Feb 27, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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statistical anomaly  Source

[ AnalyticsWeek BYTES]

>> Voices in AI – Episode 101: A Conversation with Cindi Howsen by analyticsweekpick

>> Machine Learning and Information Security: Impact and Trends by administrator

>> Autoscaling of Cloud-Native Apps Lowers TCO and Improves Availability by analyticsweek

Wanna write? Click Here

[ FEATURED COURSE]

The Analytics Edge

image

This is an Archived Course
EdX keeps courses open for enrollment after they end to allow learners to explore content and continue learning. All features and materials may not be available, and course content will not be… more

[ FEATURED READ]

Hypothesis Testing: A Visual Introduction To Statistical Significance

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Statistical significance is a way of determining if an outcome occurred by random chance, or did something cause that outcome to be different than the expected baseline. Statistical significance calculations find their … more

[ TIPS & TRICKS OF THE WEEK]

Fix the Culture, spread awareness to get awareness
Adoption of analytics tools and capabilities has not yet caught up to industry standards. Talent has always been the bottleneck towards achieving the comparative enterprise adoption. One of the primal reason is lack of understanding and knowledge within the stakeholders. To facilitate wider adoption, data analytics leaders, users, and community members needs to step up to create awareness within the organization. An aware organization goes a long way in helping get quick buy-ins and better funding which ultimately leads to faster adoption. So be the voice that you want to hear from leadership.

[ DATA SCIENCE Q&A]

Q:What is an outlier? Explain how you might screen for outliers and what would you do if you found them in your dataset. Also, explain what an inlier is and how you might screen for them and what would you do if you found them in your dataset
A: Outliers:
– An observation point that is distant from other observations
– Can occur by chance in any distribution
– Often, they indicate measurement error or a heavy-tailed distribution
– Measurement error: discard them or use robust statistics
– Heavy-tailed distribution: high skewness, can’t use tools assuming a normal distribution
– Three-sigma rules (normally distributed data): 1 in 22 observations will differ by twice the standard deviation from the mean
– Three-sigma rules: 1 in 370 observations will differ by three times the standard deviation from the mean

Three-sigma rules example: in a sample of 1000 observations, the presence of up to 5 observations deviating from the mean by more than three times the standard deviation is within the range of what can be expected, being less than twice the expected number and hence within 1 standard deviation of the expected number (Poisson distribution).

If the nature of the distribution is known a priori, it is possible to see if the number of outliers deviate significantly from what can be expected. For a given cutoff (samples fall beyond the cutoff with probability p), the number of outliers can be approximated with a Poisson distribution with lambda=pn. Example: if one takes a normal distribution with a cutoff 3 standard deviations from the mean, p=0.3% and thus we can approximate the number of samples whose deviation exceed 3 sigmas by a Poisson with lambda=3

Identifying outliers:
– No rigid mathematical method
– Subjective exercise: be careful
– Boxplots
– QQ plots (sample quantiles Vs theoretical quantiles)

Handling outliers:
– Depends on the cause
– Retention: when the underlying model is confidently known
– Regression problems: only exclude points which exhibit a large degree of influence on the estimated coefficients (Cook’s distance)

Inlier:
– Observation lying within the general distribution of other observed values
– Doesn’t perturb the results but are non-conforming and unusual
– Simple example: observation recorded in the wrong unit (°F instead of °C)

Identifying inliers:
– Mahalanobi’s distance
– Used to calculate the distance between two random vectors
– Difference with Euclidean distance: accounts for correlations
– Discard them

Source

[ VIDEO OF THE WEEK]

@AnalyticsWeek Panel Discussion: Big Data Analytics

 @AnalyticsWeek Panel Discussion: Big Data Analytics

Subscribe to  Youtube

[ 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]

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

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

Subscribe 

iTunes  GooglePlay

[ 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

AI can help prevent mass shootings

Awesome, not awesome.

