Visualization? In simple terms, presenting data and information in an easy-to-understand visual or graphical format is known as data visualization. Of course, it is not as limited in its usage and helps with more than visualizing data. Data visualization tools provide a range of features and can be used for multiple purposes. A few data visualization examples include- presenting data in various charts, graphs, infographics, maps, etc., in highly customizable interactive dashboards to facilitate a better understanding of the derived insights. It is a form of visual storytelling for business purposes. It is all about how effectively you can analyze and present the data in real-time to derive accurate and meaningful insights. These insights allow you to make better decisions to expand the business, grab new opportunities, enhance customer satisfaction, and increase ROI. These visualization techniques effectively present the analytics for easier understanding. Types of Data Visualization The types here are nothing but the numerous ways in which data can be presented in a visual format. There are as many as 67 types of data visualizations to choose from. Nevertheless, these 10 are the most popular choices for enterprises around the world. Tree graphs, heat maps, dot maps, networks, and text tables are a few more types of data visualizations we see often. You can design any type of charts and graphs to generate reports. In fact, most enterprises rely on more than one type to process data efficiently and create better presentations. This ultimately results in better decisions. But how are such charts, graphs and effective visualizations created? Employees can’t spend days and weeks toiling on data, right? By the time they manually create a chart, the data would be outdated, and the market trends would have changed. The charts and graphs become unworthy and end up as an additional expense for the business. Even though historical data and real-time data are necessary for decision-making, using past data alone will not be enough. Investing in tools for data visualization is the right way to process historical and real-time data in less time and investing in data visualization software will aid your business in many ways. If using data connectors and working on data exploration seems hard, hire offshore professionals and data analysts. They manage data files and data integration to simplify complex data. They create interactive maps, charts, and transform large data sets into interactive data visualizations. Outsourcing experts is a great way to learn and empower your business. You can compare values and get the gist of what’s better and what’s not just by looking at the charts. You can identify the patterns in huge volumes of data to see how a decision or a change has impacted different aspects of your business. Data visualization techniques make the process effective. The information you own will have a new and effective meaning. It becomes easier to identify emerging trends in the industry and plan in advance to make the most of the changes. This fetches you a definite edge over your competitors. You can ensure your success by investing in visualization capabilities that process data from multiple data sources and present the analysis through customizable dashboards. There are times when we want to see how certain variables can change the course of the business plan. Or, we might want to correlate two or more types of data visualizations to identify common variables. The data visualization tools and online applications can assist us in finding what we are looking for. These can be used through the cloud, through browsers, and/ or by installing in-house servers. You can also find open-source data visualizations and applications. 10 Best Data Visualization Tools 1. Tableau Tableau is counted among the best data visualization software. Since it was released in 2013, many small, medium, and large data analytics consulting firms have started working on the software and became Tableau Alliance Partners. You can find a lot of firms offering Tableau capabilities to manage big data. You can work on the Tableau desktop app, via servers, or hosted online through the cloud, or start with the Tableau public version available for free and proceed to buy the paid version. Data from CSV, Salesforce Einstein analytics platform, Google Analytics, and various other sources can be processed and presented on the interactive dashboard. You can also try the Tableau mobile version. The rich gallery of templates for infographics is a delight to every Tableau consultant. With such extensive options and components included, it’s no surprise that Tableau is an impressive and important business intelligence storytelling app. The tutorials shared on the website guide consultants in brushing up their skills and becoming efficient in using Tableau software for data visualization. Robust performance and scalability are other advantages of using this software. Data privacy is another advantage of investing in Tableau. However, the free public version doesn’t allow you to keep your analytics private as the Tableau server does. 2. FusionCharts FusionCharts is a JavaScript-based data visualization tool famous for offering around 1000 types of maps and 150 types of charts and graphs to present the data in a visual format. The aim of this tool is to create

