BOB in The Netherlands

I recently gave the keynote address at the Custon Customer Congress in The Netherlands. The presentation highlighted how B2B companies can improve their customer loyalty. A copy of the presentation can be found here. For the Dutch reader, you can find a summary of my presentation online at ITCommercie (note regarding my picture for the article: I was totally sober for the presentation).

Source

Dec 26, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

[  COVER OF THE WEEK ]

image
Data interpretation  Source

[ AnalyticsWeek BYTES]

>> @ReshanRichards on creating a learning startup for preparing for #jobsoffuture #podcast by admin

>> PHP Exceeds the Generic Human Expectations. Here’s how the Brand got it Done by thomassujain

>> Guide to business intelligence and health IT analytics by analyticsweekpick

Wanna write? Click Here

[ FEATURED COURSE]

Deep Learning Prerequisites: The Numpy Stack in Python

image

The Numpy, Scipy, Pandas, and Matplotlib stack: prep for deep learning, machine learning, and artificial intelligence… more

[ FEATURED READ]

The Signal and the Noise: Why So Many Predictions Fail–but Some Don’t

image

People love statistics. Statistics, however, do not always love them back. The Signal and the Noise, Nate Silver’s brilliant and elegant tour of the modern science-slash-art of forecasting, shows what happens when Big Da… 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:What is star schema? Lookup tables?
A: The star schema is a traditional database schema with a central (fact) table (the “observations”, with database “keys” for joining with satellite tables, and with several fields encoded as ID’s). Satellite tables map ID’s to physical name or description and can be “joined” to the central fact table using the ID fields; these tables are known as lookup tables, and are particularly useful in real-time applications, as they save a lot of memory. Sometimes star schemas involve multiple layers of summarization (summary tables, from granular to less granular) to retrieve information faster.

Lookup tables:
– Array that replace runtime computations with a simpler array indexing operation

Source

[ VIDEO OF THE WEEK]

Want to fix #DataScience ? fix #governance by @StephenGatchell @Dell #FutureOfData #Podcast

 Want to fix #DataScience ? fix #governance by @StephenGatchell @Dell #FutureOfData #Podcast

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Information is the oil of the 21st century, and analytics is the combustion engine. – Peter Sondergaard

[ PODCAST OF THE WEEK]

@DrewConway on creating socially responsible data science practice #FutureOfData #Podcast

 @DrewConway on creating socially responsible data science practice #FutureOfData #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

73% of organizations have already invested or plan to invest in big data by 2016

Sourced from: Analytics.CLUB #WEB Newsletter

Dec 19, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

[  COVER OF THE WEEK ]

image
Conditional Risk  Source

[ AnalyticsWeek BYTES]

>> June 5, 2017 Health and Biotech analytics news roundup by pstein

>> Make Every Sprint Count with DevOps Analytics by analyticsweek

>> [Step-by-step] Using Talend for cloud-to-cloud deployments and faster analytics in Snowflake by analyticsweekpick

Wanna write? Click Here

[ FEATURED COURSE]

A Course in Machine Learning

image

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]

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

image

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]

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:You have data on the durations of calls to a call center. Generate a plan for how you would code and analyze these data. Explain a plausible scenario for what the distribution of these durations might look like. How could you test, even graphically, whether your expectations are borne out?
A: 1. Exploratory data analysis
* Histogram of durations
* histogram of durations per service type, per day of week, per hours of day (durations can be systematically longer from 10am to 1pm for instance), per employee…
2. Distribution: lognormal?

3. Test graphically with QQ plot: sample quantiles of log(durations)log?(durations) Vs normal quantiles

Source

[ VIDEO OF THE WEEK]

Agile Data Warehouse Design for Big Data Presentation

 Agile Data Warehouse Design for Big Data Presentation

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

War is 90% information. – Napoleon Bonaparte

[ PODCAST OF THE WEEK]

@chrisbishop on futurist's lens on #JobsOfFuture #FutureofWork #JobsOfFuture #Podcast

 @chrisbishop on futurist’s lens on #JobsOfFuture #FutureofWork #JobsOfFuture #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

Data is growing faster than ever before and by the year 2020, about 1.7 megabytes of new information will be created every second for every human being on the planet.

Sourced from: Analytics.CLUB #WEB Newsletter

Why On-Premises App Analytics Is the Way to Go

The app market is thriving, and that is opening up more opportunities for app development. However, despite a significant uptick in app downloads, this doesn’t mean users actually interact with your product. According to Statista, about 24% of apps downloaded are used only once. The problem lies in user engagement. It turns out that creating a great app is just part of the game.

That’s why marketers turn to app analytics. It lets them measure app usage and optimize the user experience to boost customer acquisition and retention. And if you want to acquire an accurate picture of your app engagement while mapping customers’ journeys across web and mobile, you need the right data.

Considering the growing concern about data protection and privacy restrictions in today’s digital ecosystem, this means you’re heading down a bumpy road. The good news is that you can buckle up and drive smoothly if you choose a reliable and comprehensible solution. That solution is self-hosted app analytics.

If you’re wondering how it does the trick, then we have a simple answer: security and control. You maintain ownership of data and know exactly where it’s stored and who can access it. But that’s just the beginning.

In this post we will walk you through all the twists and turns of on-premises app analytics to demonstrate how it can benefit your organization. Of course, we understand that your organization is like no other and has specific requirements, but some functionalities are bread and butter for every company. So, off we go!

