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.

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.

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

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

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.

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.

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

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.

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

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.

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

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.

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.

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.

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

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

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

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.

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

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

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

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.

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.

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).

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.

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

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?

The Future of Lead Generation CallTrack.AI stands at the forefront of a new era in lead generation. By harnessing the capabilities of AI, businesses can not only improve their lead generation processes but also revolutionize the way they interact with prospects. The result is a more efficient, personalized, and successful approach to converting leads into loyal customers. As AI continues to evolve, CallTrack.AI remains a pivotal tool for businesses looking to thrive in the digital marketplace. Read more at CallTrack.Ai

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.

As data analytics, superfast computers, digital technology, and other breakthroughs enabled by science play a bigger and bigger role in informing medical decision-making, science has carved out a new and powerful role as the steadfast partner of the business of medicine—which is also enjoying a new day in the sun. It may surprise some people to learn that the business of medicine is not a twenty-first-century invention. Health care has always been a business, as far back as the days when Hippocrates and his peers practiced medicine. Whether it was three goats, a gold coin, or a bank note, some type of payment was typically exchanged for medical services, and institutions of government or learning funded research. However, since the 1970s, business has been the major force directing the practice of medicine. Together, the business and science of medicine are the new kids on the block—the bright, shiny new things. Ideally, as I’ve suggested, the art, science, and business of medicine would work together in a harmonious partnership, each upholding the other and contributing all it has to offer to the whole. And sometimes (as we’ll find in later chapters) this partnership works well. When it does, the results are magnificent for patients and doctors, not to mention for scientists and investors.

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.

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.

But increasing the amount of equity finance in an economy is easier said than done: it is a project that would take decades rather than years. Some of the barriers are institutional: outside of the very small world of venture capital (of which more later) and the even smaller and newer field of equity crowdfunding, most businesses do not raise equity, and most financial institutions do not provide it. There are established agencies that can rate the creditworthiness of even quite small businesses, and algorithms to allow banks to quickly and cheaply decide whether to lend to them. Nothing similar exists for equity investment, and the equivalent analytical task (working out a company's likely future value, rather than its likelihood of servicing a fixed debt) is more complex. And cultural factors stand in the ways too: despite a very elegant financial economics theorem that shows that business owners should be indifferent between equity and debt finance, for many small business owners there seems a cognitive and cultural bias against giving away equity.

There was little effort to conceal this method of doing business. It was common knowledge, from senior managers and heads of research and development to the people responsible for formulation and the clinical people. Essentially, Ranbaxy’s manufacturing standards boiled down to whatever the company could get away with. As Thakur knew from his years of training, a well-made drug is not one that passes its final test. Its quality must be assessed at each step of production and lies in all the data that accompanies it. Each of those test results, recorded along the way, helps to create an essential roadmap of quality. But because Ranbaxy was fixated on results, regulations and requirements were viewed with indifference. Good manufacturing practices were stop signs and inconvenient detours. So Ranbaxy was driving any way it chose to arrive at favorable results, then moving around road signs, rearranging traffic lights, and adjusting mileage after the fact. As the company’s head of analytical research would later tell an auditor: “It is not in Indian culture to record the data while we conduct our experiments.

A good metric is a ratio or a rate. Accountants and financial analysts have several ratios they look at to understand, at a glance, the fundamental health of a company. You need some, too. There are several reasons ratios tend to be the best metrics: • Ratios are easier to act on. Think about driving a car. Distance traveled is informational. But speed—distance per hour—is something you can act on, because it tells you about your current state, and whether you need to go faster or slower to get to your destination on time. • Ratios are inherently comparative. If you compare a daily metric to the same metric over a month, you’ll see whether you’re looking at a sudden spike or a long-term trend. In a car, speed is one metric, but speed right now over average speed this hour shows you a lot about whether you’re accelerating or slowing down. • Ratios are also good for comparing factors that are somehow opposed, or for which there’s an inherent tension. In a car, this might be distance covered divided by traffic tickets. The faster you drive, the more distance you cover—but the more tickets you get. This ratio might suggest whether or not you should be breaking the speed limit.

