The Analytics Club
ANALYTICA Issue 6 | Volume 3 | April 22
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Role of Analytics in disrupting the Healthcare Industry
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Data analytics used in Circular Supply Chain
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Emerging role of data driven marketing
About Us Organon, the Analytics Club of IIM Rohtak is a student-driven initiative whose basic idea is to generate and cultivate student interest in Analytics and technology. Our primary goals and responsibilities include conducting seminars and webinars covering on-demand topics, skills and tools that will help students of IIM Rohtak fraternity gain expertise in various analytical tools. These skills will help students apply analytical tools in their respective fields during placements. We conduct Analytics related events, competency builder events, Case Study Competitions, Quiz questions
About Us Role of Analytics in disrupting the Healthcare Industry
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Data Analytics Used in Circular Supply Chain
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The Emerging Role of Data Driven Marketing
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Newbits
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Quiz
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Analytica | April 2022
Role of Analytics in disrupting the Healthcare Industry Gunjan Ahuja Great Lakes of Management, Gurgaon
ABSTRACT Data is the new oil of the contemporary world and analytics is the new engine of the healthcare industry! Analytics solutions are the top priority for health CIOs (chief information officers), particularly as health information systems attempt to use big data to prevent diseases, offer better care, and improve all spectrum sectors. Healthcare analytics is the process of gathering and evaluating data in the healthcare industry to obtain knowledge and support decision making. From critical fields such as clinical data, pharmaceuticals, medical costs, and patient behaviour, healthcare analytics can be employed on micro and macro levels to enhance patient care effectively, streamline operations, and minimize total costs. Compared to other industries, healthcare data is the most complex. From monitoring real-time critical signs to EHR (electronic health records), data originates from different sources and have to abide by federal regulations. It is a delicate and challenging process that needs a level of connectivity and security that only an integrated analytics solution can offer. “Disruption” is one such word that any industry fears and loves the most. The key driver in the field of healthcare would be information and data analysis. And as the events went downhill in the year 2020, it only has raised the alarms for much faster change.
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ANALYTICS FOR MEDICAL PROVIDERS As healthcare providers change from service charges to a value-based care framework, the necessity to enhance care and efficiency makes analytics a critical function of daily operations. With an integrated analytics solution, providers: ·Minimize patient wait times by evaluating and leveraging staffing and scheduling procedures. ·Offer personalized treatment to patients and boost the general patient experience. ·Enhance performance by providing quality data-based care. ·Enhance care quality and patient satisfaction by streamlining timeconsuming procedures associated with processing insurance, making appointments, and offering referrals. From the perspective of the health care sector, fraud detection would be one of the most noticeable improvements brought on by the emergence of data analytics. Hospital insurance fraud and pharmacy fraud were always challenging to notice, let alone verify. Data analytics quickly detects potential issues and notable medical record trends to deter fraud and show that it occurred where a crime occurred. Data analytics has had a widespread effect on the health sector, which is just beginning to happen. If technology advances and care providers get
Analytica | April 2022 more confident with it, people see the healthcare situation improve. Doctor visits are now supplemented with video conferences and automatic notifications, the focus on health treatment has begun to increase, and eventually, the life quality would grow. It is a bumpy road incorporating data analytics into healthcare, but the result is worth it. HIGH-RISK PATIENT CARE Healthcare can be complicated and costly for patients looking for emergency services. The price increase is not a guarantee that the patients will receive better results; hospitals need a substantial change in their operations. With digitized clinical records, it is easy to identify patient histories and patterns. Predictive analytics separates individuals with a high risk of experiencing chronic health issues, providing doctors with an opportunity to offer corrective plans that minimize hospital visits. Providing personalized care solutions and monitoring these patients is unattainable without adequate data. Therefore, the utilization of healthcare analytics is of great significance to safeguarding high-risk patients.