Yashica Chopra
April 27, 2021
1 Comment

10 Things to Consider While Building your Right Data Science Team

Enterprises these days no longer follow outdated business processes. The focus is more on adopting the latest technology and relying on new software and tools to increase productivity and ROI. But working with advanced systems means hiring experts who have experience in the said field. Data is a major part of every business. Be it artificial intelligence, machine learning, natural language processing, or business intelligence, these technologies work with vast amounts of data. Organizations need employees who can work with data and the latest software to derive insights and generate reports. Ensuring data quality helps an organization make better decisions. Many of you would have already heard of data scientists. A core data scientist is supposed to know everything and manage it all. But that is hardly possible. Even though the same person should have a varied skill set, a data scientist alone will not be enough. What enterprises need is a fully equipped data science team to work on analysis. What is Data Science? Before we talk in detail about why and how you should build a successful data science team, let us first see what it actually is. The simplest definition is- In computer science, data analytics is the process of analyzing raw data to make conclusions about it. A data science company offers multiple services related to data analytics, data infrastructure, data strategy, big data, deep learning, machine learning, artificial intelligence, and much more. The technologies and services are interlinked with the teams working on different aspects of data science and advanced analytics. Not every business has the suitable infrastructure to build a data science team. While some find it easy, others have to make a lot of effort. A global digital framework can introduce governance, the social environment, business, and technology. In such instances, hiring the services of a data science company is a better option. How Data Scientists are different from Data Architects? A data architect has an evolving role, so there is no industry-standard certification program. As data engineers, data scientist experts, or solutions architects, individuals typically gain experience in data design, data management, and data storage work as they work their way up to the role of a data architect. The Importance of Building the Right Data Science Team What if you want to build a team for data science projects? At what stage should you introduce data science into your enterprise? Mostly, the decision has the highest impact at early stages only. How important is it to build the right team? Introducing data science roles into your business processes requires a lot of planning. You will need to be sure that you have enough budget to invest in systems, people, and processes. You also need to be assured that your existing employees will welcome the changes and embrace them. If your employees do not value the insights offered by data scientists, the purpose will be lost. Many companies expect data analysts to be able to convert alienating numbers in order to provide tangible insights. The following are some reasons you should invest in building data science for your business. Empower the employees and management Recommending future actions based on insights derived from past and real-time data Identifying growth opportunities in the market Helping in better decision making based on data-driven reports Assisting the employees to adopt industry best practices Analyzing and evaluating the decisions made by the management Identifying the target audiences Identifying customers’ issues and finding solutions Helping recruit the right talent for the business Data Science Team Roles A data science team has multiple experts, each dealing with different aspects of the field. The roles and responsibilities of the team members depend on their experience in domain expertise, technical knowledge, and quantitative skills. Team Leader- Chief Analytics Officer or Chief Data Officer Data Strategist Data Scientist Data Engineers and Architects Data Analysts Machine Learning Engineer Business Analyst Data Journalists Data Visualization Engineer The actual team positions might differ, depending on the types of data science teams an enterprise wants to build and how much it can invest into it. How to Structure the Data Science Team? The data science team structure can further be classified. You will first need to decide the type of team you want to build in your organization and then hire the right kind of experts who require data analysts with market-tested skills. Decentralized: Works the best for short-term, initial data science integration activities and SMEs that don’t want to become a full-fledged data-driven business. It totally depends on the business objectives to opt for a decentralized model. Centralized: These are data science teams that work on multiple projects scattered in different departments throughout the enterprise. This structure works well for enterprises focusing on long-term growth and development. Functional: One team works with one department like the marketing or the logistics. The focus area is limited to that department. This structure is best suited for startups where there is no need to analyze every single piece of information. Center of Excellence (CoE): This is similar to the centralized structure but with a separate unit for data scientists. It is known as one of the most balanced structures since there is a higher level of coordination between the teams. Consulting: This is similar to having a data analytics company within the enterprise. The data engineering team can be hired by different departments to work on specific projects. This structure works from SMEs where the management cannot allocate many external resources to the teams. Democratic: This allows you to combine and integrate the simple or specialized data science model with other systems in the enterprise. Employees have access to data science systems and can make changes to them. This works when businesses focus on building data science infrastructure for the enterprise. Federated: This is similar to employing a SWOT team in the organization. The federated structure is a combination of decentralized and CoE types. So does this make you wonder who should the data science team report