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.

ever. Amen. Thank God for self-help books. No wonder the business is booming. It reminds me of junior high school, where everybody was afraid of the really cool kids because they knew the latest, most potent putdowns, and were not afraid to use them. Dah! But there must be another reason that one of the best-selling books in the history of the world is Men Are From Mars, Women Are From Venus by John Gray. Could it be that our culture is oh so eager for a quick fix? What a relief it must be for some people to think “Oh, that’s why we fight like cats and dogs, it is because he’s from Mars and I am from Venus. I thought it was just because we’re messed up in the head.” Can you imagine Calvin Consumer’s excitement and relief to get the video on “The Secret to her Sexual Satisfaction” with Dr. GraySpot, a picture chart, a big pointer, and an X marking the spot. Could that “G” be for “giggle” rather than Dr. “Graffenberg?” Perhaps we are always looking for the secret, the gold mine, the G-spot because we are afraid of the real G-word: Growth—and the energy it requires of us. I am worried that just becoming more educated or well-read is chopping at the leaves of ignorance but is not cutting at the roots. Take my own example: I used to be a lowly busboy at 12 East Restaurant in Florida. One Christmas Eve the manager fired me for eating on the job. As I slunk away I muttered under my breath, “Scrooge!” Years later, after obtaining a Masters Degree in Psychology and getting a California license to practice psychotherapy, I was fired by the clinical director of a psychiatric institute for being unorthodox. This time I knew just what to say. This time I was much more assertive and articulate. As I left I told the director “You obviously have a narcissistic pseudo-neurotic paranoia of anything that does not fit your myopic Procrustean paradigm.” Thank God for higher education. No wonder colleges are packed. What if there was a language designed not to put down or control each other, but nurture and release each other to grow? What if you could develop a consciousness of expressing your feelings and needs fully and completely without having any intention of blaming, attacking, intimidating, begging, punishing, coercing or disrespecting the other person? What if there was a language that kept us focused in the present, and prevented us from speaking like moralistic mini-gods? There is: The name of one such language is Nonviolent Communication. Marshall Rosenberg’s Nonviolent Communication provides a wealth of simple principles and effective techniques to maintain a laser focus on the human heart and innocent child within the other person, even when they have lost contact with that part of themselves. You know how it is when you are hurt or scared: suddenly you become cold and critical, or aloof and analytical. Would it not be wonderful if someone could see through the mask, and warmly meet your need for understanding or reassurance? What I am presenting are some tools for staying locked onto the other person’s humanness, even when they have become an alien monster. Remember that episode of Star Trek where Captain Kirk was turned into a Klingon, and Bones was freaking out? (I felt sorry for Bones because I’ve had friends turn into Cling-ons too.) But then Spock, in his cool, Vulcan way, performed a mind meld to determine that James T. Kirk was trapped inside the alien form. And finally Scotty was able to put some dilithium crystals into his phaser and destroy the alien cloaking device, freeing the captain from his Klingon form. Oh, how I wish that, in my youth or childhood,

The author believes that epistemology has kidnapped modern philosophy, and well nigh ruined it; he hopes for the time when the study of the knowledge-process will be recognized as the business of the science of psychology, and when philosophy will again be understood as the synthetic interpretation of all experience rather than the analytic description of the mode and process of experience itself.

If the highly paid, publicly scrutinized employees of a business that had existed since the 1860s could be misunderstood by their market, who couldn’t be? If the market for baseball players was inefficient, what market couldn’t be? If a fresh analytical approach had led to the discovery of new knowledge in baseball, was there any sphere of human activity in which it might not do the same?

Some use Salesforce. Others use Excel. Others use analytics tools. Each leader has their own scorecard. Enablement professionals need to be part of the conversation about what is working and what is not. We need to connect the dots between the great work we do and the results. We want to be able to walk into our leadership’s office and show the correlation between performance by seller and by team and enablement activity. We need to be able to turn a subjective dialogue into a fact-based and analytical conversation. We need to be able to provide answers to the most important question: What can we do to improve performance and achieve our go-to-market goals?

So was the spike in traffic they saw under Bezos, attributable mainly to the website’s new practice of publishing content in the early morning, when audience attention was at its peak. In October 2015, exactly a year after Bezos officially took control, the Post actually passed the Times in unique monthly users. The victory was by a hair, according to the analytics firm comScore—66.9 million to 65.8 million—but for the Post it was an almost 60 percent increase over the previous year.

In the years before Julia joined the group, People Analytics had determined that Google needed to interview a job applicant only four times to predict, with 86 percent confidence, if they would be a good hire. The division had successfully pushed to increase paid maternity leave from twelve to eighteen weeks because computer models indicated that would reduce the frequency of new mothers quitting by 50 percent.

