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
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Unleashing predictive analytics 01 Machine Learning and ESG: A desirable crossover 05 Metaverse fictional universe 09 Newsbits 13 Quiz 14 Content
When paired with the expert system or artificial intelligence (AI), the understandings amassed from these innovative systems are the trick to more accurate and timely projecting moving forward. Predictive analytics boost processes utilizing machine learning as well as historical data like weather patterns, consumer habits, and gas rate variations But what occurs when historical data is no longer predictive of the future? Bansal
The companies who were well established and had the resources to manufacture large amounts of medicines were not able to cope with the demand and the supply was abruptly hampered leading to black marketing and hoarding of essential items
UNLEASHING PREDICTIVE ANALYTICS Tushar
Consider the Covid 19 pandemic, during which an external, disruptive occasion shattered the global economy and skewed forecasts. Supply chain leaders making use of predictive analytics in early 2019 and also 2020 did not as well as could not make up for the international financial shock caused by the pandemic
As the supply chain stabilizes, several manufacturers are returning to regular procedures with more durable technical abilities. Nearly half of supply chain leaders raised costs on innovations and systems throughout the pandemic consisting of predictive analytics.
For example, firms that manufacture PPE or toilet paper had no way of predicting just how much the demand for those products would certainly increase throughout the pandemic
Amity Business School Analytica|August2022 Page 1
Consumers' behavior and acquiring patterns during the pandemic were not predictive, either Businesses were fighting with forecasting because of abnormalities in customer habits throughout 2020, and also the choice to also consist of data from 2020 in anticipating models was arguable.
PREDICTIVE ANALYTICS SERVICES
Neither did the raw product carriers that make these items feasible On the other hand, local businesses, dining establishments, and providers had no dependable way of readjusting their inventories or operations to match demand.
Predictive analytics uses analytical algorithms incorporated with interior and exterior information to forecast future fads, making it possible for businesses to enhance stock, enhance shipment times, boost sales, and ultimately reduce operational expenses.
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Here is a more detailed look at how you can boost predictive analytics with AI in the supply chain: 1.Inventory management: Also, before the pandemic, supply overstock, as well as understock, added to countless lost dollars yearly an absence of real time inventory exposure played a critical duty in these issues. When you sync AI with real time, you can enhance your stock monitoring past fundamental reordering. You are checking modern technologies like internet of things (IoT) tools in the storehouse to supply real time signals for reduced inventory so you can replenish items before they head out of stock In time, an AI based solution can gather information and acknowledge patterns, allowing you to plan inventory better.To get started, one needs the data to, in fact, examine From the fundamentals of barcode scanning to RFID and other stockroom automation modern technologies, they can catch every one of the data factors When data like every barcode scan is fed into an AI/analytics engine, this data can give them insights into the patterns of its supply movement and sales and also understand how to maximize employees' functions.
Over the next 2 centuries, the Society of Lloyds became the world's leading market for speciality insurance coverage, mainly since they used historical data and complete knowledge to quickly and efficiently identify risk. AI is anticipated to grow to a $309 billion sector by 2026, and 44% of executives report reduced operational costs as a direct outcome of implementing AI
There will certainly constantly be out of the way elements that skew information Yet the more information resources one has, whether internal or external, the more exact predictions will be when coupled with AI and anticipating analytics The difficulty can be understanding where to find it. Matching predictive analytics versions with AI is essential in improving forecast accuracy post pandemic. In technique, this indicates having up to the second information for each resource. Plastic supply, as an example, might be affected by the lack of particular basic materials due to natural catastrophes or unforeseen shipping hold ups An AI system can proactively flag most likely occasions, leading to more informed decision making. Though there have been breakthroughs in the last couple of decades as computer system handling and information storage space capacities grow the initial application of predictive analytics remained in 1688, by Lloyd's of London. One of the first insurance policy and reinsurance markets ever developed, Lloyd's was a driver for circulating vital info required to analyze risk on abroad shipping and trading. Founder Edward Lloyd could utilize the trusted shipping news and information from his famous coffee house to help sailors, vendors, ship owners, and bankers in their insurance policy and underwriting service ventures
In addition to path optimization, IoT tools can collect real time sensing unit data on vehicles to enhance functional elements of distributions For instance, this technology can sense object changes en route, inequalities, and unexpected quits, revealing understandings for more intelligent decision making
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2.Distribution optimization: In the last few years, predictive analytics has been utilized to maximize trucking paths and guarantee on time distributions However, what happens when mishaps, traffic congestion or severe weather conditions occur? These, and other unforeseen circumstances, might cause roadblocks in delivering or backhauling goods in the supply chain. It is where the power of analytics, as well as AI, enter play Analyzing these occasions will give future understandings of just how to handle and prepare for these situations.