ROLE OF ANALYTICS IN DISRUPTING HEALTHCARE INDUSTRY 4 PATIENT SATISFACTION Patient engagement and satisfaction are a priority for many medical facilities. With health tracking gadgets and wearables, doctors actively provide preventive care to patients, and patients also gain insights into their health role. Such knowledge strengthens patients' and doctors' relationship, decreases hospitalization rates, and prevent major health issues that could potentially affect the patient. ANALYTICS FOR MEDICAL PAYERS The ever-changing regulations affect health insurance providers, and since it is the highest cost for families, health insurance depends on performance quality. Through gathering and translating data using an analytics solution, payers: Evaluate prescription fulfilment and hospital claims data to develop intended campaigns for particular health problems. Quickly adapt to any law modifications
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Analytica | April 2022
through integrating an analytic solution that accommodates the current security model. Use pricing information instead of quality criteria to recognize less costly but highest value providers for specific procedures. ANALYTICS FOR POPULATION HEALTH PHM (population health management) is fostering a change in healthcare, with the industry concentrating on prevention and prediction in health instead of treatment and response. Healthcare facilities can leverage predictive analytics to identify patients likely to get chronic diseases in the early level of disease development, offering them a golden opportunity to prevent long-term health complications that lead to repeat hospitalization and expensive care. By gathering and assessing large data sets, well-established analytics. ROLE OF ANALYTICS IN HEALTHCARE INDUSTRY 5
DISRUPTING
Determine high-risk patients to enhance funding and staff allocation. Estimate patient health results to determine the effectiveness of specific treatments and programs. Eliminate care gaps by evaluating ratios between a patient and a provider based on particular conditions.
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Carefully measure and track patient conditions and intake to intervene and predict potential epidemics. ADVANTAGES HEALTHCARE
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ANALYTICS
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Staffing: Having a difference in the number of staffers required on a patient adds cost to the management and the patient both. A paper by Intel describes how four hospitals in Paris are utilizing “time series analysis”. The team developed a web-based solution that predicts and helps them in resource allocation for best patient care practices. (Kyle Ambert, 2016) Enhancing patient engagement: Patients are these days very much concerned about their health and smart wearables help them keep a track of their heart rate and blood oxygen levels. Monitoring their own health and taking timely precautions is a way of helping people save lives as well as giving them back the cost of wearing a smartwatch. Predictive Analytics: The goal here is to help doctors track and make decisions in seconds as to who might be at the risk of diabetes or obesity. Predictive analytics is a major role player when it comes to people with complex and hereditary medical conditions. (Wullianular Raghupathi, 2014)
Analytica | April 2022 Augmenting diagnostics and decisionmaking: Big data can come in any form digital or handwritten. Clinical Decision Support (CDS) services are one such offering, which is used in various healthcare providers to evaluate medical data in real-time, thereby influencing treatment, diagnosis and prescriptive decisions. We current observe that the radiologist department isn’t even well connected with cardiology from same hospital, this will be helpful for a patient suffering from multiple ailments. Managing Supply Chain: Exploiting analytics in supply chain management becomes an important aspect because either under storage or over, both are will become a boon for patient care and long-term finances. Optimized storage and housing can help a hospital save up to $10million per year. (LaPointe, 2017) Integrating Big Data with Medical Imaging: Medical Imaging is an important aspect in patient diagnosis and with the usage of algorithmic pattern recognitions and converting single pixel deviation in MRIs can add larger benefits to the healthcare system. Medical imaging diagnosis is a hefty business and with the usage of artificial intelligence it could save both time and money. Fraud Prevention: Medical Insurance scams and prescription frauds have always been difficult to track let alone to be proven. New data analytics and information technology usage by healthcare providers can become a significant player to recognize patterns, red flag them and prove that these patterns have happened when a crime occurs. Challenges in Healthcare Analytics Electronic Health Records (EHR): The problem currently is that there are thousands of medical conventions
and open access to it is restricted for researchers. The data comes in a variety of forms and building a granular data collection out of it becomes more of a cost than the value extracted out of it. (Cohen, 2019) Real-time alerting: Not everybody is wearing a smartwatch and the lack of infrastructure for real-time collection of data, analysing and then monitoring it becomes a hindrance to the overall idea of real-time healthcare provision. Telemedicine: Clinicians can use teleconferencing tools in providing healthcare benefits to the patients but the lack of precision and availability of tools might just cause somebody their life. (Steve Grifiths, 2019)
CONCLUSION Analytics will play a disruptive role in changing the dynamics of the healthcare industry but how it shapes out will depend on its fair usage. Although the current data privacy and security laws are not turning out to be sufficient to protect the civilians, with the right planning and consideration and keeping an eye on social responsibility as a whole it can change the gameplay in a positive direction.
