Mar 29, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Correlation-Causation  Source

[ AnalyticsWeek BYTES]

>> Why You Must Not Have Any Doubts About Cloud Security by thomassujain

>> Investing in Big Data by Bill Pieroni by thebiganalytics

>> The backlash against big data by analyticsweekpick

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[ NEWS BYTES]

>>
 Apartment Investment & Management Co (NYSE:AIV) Institutional Investor Sentiment Analysis – Frisco Fastball Under  Sentiment Analysis

>>
 Hadoop Infrastructure Engineer – Built In Chicago Under  Hadoop

>>
 Watch the #MeToo campaign spread around the world on Facebook, Twitter, and Instagram – Fast Company Under  Social Analytics

More NEWS ? Click Here

[ FEATURED COURSE]

Pattern Discovery in Data Mining

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Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods, and applications of pattern disc… more

[ FEATURED READ]

Storytelling with Data: A Data Visualization Guide for Business Professionals

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Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You’ll discover the power of storytelling and the way to make data a pivotal point in your story. Th… more

[ TIPS & TRICKS OF THE WEEK]

Save yourself from zombie apocalypse from unscalable models
One living and breathing zombie in today’s analytical models is the pulsating absence of error bars. Not every model is scalable or holds ground with increasing data. Error bars that is tagged to almost every models should be duly calibrated. As business models rake in more data the error bars keep it sensible and in check. If error bars are not accounted for, we will make our models susceptible to failure leading us to halloween that we never wants to see.

[ DATA SCIENCE Q&A]

Q:Explain what a long-tailed distribution is and provide three examples of relevant phenomena that have long tails. Why are they important in classification and regression problems?
A: * In long tailed distributions, a high frequency population is followed by a low frequency population, which gradually tails off asymptotically
* Rule of thumb: majority of occurrences (more than half, and when Pareto principles applies, 80%) are accounted for by the first 20% items in the distribution
* The least frequently occurring 80% of items are more important as a proportion of the total population
* Zipf’s law, Pareto distribution, power laws

Examples:
1) Natural language
– Given some corpus of natural language – The frequency of any word is inversely proportional to its rank in the frequency table
– The most frequent word will occur twice as often as the second most frequent, three times as often as the third most frequent…
– The” accounts for 7% of all word occurrences (70000 over 1 million)
– ‘of” accounts for 3.5%, followed by ‘and”…
– Only 135 vocabulary items are needed to account for half the English corpus!

2. Allocation of wealth among individuals: the larger portion of the wealth of any society is controlled by a smaller percentage of the people

3. File size distribution of Internet Traffic

Additional: Hard disk error rates, values of oil reserves in a field (a few large fields, many small ones), sizes of sand particles, sizes of meteorites

Importance in classification and regression problems:
– Skewed distribution
– Which metrics to use? Accuracy paradox (classification), F-score, AUC
– Issue when using models that make assumptions on the linearity (linear regression): need to apply a monotone transformation on the data (logarithm, square root, sigmoid function…)
– Issue when sampling: your data becomes even more unbalanced! Using of stratified sampling of random sampling, SMOTE (‘Synthetic Minority Over-sampling Technique”, NV Chawla) or anomaly detection approach

Source

[ VIDEO OF THE WEEK]

#FutureOfData with Rob(@telerob) / @ConnellyAgency on running innovation in agency

 #FutureOfData with Rob(@telerob) / @ConnellyAgency on running innovation in agency

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[ QUOTE OF THE WEEK]

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

[ PODCAST OF THE WEEK]

#FutureOfData Podcast: Peter Morgan, CEO, Deep Learning Partnership

 #FutureOfData Podcast: Peter Morgan, CEO, Deep Learning Partnership

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[ FACT OF THE WEEK]

IDC Estimates that by 2020,business transactions on the internet- business-to-business and business-to-consumer – will reach 450 billion per day.

Sourced from: Analytics.CLUB #WEB Newsletter

The Horizontal Impact of Advanced Machine Learning: Network Optimization

A substantial amount of literature has been dedicated to Artificial Intelligence’s analytic capabilities. Recurring use cases include the machine learning potential of its algorithms for fraud detection, churn reduction, recommendation engines, and personalization of individual preferences via digital assistants.

