Big Data Analytics Quotes

They all looked at Holly. She turned to face the cheerleader and said, “You need to learn that some things are more valuable than good looks. Data manipulation is more important than big boobs. Analytics is more useful than lip gloss.” Wow, she said that? Everyone laughed a bit, surprised, shocked. Holly turned and headed toward the concert hall. Grinning.

Although the method is simple, it shows how, mathematically, random brute force can overcome precise logic. It's a numerical approach that uses quantity to derive quality.

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

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.

We are moving slowly into an era where Big Data is the starting point, not the end.

Those companies that view data as a strategic asset are the ones that will survive and thrive.

Bitcoin is often mistakenly characterized as “anonymous” currency. In fact, it is relatively easy to connect identities to bitcoin addresses and, using big-data analytics, connect addresses to each other to form a comprehensive picture of someone’s bitcoin spending habits.

We are hyperreactive to even small stimuli in our environment We have trouble distinguishing between information or sensory data that should be ignored versus data that should be carefully considered We are highly focused on details rather than “big picture” concepts We’re deeply and deliberatively analytical Our decision-making process is methodical rather than efficient; we don’t rely on mental shortcuts or “gut feelings

Exfiltrated metadata from internet service providers and social media platforms can be plugged into big data analytics and once the right algorithm is applied, can allow an adversary surgically precise psychographic targeting of critical infrastructure executives with elevated privileges. Why is no one talking about this?

The human side of analytics is the biggest challenge to implementing big data.

Predictions have an expiry date. Action is needed before predictions expire.

Already law enforcement agencies make use of predictive analytic tools to identify suspects and direct investigations. It’s a short step from there to the world of Big Brother and thoughtcrime.

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

Jack's marketing books had been a part of her life for so long that she had ceased to register their presence, simply moving them from the couch to the coffee table, from the bed to the nightstand. How to Sell Everything to Anybody. Eight Great Habits of CEOs. They all seemed to involve numbers, as if you could simply count yourself to riches, like following sheep to sleep.

There is another issue with the largely cognitive approach to management, which we had big-time at Google. Smart, analytical people, especially ones steeped in computer science and mathematics as we were, will tend to assume that data and other empirical evidence can solve all problems. Quants or techies with this worldview tend to see the inherently messy, emotional tension that’s always present in teams of humans as inconvenient and irrational—an irritant that will surely be resolved in the course of a data-driven decision process. Of course, humans don’t always work that way. Things come up, tensions arise, and they don’t naturally go away. People do their best to avoid talking about these situations, because they’re awkward. Which makes it worse.

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?

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

Artificial Intelligence, deep learning, natural language processing, computer vision, and other related characteristics: super-computing, eventually quantum-computing, and nano and bio technologies; advanced big-data analytics; and other emerging technologies are beginning to offer an entirely new way of war, and at command speeds hitherto unimaginable. The revolution in sensor and command and control technologies is matched and enabled by developments in long-range, hypersonic “intelligent” weaponry and new swarms of killing machines allied to a range of directed-energy weapons. Such a potentially revolutionary change in the character and conduct of war must necessarily impose entirely new ways of defense.

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.

This happens because data scientists all too often lose sight of the folks on the receiving end of the transaction. They certainly understand that a data-crunching program is bound to misinterpret people a certain percentage of “he time, putting them in the wrong groups and denying them a job or a chance at their dream house. But as a rule, the people running the WMDs don’t dwell on those errors. Their feedback is money, which is also their incentive. Their systems are engineered to gobble up more data and fine-tune their analytics so that more money will pour in. Investors, of course, feast on these returns and shower WMD companies with more money. And the victims? Well, an internal data scientist might say, no statistical system can be perfect. Those folks are collateral damage. And often, like Sarah Wysocki, they are deemed unworthy and expendable. Big Data has plenty of evangelists, but I’m not one of them. This book will focus sharply in the other direction, on the damage inflicted by WMDs and the injustice they perpetuate. We will explore harmful examples that affect people at critical life moments: going to college, borrowing money, getting sentenced to prison, or finding and holding a job. All of these life domains are increasingly controlled by secret models wielding arbitrary punishments.