#Awesome

“Millions of people communicate using sign language, but so far projects to capture its complex gestures and translate them to verbal speech have had limited success. A new advance in real-time hand tracking from Google’s AI labs, however, could be the breakthrough some have been waiting for. The new technique uses a few clever shortcuts and, of course, the increasing general efficiency of machine learning systems to produce, in real time, a highly accurate map of the hand and all its fingers, using nothing but a smartphone and its camera.” — Devin Coldewey, Writer and Photographer Learn More from TechCrunch >

#Not Awesome

“…[A]rtificial intelligences, in seeking to please humanity, are likely to be highly emotional. By this definition, if you encoded an artificial intelligence with the need to please humanity sexually, their urgency to follow their programming constitutes sexual feelings. Feelings as real and valid as our own. Feelings that lead to the thing that feelings, probably, evolved to lead to: sex. One gets the sense that, for some digisexual people, removing the squishiness of the in-between stuff — the jealousy and hurt and betrayal and exploitation — improves their sexual enjoyment. No complications. The robot as ultimate partner. An outcome of evolution.” — Emma Grey Ellis, Writer Learn More from WIRED >

What we’re reading.

Originally Posted at: AI can help prevent mass shootings

The Importance of Your Relative Performance

Customer Experience Management (CEM) is the process of understanding and managing customers’ interaction with and perceptions about the company/brand. In these programs, customer experience metrics are tracked and used to identify improvement opportunities in order to increase customer loyalty. These customer experience metrics, used to track performance against oneself, may not be adequate for understanding why customers spend more with a company.  Keiningham et al. (2011) found that a company’s ranking (against the competition) was strongly related to share of wallet of their customers. In their two-year longitudinal study, they found that top-ranked companies received greater share of wallet of their customers compared to bottom-ranked companies.

Relative Performance Assessment (RPA): A Competitive Analytics Approach

I developed the Relative Performance Assessment (RPA), a competitive analytics solution that helps companies understand their relative ranking against their competition and identify ways to increase their ranking, and consequently, increase purchasing loyalty. The purpose of this post is to present some data behind the method.

This method is appropriate for companies who have customers who use a variety of competitors. In its basic form, the RPA method requires two additional questions in your customer relationship survey:

  • RPA Question 1: What best describes our performance compared to the competitors you use?  This question allows you to gauge each customer’s perception of where they think you stand relative to other companies/brands in their portfolio of competitors they use.  The key to RPA is the rating scale. The rating scale allows customers to tell you where your company ranks against all others in your space. The 5-point scale for the RPA is:
    1. <your company name> is the worst
    2. <your company name> is better than some
    3. <your company name> is average (about the same as others)
    4. <your company name> is better than most
    5. <your company name> is the best
  • RPA Question 2: Please tell us why you think that “insert answer to question above”. This question allows each customer to indicate the reasons behind his/her ranking of your performance. The content of the customers’ comments can be aggregated to identify underlying themes to help diagnose the reasons for high rankings (e.g., ranked the best / better than most) or low rankings (ranked the worst / better than some).

RPA in Practice

Figure 1. Percent of responses regarding relative performance

I have applied the RPA method in a few customer relationship surveys. I will present the results of a relationship survey for a B2B software company. This particular company had customers that used several competitors, so the RPA method was appropriate. The results in Figure 1 show that, on average, customers think the company is a typical supplier in the space, with a few customers indicating extreme ratings.

Additionally, similar to the findings in the Keiningham study, I found that the RPA was related to loyalty measures (see Figure 2). That is, customers who rank a company high also report high levels of customer loyalty toward that company. Conversely, customers who rank a company low also report low levels of customer loyalty toward that company. This relationship is especially strong for Advocacy and Purchasing loyalty.

Figure 2. Relative performance (RPA) is related to different types of customer loyalty.

Relative Performance, Customer Experience and Customer Loyalty

To understand the importance of the relative performance, I wanted to determine how well the RPA explained customer loyalty after accounting for the effects of the customer experience. Along with the RPA, this relationship survey also included seven (7) general customer experience questions (e.g., product quality, support quality, communications from the company) that allowed the customer to rate their experience across different customer touchpoints and 5 customer loyalty questions measuring the three types of customer loyalty, retention, advocacy and purchasing.