What is less talked about, yet perhaps even more influential, is its potential for network optimization. Whereas the aforementioned use cases are specific to particular verticals, the optimization capabilities of advanced machine learning and deep learning have a horizontal effect that reinforces business value regardless of an organization’s area of specialization.

According to One Network founder and CEO Greg Brady, with traditional network planning tools for the supply chain industry “the user has to be involved and decide what the planning tool is going to do. The deployment we have [with machine learning], the algorithms run on the execution system and do the work that the user used to do. They learn from their experience and get smarter over time.”

The basis of this approach—deploying AI on execution systems to learn from experience and optimize based on timely changes to data—is applicable to operations for any industry or line of business. As such, it may very well be advanced machine learning’s most redeeming quality for the enterprise.

Deep Learning Pattern Recognition

The central component in the network optimization capabilities of advanced machine learning is the profound pattern recognition of its deep learning algorithms. Those algorithms are peerless in their ability to identify patterns in enormous amounts of big data related to specific business applications or overall network optimization. Moreover, they can identify these patterns quicker than alternative methods can, using those results to inform network operation and optimize its functionality. Regarding AI’s utility in this regard, “Everything is predictive,” Brady said. “It’s always looking forward in time. It’s trying to understand what the problem’s going to be before it exists. With enough speed, it fixes the problem before it ever occurs.” Leveraging this capability of deep learning for networking concerns requires sizable amounts of data for its algorithms to analyze. It also involves predictions about what networking results should be, then uses other advanced machine learning techniques to measure the results of data in near real-time to see how they compare to the predictions.

Autonomous Agents

Composite approaches of deep learning in tandem with other aspects of machine learning can optimize networks with a striking degree of accuracy. By deploying autonomous agents, which Brady described as “like Jarvis on Iron Man but instead they actually do the work”, within networks to compare the results of deep learning pattern recognition to incoming data about network efficiency, organizations can determine how their actual networking compares to optimal levels. “What these agents do is every hour look at if the pattern is being met as we assumed,” Brady revealed. “If not, it adjusts its forecast.” Oftentimes, those adjustments are made while seeking to identify reasons why patterns are not met. A restaurant chain looking to optimize its supply chain ordering process while accounting for real-time factors such as inventory consumption and store traffic implemented autonomous agents with a deep learning pattern recognition engine. In this example, the store used historical data “for a pattern recognition engine to determine what the traffic buying patterns are,” Brady said. “You get a level of detail in the data that’s unbelievable.” The pattern recognition capabilities then devise daily forecasts for each store. Millions of autonomous agents are deployed to “read the point of sales data all day long; every hour we give them more data to chew on. The agents will interpret what it’s learned from the data consumption.” Agents not only adjust the forecasts for ordering supplies, but also “automatically generate orders on their own,” Brady added, implementing a pivotal layer of automation for time-sensitive event data. The result was order accuracy at approximately 80 percent, which Brady remarked was “completely unheard of” for the organization.

Crucial Caveats

Although there may not necessarily be limits to the automation capabilities of advanced machine learning for networking concerns, there certainly are to the prudent implementation of them. According to Brady, it’s necessary for users to understand the impact of the decision-making process of machine learning for networking because some situations may “cost you more than what it’s worth to fix”, especially when considering downstream ramifications of decisions for distributed locations. It’s equally valuable for advanced machine learning engines to “see all the data from all the parties” Brady cautioned, primarily for the same reason—to avoid situations in which there are repeated “fast local decisions that just cause all kind of problems in the network.” By taking measures to prevent these situations from occurring, users can ensure that they optimize their networks for maximum productivity, regardless of the specific system or type of network it may be.

Source by jelaniharper

Using Big Data to Kick-start Your Career

Gordon Square Communications and WAAT offers tips about how to make the most of online resources to land a dream job – all without spending a penny.

Left to right: Vamory Traore, Sylvia Arthur and Grzegorz Gonciarz

You are probably familiar with Monster.com or Indeed.com, huge jobs websites where you can upload your CV together with other 150 million people every month.

The bad news is that it is unlikely that your CV will ever get seen on one of these websites, discovered attendees of London Technology Week event Using Tech to Find a Job at Home or Abroad.

“There are too many people looking for a small number of jobs,” says Sylvia Arthur, Communicator Consultant at Gordon Square Communications and author of the book Get Hired! out on 30th June.