In the EPJ results, there were two statistically distinguishable groups of experts. The first failed to do better than random guessing, and in their longer-range forecasts even managed to lose to the chimp. The second group beat the chimp, though not by a wide margin, and they still had plenty of reason to be humble. Indeed, they only barely beat simple algorithms like “always predict no change” or “predict the recent rate of change.” Still, however modest their foresight was, they had some. So why did one group do better than the other? It wasn’t whether they had PhDs or access to classified information. Nor was it what they thought—whether they were liberals or conservatives, optimists or pessimists. The critical factor was how they thought. One group tended to organize their thinking around Big Ideas, although they didn’t agree on which Big Ideas were true or false. Some were environmental doomsters (“We’re running out of everything”); others were cornucopian boomsters (“We can find cost-effective substitutes for everything”). Some were socialists (who favored state control of the commanding heights of the economy); others were free-market fundamentalists (who wanted to minimize regulation). As ideologically diverse as they were, they were united by the fact that their thinking was so ideological. They sought to squeeze complex problems into the preferred cause-effect templates and treated what did not fit as irrelevant distractions. Allergic to wishy-washy answers, they kept pushing their analyses to the limit (and then some), using terms like “furthermore” and “moreover” while piling up reasons why they were right and others wrong. As a result, they were unusually confident and likelier to declare things “impossible” or “certain.” Committed to their conclusions, they were reluctant to change their minds even when their predictions clearly failed. They would tell us, “Just wait.” The other group consisted of more pragmatic experts who drew on many analytical tools, with the choice of tool hinging on the particular problem they faced. These experts gathered as much information from as many sources as they could. When thinking, they often shifted mental gears, sprinkling their speech with transition markers such as “however,” “but,” “although,” and “on the other hand.” They talked about possibilities and probabilities, not certainties. And while no one likes to say “I was wrong,” these experts more readily admitted it and changed their minds. Decades ago, the philosopher Isaiah Berlin wrote a much-acclaimed but rarely read essay that compared the styles of thinking of great authors through the ages. To organize his observations, he drew on a scrap of 2,500-year-old Greek poetry attributed to the warrior-poet Archilochus: “The fox knows many things but the hedgehog knows one big thing.” No one will ever know whether Archilochus was on the side of the fox or the hedgehog but Berlin favored foxes. I felt no need to take sides. I just liked the metaphor because it captured something deep in my data. I dubbed the Big Idea experts “hedgehogs” and the more eclectic experts “foxes.” Foxes beat hedgehogs. And the foxes didn’t just win by acting like chickens, playing it safe with 60% and 70% forecasts where hedgehogs boldly went with 90% and 100%. Foxes beat hedgehogs on both calibration and resolution. Foxes had real foresight. Hedgehogs didn’t.

the real problem is that numerical correlations say nothing of people’s real needs, of their desires, and of the reasons for their activities. As a result, these numerical data can give a false impression of people. But the use of big data and market analytics is seductive: no travel, little expense, and huge numbers, sexy charts, and impressive statistics, all very persuasive to the executive team trying to decide which new products to develop.

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

Online Course in Big Data Analytics - Training and Placement Our training in big data analytics is a beginner level and advanced level online program so learn concepts of big data technologies with assured placements 361online,com/bigdata/PGP_visualization_asured.php

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.

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.

La analítica de datos es la fase de preguntas y respuestas que conduce a la toma de decisiones de todo el esquema de inteligencia empresarial.

A task force of the American Society for Cell Biology (ASCB) distinguished four kinds of reproducibility: analytic reproducibility, which tries to duplicate original conclusions by reanalyzing original data; direct reproducibility, which tries to get the same experimental results using the same experimental conditions as in the original report; systematic reproducibility, which tries to get the same results as the original study under different experimental conditions than the original ones; and conceptual reproducibility, which uses new experimental approaches and aims “to demonstrate the validity of a concept or finding using a different paradigm.

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.

The three tenets of upstream data are: Data management Quantification of uncertainty Risk assessment

Exploratory wells located invariably by a set of deterministic seismic interpretations are drilled into reservoirs under uncertainty that is invariably poorly quantified, the geologic models yawning to be optimized by a mindset that is educated in a data-driven methodology.

