Business Analytics Quotes

Organizational structure and management style are those two factors that we always forget to analyze when the performance of our businesses goes down.

You are dealing with emotional customers and not analytical bots.

Maintaining a healthy balance of analytics, strategy and creativity is very important as they’re all equally important.

A discontent employee means not getting the results 100% and the loss of a company advocate as well.

When it comes to riding a trend for business growth, there are three important steps that we should always remember: data analysis, trend identification, and fast and effective decision making.

Tweet others the way you want to be tweeted.

When we pair modern tech like Blockchain technology, cryptography and data analytics with the ancient practice of bartering, a lot of business opportunities emerge.

Good decision-making is based on access to the correct information at the right time.

Keep learning more and more about your customers, your competitors, your brand, your target market, and business opportunities.

Let market research be a permanent, ongoing part of your business strategy.

When we master customer behavior and are able to offer them exactly what they want, we can achieve the biggest business opportunities.

Making the right decision at the right time is what sets a successful entrepreneur apart from the others.

In current times, we have access to so much data. Having said that, data analysis can uncover so many hidden patterns about customer behavior and how they interact with various products.

The kind of data and the data-analytical perspective privy to banks is quite unique to banks.

That is why a good reader does not cheer an apt sentence or pause to applaud even an inspired paragraph. Analytic thought is too busy for that, and too detached.

Ratios matter in Data Science. Dreams should be big and worries small.

If you can’t explain it simply, you don’t understand it well enough. — Albert Einstein

The critic's proper business is explanation and evaluation, which means he must make use of his analytic powers to translate the concrete to the abstract.

As business leaders we need to understand that lack of data is not the issue. Most businesses have more than enough data to use constructively; we just don't know how to use it. The reality is that most businesses are already data rich, but insight poor.

Your job isn’t to build a product; it’s to de-risk a business model.

Someday soon, say predictive analytics experts, it will be possible for companies to know our tastes and predict our habits better than we know ourselves.

data mining is an exploratory undertaking closer to research and development than it is to engineering.

Data Analytics is critical to wise investing, but so is good old fashioned understanding of business and markets.

almost every scholar who has grappled with the question of what reading does to one’s habits of mind has concluded that the process encourages rationality; that the sequential, propositional character of the written word fosters what Walter Ong calls the “analytic management of knowledge.

Forward-thinking organizations seek hybrid professionals who are highly proficient writers, analytical, creative, and tech savvy, with strong competencies in business management, information technology (IT), and human behavior.

To engage the written word means to follow a line of thought, which requires considerable powers of classifying, inference-making and reasoning. It means to uncover lies, confusions, and overgeneralizations, to detect abuses of logic and common sense. It also means to weigh ideas, to compare and contrast assertions, to connect one generalization to another. To accomplish this, one must achieve a certain distance from the words themselves, which is, in fact, encouraged by the isolated and impersonal text. That is why a good reader does not cheer an apt sentence or pause to applaud even an inspired paragraph. Analytic thought is too busy for that, and too detached.

It is useful for companies to look at AI through the lens of business capabilities rather than technologies. Broadly speaking, AI can support three important business needs: automating business processes, gaining insight through data analysis, and engaging with customers and employees.

All good decisions are Data dependent. To make good decisions, you need good data. And you need that good data to be organized according to it's applicable use value. So every business should be mining data and organizing data to enable business leaders to make good decisions on behalf of the business.

As Paul Saffo, a forecaster of large-scale change at Discern Analytics, observes wisely, 'Change is never linear. Our expectations are linear, but new technologies come in S curves, so we routinely overestimate short-term change and underestimate long-term change.' Never mistake a clear view for a short distance, he adds.