Clutch
April 21, 2021
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DataToBiz Becomes Clutch’s Top Artificial Intelligence Company in India

Taking data-driven decisions instead of intuition is what we do here at DataToBiz. We are an AI and data science service company that connects businesses to well-studied data. Our company consists of experts with the goal of helping clients in managing their data assets and looking for the best ways to implement insights from their data. DataToBiz becomes Clutch’s Top Artificial Intelligence Company in India. The DataToBiz team is open to working with all kinds of businesses regardless of their size. We focus on making them understand the potential of data and implement the perfect structure to process it and make decisions. Recently, Clutch, a B2B reviews platform, crowned DataToBiz as one of the 2021 leading agencies in artificial intelligence. This award not only acknowledges our hard work but also inspires us to strive higher. “Great to see ourselves amongst the contributors of Data Science, AI, Power BI, Tableau and Big Data Solutions.” – CEO, DataToBiz We also would like to take this opportunity to extend our gratitude to our clients who willingly shared their experiences with us. Here are some of the feedback they left on Clutch’s website: “The team at DataToBiz was patient, and they listened to all of our needs. We would occasionally make last-minute requests at odd hours, but they were always available to help us resolve the issue quickly.” – Senior Analyst, Absolutdata Analytics “We’re happy with the outcomes of DataToBiz’s work.” – Amritpal Singh, CTO, Spice Labs Pvt Ltd We are overwhelmed by the award we received from Clutch! If you are interested in our services, you can contact us anytime. Let’s make your business AI-driven! DataToBiz has not only made its mark in the Indian subcontinent but also provides data strategy consulting solutions in countries such as the United Kingdom, United States, Canada, Australia, and the middle-east.