There are two distinct branches of data mining, predictive and descriptive/exploratory (Figure 1.2), that can turn raw data into actionable knowledge. Sometimes you hear these two categories called directed (predictive) and undirected (descriptive). Predictive models use known results to develop (or train or estimate) a model that can be used to predict values for different data. Descriptive models describe patterns in existing data that may be found in new data. With descriptive models, there is no target variable for which you are striving to predict the value. Most of the big payoff has been in predictive modeling when the models are operationalized in a real-world setting.

predictive Analytics enabled the Big Data to deliver the actual usage and value to the businesses by putting the processed information to a real use.

The secret to getting the most from Big Data isn’t found in huge server farms or massive parallel computing or in-memory algorithms. Instead, it’s in the almighty pencil.

key is to focus on the big decisions for which if you had better data, if you had better predictive ability, if you had a better ability to optimize, you’d make more money.

One other problem is that too many people—and vendors in particular—are already using big data to mean any use of analytics, or in extreme cases even reporting and conventional business intelligence.

technologies like big data and analytics, high-speed communications, and rapid prototyping have augmented the contributions made by more abstract and data-driven reasoning, and in turn have increased the value of people with the right engineering, creative, or design skills.

data, models, transformation. Data is the creative use of internal and external data to give you a broader view on what is happening to your operations or your customer. Modeling is all about using that data to get workable models that can either help you predict better or allow you to optimize better in terms of your business.

It’s not enough to analyze “Big Data” or even extract insights from them. The best sales leaders mask all that complexity and keep it simple for the front lines so sales staff like Maria can get on with what they do best — sales, not statistics.

Research shows that personalization can deliver five to eight times the ROI on marketing spend and lift sales 10% or more.

Traveling cookie” data build a digital footprint of a consumers based on their logins at popular sites (for example, on airline sites or Facebook). Once the customer logs in, the cookie follows that customer wherever he or she goes on the web.

ARTICLE Five steps to squeeze more ROI from your marketing To keep up with today’s channel-surfing customer, marketers need to move beyond traditional Marketing Mix Modeling.

One of the most critical design and architecture decisions adopters of advanced analytics must make is whether to store analytic data in a data warehouse or in a standalone analytic database. Where does the data go? Where is it managed? Where are we going to do our analytical processes? Philip Russom, Senior Manager, TDWI Research

There are four main predictive modeling techniques detailed in this book as important upstream O&G data-driven analytic methodologies: Decision trees Regression Linear regression Logistic regression Neural networks Artificial neural networks Self-organizing maps (SOMs) K-means clustering

It’s about the ability to make better decisions and take meaningful actions at the right time. It’s about detecting fraud while someone is swiping a credit card, or triggering an offer while a shopper is standing on a checkout line, or placing an ad on a website while someone is reading a specific article. It’s about combining and analyzing data so you can take the right action, at the right time, and at the right place.

Crunching data is not an automatic ticket for success, any more than putting up a website turned every company in the dotcom era into an e-commerce juggernaut. If the rollout of IT in the corporate world over the last 30 years has taught one lesson, it’s that the adoption of a transformative technology always requires careful and creative management grounded in facts. The new new thing never succeeds without a lot of help from the old old thing.

Given the overwhelmingly positive effects of this Facebook effort, the brand considered making massive marketing budget cuts to TV and print advertising in favor of more spend on social media channels. MMM analysis suggested that digital marketing (online display, Facebook advertising and Facebook viral) would deliver the same impact as traditional marketing (TV and print), but at only 15 percent of the cost.

When long-term effects were included in the calculations, however, the contribution of digital dropped by half. Online displays and Facebook advertising just cannot deliver the same emotional connection that brand equity requires that TV advertising does. Significant cuts to TV spend as suggested by traditional MMM would have reduced the NPV of the brand’s profit. In

These data rarely exist in one place in the organization so you’ll need to pull in people from multiple functions such as marketing, sales, in-store operations, IT, and beyond. We’ve seen companies create small “SWAT” teams that assemble people from these functions to break through bureaucratic logjams.

The organizational and technology challenges are significant, and we have touched on only a few of them here. But we’ve seen big pay-offs for retailers who can follow individual customers across media and channels. Increasingly,

What, then, are the practical steps that pricing managers can take to master Big Data? Companies must recruit a new generation of pricing talent with more of a “trader” profile than an “analyst” one.