Quick Review of Core Behavior Patterns Reds are quick and more than happy to take command if needed. They make things happen. However, when they get going, they become control freaks and can be hopeless to deal with. And they repeatedly trample on people’s toes. Yellows can be amusing, creative, and elevate the mood regardless of who they’re with. However, when they are given unlimited space, they will consume all the oxygen in the room, they won’t allow anyone into a conversation, and their stories will reflect reality less and less. The friendly Greens are easy to hang out with because they are so pleasant and genuinely care for others. Unfortunately, they can be too wishy-washy and unclear. Anyone who never takes a stand eventually becomes difficult to handle. You don’t know where they really stand, and indecision kills the energy in other people. The analytical Blues are calm, levelheaded, and think before they speak. Their ability to keep a cool head is undoubtedly an enviable quality for all who aren’t capable of doing that. However, Blues’ critical thinking can easily turn to suspicion and questioning those around them. Everything can become suspect and sinister.

Like large areas of analytic philosophy today, scholasticism, too, preferred to busy itself with the fetishization of fine distinctions on an apparently secure investigative foundation, rather than engaging in the adventure of providing a relevant contribution to the understanding of its own age, with its shifting foundational structures.

Now is the moment to define our terms. In this book, Fast and Slow do more than just describe a rate of change. They are shorthand for ways of being, or philosophies of life. Fast is busy, controlling, aggressive, hurried, analytical, stressed, superficial, impatient, active, quantity-over-quality. Slow is the opposite: calm, careful, receptive, still, intuitive, unhurried, patient, reflective, quality-over-quantity. It is about making real and meaningful connections - with people, culture, work, food, everything.

While CEO of P&G, John Pepper was once asked in an interview which skill or characteristic was most important to look for when hiring new employees. Was it leadership? Analytical ability? Problem solving? Collaboration? Strategic thinking? Or something else? His answer was integrity. He explained, “All the rest, we can teach them after they get here.

All revolutions are impossible till they happen, then they become inevitable.

Most people use statistics the way a drunkard uses a lamp post, more for support than illumination.

opinion-based decision making, statistical malfeasance, and counterfeit analysis are pandemic. We are swimming in make-believe analytics.

We have met the enemy and he is us.” We need to change the ways we do our job.

All models are wrong, but some are useful.” In other words, models intentionally simplify our complex world.

You can't manage what you don't measure.

An intelligent organization is not about the “cleverness” of one analytics team but the insightful nature of the entire business.

All data has its beauty, but not everyone sees it.

You need to know which aspects of your business are too risky and then work to improve the metric that represents that risk.

The human job losses we’ve seen were primarily due to attrition of workers who were not replaced or through automation of outsourced work.

Unfortunately, creating an objective function that matches the true goal of the data mining is usually impossible, so data scientists often choose based on faith[22] and experience.

Anyone who knows anything about data knows that it is critical to have authentic data – data that holistically represents the truth of something, as opposed to fragments or biased portions.

Often, the answer to a problem or dilemma is not immediately obvious, and we simply don’t know what to do next. As we mentioned earlier, a problem cannot be solved at the same level of thinking in which it was created; we need a shift in our level of understanding. This logic reminds us that if we do not know the answer to a specific problem, recycling the same information over and over usually will not produce a solution. It will, however, keep our minds busy and speeded up. It will create stress. We’ve all had the experience of being stuck in “thought quicksand,” where our mental struggling sucks us deeper into our analytical thinking. This is an example of the misuse of the analytical thought process.

Create mode is when you’re imaginative, creative, and open to new ideas. Edit mode is when you are logical, regulated, and analytical. Most of us constantly switch back and forth between the two within a given piece of work, like when we write an email. You write a small part, read it, make edits, and then write some more. The major issue is that your editor brain gets in the way of your creator brain. It stops the flow, which can remove the potential of amazing thoughts that you didn’t even know exist in your head from ever coming out. You need these thoughts to surface during this experiment, but your editor brain can get in the way because it’s too focused on making everything right or perfect. Thinking puts your editor brain into the driver’s seat.

The construction industry is the world’s second largest (after agriculture), worth $8 trillion a year. But it’s remarkably inefficient. The typical commercial construction project runs 80% over budget and 20 months behind schedule, according to McKinsey.

In spite of its undeniable power, so many leaders struggle to embrace organizational health (which I’ll be defining shortly) because they quietly believe they are too sophisticated, too busy, or too analytical to bother with it. In other words, they think it’s beneath them.