As a result, the most important recommendation for organizations of all shapes and sizes moving forward is to anticipate worst case scenarios at a minimum. Even in cases where organizations cannot or will not make some of the operational changes recommended below, the exercise of focusing on nonsoftware areas of a given business can help identify under-realized or -appreciated assets within an organization. Particularly ones for whom the sale of software has been low effort, brainstorming about other potential revenue opportunities is unlikely to be time wasted. One vendor in the business intelligence and analytics space has privately acknowledged doing just this; based on current research and projecting current trends forward, it is in the process of building out a 10-year plan over which it assumes that the upfront licensing model will gradually approach zero revenue. In its place, the vendor plans to build out subscription and data-based revenue streams. Even if the plan ultimately proves to be unnecessary, the exercise has been enormously useful internally for the insight gained into its business.

A calm mind is not about doing nothing’, says Ajinkya Rahane. ‘It’s about keeping yourself busy, particularly if you are going through a tough phase. It prevents your mind from being flooded with negative thoughts.’14 Rahane found that ‘switching off’ after about fifteen minutes of introspection post a match made him more relaxed; very important for someone as intense and analytical as him.

I’m not here to say that analytics are bad. I’m here to say that analytics are human. Or at least, they represent the real needs and genuine interests of actual human beings; they’re proxies for people. And as such, we are honor bound to be respectful with them, to consider them with nuance and care, to let them guide us toward creating experiences of delight or at least outcomes that fulfill mutual needs, not to use them, manipulate them, and exploit them.

Analytics are people. And relevance, in terms of offering targeted messages and experiences, is a form of showing respect for your customer’s time and interests. So is discretion regarding their privacy.

When he got out of the car to do his business, my mother stared straight ahead. But I turned to watch. There was always something wild and charismatically uncaring about my father’s demeanor in these moments, some mysterious abandonment of his frowning and cogitative state that already meant a lot to me, even though at that age I understood almost nothing about him. Paulie had long ago stopped whispering 'perv' to me for observing him as he relieved himself. She of course, kept her head n her novels. I remember that it was cold that day, and windy but that the sky had been cut from the crackling blue gem field of a late midwestern April. Outside the car, as other families sped past my father stepped to the leeward side of the open door then leaning back from the waist and at the same time forward the ankles. His penis poked out from his zipper for this part, Bernie always stood up at the rear window. My father paused fo a moment rocking slightly while a few indistinct words played on his lips. Then just before his stream stared he tiled back his head as if there were a code written in the sky that allowed the event to begin. This was the moment I waited for, the movement seemed to be a marker of his own private devotion as though despite his unshakable atheism and despite his sour, entirely analytic approach to every affair of life, he nonetheless felt the need to acknowledge the heavens in the regard to this particular function of the body. I don't know perhaps I sensed that he simply enjoyed it in a deep way that I did. It was possible I already recognized that the eye narrowing depth of his physical delight in that moment was relative to that paucity of other delights in his life. But in any case the prayerful uplifting of his cranium always seemed to democratize him for me, to make him for a few minutes at least, a regular man. Bernie let out a bark. ‘’Is he done?’’ asked my mother. I opened my window. ‘’Almost.’’ In fact he was still in the midst. My father peed like a horse. His urine lowed in one great sweeping dream that started suddenly and stopped just as suddenly, a single, winking arc of shimmering clarity that endured for a prodigious interval and then disappeared in an instant, as though the outflow were a solid object—and arch of glittering ice or a thick band of silver—and not (as it actually approximated) a parabolic, dynamically averaged graph of the interesting functions of gravity, air resistance, and initial velocity on a non-viscous fluid, produced and exhibited by a man who’d just consumed more than a gallon of midwestern beer. The flow was as clear as water. When it struck the edge of the gravel shoulder, the sound was like a bed-sheet being ripped. Beneath this high reverberation, he let out a protracted appreciative whistle that culminated in a tunneled gasp, his lips flapping at the close like a trumpeters. In the tiny topsoil, a gap appeared, a wisp entirely unashamed. Bernie bumped about in the cargo bay. My father moved up close to peer through the windshield, zipping his trousers and smiling through the glass at my mother. I realized that the yellow that should have been in his urine was unmistakable now in his eyes. ‘’Thank goodness,’’ my mother said when the car door closed again. ‘’I was getting a little bored in here.

If your main interest is in the business uses of machine learning, this book can help you in at least six ways: to become a savvier consumer of analytics; to make the most of your data scientists; to avoid the pitfalls that kill so many data-mining projects; to discover what you can automate without the expense of hand-coded software; to reduce the rigidity of your information systems; and to anticipate some of the new technology that’s coming your way. I’ve seen too much time and money wasted trying to solve a problem with the wrong learning algorithm, or misinterpreting what the algorithm said. It doesn’t take much to avoid these fiascoes. In fact, all it takes is to read this book.