One of the main ​reasons that MMM doesn’t deliver the benefits it should is because CMOs and marketers aren’t involved in the analysis. In many cases companies outsource the analysis or throw it over the wall to an internal analytics team. The result we often see is that the CMO pushes back on implementing the findings of the analysis, either because it’s too complex or challenges the status quo. Often times there’s a high level of distrust due to a lack of transparency into the process, so even if there’s great analysis there, the CMO won’t act on it.

Set hard and measurable goals. Lots of folks talk about improving customer experience, a worthy goal. But how do you measure it? Goals like revenue growth, increased profitability, or increased customer use are good because they’re measurable. But don’t stop there. Write out how the improvement you’re shooting for will impact the P&L. For

information. The regulars around the bar of the National Press Club never thought to reuse online data about media consumption. Nor might the analytics specialists in Armonk, New York, or Bangalore, India, have harnessed the information in this way. It took Cross, a louche outsider with disheveled hair and a slacker’s drawl, to presume

The private-equity approach can take the form of simple improvements, such as changing irrigation from antiquated dykes and canal networks to automatic spray systems: these are the equivalent of picking low-hanging fruit. Pricey robots can boost milk per cow by 10-15%. Using “big-data” analytics to plant and cultivate seeds can push crop yields up 5%. “This is an industry where the gap between the top and bottom quartile is greater than anywhere else,” says Detlef Schoen of Aquila Capital, an alternative-investment firm. And yet the 36 agriculture-focused funds, with $15 billion under management, pale in comparison to the 144 funds focused on infrastructure ($89 billion) and 473 targeting real estate ($163 billion), according to Preqin, a data provider. TIAA-CREF, an American financial group, is a market leader with $5 billion in farmland, from Australia to Brazil, and its own agricultural academic centre at the University of Illinois. Canadian pension funds and Britain’s Wellcome Trust are among those bolstering their farming savvy.

The availability of all this data means that virtually every business or organizational activity can be viewed as a big data problem or initiative. Manufacturing, in which most machines already have one or more microprocessors, is increasingly a big-data environment. Consumer marketing, with myriad customer touchpoints and clickstreams, is already a big data problem. Google has even described its self-driving car as a big-data project.

Transforming your company to take advantage of data and analytics is the hard part, OK?

If you’re good at data analytics but you don’t have this feel for the business, you’ll make naïve decisions. If you’re comfortable with the feel of the business but you never use analytics, you’re just leaving a lot of money on the table that your competitors are going to be able to exploit. So the challenge is how to build that bimodal athlete and how to get the technical talent.

Finding growth with big data is more than an add-on; it affects every aspect of a business, requiring a change in mind-set from leadership down to the front lines. Micromarket strategies

Once management is on board, the sales team needs to understand the rationale behind the micromarket strategy and have simple tools that make it easy to implement. That means aligning sales coverage with opportunity and creating straightforward sales “plays” for each type of opportunity.

In a recent Harvard Business Review article we explore how companies require three mutually supportive capabilities to fully exploit data and analytics: an ability to identify and manage multiple sources of data, the capacity to build advanced analytic models, and the critical management muscle to transform the organization.

Algorithmic profits Algorithmic marketing is allowing companies to do things they couldn’t do before, and some early signs show it can deliver big value, especially in financial or information services. In North America, Amazon.com grew 30 to 40 percent, quarter after quarter, throughout the United States’ 2008-2012 recession, while other major retailers shrank or went out of business. From 2006 to 2010, Amazon spent 5.6 percent of its sales revenue on IT, while rivals Target and Best Buy spent 1.3% and 0.5%, respectively. That investment and focus has yielded increasingly sophisticated recommendation engines that deliver over 35 percent of all sales, an automated e-mail/customer service systems (90 percent are automated, versus 44 percent for the average retailer) that are a key component of its best-in-class customer satisfaction, and dynamic pricing systems that crawl the Web and react to competitor pricing and stock levels by altering prices on Amazon.com, in some cases every 15 seconds.

they need to develop a smart strategy that focuses on the data they really need to achieve their goals.