Digital analytics is the analysis of qualitative and quantitative data from your business and the competition to drive a continual improvement of the online experience that your customers and potential customers have which translates to your desired outcomes (both online and offline).

20th Century 21st Century Scale and Scope Speed and Fluidity Predictability Agility Rigid Organization Boundaries Fluid Organization Boundaries Command and Control Creative Empowerment Reactive and Risk Averse Intrapreneur Strategic Intent Profit and Purpose Competitive Advantage Comparative Advantage Data and Analytics Synthesizing Big Data

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In the competitive world of digital marketing, converting prospects into loyal customers is the ultimate goal for any business. CallTrack.AI emerges as a revolutionary tool in this quest, leveraging the power of artificial intelligence to transform the lead generation process. How CallTrack.AI redefines the approach to capturing and nurturing leads, ultimately leading to higher conversion rates and a robust customer base?

I was thinking about Leon and our affinity for busyness, when I happened upon a book called In Praise of Slowness, written by Carl Honoré. In that book he describes a New Yorker cartoon that illustrates our dilemma. Two little girls are standing at a school-bus stop, each clutching a personal planner. One says to the other, “Okay, I’ll move ballet back an hour, reschedule gymnastics, and cancel piano. You shift your violin lessons to Thursday and skip soccer practice. That gives us from 3:15 to 3:45 on Wednesday the sixteenth to play.” This, I suppose, is how the madness starts. Pay close attention to the words Honoré uses to describe this fast-life/slow-life dichotomy. “Fast is busy, controlling, aggressive, hurried, analytical, stressed, superficial, impatient, active, quantity-over-quality. Slow is the opposite: calm, careful, receptive, intuitive, unhurried, patient, reflective, quality-over-quantity…. It is seeking to live at what musicians call the tempo giusto—the right speed.”* Which of those lifestyles would you prefer?

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Charles Munger, right-hand adviser to Warren Buffett, the richest man on the planet, is known for his unparalleled clear thinking and near-failure-proof track record. How did he refine his thinking to help build a $3 trillion business in Berkshire Hathaway? The answer is “mental models,” or analytical rules-of-thumb4 pulled from disciplines outside of investing, ranging from physics to evolutionary biology. Eighty to 90 models have helped Charles Munger develop, in Warren Buffett’s words, “the best 30-second mind in the world. He goes from A to Z in one move. He sees the essence of everything before you even finish the sentence.

Minds are great when it comes to inventing new devices, constructing business plans, or organizing daily schedules. But, by themselves, minds are far less useful in learning to be present, learning to love, or discovering how best to carry the complexities of a personal history. Verbal knowledge is not the only kind of knowledge there is. We must learn to use our analytical and evaluative skills when doing so promotes workability and to use other forms of knowledge when they best serve our interests. In effect, the ultimate goal of ACT is to teach clients to make such distinctions in the service of promoting a more workable life.

That you have to ask Krishnamurti, not me. That is not my business. He loves it, that’s how he has grown. For centuries, for many, many lives, he has been moving towards a tunnel vision. And the tunnel vision has its own beauties, because whatsoever you see, you see very clearly because your eyes are focused. Hence the clarity of Krishnamurti. Nobody has ever been so clear, so crystal clear. Nobody has ever been so logical, so rational; nobody has ever been so analytical. His profundity in going into things and their details is simply unbelievable. But that is part of his tunnel vision. You cannot have everything, remember. If you want clarity you will need tunnel vision; you will have to become more and more focused on less and less.

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My “10 Smart Market Diagnosis and Profiling Questions” What keeps them awake at night, indigestion boiling up their esophagus, eyes open, staring at the ceiling? What are they afraid of? What are they angry about? Who are they angry at? What are their top three daily frustrations? What trends are occurring and will occur in their businesses or lives? What do they secretly, ardently desire most? Is there a built-in bias to the way they make decisions? (Example: engineers = exceptionally analytical) Do they have their own language? Who else is selling something similar to their product, and how? Who else has tried selling them something similar, and how has that effort failed? So, Step 1 in our system is to analyze thoroughly, understand, and connect with the customer.