The factors that usually decide presidential elections—the economy, likability of the candidates, and so on—added up to a wash, and the outcome came down to a few key swing states. Mitt Romney’s campaign followed a conventional polling approach, grouping voters into broad categories and targeting each one or not. Neil Newhouse, Romney’s pollster, said that “if we can win independents in Ohio, we can win this race.” Romney won them by 7 percent but still lost the state and the election. In contrast, President Obama hired Rayid Ghani, a machine-learning expert, as chief scientist of his campaign, and Ghani proceeded to put together the greatest analytics operation in the history of politics. They consolidated all voter information into a single database; combined it with what they could get from social networking, marketing, and other sources; and set about predicting four things for each individual voter: how likely he or she was to support Obama, show up at the polls, respond to the campaign’s reminders to do so, and change his or her mind about the election based on a conversation about a specific issue. Based on these voter models, every night the campaign ran 66,000 simulations of the election and used the results to direct its army of volunteers: whom to call, which doors to knock on, what to say. In politics, as in business and war, there is nothing worse than seeing your opponent make moves that you don’t understand and don’t know what to do about until it’s too late. That’s what happened to the Romney campaign. They could see the other side buying ads in particular cable stations in particular towns but couldn’t tell why; their crystal ball was too fuzzy. In the end, Obama won every battleground state save North Carolina and by larger margins than even the most accurate pollsters had predicted. The most accurate pollsters, in turn, were the ones (like Nate Silver) who used the most sophisticated prediction techniques; they were less accurate than the Obama campaign because they had fewer resources. But they were a lot more accurate than the traditional pundits, whose predictions were based on their expertise. You might think the 2012 election was a fluke: most elections are not close enough for machine learning to be the deciding factor. But machine learning will cause more elections to be close in the future. In politics, as in everything, learning is an arms race. In the days of Karl Rove, a former direct marketer and data miner, the Republicans were ahead. By 2012, they’d fallen behind, but now they’re catching up again.

In a report released on the topic of IoT, Accenture notes that this technology “is driving innovation and new opportunities by bringing every object, consumer and activity into the digital realm”1. Being a tool that can only be used in physical environments, the IoT’s immense power means that this data-empowered category of technology can help swing the pendulum back towards brick-and-mortar shopping. As

A company can also use the technology to gain insights to help make informed business decisions, setting in place even greater improvements. Thinking

Big data is, in a nutshell, large amounts of data that can be gathered up and analyzed to determine whether any patterns emerge and to make better decisions.

AS A KID GROWING UP IN A RURAL PENNSYLVANIA COAL COUNTRY in the 1930s and 1940s, Bill McGowan never dreamed of a career as a businessman, unaware that such a profession even existed. The son of a railroad engineer and a schoolteacher, McGowan got his first glimpse of the wider world during a three-year stint in the U.S. Army in postwar Europe, after which he returned home to complete an undergraduate degree in chemistry at King’s College in Wilkes-Barre, Pennsylvania. McGowan excelled at chemistry, thanks to his talent for comprehending the rules of complex systems, but found little joy in the subject. His plans for a career in medicine left him similarly lukewarm. One King’s College professor surmised the gregarious, hyper-analytical student’s true calling and suggested he apply for a seat in Harvard Business School’s class of 1954.

With all of the data and analytical tools at our disposal, you would not expect this, but a substantial proportion of business and investment decisions are still based on the average. I see investors and analysts contending that a stock is cheap because it trades at a PE that is lower than the sector average or that a company has too much debt because its debt ratio is higher than the average for the market. The average is not only a poor central measure on which to focus in distributions that are not symmetric, but it strikes me as a waste to not use the rest of the data.

Creating something from nothing is ingenious. Creating something from something is smart. To rule your world, be ingeniously smart.

This may sound like a terrible generalization but the Japanese language has taught me that a person's understanding of the world need not be so well articulated -- so rationally articulated -- the way it tends to be in Western languages. The Japanese language has the full potential to be logical and analytical, but it seems to me that it isn't its real business to be that way. At least, not the Japanese language we still use today. You can mix the present and the past tense. You don't have to specify whether something is singular or plural. You aren't always looking for a cogent progression of sentences; conjunctions such as "but," "and," and "so" are hence not all that important. Many Japanese people used to criticize their language for inhibiting rational thought. It was quite liberating to me when I realized that we can understand the world in different ways depending on the language we use. There isn't a right way or a wrong way.