Understanding the causes of customer loyalty is essential to any Customer Experience Management (CEM) program. To be of value, the RPA needs to explain differences in customer loyalty beyond traditional customer experience measures. I ran a stepwise regression analysis for each loyalty question to see if the Relative Performance Assessment helped us explain customer loyalty differences beyond what can be explained by general experience questions.

Figure 3. Relative performance (RPA) helps explain purchasing loyalty behavior. Improving relative performance will increase purchasing loyalty and share of wallet.

For each customer loyalty question, I plotted the percent of variance in loyalty that is explained by the general questions and the one RPA question.  As you can see in Figure 3, the 7 general experience questions explain advocacy loyalty better than they do for purchasing and retention loyalty. Next, looking at the RPA question, we see that it has a significant impact on purchasing loyalty behaviors. In fact, the RPA improves the prediction of purchasing loyalty by almost 50%. This finding shows us that 1) there is value in asking your customers about your relative performance and 2) improving the company’s ranking will increase purchasing loyalty and share of wallet.

Understanding your Ranking

Further analysis of the data can help you understand your competitive (dis)advantage and the reasons behind your ranking. First, you can correlate the experience ratings with the RPA to see which customer experience area has the biggest impact on your relative performance.  Second, content analysis of the second RPA question (e.g., why customers gave that ranking) can reveal the reasons behind your ranking.  Applying both of these methods on the current data, I found a common product-related theme that might be responsible for their ranking. Specifically, results showed that the biggest customer experience driver of relative performance (RPA) was product quality. Additionally, the open-ended comments by customers who gave low RPA rankings were primarily focused on product-related issues (e.g., making the product easier to use, adding more customizability).

Summary

Companies that have higher industry rankings receive more share of wallet than companies who have lower industry rankings.  The Relative Performance Assessment helps companies measure their performance relative to their competitors and helps them identify ways to improve their competitive advantage.

Source by bobehayes

Feb 20, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Trust the data  Source

[ AnalyticsWeek BYTES]

>> AI systems claiming to ‘read’ emotions pose discrimination risks by administrator

>> How savvy execs make the most of data analytics by analyticsweekpick

>> Bias: Breaking the Chain that Holds Us Back by analyticsweek

Wanna write? Click Here

[ FEATURED COURSE]

Process Mining: Data science in Action

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Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be ap… more

[ FEATURED READ]

How to Create a Mind: The Secret of Human Thought Revealed

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Ray Kurzweil is arguably today’s most influential—and often controversial—futurist. In How to Create a Mind, Kurzweil presents a provocative exploration of the most important project in human-machine civilization—reverse… more

[ TIPS & TRICKS OF THE WEEK]

Data Analytics Success Starts with Empowerment
Being Data Driven is not as much of a tech challenge as it is an adoption challenge. Adoption has it’s root in cultural DNA of any organization. Great data driven organizations rungs the data driven culture into the corporate DNA. A culture of connection, interactions, sharing and collaboration is what it takes to be data driven. Its about being empowered more than its about being educated.

[ DATA SCIENCE Q&A]

Q:How would you define and measure the predictive power of a metric?
A: * Predictive power of a metric: the accuracy of a metric’s success at predicting the empirical
* They are all domain specific
* Example: in field like manufacturing, failure rates of tools are easily observable. A metric can be trained and the success can be easily measured as the deviation over time from the observed
* In information security: if the metric says that an attack is coming and one should do X. Did the recommendation stop the attack or the attack never happened?

Source

[ VIDEO OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with Juan Gorricho, @disney

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Juan Gorricho, @disney

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

For every two degrees the temperature goes up, check-ins at ice cream shops go up by 2%. – Andrew Hogue, Foursquare

[ PODCAST OF THE WEEK]

Andrea Gallego(@risenthink) / @BCG on Managing Analytics Practice #FutureOfData #Podcast

 Andrea Gallego(@risenthink) / @BCG on Managing Analytics Practice #FutureOfData #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

In late 2011, IDC Digital Universe published a report indicating that some 1.8 zettabytes of data will be created that year.