The need for managers with data-analytic skills The consulting firm McKinsey and Company estimates that “there will be a shortage of talent necessary for organizations to take advantage of big data. By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.” (Manyika, 2011). Why 10 times as many managers and analysts than those with deep analytical skills? Surely data scientists aren’t so difficult to manage that they need 10 managers! The reason is that a business can get leverage from a data science team for making better decisions in multiple areas of the business. However, as McKinsey is pointing out, the managers in those areas need to understand the fundamentals of data science to effectively get that leverage.

Although some organizations today may survive and prosper because they have intu- itive geniuses managing them, most are not so fortunate. Most organizations can benefit from strategic management, which is based upon integrating intuition and analysis in decision making. Choosing an intuitive or analytic approach to decision making is not an either–or proposition. Managers at all levels in an organization inject their intuition and judgment into strategic-management analyses. Analytical thinking and intuitive thinking complement each other. Operating from the I’ve-already-made-up-my-mind-don’t-bother-me-with-the-facts mode is not management by intuition; it is management by ignorance. Drucker says, “I believe in intuition only if you discipline it. ‘Hunch’ artists, who make a diagnosis but don’t check it out with the facts, are the ones in medicine who kill people, and in management kill businesses.

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Let us begin, then, with the mad-house; from this evil and fantastic inn let us set forth on our intellectual journey. Now, if we are to glance at the philosophy of sanity, the first thing to do in the matter is to blot out one big and common mistake. There is a notion adrift everywhere that imagination, especially mystical imagination, is dangerous to man’s mental balance. Poets are commonly spoken of as psychologically unreliable; and generally there is a vague association between wreathing laurels in your hair and sticking straws in it. Facts and history utterly contradict this view. Most of the very great poets have been not only sane, but extremely business-like; and if Shakespeare ever really held horses, it was because he was much the safest man to hold them. Imagination does not breed insanity. Exactly what does breed insanity is reason. Poets do not go mad; but chess-players do. Mathematicians go mad, and cashiers; but creative artists very seldom. I am not, as will be seen, in any sense attacking logic: I only say that this danger does lie in logic, not in imagination. Artistic paternity is as wholesome as physical paternity. Moreover, it is worthy of remark that when a poet really was morbid it was commonly because he had some weak spot of rationality on his brain. Poe, for instance, really was morbid; not because he was poetical, but because he was specially analytical. Even chess was too poetical for him; he disliked chess because it was full of knights and castles, like a poem. He avowedly preferred the black discs of draughts, because they were more like the mere black dots on a diagram. Perhaps the strongest case of all is this: that only one great English poet went mad, Cowper. And he was definitely driven mad by logic, by the ugly and alien logic of predestination. Poetry was not the disease, but the medicine; poetry partly kept him in health. He could sometimes forget the red and thirsty hell to which his hideous necessitarianism dragged him among the wide waters and the white flat lilies of the Ouse. He was damned by John Calvin; he was almost saved by John Gilpin. Everywhere we see that men do not go mad by dreaming. Critics are much madder than poets. Homer is complete and calm enough; it is his critics who tear him into extravagant tatters. Shakespeare is quite himself; it is only some of his critics who have discovered that he was somebody else. And though St. John the Evangelist saw many strange monsters in his vision, he saw no creature so wild as one of his own commentators. The general fact is simple. Poetry is sane because it floats easily in an infinite sea; reason seeks to cross the infinite sea, and so make it finite. The result is mental exhaustion, like the physical exhaustion of Mr. Holbein. To accept everything is an exercise, to understand everything a strain. The poet only desires exaltation and expansion, a world to stretch himself in. The poet only asks to get his head into the heavens. It is the logician who seeks to get the heavens into his head. And it is his head that splits.

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Emotional intelligence is born largely in the neurotransmitters of the brain’s limbic system, which governs feelings, impulses, and drives. Research indicates that the limbic system learns best through motivation, extended practice, and feedback. Compare this with the kind of learning that goes on in the neocortex, which governs analytical and technical ability. The neocortex grasps concepts and logic.