Red Dino Sdn Bhd aims to provide e-commerce solutions to setting up marketplace accounts, managing and retaining seller's accounts, along with photography and graphic design - for ease of retailers and manufacturers to kick-start their online businesses. Using our platform - DinoSync, we integrate multiple marketplaces, website providers, point of sales system (POS), multi-warehouse and shipping management, and business analytics into a single web application.

This book is a compilation of interesting ideas that have strongly influenced my thoughts and I want to share them in a compressed form. That ideas can change your worldview and bring inspiration and the excitement of discovering something new. The emphasis is not on the technology because it is constantly changing. It is much more difficult to change the accompanying circumstances that affect the way technological solutions are realized. The chef did not invent salt, pepper and other spices. He just chooses good ingredients and uses them skilfully, so others can enjoy his art. If I’ve been successful, the book creates a new perspective for which the selection of ingredients is important, as well as the way they are smoothly and efficiently arranged together. In the first part of the book, we follow the natural flow needed to create the stimulating environment necessary for the survival of a modern company. It begins with challenges that corporations are facing, changes they are, more or less successfully, trying to make, and the culture they are trying to establish. After that, we discuss how to be creative, as well as what to look for in the innovation process. The book continues with a chapter that talks about importance of inclusion and purpose. This idea of inclusion – across ages, genders, geographies, cultures, sexual orientation, and all the other areas in which new ways of thinking can manifest – is essential for solving new problems as well as integral in finding new solutions to old problems. Purpose motivates people for reaching their full potential. This is The second and third parts of the book describes the areas that are important to support what is expressed in the first part. A flexible organization is based on IT alignment with business strategy. As a result of acceleration in the rate of innovation and technological changes, markets evolve rapidly, products’ life cycles get shorter and innovation becomes the main source of competitive advantage. Business Process Management (BPM) goes from task-based automation, to process-based automation, so automating a number of tasks in a process, and then to functional automation across multiple processes andeven moves towards automation at the business ecosystem level. Analytics brought us information and insight; AI turns that insight into superhuman knowledge and real-time action, unleashing new business models, new ways to build, dream, and experience the world, and new geniuses to advance humanity faster than ever before. Companies and industries are transforming our everyday experiences and the services we depend upon, from self-driving cars, to healthcare, to personal assistants. It is a central tenet for the disruptive changes of the 4th Industrial Revolution; a revolution that will likely challenge our ideas about what it means to be a human and just might be more transformative than any other industrial revolution we have seen yet. Another important disruptor is the blockchain - a distributed decentralized digital ledger of transactions with the promise of liberating information and making the economy more democratic. You no longer need to trust anyone but an algorithm. It brings reliability, transparency, and security to all manner of data exchanges: financial transactions, contractual and legal agreements, changes of ownership, and certifications. A quantum computer can simulate efficiently any physical process that occurs in Nature. Potential (long-term) applications include pharmaceuticals, solar power collection, efficient power transmission, catalysts for nitrogen fixation, carbon capture, etc. Perhaps we can build quantum algorithms for improving computational tasks within artificial intelligence, including sub-fields like machine learning. Perhaps a quantum deep learning network can be trained more efficiently, e.g. using a smaller training set. This is still in conceptual research domain.

Cleaning data in the analytics value chain violates the third of quality guru W. Edwards Deming’s 14 principles19 of business transformation: Cease dependence on inspection to achieve quality. Eliminate the need for massive inspection by building quality into the product in the first place. Rather than inspecting cars at the end of an assembly line and scrapping the ones that fail, it makes much better sense to design quality into the process and build high-quality cars. Similarly, it is much smarter to build data quality directly into the source systems that generate data than it is to trap and correct errors farther down the chain.

Typically introverted and analytical, Fives don’t believe they have enough inner resources or energy to meet the demands of life. They feel drained by prolonged involvement with other people or by having too many expectations placed on them. Every handshake, phone call, business meeting, social gathering or unexpected encounter seems to cost them more than it does other people. Fearful they don’t have sufficient inner resources to function in the world, they detach and withdraw into the mind, where they feel more at home and confident.

Product management is a career, not just a role you play on a team. The product manager deeply understands both the business and the customer to identify the right opportunities to produce value. They are responsible for synthesizing multiple pieces of data, including user analytics, customer feedback, market research, and stakeholder opinions, and then determining in which direction the team should move. They keep the team focused on the why — why are we building this product, and what outcome will it produce?