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Analytica | April 2022
Data Analytics Used in Circular Supply Chain Ujjwal Rawat SRM University (SRMAP), Andhra Pradesh
INTRODUCTION Big data analytics is the process of transforming terabytes of low-value data into small amounts of high-value data to provide an overview of a company using only a small part of the big picture . Big data systems can be divided into four sequential steps: data generation, data collection, data storage, and data analysis. The new era developments, acquiring information isn`t always a tough mission anymore though the green use of information to attain strategic and operational desires remains a place of concern. Today`s commercial enterprise surroundings affords a massive possibility, when you consider that a massive extent of information is generated each minute. The important step is to make sure that the use of superior analytics consisting of predictive analytics, automatic algorithms, and realtime information evaluation is authentic and verified.
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For example, most of the logistics methods in production flowers are carried out via way of means of equipment with radio-frequency identification (RFID) tags, which lets in realtime monitoring of the goods. Using information evaluation on the store ground allows the machine to effectively put into effect real-time production, planning, and scheduling, that is without delay tormented by each the fabric shipping time and the realtime records coming from the producing methods. Moreover, studying the huge information can degree the fabric go with the drift and assist the plant supervisor to higher plan area obstacles concerning fabric go with the drift and warehousing operations. In every stage, quite a few information is generated, and via way of means of accumulating this information for all merchandise, we will have a dataset with huge information characteristics. In spite of this massive extent of information - that is
Analytica | April 2022 generated and saved via way of means of production flowers - the variety of research on huge information. BIG DATA IN SUPPLY CHAIN MANAGEMENT (LOGISTICS AND CLOUD COMPUTING) The utility of big information strategies and techniques in numerous actors in supply chain management, along with forecasting, sales control, risk analysis, and more. The facts available in the supply chain are usually related to customer, sales, market, supplier level requirements, demand forecasting, inventory, potential distribution, first class control, human resources, competency level, logistics, sourcing, warehousing planning and pricing. Additionally, large amounts of information can undoubtedly influence demand forecasting, inventory management, production and supplier planning, and product improvement in the supply chain. Supply chains can leverage massive amounts of information by reducing cycle times, cross-viewing, improving decision-making and optimizing overall chain performance. The use of big data analytics has proven useful in improving logistics and implementing supply chain management strategies. Supply chains that manage uncertainty in decision-making strategies use risk control strategies to some degree.
Cloud computing is one of the practices used to store, expand, and install huge information in commercial enterprise methods. The production enterprise incorporates a massive extent of information created via way of means of sensors, digital devices, and virtual machines in factories.
CONCLUSION THE BARRIERS Management aid performs an crucial position with the a success implementation of massive records analytics structures. It isn't always smooth to decide that the software of massive records evaluation is beneficial for corporations with much less than a positive each day buying and selling volume. In maximum human deliver chains, the growing quantity of product elements and the centralization of manufacturing acquire crucial goals: productiveness via component specialization (many specialised substances and designs that upload functionality) and monetary performance via economies of scale (huge factories). The backside line is that, at the least for the foreseeable future, large adoption of round deliver chains will pressure corporations to forego financial savings in huge production flowers and decrease specialization (and for that reason capabilities) of elements.