Sourced from: Analytics.CLUB #WEB Newsletter

17 equations that changed the world

Ian Stewart compiled an interest summation of 17 equations that practically changed the world

Here are 17 equations:
Pythagoras’s Theorem
In mathematics, the Pythagorean theorem, also known as Pythagoras’s theorem, is a fundamental relation in Euclidean geometry among the three sides of a right triangle. It states that the square of the hypotenuse (the side opposite the right angle) is equal to the sum of the squares of the other two sides.

 
Logarithms
a quantity representing the power to which a fixed number (the base) must be raised to produce a given number.

 
Calculus
the branch of mathematics that deals with the finding and properties of derivatives and integrals of functions, by methods originally based on the summation of infinitesimal differences. The two main types are differential calculus and integral calculus.

 
Law of Gravity
Newton’s law of universal gravitation states that a particle attracts every other particle in the universe using a force that is directly proportional to the product of their masses and inversely proportional to the square of the distance between their centers.

 
The Square Root of Minus One
The “unit” Imaginary Number (the equivalent of 1 for Real Numbers) is √(−1) (the square root of minus one). In mathematics we use i (for imaginary) but in electronics they use j (because “i” already means current, and the next letter after i is j).

 
Euler’s Formula for Polyhedra
This theorem involves Euler’s polyhedral formula (sometimes called Euler’s formula). Today we would state this result as: The number of vertices V, faces F, and edges E in a convex 3-dimensional polyhedron, satisfy V + F – E = 2.

 
Normal Distribution
In probability theory, the normal (or Gaussian) distribution is a very common continuous probability distribution. Normal distributions are important in statistics and are often used in the natural and social sciences to represent real-valued random variables whose distributions are not known.

 
Wave Equation
The wave equation is an important second-order linear hyperbolic partial differential equation for the description of waves—as they occur in physics—such as sound waves, light waves and water waves. It arises in fields like acoustics, electromagnetics, and fluid dynamics.

 
Fourier Transform
a function derived from a given function and representing it by a series of sinusoidal functions.

 
Navier-Stokes Equation
In physics, the Navier–Stokes equations /nævˈjeɪ stoʊks/, named after Claude-Louis Navier and George Gabriel Stokes, describe the motion of viscous fluid …

 
Maxwell’s Equation
Maxwell’s equations are a set of partial differential equations that, together with the Lorentz force law, form the foundation of classical electromagnetism, classical optics, and electric circuits.

 
Second Law of Thermodynamics
the branch of physical science that deals with the relations between heat and other forms of energy (such as mechanical, electrical, or chemical energy), and, by extension, of the relationships between all forms of energy.

 
Relativity
the dependence of various physical phenomena on relative motion of the observer and the observed objects, especially regarding the nature and behavior of light, space, time, and gravity.

 
Schrodinger’s Equation
After much debate, the wavefunction is now accepted to be a probability distribution. The Schrodinger equation is used to find the allowed energy levels of quantum mechanical systems (such as atoms, or transistors). The associated wavefunction gives the probability of finding the particle at a certain position.

 
Information Theory
the mathematical study of the coding of information in the form of sequences of symbols, impulses, etc., and of how rapidly such information can be transmitted, e.g., through computer circuits or telecommunications channels.

 
Chaos Theory
Chaos theory is a branch of mathematics focused on the behavior of dynamical systems that are highly sensitive to initial conditions.

 
Black-Scholes Equation
In mathematical finance, the Black–Scholes equation is a partial differential equation (PDE) governing the price evolution of a European call or European put under the Black–Scholes model. Broadly speaking, the term may refer to a similar PDE that can be derived for a variety of options, or more generally, derivatives.
 

17 equations that changed the world
17 equations that changed the world

Source

Feb 13, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Data shortage  Source

[ AnalyticsWeek BYTES]

>> Four Years Running: Gartner Names Logi a Leader in Embedded Analytics by analyticsweek

>> From Dust to Trust: How to Make Your Salesforce Data Better by analyticsweekpick

>> Data looks better naked by analyticsweek

Wanna write? Click Here

[ FEATURED COURSE]

The Analytics Edge

image

This is an Archived Course
EdX keeps courses open for enrollment after they end to allow learners to explore content and continue learning. All features and materials may not be available, and course content will not be… more

[ FEATURED READ]