It is difficult to evaluate their recommendations of individual securities. Each service is entitled to be judged separately, and the verdict could properly be based only on an elaborate and inclusive study covering many years. In our own experience we have noted among them a pervasive attitude which we think tends to impair what could otherwise be more useful advisory work. This is their general view that a stock should be bought if the near-term prospects of the business are favorable and should be sold if these are unfavorable—regardless of the current price. Such a superficial principle often prevents the services from doing the sound analytical job of which their staffs are capable—namely, to ascertain whether a given stock appears over- or undervalued at the current price in the light of its indicated long-term future earning power. The intelligent investor will not do his buying and selling solely on the basis of recommendations received from a financial service. Once this point is established, the role of the financial service then becomes the useful one of supplying information and offering suggestions.

I know I saw nothing wrong with insurance fraud, just as I saw nothing wrong with drug smuggling, or with anything else I considered a victimless crime. Draft dodging, still well in the future for me but already upending the lives of the older brothers of friends, I vehemently endorsed. The Vietnam War was wrong, rotten to the core. But the military, the government, the police, big business were all congealing in my view into a single oppressive mass—the System, the Man. These were standard-issue youth politics at the time, of course, and I was soon folding school authorities into the enemy force. And my casual, even contemptuous attitude toward the law was mostly a holdover from childhood, when a large part of glory was defiance and what you could get away with. But a more conscious, analytic, loosely Marxist disaffection was also taking root in my politics in my midteens. (And disaggregating, intellectually and emotionally, the mass of institutional power—sorting out how things actually worked, beyond how they felt as a whole—would turn out to be the work of many years.) In the meantime, surfing became an excellent refuge from the conflict—a consuming, physically exhausting, joy-drenched reason to live. It also, in its vaguely outlaw uselessness, its disengagement from productive labor, neatly expressed one’s disaffection. Where was my sense of social responsibility? Not much in evidence. I marched in peace marches. I was still a good student, which really proved nothing except that I liked to read and was hedging my bets.

Businesses should free themselves from dogma, especially when leveraging data to build a business. No one got very far living out other people’s thinking.

The methodical process of analytical thinking brings clarity to effectively define a clear strategy.

4. The potential levers to improve employees’ experience We have identified three levers to enable the transition from the current breakdown of employee activities to the ideal division of activities. They are: Automate: companies should identify and automate routine activities, such as generating a PowerPoint presentation for a weekly meeting or recording invoices in accounting software. Augment: organizations should seize the opportunity to increase the value of work activities delivered by employees. IA is used as a crucial component here, with, for example, the generation of insights through advanced analytics to help decision making. Abandon: some work activities do not fit with leading practices for efficient work, and represent an obstacle to the employee’s experience. These activities should be reduced or eliminated. For example, restricting the volume of meetings and email traffic is essential. We call these levers the “Triple-A artifact”. It has proven to be a handy framework to help organizations build their action plans to boost their employee experience.

This came into play recently in a partnership between an Australian supermarket and an auto insurance company. Combining data from the supermarket’s loyalty card program with auto claims information revealed interesting correlations. The data showed that people who buy red meat and milk are good car insurance risks while people who buy pasta and spirits and who fuel their cars at night are poor risks. Though this statistical relationship could be an indicator of risky behaviors (driving

stable competitive forces—take the time and effort to deeply understand these forces, and voilà, you can create a road map for your other decisions that is likely to last for some time. The emphasis in strategy was therefore analytical: because industries were assumed to be relatively

Management becomes more complex, too, because the staff has to reach across the organization at a level and consistency you never had to before: different departments, groups, and business units. For example, analytics and business intelligence teams never had to have the sheer levels of interaction with IT or engineering. The IT organization never had to explain the data format to the operations team. From both the technical and the management perspectives, teams didn’t have to work together before with as high of a bandwidth connection. There may have been some level of coordination before, but not this high. Other organizations face the complexity of data as a product instead of software or APIs as the product. They’ve never had to promote or evangelize the data available in the organization. With data pipelines, the data teams may not even know or control who has access to the data products. Some teams are very siloed. With small data, they’ve been able to get by. There wasn’t ever the need to reach out, coordinate, or cooperate. Trying to work with these maverick teams can be a challenge unto itself. This is really where management is more complicated.