Path optimization software can be paired with AI to make it possible for real time rerouting based on previous aspects AI formulas would currently be capable of even predicting the most effective times for distributions, possible hold ups, and other transport and distribution factors
CONCLUSION
The current pandemic highlighted the power of predictive analytics coupled with AI Data collection is critical in the supply chain, yet it is useless if it does not result in activity. are collecting even more information than ever; however, we require AI to transform it into predictive and workable understandings. get started today, one must have a good strategy and group buy in to start recording the information factors and the appropriate innovation on one ’ s trip in the direction of fully executing and anticipating analytics utilizing AI.
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The ESG disclosure is based on a long history of providing annual updates on environmental and social performance. In 1980, Chemical companies published sustainability reports to help mitigate issues regarding image and environmental action Reports didn’t follow a framework and were market driven rather than analytical in nature. These disclosures are important to inform internal and external stakeholders about a company ’ s integration and management of ESG risks & opportunities.
ESG Disclosures
Machine
Learning and ESG: A desirable crossover Nupur Chiragbhai Shah Ahmedabad University Analytica|August2022 Page 5
It is not esoteric to believe that the recent explosion of ESG awareness has moved into mainstream consciousness. There is a lot of subjectivity in how the terminology is understood, even among people who are aware that ESG stands for Environmental, Social, and Governance It serves as a catch all phrase for a number of closely related concepts, such as sustainable investment, socially responsible investment, impact investment, social impact investment, ethical investment, and even conscious capital. While each concept has its own specifics, they are all connected by the idea of adopting a strategy that goes beyond the conventional "bottom line" of shareholder returns.
Unlimited ESG variables can be analyzed using machine learning to find sources of alpha. As opposed to market cap weighted benchmarks, smart beta is rules based and intended to increase returns rather than just minimize risk
Making ESG data accessible Artificial intelligence (AI), machine learning, and similar programmes will help sift through the data and background noise to give the information that matters for your investment analysis. Researchers and engineers are applying ML in similarly revolutionary ways across the finance community They are leveraging emotive data to analyze linguistic data from material, such as earnings releases and LinkedIn posts, to determine whether a firm is actually committed to ESG and what effect this commitment has on its stakeholders
In addition to lowering the inherent volatility of market indices, this strong combination may provide investors with significant alpha compared to equities benchmarks. Some ESG tactics fall short of what investors want. For instance, the negative exclusion method, which excludes contentious activities from the portfolio, is appealing to investors but only partially meets their needs The several best in class techniques evaluate a wide range of variables and frequently provide portfolios with excellent overall ESG scores
The capacity to quantify and analyse risk in portfolios and positions has now become crucial for industry players since investors are eager to take advantage of the expanding ESG prospects. This is true whether investors are screening the investable universe with negative and positive ESG metrics, performing bottom up studies of companies, determining what indicators are material to a particular company or sector, or interacting with companies on policies. With ESG investors are growing more selective, they expect their investments to achieve both their ESG goals and excellent profits. While some investors prefer managers who include ESG risk into conventional equities portfolios, others look for funds that concentrate only on long term ESG trends. Real, measurable value may now also be created using a cutting edge approach that combines smart beta investing with machine intelligence.