Yashica Chopra
April 19, 2021
1 Comment

12 Great Facts About Analytics for Retail Price Optimization

What is one of the most important factors a retailer should always monitor? It’s the price of products they sell. We all know that the selling price of a product is pre-fixed. But that doesn’t stop some retailers from offering discounts and giveaways. In fact, customers today expect you to offer products at competitive prices. With so many stores mushrooming (online and offline), it has become necessary to have a flexible pricing policy. Guesswork won’t help you in the long run. It is a sure way to end up in losses. Why not give a shot to analytics for retail price optimization? What is Retail Analytics? In simple words, analyzing retail business & customer behavior data to help retailers make better decisions is known as retail analytics. Of course, it is hardly that easy in real life. Retail analytics is the process of using AI tools to collect and analyze historical and real-time data to derive in-depth insights. It allows you to make better decisions based on these insights. This will be a win-win situation for you and your customers. Before we read more about analytics for retail price optimization, let us answer the following questions. These are some key factors that you must consider as a part of your retail analytics. How willing are Customers to Pay for a Product? Also known as price sensitivity, this factor deals with the maximum amount a customer would pay for a product. Unless you know this, you cannot make adjustments to the prices of products you sell in your retail stores. What is the Average Revenue Generated per User? How much does each customer contribute to your revenue each month? Knowing the answer to this question will help you understand who your most valued customers are and which products they are buying. What Makes a Product Famous Among the Customers (Product Value)? It can also be termed as feature value analysis, where you try to identify the most liked and least liked features of a product. Depending on the number of features/ aspects customers like or prefer in a product, they will fix a price for it in their minds. If you set your selling price over that amount, it will affect your sales as they may not wish to pay as much. How do Customer Acquisition Costs and Customer Lifetime Value Affect Your Pricing Decisions? You need to know how much you can invest in a customer. There is no point in running an extensive campaign if the customer doesn’t buy from your retail stores, right? Retail intelligence and analytics for retail price optimization give an insight-based understanding. Here’s how retail analytics can optimize your pricing strategies, streamline your business operations and promotional plans, and help you become a leading retailer in the market. 1. You can Get Immediate Returns on Your Investment Getting faster returns is the dream of every retailer. Who would want to wait for months and years as the interest accumulates and reduces the profit margin? Computer vision solutions for retail allow you to come up with a pricing policy that can be changed in real-time. Each time you adjust your short-term goals, you can tweak the prices accordingly. That too, without worrying whether the customer will pay as much or not. A study by PwC found that 60% of the customers decide whether to buy a product or not solely based on its price. Fixing the price without knowing how things stand could lead to losses instead of increasing ROI. 2. You can Understand Your Customers’ Purchasing Behavior It is eventually up to the customers to buy a product, isn’t it? Even the best discount offers don’t result in sales at times. This could be because- Customers don’t prefer that product The timing of the discount was wrong The offer didn’t reach the target audience How can you make sure such mistakes don’t happen? By using retail analytics to get insights about customers’ purchase behavior and interests. What drivers influence customers to buy a product? The answer to this question can help you optimize the price to increase sales and profits. 3. Automate Your Business Operations to Gain Competitive Edge Who said automation is not meant for retailers? Why spend your precious time calculating and analyzing the market trends, measuring the price changes, and monitoring customer demands? Let technology do it on your behalf. It would also reduce the risk of human error and give you more time to focus on implementing the pricing and promotional strategies. Machine learning algorithms can help automate pricing. It can be integrated with other retail applications you use to streamline your business operations. You can stay up to date with the latest changes in the market (check the next point). 4. Be Ready to React to the Changes in the Market A successful retailer is the one who can make fast and accurate pricing changes in real-time. Effectively managing both offline and online sales is becoming more of a necessity in recent times. When you know that the demand for a product will increase or decrease in the coming days, you can plan your pricing strategies to attract more customers and increase your return on investment. 5. Make Use of the Feedback to Correct Your Pricing Strategies The feedback here comes from the retail analytics software you use. The regular reports generated by the software will tell you whether the current pricing plan is effective or if changes have to be made. Real-time insights are derived from the latest information available. This helps you make quick changes to the prices and adjust them immediately to suit the customers’ demands. Instead of taking feedback through surveys, you can get the required reports from the software. The constant feedback will keep you at the top of your game and ahead of your competition. 6. Support Your Decisions with Processed Data A wrong decision could prove to be very costly for a retailer. While gut feeling cannot be ignored, relying entirely on it is a risky

Yashica Chopra
April 14, 2021
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12 Top Tableau Consulting Firms to Hire