“The problem is that only 20% of jobs are advertised, while 25% of people are seeking a new job. If you divide twenty by twenty-five, the result of the equation is that you lose,” explains Ms Arthur.

So, how can we use technology to effectively find a job?

The first step is to analyse the “Big Data” – all the information that tells us about trends or associations, especially relating to human behaviour.

For example, if we were looking for a job in IT, we could read in the news that a new IT company has opened in Shoreditch, and from there understand that there are new IT jobs available in East London.

Big Data also tells us about salaries and cost of living in different areas, or what skills are required.

“Read job boards not as much to find a job as to understand what are the growing sectors and the jobs of the future,” is Ms Arthur’s advice.

Once you know where to go with the skills you have, you need to bear in mind that most recruiters receive thousands of CVs for a single job and they would rather ask a colleague for a referral than scan through all of them.

So if you are not lucky enough to have connections, you need to be proactive and make yourself known in the industry. “Comment, publish, be active in your area, showcase your knowledge,” says Ms Arthur.

“And when you read about an interesting opportunity, be proactive and contact the CEO, tell them what you know and what you can do for them. LinkedIn Premium free trial is a great tool to get in touch with these people.”

Another good advice is to follow the key people in your sector on social media. Of all the jobs posted on social media, 51% are on Twitter, compared to only 23% on LinkedIn.

And for those looking for jobs in the EEA, it is worth checking out EURES, a free online platform where job seekers across Europe are connected with validated recruiters.

“In Europe there are some countries with shortage of skilled workforce and others with high unemployment,” explains Grzegorz Gonciarz and Vamory Traore from WAAT.

“The aim of EURES is to tackle this problem.”

Advisers with local knowledge also help jobseekers to find more information about working and living in another European country before they move.

As for recent graduates looking for experience, a new EURES program called Drop’pin will start next week.

The program aims to fill the skills gap that separates young people from recruitment through free training sessions both online and on location.

To read the original article on London Technology Week, click here.

Source: Using Big Data to Kick-start Your Career

Mar 22, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Data Mining  Source

[ NEWS BYTES]

>>
 Trick or treat! Halloween spending statistics – Seeking Alpha Under  Statistics

>>
 Battleground on drug price legislation shifts to states and so does lobbying by PhRMA – MedCity News Under  Health Analytics

>>
 Increase in Data Discovery Tools to Propel the Global Prescriptive Analytics Market – Edition Truth Under  Prescriptive Analytics

More NEWS ? Click Here

[ FEATURED COURSE]

Statistical Thinking and Data Analysis

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This course is an introduction to statistical data analysis. Topics are chosen from applied probability, sampling, estimation, hypothesis testing, linear regression, analysis of variance, categorical data analysis, and n… more

[ FEATURED READ]

Rise of the Robots: Technology and the Threat of a Jobless Future

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What are the jobs of the future? How many will there be? And who will have them? As technology continues to accelerate and machines begin taking care of themselves, fewer people will be necessary. Artificial intelligence… more

[ TIPS & TRICKS OF THE WEEK]

Save yourself from zombie apocalypse from unscalable models
One living and breathing zombie in today’s analytical models is the pulsating absence of error bars. Not every model is scalable or holds ground with increasing data. Error bars that is tagged to almost every models should be duly calibrated. As business models rake in more data the error bars keep it sensible and in check. If error bars are not accounted for, we will make our models susceptible to failure leading us to halloween that we never wants to see.

[ DATA SCIENCE Q&A]

Q:Explain what resampling methods are and why they are useful?
A: * repeatedly drawing samples from a training set and refitting a model of interest on each sample in order to obtain additional information about the fitted model
* example: repeatedly draw different samples from training data, fit a linear regression to each new sample, and then examine the extent to which the resulting fit differ
* most common are: cross-validation and the bootstrap
* cross-validation: random sampling with no replacement
* bootstrap: random sampling with replacement
* cross-validation: evaluating model performance, model selection (select the appropriate level of flexibility)
* bootstrap: mostly used to quantify the uncertainty associated with a given estimator or statistical learning method

Source

[ VIDEO OF THE WEEK]

Data-As-A-Service (#DAAS) to enable compliance reporting

 Data-As-A-Service (#DAAS) to enable compliance reporting

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[ QUOTE OF THE WEEK]

He uses statistics as a drunken man uses lamp posts—for support rather than for illumination. – Andrew Lang

[ PODCAST OF THE WEEK]

Pascal Marmier (@pmarmier) @SwissRe discusses running data driven innovation catalyst

 Pascal Marmier (@pmarmier) @SwissRe discusses running data driven innovation catalyst

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[ FACT OF THE WEEK]

Data production will be 44 times greater in 2020 than it was in 2009.