Next step: ESG in asset management
As businesses with various ESG strengths are integrated in a single portfolio, the key factors that produce exceptional performance are lost As a result, portfolios built using rating systems often reflect broad market benchmarks, at best. If any can make the claim of producing the required alpha to regularly surpass benchmarks, it is rare. Investors are looking more and more for techniques that can both find good ESG firms and create financial value from them The ESG proposition must be advanced at this point Search for ESG alpha There is an alpha in ESG data; the challenge is to utilize the now available enormous datasets effectively Given that there are roughly 150 ESG indicators for each organization, with each indicator providing a specific response to a particular ESG question, it is difficult to truly leverage this data.
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Filtering versus machine learning approaches Ossiam's plan calls for purging companies from a worldwide benchmark of equities that do not adhere to cutting edge ESG guidelines Investors that do not desire exposure to contentious business practices are satisfied by this purging process. So far, so easy The machine learning method, which finds predictable patterns in ESG data even when the number of variables is very big and the interactions between them are exceedingly complicated, provides the strategy's main value.
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Every company that is being taken into consideration must have its performance compared to every other organization. Using conventional methods would be impossible for this complicated operation. The linear regression model is the oldest and most well known of the conventional techniques For example, using macroeconomic variables like GDP growth, industrial growth, inflation, interest rates, and labor indicators, this model may predict the likely financial success of businesses.
These concerns range from how unbiased the board is to workforce diversity, the firm's impact on the environment, the sustainability of its infrastructure, etc To identify patterns between ESG profiles and the chance of outperforming their peers, it is necessary to evaluate these indicators company by company in order to identify the businesses that are most likely to succeed.
However, when there are many predictors, linear regression produces subpar results Given that there are 150 ESG indicators and that they are expanding quickly, the linear regression model has a poor chance of predicting where future alpha will be Machine learning comes into play in this situation.
are then plucked out to satisfy investors' need for a low carbon impact. This results in a portfolio of equities with superior ESG ratings than the benchmark, a higher likelihood of financial outperformance, no contentious activities, and a small carbon footprint. Businesses like State Street Corporation employ AI apps throughout many facets of their operations and examine investment strategies utilizing predictive analytics They use natural language processing to generate signals in several of their strategies In a similar vein, they employ AI methods to produce signals that support established tactics and improve alpha creation Additionally, the business delivers personalized news feed to portfolio managers using AI tools, for instance, they have improved corporate ESG risk measurement using AI and machine learning (ML) in order to improve investment portfolio performance
Because ESG is still very much in its development as an investing category, the "understanding" component of machine learning is essential. It is expected to change as investors demand more from their ESG investments and databases grow as businesses reveal more information, either out of conviction or in compliance with regulations. This implies that adaptability is crucial Investors will miss advancements in the ESG system and forgo returns if the algorithm's criteria are set in stone
The best performing equities are generated by machine learning, and the worst polluters
However, perhaps in a few years environmental issues will be properly incorporated into ESG strategies and social factors will have become more important to performance. Today, many ESG variables are focused on environmental difficulties Road to a pioneering approach
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As soon as Mark Zuckerberg announced that Facebook would now be known as meta on October 29, 2021, the media went crazy, social media erupted, and everyone realised that the next big thing in technology had indeed arrived. Mr. Satya Nadella then announced Microsoft's interpretation of the metaverse. H&M has already established a store in metaverse and Nike has registered its assets for metaverse and all this support the fact that metaverse is not a fancy concept but the futureof upcoming generation. But although the majority of us know that it's the future, very few of us actually understand the importance of the metaverse Three key topics will be covered: Why the metaverse is such a big thing, when will people like you and me actually be able to use this technology, and most importantly what exactly the metaverse's utilitarian value is