Most companies’ creative decisions don’t live or die by the sword of data; they are based on often very subjective expert opinions or, even worse, on what web analytics author Avinash Kaushik calls the Hippo—the Highest Paid Person’s Opinion.

It's also important for tech product managers to have a broad understanding of the types of analytics that are important to your product. Many have too narrow of a view. Here is the core set for most tech products: User behavior analytics (click paths, engagement) Business analytics (active users, conversion rate, lifetime value, retention) Financial analytics (ASP, billings, time to close) Performance (load time, uptime) Operational costs (storage, hosting) Go‐to‐market costs (acquisition costs, cost of sales, programs) Sentiment (NPS, customer satisfaction, surveys)

Business intelligence (BI) is an umbrella term that includes a variety of IT applications that are used to analyze an organization’s data and communicate the information to relevant users.

Mastering Social Media Advertising Mastering social media marketing requires a comprehensive understanding of the platforms, audience targeting, content creation, and analytics. Social media has become an inevitable part of human life so the business. An effective social media marketing helps in attracting a huge number of audience. It also increase brand promotion. Social media advertising is getting tricky and new day by day. So it is always important to go with the trend and have updated knowledge in changing markets.

When it comes to developing and maintaining a competitive advantage for your business, there is no question in my mind that you’re going to need to incorporate both data science and business intelligence together in order to survive the future hyper-competitive environment.  If you are not doing so, I can guarantee that your competitors will be doing it.

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.

Oh, Timothy . . . your father always says you’re a Kingston man through and through, but deep down, I think you take after me more. All he cares about is business and data and analytics, but you and I—we both love the arts and are able to appreciate the softer beauty in our world.

Simple Fast Funnels may be the new kid on the block when it comes to a complete bumper to bumper CRM system, but it’s a force to be reckoned with! Business owners are switching over right and left and I’m going to outline 10 of the best features of Simple Fast Funnels so you can see what all the buzz is about! Funnel builder: Simple Fast Funnels has easy intuitive software so you can build your own landing pages, funnels, websites, sales pages etc. No developer needed, everything included and simple to use Email Software: Instead of paying hundreds or thousands per month to send emails, this software does it for you! You can have your entire email list automated or send emails on the fly, whatever fits the bill for you, they’ve got you covered and it’s so easy to track your email results so you can modify and make improvements as you go. Online Membership Area: Now, for no additional fees that lot’s of CRM software likes to charge, you can build glorious membership areas for your clients. You can control timing on video releases, give access for certain time periods upset packages… whatever your business looks like, if you can dream it, you can build it in the membership area. Survey and quiz generator: Ramp up your lead capture game to grow your customer list! One of the best ways to get leads is to get your customers talking about themselves. Not only do people love to take surveys and quizzes, but it can help you gather information about your clients to serve them better and grow your sales! SMS Marketing Software: If you’re not messaging your customers, you’re missing out, and if you are messaging your customers you’re probably over paying. Amazing automated intuitive SMS marketing can make your life much easier and allow you to reach your customers in more ways. Being where your customers are more present is always good for business. Simple Fast Funnels helps you get the cheapest SMS rates around and it automatically integrates into the system for your unified messages. Appointment booking: Another expensive thing you used to have to pay for and try to get to work properly with your website AND look decent is also built right in. Now, without leaving Simple Fast Funnels, you’re able to capture the lead, follow up with the lead all over the place, engage with them, build trust, book appointments, schedule calls and even send them automated text reminders. E com Purchases: Directly on your website, you’ll be able to take payments. No more invoices sent from other platforms, everything buttoned up nice and clean. Unified messaging: From now on, whether a client emails, texts, calls etc, it all shows up in one place at your end. This might not seem like a big deal, but it’s a HUGE pain to have to follow customers about and keep track of conversations. Now you see all your communication with customers in a neat little area. Blogs: Blogs these days can really help your marketing efforts across the board, and of course your blogs will be a perfect fit in your simple fast funnel account. Analytics: Data tracking when you’re dealing with features on various platforms is a nightmare. If you capture a lead on a Word press landing page, send it an email software like Keep, mail chimp or whatever, send them to a new website to schedule calls and another to make purchases… How could you possibly expect to get good customer data? Hosting all of your “business” in one location makes tracking flawless. The more customers you have the more data you need to be efficient. Cheers to making it easy. All that software and that’s just the top 10, guys there’s more. Simplefastfunnels.com also lets you have a 2 week free trial. Don’t take anyone word for anything. Go try it for yourself.

Context makes analytics actionable.