Big Data: A Revolution That Will Transform How We Live, Work, and Think

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“Illuminating and very timely . . . a fascinating — and sometimes alarming — survey of big data’s growing effect on just about everything: business, government, science and medicine, privacy, and even on the way we think… more

[ TIPS & TRICKS OF THE WEEK]

Grow at the speed of collaboration
A research by Cornerstone On Demand pointed out the need for better collaboration within workforce, and data analytics domain is no different. A rapidly changing and growing industry like data analytics is very difficult to catchup by isolated workforce. A good collaborative work-environment facilitate better flow of ideas, improved team dynamics, rapid learning, and increasing ability to cut through the noise. So, embrace collaborative team dynamics.

[ DATA SCIENCE Q&A]

Q:How would you define and measure the predictive power of a metric?
A: * Predictive power of a metric: the accuracy of a metric’s success at predicting the empirical
* They are all domain specific
* Example: in field like manufacturing, failure rates of tools are easily observable. A metric can be trained and the success can be easily measured as the deviation over time from the observed
* In information security: if the metric says that an attack is coming and one should do X. Did the recommendation stop the attack or the attack never happened?

Source

[ VIDEO OF THE WEEK]

@DrewConway on fabric of an IOT Startup #FutureOfData #Podcast

 @DrewConway on fabric of an IOT Startup #FutureOfData #Podcast

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Data matures like wine, applications like fish. – James Governor

[ PODCAST OF THE WEEK]

@AmyGershkoff on building #winning #DataScience #team #FutureOfData #Podcast

 @AmyGershkoff on building #winning #DataScience #team #FutureOfData #Podcast

Subscribe 

iTunes  GooglePlay

[ 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

Customer centric fix to save Indian Maharaja (Air India) from financial mess

Customer centric fix to save Indian Maharaja (Air India) from financial messDuring my recent visit to New Delhi, India, I had an interesting discussion with one of senior Air India official about Air India. Certainly the discussion and the findings were not very different from what has already been said and shared in news web. The corporate tone is certainly not in line with the tone of a customer centric service based companies. That makes me write just another article to add my fair share of what I think will be needed to revamp India Maharaja (Air India) from a cost center for tax payers to a company that is a pride of India.

For some number geeks: Here is the story about Air India in numbers: As per a consulting firm Capa India, Air India is once again the worst performer with expectation to report loss of $1.3b in 12 months ending 31 March 2013. It received $66m bailout, has ~$100m debt. Its market share stands around 16 percent (May’ 12) and is decreasing. Some more interesting facts: Comptroller and Auditor General(CAG) has found that Air India’s workforce fell by 12% from 2006 to 2010 but employee cost shot up to 43% and the irony of it is that, the most important area for airlines – providing amenities and comfort to flyer: fell by almost 15%. : “The study revealed that in the international arena, fliers were demanding much more than basic facilities with overall travelling experience and comforts that the AI brand was no longer preferred as it was not meeting the standard. This study further stated that the services of AI were not oriented towards customer satisfaction, the personnel had an indifferent sarkari attitude and brand AI was kept as a substitute airline to travel.”

Once an Indian pride, it is now being discussed among businesses that are too frail to survive. With ever-rising competition, identical offerings, airline industry competes on thin differentiation. Customer experience is one of the core differentiator for any service industry let alone identical looking aviation companies. With success stories from few US carriers, who were able to turn to sustained profitability by positioning towards customer centricity has proven to be new mantra to survive in this competitive landscape.

With plethora of tools and infrastructures available, re-focusing a company to customer centricity is not difficult or expensive, but requires a consistent buy-in and commitment from staff, management and leadership towards “customer first” approach.