Sourced from: Analytics.CLUB #WEB Newsletter

How Sports Data Analytics Is Upsetting The Game All Over Again

One or two games in MLB is often the difference between advancing to the post-season or staying home, and an entire season can be determined by a couple of good or bad pitches. There is a huge competitive advantage to knowing the opponent’s next step. That’s one reason sport analytics is a booming field. And it explains why data scientists, both fan and professional, are figuring out how to do more accurate modeling than ever before.

One notable example is Ray Hensberger, baseball-loving technologist in the Strategic Innovation Group at Booz Allen Hamilton.

At a workshop during the GigaOm Structure conference, Hensberger shared his next-level data crunching and the academic paper his team prepared for the MIT Sloan Sports Analytics Conference. His team modeled MLB data to show with 74.5% accuracy what a pitcher is going to throw—and when.

Hensberger’s calculations are more accurate than anything else published to date. But as Hensberger knows, getting the numbers right isn’t easy. The problem: How to build machine-learning build models that understand baseball decision-making? And how to make them solid enough to actually work with new data in real-time game situations?

“We started with 900 pitchers,” says Hensberg. “By excluding players having thrown less than 1,000 pitches total over the three seasons considered, we drew an experimental sample of about 400,” he says. “We looked at things like the number of people on base, a right-handed batter versus a left-handed batter.”

They also looked at the current at-bat (pitch type and zone history, ball-strike count); the game situation (inning, number of outs, and number and location of men on base); and pitcher/batter handedness; as well as other features from observations on pitchers that vary across ball games, such as curveball release point, fastball velocity, general pitch selection, and slider movement.

The final result? A set of pitcher-customized models and a report about what those pitchers would throw in a real game situation.

“We took the data, looked at the most common pitches they threw, then built a model that said ‘In this situation, this pitcher will throw this type of pitch—be that a slider, curveball, split-finger. We took the top four top favorite pitches of that pitcher, and we built models for each one of those pitches for each one of those pitchers,” Hensberger said.

They are methods he and his team outline in a book published by his team called The Field Guide To Data Science. “Most of [the data],” he says, “was PITCHf/x data from MLB. There’s a ton of data out there.”

Modern Baseball Analytics.Booz Allen Hamilton

Cross Validation Is Key

“Each pitcher-specific model was trained and tested by five-fold cross-validation testing,” Hensberger says. Cross-validation is an important part of training and testing machine learningmodels. Its purpose, in English: to ensure that the models aren’t biased by the data they’re triangulated by.

“The cross-validation piece, the goal of it, you’re defining a data set you can test the model with,” says Hensberger. “You’ve got to have a way of testing the model out when you’re training it, and to provide insight on how the model will generalize to an unknown data set. In this case, that would be real-time pitches.”

“You don’t want to just base your model on purely 100% on what was done historically. If we just put out this model without doing that cross-validation piece, people would probably say your model is overfit for the data that you have.”

Once the models were solid, Hensberger and his team used a machine-learning strategy known as “one-versus-rest” to run experiments to predict the type of the next pitch for each pitcher. It is based on an algorithm that allowed them to establish an “index of predictability” for a given pitcher. Then they looked at the data in three different ways:

  1. Predictability by pitch count, looking at pitcher predictability: When the batter is ahead (more balls than strikes), when the batter is behind (more strikes than balls), and when the pitch count is even.
  2. Predictability by “platooning” which looks at how well a right-handed batter will fare against a left-handed pitcher, and vice versa.
  3. Out-of-sample test, a test to verify the predictions by running trained models with new data to make sure they work. “We performed out-of-sample predictions by running trained classifier models using previously unseen examples from the 2013 World Series between the Boston Red Sox and the St. Louis Cardinals.”

“Overall our rate was about 74.5% predictability across all pitchers, which actually beats the previous published result at the MIT Sloan Sports Analytics conference. That that was 70%,” says Hensberger. The report published by his team was also able to predict exact pitch type better than before. “The other study only said if a fastball or not a fastball that’s going to come out of a pitcher’s hand,” says Hensberger. “The models we built were for the top four pitches, so [they show] what the actually pitches were going to be.”