Zach Nies suggests going even further, segmenting customers into three groups. “‘A customers’ are your really big customers who negotiated a big discount and expect the world from you. ‘B customers’ are customers who are fairly low maintenance, didn’t get a big discount, see themselves as partners with you, and provide useful insights. ‘C customers’ cause trouble, are a pain to deal with, and demand things from you that you feel will damage your business,” he explains. “Don’t spend too much time on the A’s—they sound good but aren’t the best for your business. Bring as many Bs on as customers as possible. And try to get your ‘C customers’ to be customers of your competitors.

The term psychographics was created to describe the process by which we took in-house personality scoring and applied it to our massive database. Using analytic tools to understand individuals’ complex personalities, the psychologists then determined what motivated those individuals to act. Then the creative team tailored specific messages to those personality types in a process called “behavioral microtargeting.

When we develop predictive analytics systems, we are not merely automating a human’s decision by using software to specify the rules for when to say yes or no; we are even leaving the rules themselves to be inferred from data. However, the patterns learned by these systems are opaque: even if there is some correlation in the data, we may not know why. If there is a systematic bias in the input to an algorithm, the system will most likely learn and amplify that bias in its output

Tracking data becomes more detailed, analyses become further-reaching, and data is retained for a long time in order to build up detailed profiles of each person for marketing purposes. Now the relationship between the company and the user whose data is being collected starts looking quite different. The user is given a free service and is coaxed into engaging with it as much as possible. The tracking of the user serves not primarily that individual, but rather the needs of the advertisers who are funding the service. I think this relationship can be appropriately described with a word that has more sinister connotations: surveillance. Surveillance As a thought experiment, try replacing the word data with surveillance, and observe if common phrases still sound so good [93]. How about this: “In our surveillance-driven organization we collect real-time surveillance streams and store them in our surveillance warehouse. Our surveillance scientists use advanced analytics and surveillance processing in order to derive new insights.” This thought experiment is unusually polemic for this book, Designing Surveillance-Intensive Applications, but I think that strong words are needed to emphasize this point. In our attempts to make software “eat the world” [94], we have built the greatest mass surveillance infrastructure the world has ever seen. Rushing toward an Internet of Things, we are rapidly approaching a world in which every inhabited space contains at least one internet-connected microphone, in the form of smartphones, smart TVs, voice-controlled assistant devices, baby monitors, and even children’s toys that use cloud-based speech recognition. Many of these devices have a terrible security record [95]. Even the most totalitarian and repressive regimes could only dream of putting a microphone in every room and forcing every person to constantly carry a device capable of tracking their location and movements. Yet we apparently voluntarily, even enthusiastically, throw ourselves into this world of total surveillance. The difference is just that the data is being collected by corporations rather than government agencies [96].

Internet services have made it much easier to amass huge amounts of sensitive information without meaningful consent, and to use it at massive scale without users understanding what is happening to their private data. Data as assets and power Since behavioral data is a byproduct of users interacting with a service, it is sometimes called “data exhaust”—suggesting that the data is worthless waste material. Viewed this way, behavioral and predictive analytics can be seen as a form of recycling that extracts value from data that would have otherwise been thrown away. More correct would be to view it the other way round: from an economic point of view, if targeted advertising is what pays for a service, then behavioral data about people is the service’s core asset.

I have worked with many companies to implement data projects, and there’s one lesson I’ve had drummed into me time and time again: data is nothing without strategy.

We are hyperreactive to even small stimuli in our environment We have trouble distinguishing between information or sensory data that should be ignored versus data that should be carefully considered We are highly focused on details rather than “big picture” concepts We’re deeply and deliberatively analytical Our decision-making process is methodical rather than efficient; we don’t rely on mental shortcuts or “gut feelings” Processing a situation takes us more time and energy than it does for a neurotypical person

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

I believe the technological industry is switching in a different direction that one may think in the Metaverse. Why spend trillions of dollars on big data when it is becoming more useless? We need dynamic content to create a boom in the tech industry for the next millennium. Why hire someone with a 4 year degree from college for a career in database administration when companies can't afford to pay 100k a year? We can manage it quite fine in google sheets or excel. The utilization of AI will then completely defeat the purpose of Data As A Service when a program can dynamically build hash objects in random access memory by simply using a small script like (via switch) while creating a [5th XYZ Stargate] just like the Diablo version, but with a smaller seed. You could then store those objects for the blockchain Inna virtualized file container ;)." - Jonathan Roy Mckinney

if every business, regardless of size, is now a data business, every business therefore needs a robust data strategy.

doesn’t matter how much data you have, it’s whether you use it successfully that counts.