The Ultimate Guide to GA4 Consulting Services:- Introduction: The Dawn of the GA4 Era- The digital landscape is changing, and with it comes the need to change our approach to analytics. Google Analytics 4 (GA4) is the future, designed to provide a more holistic understanding of the customer journey. However, adopting this new technology often presents a complex challenge that necessitates professional help. That's where GA4 Consulting Services come into the picture. In this blog post, we'll navigate the intricacies of GA4 Consulting. Whether you're a business owner wanting to transition from Universal Analytics to GA4 or a newbie eager to set up your analytics the right way, this guide has got you covered. So let's dive in.

Understanding and tracking 10 most crucial google analytics metrics Google Analytics proves to be an invaluable asset for businesses of varying sizes, offering comprehensive analysis capabilities. It empowers users to gain insights into their content, websites, and incoming traffic, thereby aiding in the enhancement of overall strategy and campaign planning. It is imperative to familiarize oneself with Google Analytics before engaging in practical application. Obtaining a Google Analytics certification serves as an excellent introductory step, and it is worth noting that this certification is available for free. By incorporating Google Analytics into your digital marketing toolkit, you can significantly bolster your online marketing endeavors. For more information on analytics metrics, Google Analytics certification, and measurement, please refer to the accompanying blog.

The value of business architecture is to provide an abstract representation of an enterprise and the business ecosystem in which it operates. By doing so, business architecture delivers value as an effective communication and analytical framework for translating strategy into actionable initiatives. The framework also enhances the enterprise’s capacity to enact transformational change, navigate complexity, reduce risk, make more informed decisions, align diverse stakeholders to a shared vision of the future, and leverage technology more effectively

How can you run Analytics “as one”? If you leave Analytics to IT, you will end up with a first-class race car without a driver: All the technology would be there, but hardly anybody could apply it to real-world questions. Where Analytics is left to Business, however, you’d probably see various functional silos develop, especially in larger organizations. I have never seen a self-organized, cross-functional Analytics approach take shape successfully in such an organization. Instead, you can expect each Analytics silo to develop independently. They will have experts familiar with their business area, which allows for the right questions to be asked. On the other hand, the technical solutions will probably be second class as the functional Analytics department will mostly lack the critical mass to mimic an organization’s entire IT intelligence. Furthermore, a lot of business topics will be addressed several times in parallel, as those Analytics silos may not talk to each other. You see this frequently in organizations that are too big for one central management team. They subdivide management either into functional groups or geographical groups. Federation is generally seen as an organizational necessity. It is well known that it does not make sense to regularly gather dozens of managers around the same table: You’d quickly see a small group discussing topics that are specific to a business function or a country organization, while the rest would get bored. A federated approach in Analytics, however, comes with risks. The list of disadvantages reaches from duplicate work to inconsistent interpretation of data. You can avoid these disadvantages by designing a central Data Analytics entity as part of your Data Office at an early stage, to create a common basis across all of these areas. As you can imagine, such a design requires authority, as it would ask functional silos to give up part of their autonomy. That is why it is worthwhile creating a story around this for your organization’s Management Board. You’d describe the current setup, the behavior it fosters, and the consequences including their financial impact. Then you’d present a governance structure that would address the situation and make the organization “future-proof.” Typical aspects of such a proposal would be The role of IT as the entity with a monopoly for technology and with the obligation to consider the Analytics teams of the business functions as their customers The necessity for common data standards across all of those silos, including their responsibility within the Data Office Central coordination of data knowledge management, including training, sharing of experience, joint cross-silo expert groups, and projects Organization-wide, business-driven priorities in Data Analytics Collaboration bodies to bring all silos together on all management levels