Business intelligence is now a part of many growing and successful firms from around the world. Markets and Markets reported that the global business intelligence and analytics software market is estimated to have a CAGR (Compound Annual Growth Rate) of 7.6% by 2025. The market size is expected to grow from $23.1 billion in 2020 to $33.3 billion by 2025. Among the various BI tools, Tableau is one of the consistently leading platforms in the market. Tableau Software was founded in 2003 in California. The company offers data visualization software and services to various businesses in different parts of the world. The success of this business intelligence tool led to many companies becoming Tableau partners or tableau consulting firms. From famous multinational organizations such as Accenture and Deloitte to companies like Ideas2IT, many of them are offering Tableau Consulting services. The diversity and potential of this tool might be amongst the reasons to invest in it. Need to find more reasons to invest in Tableau Consulting firms? We have another post for you. Read Now >> 14 Powerful Reasons to Invest in Tableau Consulting You can consider these factors when hiring Tableau consulting firms- Though Tableau is easy to use and implement, taking the assistance of experts will help MSMEs and Large Enterprises in streamlining the implementation process. It helps avoid unnecessary glitches and complications. As we’ve seen, when there is competition in the market, it gets rather tough to choose the right consulting firm to help the business. In this blog, we’ve listed the top 12 Tableau consulting firms based on experience, the flexibility of approach, responsibility, and quality of services, company portfolio, client testimonials, etc. 1. Accenture Accenture is an Irish MNC with operations in 52 countries. It is also a Fortune Global 500 company with reported revenue of $44.5 billion in 2021. The MNC offers various services, ranging from application services to supply chain management, security, and more. Accenture is a Tableau Alliance partner firm and helps numerous businesses implement business intelligence and data visualization services. The company is best known for providing- 2. DataToBiz DataToBiz is one of the fastest-growing data analytics consulting companies. The company offers services such as- It is an India Private Limited company that aims to help businesses from various industries leverage and make the most of the insights derived from processed and structured data. This famous Tableau consulting company is known for its planned approach to handling huge amounts of data by – 3. Ideas2IT Ideas2IT was founded with a focus on Silicon Valley startups. It is also one of the leading Tableau consulting firms with around 240 experts who operate from different countries such as India, the USA, Canada, Switzerland, and Ecuador. The company works mainly with IoT (Internet of Things), data science, and blockchain technology. As a part of data science, the company deals with DevOps, application development, data modeling, and data visualization using Tableau software. From healthcare to retail to finance sectors, Ideas2IT works with businesses in different industries. The company offers flexible services and has experienced professionals who work with business on- 4. Deloitte Deloitte Touche Tohmatsu Limited, an Anglo-American MNC, was founded in 1845. Yeah! The company has operations in more than a hundred countries from around the world. From more than 90,000 employees, the company has 5000 Tableau experts who have worked on 100+ projects. Deloitte reported revenue of $50.2 billion in 2021. Some of the leading names, such as Adobe, Yamaha, Dell EMC, etc., are clients of Deloitte. The company’s Tableau services help enterprises implement self-reliant and agile data-driven culture to increase their business value. 5. Slalom Slalom is a modern business and technology consulting firm in Seattle. The company has delivered more than 4000 Tableau projects. Slalom has been a Tableau Alliance Partner for years with 400+ certifications. In that scenario, building a modern data ecosystem on the cloud and using Tableau to streamline the business operations will become a priority. Slalom offers exactly that and much more. The company helps firms embed Tableau into other enterprise-level applications to deploy a scalable and flexible system throughout the business. By using machine learning and data analytics services offered by the company, MNCs can become better equipped to achieve their long-term goals. 6. Wipro Wipro Limited was founded in 1945 with its headquarters in Bangalore, India. The company’s offices are located in six continents and offer services related to IT infrastructure, artificial intelligence and data analytics, application and consulting, digital operations and platforms. Wipro works with numerous organizations and manufacturers from various industries – aerospace & defense, education, pharma, travel, public sector, retail, banking, and many more. Being a well-known consulting company, Wipro uses cutting-edge technologies and advanced visualization techniques to help its clients unlock the power of big data. The company believes in cloud-based delivery of services so that enterprises can speed up their business processes and empower the employees to become more productive. The company has been a Tableau partner since 20l6, with Wipro’s primary focus on helping clients from the financial, retail, and CPG (Consumer Packaged Goods) sectors. 7. Iflexion Iflexion has its headquarters located in Denver, USA. With more than 850 employees, the company caters to a mid and large-scale organization from around the globe. The services offered by the company are IT-based, such as QA, mobile and web development, integration, and more. Iflexion works with various industries such as eCommerce, security, finance, etc. The company has been offering Tableau services ever since Tableau was released in 2004. From migration to training employees to provide support, Iflexion is a one-stop destination for Tableau services. The company helps its clients by offering the offering BI services- The company works with clients from the healthcare, education, media, finance, marketing, and government agencies. 8. Sojourn Solutions Sojourn Solutions has been helping firms by offering Marketing Operations and Marketing Automation services. The company focuses on B2B marketing while making it a point to align the strategies with data and business processes. Vodafone and O2 are among their top clients. Sojourn Solutions

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