There are five ways technology can boost marketing practices: Make more informed decisions based on big data. The greatest side product of digitalization is big data. In the digital context, every customer touchpoint—transaction, call center inquiry, and email exchange—is recorded. Moreover, customers leave footprints every time they browse the Internet and post something on social media. Privacy concerns aside, those are mountains of insights to extract. With such a rich source of information, marketers can now profile the customers at a granular and individual level, allowing one-to-one marketing at scale. Predict outcomes of marketing strategies and tactics. No marketing investment is a sure bet. But the idea of calculating the return on every marketing action makes marketing more accountable. With artificial intelligence–powered analytics, it is now possible for marketers to predict the outcome before launching new products or releasing new campaigns. The predictive model aims to discover patterns from previous marketing endeavors and understand what works, and based on the learning, recommend the optimized design for future campaigns. It allows marketers to stay ahead of the curve without jeopardizing the brands from possible failures. Bring the contextual digital experience to the physical world. The tracking of Internet users enables digital marketers to provide highly contextual experiences, such as personalized landing pages, relevant ads, and custom-made content. It gives digital-native companies a significant advantage over their brick-and-mortar counterparts. Today, the connected devices and sensors—the Internet of Things—empowers businesses to bring contextual touchpoints to the physical space, leveling the playing field while facilitating seamless omnichannel experience. Sensors enable marketers to identify who is coming to the stores and provide personalized treatment. Augment frontline marketers’ capacity to deliver value. Instead of being drawn into the machine-versus-human debate, marketers can focus on building an optimized symbiosis between themselves and digital technologies. AI, along with NLP, can improve the productivity of customer-facing operations by taking over lower-value tasks and empowering frontline personnel to tailor their approach. Chatbots can handle simple, high-volume conversations with an instant response. AR and VR help companies deliver engaging products with minimum human involvement. Thus, frontline marketers can concentrate on delivering highly coveted social interactions only when they need to. Speed up marketing execution. The preferences of always-on customers constantly change, putting pressure on businesses to profit from a shorter window of opportunity. To cope with such a challenge, companies can draw inspiration from the agile practices of lean startups. These startups rely heavily on technology to perform rapid market experiments and real-time validation.

The data mining tools used are nearest neighbor, genetic algorithms, rule induction, decision trees, and artificial neural networks.

analytics startup wasn’t disrupting anything so much as unseating big-data incumbents:

Surveillance As a thought experiment, try replacing the word data with surveillance, and observe if common phrases still sound so good [93]. How about this: “In our surveillance-driven organization we collect real-time surveillance streams and store them in our surveillance warehouse. Our surveillance scientists use advanced analytics and surveillance processing in order to derive new insights.

("SELECT *" queries are rarely needed for analytics)

Snapshot isolation is a boon for long-running, read-only queries such as backups and analytics.

Cluster analysis

If you’re good at data analytics but you don’t have this feel for the business, you’ll make naïve decisions.

If you’re comfortable with the feel of the business but you never use analytics, you’re just leaving a lot of money on the table that your competitors

Very few companies know how to exploit the data already embedded in their core operating systems. THE SOLUTION Evidence-based, data-driven decision making provides the answer, but it requires a big cultural shift and four changes in how operations are managed. Who Benefits from Big Data? 496 words Big data is big business. The IT research firm Gartner estimates that total software, social media, and IT services spending related to big data and analytics topped $28 billion worldwide in 2012. All estimates predict rapid growth. In addition to vendors, at least three types of organizations are harvesting value from big data.

You can keep the Office of Personnel Management records, I don't need Electronic Health Records, give me the metadata, big data analytics and a custom tailored algorithm and a budget and during election time, I can cut to the psychological core of any population, period!

the future of connected health will rest more heavily in the area of analytics and data.

Zeo, a manufacturer of a sleep tracking device has amassed the largest database in the world on sleep cycles. If these data can help consumers and insurers change behaviors and improve outcomes, this could potentially be a lucrative business model built upon data analytics.

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