Few methods, which are low cost and easier to implement on complex service delivery framework like airlines to achieve sustained growth over customer satisfaction:
1.      Build a culture that cares about customers: No, it is not that expensive to deploy, and yes it works, and has been working for many profitable businesses. If company adopts a culture that embraces customer satisfaction, every effort is made to cross miles to improve satisfaction. As a starter, leadership buy-in is obtained and that messaging is flown to management and down the value chain.
2.      Let customer be your innovation hub: How difficult is it to invent products/services that sells? Not much, if customers are playing pivotal role in suggesting it. It is important that every effort is made to gather as much insight from customer and use it to analyze week areas and opportunities to introduce product to fill gap and holes.
3.      Put feedback mechanisms like surveys in every corner of service delivery: What will enable a consistent beacon that communicates loopholes, and strong areas? A culture to listen to customer at every customer experience touch-points. The more channels we produce around customers, the better communication is built, that results in more transparency, better loyalty and improved brand value. Surveys play a great role in that. Collectively employee, transactional and relational surveys are used in harmony to provide overall understanding of health of the company. So, they must be taken advantage of to improve the operation and keep a tap on vulnerabilities.
4.      Create a tribe of brand enthusiasts and loyalists and work with them: One great way to create a positive service culture is by allowing tribes to form and flourish in organization. Here tribe signifies a group of people with common interests and passion. Every effort should be made to bring closer all loyalists, brand lovers and critics to mingle together to utilize their passion towards building a better company and every effort is made by corporations to harness that network and make it an integral part of corporate DNA. It is commonly seen among all successful brands how they work tirelessly to build communities and facilitate the discussion and learn as much as possible from it.
5.      Make Customer Satisfaction as one of primary key performance metric for growth: One starter for creating a customer centric company is to track customer satisfaction as key performance indicator to help better track organizational growth. This automatically steers all operations towards the direction to facilitate customer satisfaction.

Now for some eye delight, here are 2 video plugs: Let us take a look at two examples of contrasting customer experiences. Good experiences are delivered seamlessly if customer experience is in core DNA of the company.

Customer Experience done right:

Customer Experience gone wrong:

Source: Customer centric fix to save Indian Maharaja (Air India) from financial mess

5 New Logi Analytics Features to Enhance Self-Service and Developer Productivity

It’s a new year, which makes it the perfect time to highlight the newest innovations in the Logi Analytics platform. The latest Logi version 12.6 is now available—and it’s full of new features for self-service functionality and process task capabilities. Logi 12.6 also delivers significant improvements for developer productivity.

Here are five new Logi Analytics features that are making a big impact with our customers:

1. Expanded Conditional Task Processing
New elements give users a wider range of conditional processing constructs in your Process task definitions. New Procedure elements for Else, Switch, Switch Case, and Switch Else give you complete flexibility in directing processing flow.

2. Run Shell Command
The new Procedure.Run Shell Command element lets users run OS shell commands or applications from a task, and handle the output. It’s supported by new tokens for output, exit code, and error messages, which allow you to handle the results. Now Logi applications can execute batch command files, external applications, and file management commands right from users’ Logi tasks.

3. New Self-Service Analysis Functionality
The Logi Analysis Grid now includes aggregate-aware calculations that allow users to select the order of mathematical operations when aggregating already aggregated and grouped data. Other enhancements include a new percent-of-total format, conditional table cell colors, and numerous usability improvements that reduce the number of clicks to achieve results.

4. Faster Development Features
The HTML Tag element now supports Conditional Class and Event Handler child elements. The conditional class element allows users to define conditional formatting on their custom HTML elements. And the event handler element lets users define events on their custom HTML elements.

5. Debugging and Usability Enhancements
Logi Studio has numerous improvements for developers, including debugging and usability. For example, users can now selectively assign access to debugging using a Security Right ID that keeps developersin debug mode even when the global debug setting is set to no debugger link. In addition, the Element Toolbox now includes a search input so that you can find the right element quickly.

These and other enhancements are available in the latest release of Logi. For more information see the Logi 12.6 Release Notes.

Ready to upgrade to the latest Logi Analytics Platform? Current customers can contact Logi Customer Support and new customers can reach out to Logi’s sales team for more information.