Hensberger’s team also made some other interesting discoveries.

“Some pitchers, just given the situation, were more predictable than others,” he says. “There is no correlation between predictability and ERA. With less predictable pitchers, you would expect them to be more effective. But that’s not true. We also found that eight of the 15 most predictable pitchers came from two teams: the Cardinals and the Reds.”

This may be a result of the catchers calling the game, influencing the pitchers and their decisions. But it also may be attributed to pitching coaches telling pitchers what to do in certain situations. “Either way,” Hensberger says, “it’s interesting to consider.”

His findings around platoon advantage are worth thinking about as well. Statistically in baseball, platoon advantage means that the batter usually has the advantage: They have better stats when they face the opposite-handed pitcher.

“What we found [in that situation] is the predictability of pitchers was around 76%. If you look at the disadvantage, the overall predictability was about 73%,” Hensberger says. “So, pitchers are a little more predictable, we found, when the batter’s at the advantage. That could play into why the stats kind of favor them.”

This work was done over the corpus of data, but Hensberger says that you run the models real-time during a game, using the time interval between pitches to compute new stats and make predictions according to the current game situation.

According to Jessica Gelman, cofounder and co-chair of the MIT Sloan Sports Analytics Conference, that type of real-time, granular data crunching is where sports analytics is headed. The field is changing fast. And Gelman proves it. Below, her overview on how dramatically it has evolved from where it was just a couple of years ago.

How Sports Data Science Has Evolved

“If you’ve read Moneyball or watched the movie, at that point in time it was no different than what bankers do in looking for an undervalued asset. Now, finding those undervalued assets is much harder. There’s new stats that are being created all the time. It’s so much more advanced,” Gelman says.

Though it may surprise data geeks, Gelman says that formalized sport analytics still isn’t yet mainstream—not every sport or team uses data. The NHL is still lagging in analytics, with the most notable exception of the Boston Bruins. The NFL is slow to adopt as well, though more teams like the Buffalo Bills are investing in the space.

However, most other leagues are with the program. And that is accelerating. In a big way. In Major League Soccer, formal analytics are now happening. Data analysis is now standard in EnglishPremier League football, augmented by global football by fan sites. And almost every baseball and basketball team has an analytics team.

“Some sports have been quicker to accept it than others,” says Gelman. “But it’s widely accepted at this point in time that there’s significant value to having analytics to support decision making.”

So how are analytics used in sports? Gelman says there’s work happening on both the team side and on the business side.

“On the team side, some leagues do a lot with, for example, managing salaries and using analytics for that. Other leagues use it for evaluating the players on the field and making decisions about who’s going to play or who to trade. Some do both,” says Gelman.

On the business side, data science increasingly influences a number of front office decisions. “That’s ticketing, pricing, and inventory management. It’s also customer marketing, enhancing engagement and loyalty, fandom, and the game-day experience,” Gelman explains. A lot of data science work looks at how people react to what in the stadium and how you keep them coming to back—versus watching at home on TV. “And then,” Gelman says, “the most recent realm of analytics is wearable technology,” which means more data will soon be available to players and coaches.

Hensberger sees this as a good thing. Ultimately, he says, the biggest winners will be the fans.

“Data science is about modeling and predicting. When this gets in the hands of everyone across the leagues, the viewing experience will get better for everybody,” he says. “You want to see competition. You don’t want to see a blowout, you want to see close games. Excitement and heart-pounding experience. That’s what brings us back to the sport.”

Originally posted via “How Sports Data Analytics Is Upsetting The Game All Over Again”

Source: How Sports Data Analytics Is Upsetting The Game All Over Again by analyticsweekpick

One Word Can Speak Volumes About Your Company Culture

Employee surveys are used to help manage the employee relationship. The questions in the employee survey are used to elicit employee responses that will be used to better understand how to improve that relationship. I crafted a new employee survey question that combines the best of both structured and unstructured measurement approaches. This approach provides both qualitative and quantitative information.

Here is the new question: What one word best describes <Company Name> as an employer?