An example is Neil Patel. You can go to his site right now and he’ll walk you through everything from launching a Wordpress site, to setting up Google analytics, learning SEO, copywriting secrets, and literally a hundred other blog posts covering everything you’d ever need to know to be an online marketer. And it’s all free. But hundreds of people pay him every single day to do that work for them.

with everything you need to know about the ins and outs of data mining. This book has been laid out in straightforward and clear chapters with each chapter focusing on a particular part of data science for business to be able to ensure that you gain the maximum amount to knowledge without having to weed through unnecessary information. I hope this book answers any question you have and leaves you feeling confident on the subject of data science, data analytics and business intelligence. Chapter 1 Wholeness of Data Analytics There is a lot of data that comes rushing towards an organization of any type and sometimes it can be hard to decipher just what it means to the team and how they can use it to benefit them. This is where data analytics is the more helpful. The data is analyzed through a process of inspecting, cleaning, transforming and modeling that makes the information easier to look at and read. By narrowing down the amount of information, an organization is looking at they are going to be better able to utilize the relevant information and use the conclusions the data suggests to make decisions that are most likely to bring rewards. Although data analytics are most frequently used in business to consumer applications, there are many different facets of the data analysis. Some of the most common places data analytics are utilized in the worlds of business, science, and social science, in a variety of ways. Regardless of the type of organization, you are involved with, and even in your personal life, there are ways to make data analysis work for you. An example of where data analytics would be used in regards to a social networking site. A social networking website collects the information which relates to user preferences as well as the community interested and can segment according to the criteria that have been specified

Understanding Financial Risks and Companies Mitigate them? Financial risks are the possible threats, losses and debts corporations face during setting up policies and seeking new business opportunities. Financial risks lead to negative implications for the corporations that can lead to loss of financial assets, liabilities and capital. Mitigation of risks and their avoidance in the early stages of product deployment, strategy-planning and other vital phases is top-priority for financial advisors and managers. Here's how to mitigate risks in financial corporates:- ● Keeping track of Business Operations Evaluating existing business operations in the corporations will provide a holistic view of the movement of cash-flows, utilisation of financial assets, and avoiding debts and losses. ● Stocking up Emergency Funds Just as families maintain an emergency fund for dealing with uncertainties, the same goes for large corporates. Coping with uncertainty such as the ongoing pandemic is a valuable lesson that has taught businesses to maintain emergency funds to avoid economic lapses. ● Taking Data-Backed Decisions Senior financial advisors and managers must take well-reformed decisions backed by data insights. Data-based technologies such as data analytics, science, and others provide resourceful insights about various economic activities and help single out the anomalies and avoid risks. Enrolling for a course in finance through a reputed university can help young aspiring financial risk advisors understand different ways of mitigating risks and threats. The IIM risk management course provides meaningful insights into the other risks involved in corporations. What are the Financial Risks Involved in Corporations? Amongst the several roles and responsibilities undertaken by the financial management sector, identifying and analysing the volatile financial risks. Financial risk management is the pinnacle of the financial world and incorporates the following risks:- ● Market Risk Market risk refers to the threats that emerge due to corporational work-flows, operational setup and work-systems. Various financial risks include- an economic recession, interest rate fluctuations, natural calamities and others. Market risks are also known as "systematic risk" and need to be dealt with appropriately. When there are significant changes in market rates, these risks emerge and lead to economic losses. ● Credit Risk Credit risk is amongst the common threats that organisations face in the current financial scenarios. This risk emerges when a corporation provides credit to its borrower, and there are lapses while receiving owned principal and interest. Credit risk arises when a borrower falters to make the payment owed to them. ● Liquidity Risk Liquidity risk crops up when investors, business ventures and large organisations cannot meet their debt compulsions in the short run. Liquidity risk emerges when a particular financial asset, security or economic proposition can't be traded in the market. ● Operational Risk Operational risk arises due to financial losses resulting from employee's mistakes, failures in implementing policies, reforms and other procedures. Key Takeaway The various financial risks discussed above help professionals learn the different risks, threats and losses. Enrolling for a course in finance assists learners understand the different risks. Moreover, pursuing the IIM risk management course can expose professionals to the scope of international financial management in India and other key concepts.