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Analytica | April 2022
Most structures initially withstand alternate, so it's miles critical to have the better stage managers` aid so that you can use the consequences of records evaluation to alternate a system. Information safety may be an impediment to the software of massive records analytics in enterprises. Moreover, it's miles critical to have a supportive records era branch which presents each the hardware and software program necessities for operating with massive records. ADVANTAGES Lastly, Big data enables companies to predict market direction and plan development strategies based on this analysis information. Big data allows companies to model digital models of their entire production process. Data collected from customers can be used to improve marketing and sales processes. Businesses
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can use big data analytics to predict customer needs and satisfy those needs to turn them into loyal customers. Information sharing has always been a barrier to supply chains, but the development of big data technologies can help streamline and speed up processes. DISCUSSION/FINAL THOUGHTS To sum up all, Both the literature and the evaluation of companies` enjoy with the region of the usage of massive records analytics in production suggests that the range of implemented case research are greater than the range of theoretical publications. It is probably that researchers will expand greater novel packages for massive records in production structures, consisting of growing techniques which can gain first rate answers the usage of much less time and money.
Analytica | April 2022 Big records have been broadly used for predictive research along with the literature, however, there isn't many prediction blunders in size research in massive records. More precisely, past virtually the pleasant of the entered records, the accuracy of massive records evaluation is notably laid low with the pleasant of the version used to examine the records. We nevertheless have a manner to move concerning growing measures which could decide the accuracy of a massive records evaluation method.
ordinarily have a look at massive records packages in modelling sustainability. Therefore, there may be nevertheless an opening along the software of massive records concerning optimizing operations (consisting of logistics and procurement) in a deliver chain.
The present-day research concerning massive records packages in deliver chain control is ordinarily theoretical and conceptual, and there may be a significant scarcity of research on analytical fashions. Moreover, the prevailing analytical fashions The use of big data analytics has proven useful in improving logistics and implementing supply chain management strategies. Supply chains that manage uncertainty in decision-making strategies use risk control strategies to some degree.
Developing an green collaboration amongst all of the selection makers, transporters, retailers, and door-to-door transport carrier providers
There are a few have a look at instructions in massive records which can notably enhance the overall performance of logistics structures:
Applying cloud-primarily based totally offerings in clever transportation structures and integrating them in a web making plans framework on the way to offer a connection among vehicles, visitors managers, and the very last customers.
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Analytica | April 2022
The Emerging Role of Data Driven Marketing G Muskaan Nasreen Institute of Public Enterprises, Secunderabad
Data-driven marketing is a strategy for optimising brand communications based on customer data. Customers' needs, desires, and future behaviours are predicted using data-driven marketing. Such knowledge aids in the development of personalised marketing strategies for the greatest possible return on investment (ROI).Marketers' roles have been evolving in our data-driven environment. 91 percent of marketers plan to increase their ROI and customer lifetime value. To utilise their data for demonstrable growth, future marketers and CX professionals will need a cloud-first data analytics platform. WHAT EXACTLY IS A CLOUD DATA ANALYTICS PLATFORM? Cloud analytics is the application of analytic algorithms in the cloud to data in a private or public cloud to produce a desired result. Cloud analytics entails the use of scalable
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cloud computing in conjunction with powerful analytic software to identify patterns in data and extract new insights. The terms cloud analytics, artificial intelligence (AI), machine learning (ML), and deep learning are frequently used interchangeably (DL). It is also widely used in industrial applications such as scientific research in genomics or in the oil and gas fields, business intelligence, security, the Internet of Things (IoT), and many others. In fact, data analytics can help any industry improve organisational performance and create new value. Organizations of all sizes can quickly make data-driven decisions to improve the efficiency of their products and services by leveraging AI and other analytics approaches. The cloud is an indispensable platform that enables rapid prototyping of ideas via proofs of concept (POCs) and
Analytica | April 2022 and provides a rich software ecosystem for developing AI applications and training DL models. DATA-DRIVEN MARKETERS OF THE FUTURE MUST IMPROVE CX TO GENERATE REVENUE AND LOWER COSTS To offer hyper-personalized, relevant, and transparent consumer experiences that drive engagement, data is the most crucial asset for marketers and customer experience (CX) specialists. In order to expand their business and achieve their goal of providing experiences that result in recurring and passionate consumers, businesses must get to know their customers. That, however, is no longer sufficient. Marketers are under increasing pressure to not only meet or exceed client expectations but also to drive revenue and growth. According to a CMO Council global marketer survey, 91% of marketers believe senior management and board members expect them to produce demonstrable growth. One-third of corporate leaders believe that driving growth is the key responsibility of marketers today.