I have one client (startup B2B technology company) who conducts an annual employee survey. Last year, a total of 157 employees completed the survey (response rate of 61%). In addition to the one-answer, open-ended question above, the employee survey included other traditional survey questions, including employee loyalty questions (likelihood to stay, likelihood to recommend) and employee experience questions (26 different questions across variety of areas – supervisor, pay, benefits, work group, promotions, training).

The one word answer can be used in a few ways to provide employee insight. First, you can examine the content of the words to understand your company’s strengths and weaknesses. Second, the employee responses can help you determine the level of sentiment employees have toward the company. Finally, you can use your current employees’ words (e.g., branding purposes) throughout your employment and recruitment collateral to attract prospects.

1. Identify Company Strengths and Weaknesses

ESI_WordDistribution
Figure 1. Examine the content of words that your employees use to describe you to understand your strengths and weaknesses.

The most frequently used words by the employees are presented in Figure 1. Some words used by many employees included general adjectives such as “Awesome,” “Exciting,” “Great” and “Good.” While these words tell you that employees are generally happy, they are less useful in pinpointing the reasons why they are happy. There were, however, a few words that reflected specific adjectives that provide some insight about the work environment (e.g., “Flexible / Flexibility,” “Teamwork,” “Innovative,” “Agile” and “Hectic”). Taken as a whole, these diverse adjectives paint a generally positive picture of a work environment that is innovative, flexible, hectic and one that supports teamwork.

2. Use Employee Sentiment as a KPI

Figure 2. ;lkj
Figure 2. The Employee Sentiment Index (ESI) is predictive of important organizational variables like employees’ intentions to stay with the employer and recommend the employer as a place to work.

Calculating a sentiment score is an exercise of mapping each word into a numeric value of sentiment. I used an existing sentiment lexicon that is based on prior research in the area of sentiment measurement (see here, here and here). Each word that the employees use is assigned a value (based on the lexicon) on a scale from 0 (negative sentiment) to 10 (positive sentiment). This value represents the Employee Sentiment Index (ESI).

The average ESI value across the entire set of employee responses was 7.2, reflecting that, on the average, the employees generally have a positive attitude about their employer.

To understand the usefulness of the ESI, I correlated it with the other employee loyalty measures. As you can see in Figure 2, the Employee Sentiment Index is positively related to employees’ intentions to stay with the employer and intentions to recommend the employer as a place to work. Employees reporting positive sentiment about the company are more likely to recommend the company to a friend as a place to work and more likely to stay with the company compared to employees reporting less positive sentiment about the company.

The ESI could be used as a key performance index for use in employee analytics efforts that identify the causes of employee sentiment. Furthermore, the ESI could be included in executive dashboards as a good overall metric of the health of the employee-employer relationship.

3. Improve Company Branding

ESI_Words
Figure 3. Use employee responses for communication and branding purposes.

The list of words that employees use to describe you paints a general picture of your company. You can create a word cloud to help you communicate the survey results to the company. Additionally, you can use the word cloud as part of your recruitment efforts to attract new employees. My client used their word cloud as part of their employee on-boarding process (see an initial mock up of their word cloud in Figure 3).

Summary

I presented a measurement approach to help businesses manage the health of the employee relationship. The proposed method reflects an intentional measurement approach using unstructured data. The measurement approach offers a variety of benefits:

  1. Identify company strengths/weaknesses. The content of the words that the employees use can be examined to to understand common themes.
  2. Use employee sentiment as a KPI. The ESI measures the extent to which employees hold positive sentiment toward their employer. This metric can be used to track progress over time. The ESI is predictive of other important organizational measures including turnover intentions and likelihood to recommend the employer.
  3. Improve company branding. Depending on the results, you can use word clouds for employment collateral to support recruitment and employee orientation activities.

Results of the analyses show that the proposed measurement method provides useful information about your company culture and your employees. This measurement approach allows you to categorize the words that employees use (unstructured data) into different levels of employee sentiment (structured data). The results of this case study show that employers can obtain useful information from a single question to help them understand how to better manage employee relationships.

This article first appeared on CustomerThink.com.