 

Source: 5 New Logi Analytics Features to Enhance Self-Service and Developer Productivity by analyticsweek

Feb 06, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Data interpretation  Source

[ AnalyticsWeek BYTES]

>> Amazon Redshift COPY command cheatsheet by john-hammink

>> It’s Time to Tap into the Cloud Data Protection Market Opportunity by analyticsweekpick

>> Four Things You Need to Know about Your Customer Metrics by bobehayes

Wanna write? Click Here

[ FEATURED COURSE]

Data Mining

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Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations… more

[ FEATURED READ]

Introduction to Graph Theory (Dover Books on Mathematics)

image

A stimulating excursion into pure mathematics aimed at “the mathematically traumatized,” but great fun for mathematical hobbyists and serious mathematicians as well. Requiring only high school algebra as mathematical bac… more

[ TIPS & TRICKS OF THE WEEK]

Strong business case could save your project
Like anything in corporate culture, the project is oftentimes about the business, not the technology. With data analysis, the same type of thinking goes. It’s not always about the technicality but about the business implications. Data science project success criteria should include project management success criteria as well. This will ensure smooth adoption, easy buy-ins, room for wins and co-operating stakeholders. So, a good data scientist should also possess some qualities of a good project manager.

[ DATA SCIENCE Q&A]

Q:Is it beneficial to perform dimensionality reduction before fitting an SVM? Why or why not?
A: * When the number of features is large comparing to the number of observations (e.g. document-term matrix)
* SVM will perform better in this reduced space

Source

[ VIDEO OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with @Beena_Ammanath, @GE

 #BigData @AnalyticsWeek #FutureOfData #Podcast with @Beena_Ammanath, @GE

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

We chose it because we deal with huge amounts of data. Besides, it sounds really cool. – Larry Page

[ PODCAST OF THE WEEK]

Venu Vasudevan @VenuV62 (@ProcterGamble) on creating a rockstar data science team #FutureOfData #Podcast

 Venu Vasudevan @VenuV62 (@ProcterGamble) on creating a rockstar data science team #FutureOfData #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

39 percent of marketers say that their data is collected ‘too infrequently or not real-time enough.’

Sourced from: Analytics.CLUB #WEB Newsletter

The Last Mile of Intuitive Decision Making

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A data driven leader, a data scientist, and a data driven expert is always put to test to help their teams by using their skills and expertise. Believe it or not, but a large part of that decision tree is derived from intuition that adds a bias in our judgement and makes it flawed. Most skilled professionals do understand and handle these biases but in some cases, we give into tiny traps and could find ourselves trapped in those biases which impair our judgement. Here are some examples of the biases and a good leader must understand and keep these under check:

 

  1. Analysis Paralysis Bias:

Every data science professional who has spent a good amount of time with data understands the problem with over analysis as well as under analysis. Many times “under analysis” leaves things undiscovered and leaves the results susceptible to failures. Imagine the “Target” debacle when a pregnant teen’s dad ended up receiving deals on maternity items. Such problems often occur when the analytical models are not completely thought through. Similarly, consider a case when one has spent way too much time on a task that requires little attention. Such shifting requirement for attention between little or more analysis is often not very clear upfront. So, as a leader one should build the right mechanism for helping data analytics team understand the shifting bias towards spending appropriate time analyzing and not falling into the trap of under or over analysis.

 

  1. Framed Bias:

When a leader is fed information, there are often the figments of framed bias. Algorithms and models are susceptible to take the bias of the designers. The more complicated a model is, the more it can learn and get influenced from its designers. Such a model or methodology is often tainted with the framed bias. Take a simple example of guessing a team size when options are 0-100, 100-500, 501-1000 viz a viz 0-2, 3-7, 8-12 etc. Both options (the span and volume of the values) when presented to a subject, influences the outcome and induces the framed bias. As a leader, it is important to understand that the data analytics teams are not working under any framed bias.

 

  1. Sunk Cost Bias:

Along the lines of analysis paralysis, consider a case when a team is working on a high profile task and has spent a lot of resources (effort, time and money). Many times invested resources often induce a bias of sunk cost. There is always a temptation to invest more time due to the fear of letting the past resources go to waste. This is one of the toughest bias to beat. This is clearly seen when you see a team trying various ways to tame the outcome when you know that the outcome is dependent on random variables beyond the control of the team. The only less painful way to deal with such bias is to understand the odds, let probability be the judge and have stage gates in your team’s analysis.