Marketers' roles are increasingly shifting from optimising the customer journey to leveraging data to drive growth, increase ROI, reduce customer churn, and increase customer lifetime value. Success is all about successfully exploiting data. IDENTIFYING AND RESOLVING THE MAIN DATA DIFFICULTIES FACED BY MARKETERS Marketers are well aware that their tactics must be based on facts. The problem isn't a shortage of data; there's more data from more sources than ever before. It's not because there aren't enough Mar Tech tools on the market; there are more than ever. The top barriers to extracting more value from internal data assets were: Gaps in technological systems that prevent data from being connected to a single consumer view Individual touchpoints and platforms are encasing inaccessible data. The team does not have the necessary expertise to completely unlock data and apply intelligence.
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Analytica | April 2022
A modern data platform that brings together all available data is a solution to these and other pain issues. Teradata VantageTM, for example, is a cloud data analytics platform that combines data lakes, data warehouses, analytics, new data sources, and data types in a hybrid multi-cloud environment. This provides marketers with a 360-degree view of their customers across all touchpoints. In the consumer economy, understanding customers and leveraging behaviour data to personalise and create relationships, as well as providing real-time experiences, can be a difference. Marketers anticipated that more successfully harnessing data would lead to engagements and companies that are more collaborative, customer-focused, efficient, connected, responsive, and profitable. OVERCOMING THE DATA SILO PROBLEM Companies are still plagued by data and analytics silos. Individuals, departments, analysts, and others want to use their own data or shadow IT to quickly obtain what they require. Businesses and users are aware that this limits insights, relies on incomplete or outdated data, and does not provide a single source of truth; however, the problem persists. Data silos continue to emerge and defy best practices.
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These data silos are frequently found in multiple technology stacks, cloud platforms, departments, and customer facing channels with varying levels of data quality. A unified data foundation is required to turn customer insights into action. Isolated channels are brought together for frictionless integration using this platform. Each channel is aware of the customer interactions that occur across the other channels. Marketers can provide consistent customer experiences across all channels. IMPROVING CUSTOMER EXPERIENCE NECESSITATES A CLOUD-FIRST MINDSET. The cloud is the future of business and marketing. A cloud-first data analytics platform provides the speed, ability, flexibility, and innovation needed to answer any question against any data. Marketers and customer experience professionals of the future will require this type of platform to gather more data from more sources in order to engage customers. Engagement will not occur in a group of many customers, or even a group of a few. Individuals will receive personalised interactions at a segment of one level. Global companies' marketing needs are met
Analytica | April 2022 by a cloud-first data analytics platform with hyper scalability. A telecommunications company with 20 million network subscribers, for example, has 5,000 interactions per day and over 100 billion data points. It processes trillions of interactions per month, processes 100 million queries per day, and has millions of models running mostly in real-time to better understand consumer behaviour changes and preferences, among other things. A platform that can handle these massive workloads enables marketers to use data as an asset to see, understand, and plan not only for what customers want now but also for what they will want in the future. Marketers' current mandate is to leverage real-time data across channels and touchpoints in a way that feels personal, relevant, and even anticipatory.