Source by bobehayes

Mar 15, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Ethics  Source

[ AnalyticsWeek BYTES]

>> How This Startup Wants To Use Big Data To Drive Online Sales by analyticsweekpick

>> Feb 08, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

>> Oct 26, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

Wanna write? Click Here

[ NEWS BYTES]

>>
 Vmware, Carbon Black transform approaches to data centre, cloud … – Telecompaper Under  Cloud Security

>>
 Global Big Data Security Market 2017 – Gemalto (Netherlands), Cloudera (US), Informatica (US), DataVisor, Inc. (US … – Technology News Extra Under  Big Data Security

>>
 Oracle shares slip as cloud growth misses but quarterly results top view – Financial Times Under  Cloud

More NEWS ? Click Here

[ FEATURED COURSE]

A Course in Machine Learning

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Machine learning is the study of algorithms that learn from data and experience. It is applied in a vast variety of application areas, from medicine to advertising, from military to pedestrian. Any area in which you need… more

[ FEATURED READ]

The Future of the Professions: How Technology Will Transform the Work of Human Experts

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This book predicts the decline of today’s professions and describes the people and systems that will replace them. In an Internet society, according to Richard Susskind and Daniel Susskind, we will neither need nor want … more

[ TIPS & TRICKS OF THE WEEK]

Analytics Strategy that is Startup Compliant
With right tools, capturing data is easy but not being able to handle data could lead to chaos. One of the most reliable startup strategy for adopting data analytics is TUM or The Ultimate Metric. This is the metric that matters the most to your startup. Some advantages of TUM: It answers the most important business question, it cleans up your goals, it inspires innovation and helps you understand the entire quantified business.

[ DATA SCIENCE Q&A]

Q:What are feature vectors?
A: * n-dimensional vector of numerical features that represent some object
* term occurrences frequencies, pixels of an image etc.
* Feature space: vector space associated with these vectors

Source

[ VIDEO OF THE WEEK]

#FutureOfData with @CharlieDataMine, @Oracle discussing running analytics in an enterprise

 #FutureOfData with @CharlieDataMine, @Oracle discussing running analytics in an enterprise

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[ QUOTE OF THE WEEK]

Getting information off the Internet is like taking a drink from a firehose. – Mitchell Kapor

[ PODCAST OF THE WEEK]

#DataScience Approach to Reducing #Employee #Attrition

 #DataScience Approach to Reducing #Employee #Attrition

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[ FACT OF THE WEEK]

And one of my favourite facts: At the moment less than 0.5% of all data is ever analysed and used, just imagine the potential here.

Sourced from: Analytics.CLUB #WEB Newsletter

The 7 Most Unusual Applications of Big Data You’ve Ever Seen!

It’s all well and good to talk about customer experience and managing inventory flow, but what has big data done for me lately?

I’ve rounded up seven of the most interesting — and unique — applications for big data I’ve seen recently and how they may be impacting your life.

Big Data Billboards

Outdoor marketing company Route is using big data to define and justify its pricing model for advertising space on billboards, benches and the sides of busses. Traditionally, outdoor media pricing was priced “per impression” based on an estimate of how many eyes would see the ad in a given day. No more! Now they’re using sophisticated GPS, eye-tracking software, and analysis of traffic patterns to have a much more realistic idea of which advertisements will be seen the most — and therefore be the most effective.

iPhone’s ResearchKit

Apple’s new health app, called ResearchKit, has effectively just turned your phone into a biomedical research device. Researchers can now create studies through which they collect data and input from users phones to compile data for health studies. Your phone might track how many steps you take in a day, or prompt you to answer questions about how you feel after your chemo, or how your Parkinson’s disease is progressing. It’s hoped that making the process easier and more automatic will dramatically increase the number of participants a study can attract as well as the fidelity of the data.

Big Data and Foraging

The website FallingFruit.org combined public information from the U.S. Department of Agriculture, municipal tree inventories, foraging maps and street tree databases to provide an interactive map to tell you where the apple and cherry trees in your neighborhood might be dropping fruit. The website’s stated goal is to remind urbanites that agriculture and natural foods do exist in the city — you might just have to access a website to find it.

Big Data on the Slopes

Ski resorts are even getting into the data game. RFID tags inserted into lift tickets can cut back on fraud and wait times at the lifts, as well as help ski resorts understand traffic patterns, which lifts and runs are most popular at which times of day, and even help track the movements of an individual skier if he were to become lost. They’ve also taken the data to the people, providing websites and apps that will display your day’s stats, from how many runs you slalomed to how many vertical feet you traversed, which you can then share on social media or use to compete with family and friends.