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What is metaverse? Short version: In the metaverse, you can go one step further and communicate with that individual, much like how you and I can connect with anyone on Facebook, even if they're on the other side of the planet. You may converse with them while laughing and even have a coffee date to simulate really meeting them. The question at hand is: Doesn't this all seem surreal? After all, they claim that wearing a large box on your head is required to enter the metaverse The price of this box, assuming it is an Oculus, is 37,000 Rupees. In addition, you have to always have something in your hands, and most crucially, using the metaverse is not even conceivable with the available internet connection. Then, why are businesses like Nike and H&M pushing so hard to embrace something that most people can't even use. The simplest way to understand this is to look at Spotify's growth and how cleverly they chose to enter India
Metaverse-A Fictional Universe Sarthak Paliwal Great Lakes Institute of Management
These metaverse producers include companies that will create worlds, maps, merchandise, and video games. The cost of an Oculus has dropped from $599 to $299 over the last five years, and by 2025, the average internet speed is expected to reach 120 mb/sec By the time both of these things happen, developers and businesses will have created enough goods and services in the metaverse for consumers like you and me to use. You must now follow the future course of these variables in this area
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Finally, we have the internet speed, which must be at least 120 to 150 mb/sec in order to access the metaverse, exactly like Joe Rogan and Ed Sheeran
ght after 2019. In light of this, if you step back and think about music streaming as a service, you will see that only when four businesses coordinate and work together can the general public use it.
Everyone would have thought it was insane if Spotify had been introduced in India in 2014 First of all, why pay for music when you can just get it for free? Second, who possessed the data pack to continuously play music for 180 Rs/Gb of internet cost. Thirdly, even if there were performers and services available at the time, the cost and accessibility of wireless earbuds limited how much music could be listened to. However, as the time went on four things started happening, the price of internet dropped dramatically, internet speed increased dramatically, wireless earphones dropped in price dramatically, notably, we started prioritizing memory allocation over data cost due to the cheap cost of internet access, making streaming a realistic and well liked service Spotify as a service became useful when three factors access technology, connectivity technology, and creators came into play, as they did in the Indian market. If you closely observe the metaverse, it is moving in a direction that is extremely similar even to this one We have services like Facebook meta or NVidia's omniverse, which are metaverse services, in place of Spotify the service A device similar to the pricey oculus is the technology needed to access the service
The most crucial query at this point is: Why do we need the metaverse, and what are its benefits? The answer to this question lies in the most fundamental characteristic of humans that transformed us from insignificant apes to the rulers of the planet This is our ability to bond together, which has allowed us to form fan clubs, states, and even national armies. This bonding, which is one of the most significant features of mankind, is built by three attributes, including experience, engagement and expression
Since we already have teams where a German and American, Chinese and Indian executive must cooperate to achieve a common goal, the metaverse can help us break down
barriers to build amazing teams that can promote creativity and innovation as they brainstorm and influence key decisions for their organisations. Organizations will get three amazing superpowers as a result First and foremost, corporate executives' training sessions could be changed from a dry video to an engaging event where participants could bond, have fun, and develop a sense of company loyalty This would result in effective training and higher fresher productivity as a result of excellent training Second, people from all over the world will be able to collaborate on ideas, which will foster team spirit and, ultimately, lead to wise output and a better mental wellness for employees because, when shared experiences are formed, employees will know that they are not operating with a stranger but with someone on whom they can trust, and if you compare it to today.
People become closer when they share their joy or sorrow with others because they are vulnerable in the same way. One of the most important first steps toward achieving this goal is the metaverse, where 25 individuals from 25 different nations will one day be able to collaborate to find solutions to issues.