Marketers can achieve this using Vantage CX. It provides the autonomy, visibility, and insights required to keep up with everchanging customer demand. It also provides the critical capability of transforming customer data into insights and insights into action. Surprisingly, it has been found that only 3% of marketers believe their organisation is exceptionally effective at turning data and intelligence into actions. Delays in insight gathering can mean the difference between failure and success. This highlights the need for a solution tailored to the specific requirements of marketers and customer experience professionals. A brief demonstration demonstrates how Vantage CX predicts customer needs and optimises end-to-end customer experiences to assist marketers in getting more value from their data and being more effective at their jobs.
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Analytica | April 2022
NEWSBITS
SALES CALLS HAVE GONE VIRTUAL, AND AI IS LISTENING IN Wooing clients over lunch is out. So companies are deploying software to analyze Zoom pitches and make recommendations.AI sales tools seem to be most popular, for now, among tech-focused companies. But the pandemic could help similar tools gain a foothold in more businesses, and even perhaps encourage a shift away from so many in-person sales meetings. PREDICTIVE ANALYTICS COULD VERY WELL BE THE FUTURE OF CYBERSECURITY Predictive analytics is gaining momentum in every industry, enabling organizations to streamline the way they do business. This branch of advanced analytics is concerned with the use of data, statistical algorithms, and machine learning to determine future performance. When it comes to data breaches, predictive analytics is making waves. Enterprises with a limited security
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staff can stay safe from intricate attacks. Predictive analytics tells them where threat actors tried to attack in the past, so it helps to see where they’ll strike next. Good security starts with knowing what attacks are to be feared. SPORTS ANALYTICS MARKET WORTH $8.4 BILLION BY 2026 According to a new market research report published by MarketsandMarkets™, the Sports Analytics Market size to grow from USD 2.5 billion in 2021 to USD 8.4 billion by 2026, at a Compound Annual Growth Rate (CAGR) of 27.3% during the forecast period. Various factors such as increasing spending on adoption of newer technologies, changing landscape of customer intelligence to drive the market, and proliferation of customer channels are expected to drive the adoption of sports analytics technologies and services.
Analytica | April 2022
QUIZ
1. Which of the following is definition of Raw Data? A. Set of Measurement on Recorded Values B. Processed Data C. Easy to use for data analysis D. None of the Mentioned 2. Which of the following technique comes under practical machine learning? A. Decision Tree B. Data Visualisation C. Forecasting D. None of the mentioned 3. ____________ is a multidisciplinary which involves extraction of knowledge from large volumes of data that are structured or unstructured. A. Data Science B. Data Analysis C. Descriptive Analysis D. None of the mentioned 4. Which of the following is definition of Raw Data? A. Set of Measurement on Recorded Values B. Processed Data C. Easy to use for data analysis D. None of the Mentioned 5. Point out the correct statement: A. Machine learning focuses on prediction, based on known properties learned from the training data B. Data Cleaning focuses on prediction, based on known properties learned from the training data. C. Representing data in a form which both mere mortals can understand and get valuable insights is as much a science as much as it is art D. None of the Mentioned
6. Data has been collected on visitors' viewing habits at a bank's website. Which technique is used to identify pages commonly viewed during the same visit to the website? A. Clustering B. Classification C. Association Rules D. Regression 7. Which of the following testing is concerned with making decisions using data? A. Probability B. Hypothesis C. Casual D. None of the mentioned 8. Pick Lazy Algorithm A. K-Mean B. CNN C. KNN D. RNN 9. Why Machine Learning in Data Science? A. For Visualization B. For Prediction C. For Cleaning D. All the above 10. Which of the following statements are correct about Regression and Correlation? (a) Correlation is a descriptive statistics while Regression is an Inferential Statistics (b) Both correlation and regression are Independent of origin and scale (c) Correlation is a relative measure while Regression is an absolute (d) Correlation is purely random while Regression is Functional
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