Big Data Weather Forecasting

Applications have long used data from phones to populate traffic maps, but an app called WeatherSignal taps into sensors already built into Android phones to crowdsource real time weather data as well. The phones contain a barometer, hygrometer (humidity), ambient thermometer and lightmeter, all of which can collect data relevant to weather forecasting and be fed into predictive models.

Yelp Hipster Watch

Whether you want to hang with the hipsters or avoid them, Yelp has you covered. With a nifty little search trick they call the Word Map, you can search major cities by words used in reviews — like hipster. The map then plots the locations for the reviews in red. The darker the red, the higher the concentration of that word used in reviews — and when it comes to hipsters, ironic tee shirts and handlebar mustaches.

Even Big Data Bras?

Website True&Co. is using big data to help women find better fitting bras. Statistics show that most women wear the wrong bra size, and so the website has stepped up to try to solve that problem. Customers fill out a fit questionnaire on the site, and based on the responses, an algorithm suggests a selection of bras to choose from. The company’s in-house brand is even developed and designed based on feedback from customers and data the company has collected.

The possibilities of using big data are endless and it might be time to find the big data applications in your business.

As always, thank you very much for reading my posts. You might also be interested in my new book: Big Data: Using Smart Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance

You can read a free sample chapter here.

book

Originally Posted at: The 7 Most Unusual Applications of Big Data You’ve Ever Seen! by analyticsweekpick

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Surge in real-time big data and IoT analytics is changing corporate thinking

Big data that can be immediately actionable in business decisions is transforming corporate thinking. One expert cautions that a mindset change is needed to get the most from these analytics.

Gartner reported in September 2014 that 73% of respondents in a third quarter 2014 survey had already invested or planned to invest in big data in the next 24 months. This was an increase from 64% in 2013.

The big data surge has fueled the adoption of Hadoop and other big data batch processing engines, but it is also moving beyond batch and into a real-time big data analytics approach.

Organizations want real-time big data and analytics capability because of an emerging need for big data that can be immediately actionable in business decisions. An example is the use of big data in online advertising, which immediately personalizes ads for viewers when they visit websites based on their customer profiles that big data analytics have captured.

“Customers now expect personalization when they visit websites,” said Jeff Kelley, a big data analytics analyst from Wikibon, a big data research and analytics company. “There are also other real-time big data needs in specific industry verticals that want real-time analytics capabilities.”

The financial services industry is a prime example. “Financial institutions want to cut down on fraud, and they also want to provide excellent service to their customers,” said Kelley. “Several years ago, if a customer tried to use his debit card in another country, he was often denied because of fears of fraud in the system processing the transaction. Now these systems better understand each customer’s habits and the places that he is likely to travel to, so they do a better job at preventing fraud, but also at enabling customers to use their debit cards without these cards being locked down for use when they travel abroad.”

Kelly believes that in the longer term this ability to apply real-time analytics to business problems will grow as the Internet of Things (IoT) becomes a bigger factor in daily life.

“The Internet of Things will enable sensor tacking of consumer type products in businesses and homes,” he said. “You will be collect and analyze data from various pieces of equipment and appliances and optimize performance.”

The process of harnessing IoT data is highly complex, and companies like GE are now investigating the possibilities. If this IoT data can be captured in real time and acted upon, preventive maintenance analytics can be developed to preempt performance problems on equipment and appliances, and it might also be possible for companies to deliver more rigorous sets of service level agreements (SLAs) to their customers.

Kelly is excited at the prospects, but he also cautions that companies have to change the way they view themselves and their data to get the most out of IoT advancement.

“There is a fundamental change of mindset,” he explained, “and it will require different ways of approaching application development and how you look at the business. For example, a company might have to redefine itself from thinking that it only makes ‘makes trains,’ to a company that also ‘services trains with data.'”

The service element, warranties, service contracts, how you interact with the customer, and what you learn from these customer interactions that could be forwarded into predictive selling are all areas that companies might need to rethink and realign in their business as more IoT analytics come online. The end result could be a reformation of customer relationship management (CRM) to a strictly customer-centric model that takes into account every aspect of the customer’s “life cycle” with the company — from initial product purchases, to servicing, to end of product life considerations and a new beginning of the sales cycle.

Originally posted via “Surge in real-time big data and IoT analytics is changing corporate thinking”

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