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Individuals bond when they cooperate to attain a common objective, but maybe more crucially, people bond when they share an enjoyable activity as an icebreaker Great teams are created as a result of this connection, and the metaverse is one of the most crucial building blocks in getting there
To put it another way, as technology becomes more accessible, the internet becomes much faster, and developers become more adept at creating excellent products and services, innovation and creativity are already suffering The idea of a metaverse will change how people communicate with one another
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You must have noticed that in your zoom meeting, if you want to talk about something, you can just ask someone else to. Therefore, it is quite difficult to feel comfortable presenting ideas nowadays even if the top members of your business are content and supportive because there is no quick reaction through expressions Because of this, innovation and creativity are already suffering, and last but not least, once this level of comfort is reached, the attrition rate the rate at which employees leave and join the organization will decrease, allowing businesses to save $1 billion in direct and indirect costs as a result
Revenue was particularly influenced and was 3.2X greater for the most mature companies, who also saw 2.4X times greater profit and 2.4X greater customer loyalty as measured by NPS scores. The independent report surveyed over 600 product builders and data scientists across company product teams
NEWSBITS Analytica|August2022 Page 13
Key findings include: New Independent Research Reveals Digital Product Analytics Teams Who Lead in Data Maturity Realize 2.5X Increase in Business Outcomes Over the Lagging Firms
SAN FRANCISCO (BUSINESS WIRE) Heap, the leading digital insights provider, announced research findings from newly released IDC white paper, sponsored by HEAP Analytics, “How Data Maturity and Product Analytics Improve Digital Experiences and Business Outcomes” (doc #US49598722, August 2022). The white paper verifies that data maturity meaning how well a company uses data and leverages it in its decision making provides up to 2.5X increase in overall business outcomes for leading teams versus lagging teams. This average includes increased revenues and profits, better efficiency, and higher customer satisfaction reflected in NPS scores.
Over 80% of leaders have fully automated data validation, have clearly defined policies on who can access the data, and have the ability to control data management.“These findings should be a wakeup call for businesses who want to efficiently grow their business and retain their customers for longer periods of time,” said David Wallace, IDC Research Director, Customer Intelligence and Analytics
98% of leaders have a good to excellent understanding of customer journey friction points, while only 29% of lagging the least data mature companies reported they have a good to excellent understanding in this area. 84% of leader teams get answers in minutes or hours compared to only 3% of lagging. Leaders are more than 2X as likely to report that leveraging data for personalization was either “easy” or “somewhat easy” compared to lagging 76% of leaders have a single source truth for data compared to only 3% of lagging firms. 89% of leading teams agree or strongly agree their organization celebrates learning from experimentation while only 23% of lagging teams have the same level of conviction.
The IDC white paper addresses how factors across people, processes and technologies affect relative data maturity and increasing business results.
Data mature organizations see over 3X improvement in revenue with shorter time to market, greater profit, and exceptional customer loyalty
6 According to a study conducted by IBM, what is the largest single source where data is gathered? A. Email B. Social Media C. Business Transactions D. Log Data 7. Analysis is used to analyze a system in terms of its requirements to identify its impact on customers’ satisfaction. Fill in the blank. A. Kano B. Paretto C RootCause D. Impact 8. What does SAAS stand for? A. System Aerosurface Actuator Simulation B. Systems as a Service C. Software acting as Service D Software as a Service 9 According to a very recent Jaspersoft Survey, what is the most popular big data store? A. Relational Databases B Hadoop HDFS C. Hadoop HDFS D MongoDB 10 Which of the following is/are correct types of data? A. Semi structured Data B Unstructured Data C. Semi Data D Both a & b 1.The process of quantifying data is referred to as A. Decoding B Structure C. Enumeration D. Coding 2. Data Analytics uses to get insights from data. A Statistical figures B. Numerical aspects C. Statistical methods D None of the mentioned above 3. A voluminous amount of structured, semi structured, and unstructured data that has the potential to be mined for information. A. Small Data B Meta Data C. Statistical Data D. Big Data 4 A free, Java based programming framework that supports the processing of large data sets in a distributed computing environment. A Hadoop B. Python C. R D Apache Groovy 5. The branch of data mining concerned with the prediction of future probabilities and trends. A. In memory Analytics B Predictive Analytics C. Behavioral Analytics D. Big Data Analytics Quiz Analytica|August2022 Page 14