 

  1. Anchored Bias:

This is another interesting pitfall that taints the judgement and the reason could be our own anchors or supplied by our teams. Anchors are often the biases that we strongly believe in. They are the most visibly picked assumptions in the data or analysis that stick with us and find their way into influencing subsequent judgement. One of the easiest example of such a bias is any socio-political analysis. Such analysis is often anchored with our pre-conceived bias/ information that closely satisfies our understanding. And subsequently, we try to influence the outcome. In many ways there is a thin line of differentiation between Anchored and framed bias. Besides the point that framed bias is influencing our judgement based on how something is framed viz-a-viz anchored bias leverages our pre-conceived notions to influence decision making. The easiest way to move around such bias is by keeping an open perspective and always staying within the bounds of data or analysis.

 

  1. Experience Bias:

This is one of the most painfully ignored bias that any leaders have. We often think that we are the best judge of our actions and we have the most adequate knowledge of what we do. Being an expert does come with an advantage that helps handle a task with great speed and comfort. However, experience bias tricks an individual in believing that an experienced judgement is often the right judgement. A typical example that I have come across is when a team is using obsolete models and techniques without realizing the problem that there is something better out there. Such a bias limits our capabilities and restricts decisions to our limited understanding about the subject. A leader must be swift in understanding such a bias and work around it. One of the easiest way to work around such bias is by asking questions. Many times such biases fade away when one questions their own knowledge and discovers cracks and pitfalls in their decisions. This is a critical bias to fix for success of data analytics teams.

  1. Effort & Reward Fallacy Bias:

We as human are designed to work smartly and we get our dopamines with every success that needs minimal effort. This definition of success has been engraved in our genepool and taints our judgement. When one sees a reward early in the process they often stop thinking beyond and get fixated on the outcome. This problem was briefly mentioned in the book: innovator’s dilemma. Normally we are designed to treat bigger reward with less effort as success. This is one of the most difficult bias to overcome / fix. When we meet a major breakthrough, we stop looking around the corners for something better or more effective. Such a bias could be easily treated by providing clear directions/goals. A clear roadmap to success often overpowers our quick wins and help the team maintain a vigilant eye on the bigger goal.

  1. Affinity towards new Bias:

Another bias that is engraved in people is our openness and friendliness towards the new. New TV, new house, new gadget, new way to think etc. Such a bias also influences data analytics teams. The most prominent occurrence of such a bias is when a new solution/model is proposed for a currently existing model and we give it a green light. Many times such decisions are tainted with the fact that we love to try new things and many times forego analyzing the hidden and undiscovered drawbacks. As a leader it is most important to be free from such a bias. One should have a clear scaled measure for substitution. It is important to complete SWAT analysis and understand the difference between the new viz-a-viz old ways. The team should be clearly instructed and trained to identify such pitfalls as many times this bias makes its way through untrained or relatively newer analysts and often gets unnoticed. Keeping the team from such a bias helps the team in siding with the best models for the job and helps improve the quality of data and analytics.

  1. First Thought Bias:

In many strange experiments humans are always seen making judgements early in the process when adequate information is not known to help derive a hypothesis. We are constantly seen using the judgement to help form the basis for future outcomes. A typical example is seen when you hear a new process and a quick thought appears in your mind that you wish to share, and once shared with public, you spend the rest of the time to prove your initial reaction. Many opinionated leaders often suffer from such a bias and find it difficult to eradicate from their decision making process. One of the quick and easy ways to keep this bias in check is to always have an open perspective, never let any bias get formed early in the process, and use the flow of the conversation to understand the situation before coming to any conclusion. Many good leaders do it in a great way and handle it well by keeping the decision process at the end of the conversation. With practice this is one of the easiest and most beneficial bias to get fixed.

So, it is important for a leader to keep their analysis and their team’s practices bias free so that the company could enjoy the benefits of bias free data driven outcomes.

I am certain, there are many other biases that are not covered here and I would love to hear from the readers about those. Please feel free to connect and email your suggestions and opinions.

 

Contributed in The Big Analytics: Leader’s Collaborative Book Project Download your FREE copy at TheBigAnalytics

Source: The Last Mile of Intuitive Decision Making