1. Data is in your hands

Maintaining high safety and privacy standards is no easy task. Your organization most probably operates within strict protocols and policies that determine who can access certain data and information you collect. By choosing self-hosting deployment, you make your job easier as you can store, process, and archive data on your own servers or other ones of your choice. This way, all processing takes place under your watchful eye.

What’s more, developing software in your stack and introducing new technologies to an organization involves the burden of more security issues. This is because the responsibility of handling data is shared with someone else. However, with app analytics hosted on your own infrastructure there’s far less need to worry. Of course you should be careful, but after all, you keep 100% control and ownership of your data. It’s entirely up to you how the data is managed. So you can be sure no data is shared with third parties, and you can establish robust security standards and policies.

Piwik PRO vs. Google Analytics

Compare Google Analytics and Piwik PRO and Find the Analytics Tool to Fit Your Business’s Needs

Download FREE Comparison

2. Stand guard over your data

If you’re a part of a digital ecosystem, you realize that data security is a valuable commodity. It should be a cardinal principle of your company. The problem of data safety is especially acute with organizations operating in the banking, finance, health, and governmental sectors. Not only do they handle a wealth of personal data, but also sensitive data – and that comes at a higher risk.

Bear in mind that any data breach or leak could have a tremendous impact on the whole organization. It’s not only a matter of your company’s reputation, but also development of your software and services.

70% of financial institutions regard “security concerns” as one of the biggest impediments to mobile banking adoption, as revealed in a survey taken in five of the Federal Reserve Bank districts in the United States.

Safety issues concerning data haunt many fields of business. In this area, prevention seems to be the best cure. Though data security can be approached in various ways, it’s vital to ensure that access to your app analytics data is highly restricted.

If your choose a reliable vendor for your app analytics software, you can rest assured that your data is thoroughly protected. Wondering how can you accomplish that? Have a look at the Piwik PRO approach.

First of all, as we’ve already said, you can store all the data on your own servers. So you know where it’s located and who has access to it. What’s more, as you store data on-premises you can properly adjust your app analytics setup to internal data security policies and procedures.

To be more precise, you gain full access control. It means that you’re in charge of your infrastructure configuration and can adjust it to comply with your internal security rules. For instance, you decide whether you will be able to change the settings of certain sites or mobile apps analytics.

Then, you can apply Single Sign On. With this functionality you can manage all your users in a central database, and employ SSO (Single Sign On) for logging into your app analytics module using LDAP (Lightweight Directory Access Protocol), SAML (Security Assertion Markup Language), or other enterprise standards.

Finally, you can take advantage of Audit Logs. These provide you with detailed logs of all activities happening on your platform. They let you closely monitor and review:

  • login attempts
  • modification of instance settings
  • password updates
  • any reporting API requests

Keep in mind that the safety of your customers’ data lies not only in protection and setting access rights to it. You also need to consider what kind of data you aggregate and how you manage it. Once you know all the requirements, you can apply further security measures like encryption, pseudonymization, and anonymization.

These methods are your allies in the struggle to achieve legal compliance. However, you don’t have to do the work by yourself. Some vendors, like Piwik PRO, provide automatic app analytics data anonymization and support implementation of all your security procedures.

All in all, with a holistic approach and an array of functionalities you can sleep sound knowing that you’re achieving top-level security.

3. Make regulators and privacy-concerned clients happy

For organizations operating within a digital ecosystem, data privacy legislation and security are of the utmost importance. But at the same time, they create serious challenges. Privacy laws vary across countries, and beside local ones there are international rules, like GDPR.

That’s why you need to consider solutions that take the burden off your shoulders and help you follow the letter of the law. On-premises app analytics is a reliable partner in your compliance drive. Multinational enterprises go for this deployment because of the privacy regulations in force in countries they operate in.

For instance, China and Russia only allow for storage of their citizens’ personally identifiable information within their respective borders. The same rule applies to the European Economic Area (including EU Member States plus Iceland, Liechtenstein, and Norway).

It means that if you want to collect and process the data of these countries’ residents, you have to have a data center in each of these countries. In the case of certain European Union countries, it may be enough to have a server located within the EU’s borders.

Finally and importantly, compliance with various privacy laws and regulations requires you to maintain control over data and its storage. That’s why it’s vital for you to find the right app analytics vendor. In the case of Piwik PRO, you have full ownership of data by design and know exactly where it’s located.

4. Wield analytics reports with raw data

Analytics demands precision. As your app analytics orchestrates your marketing strategy, you need to be sure it has all the instruments in line; in other words, you acquire accurate data. That is the key to good reporting. You need data that you can trust, otherwise you simply won’t get any benefit from it.

Experts in various IT fields stress that among the numerous dimensions that impacts data quality, the most prominent ones are accuracy and completeness. You need to be sure that the data you collect represents exactly what happens on your mobile app or website. Also, you should be able to tap into precise information from across your whole digital business so you can better measure, analyze, and grasp it.

When we talk about data accuracy we mean raw, unsampled data. Unfortunately, not all vendors offer this.

However, you can find partners on the market, like Piwik PRO, that offer on-premises app analytics to deliver full data sets to you, not just samples. Access to raw data gives you precise and in-depth reports, a prerequisite for a sound strategy and decision-making process.

Piwik PRO vs. Google Analytics

Compare Google Analytics and Piwik PRO and Find the Analytics Tool to Fit Your Business’s Needs

Download FREE Comparison

To sum up: on-premises app analytics should be a key tool in your marketing arsenal. But maybe you want to know more about how to optimize the customer journey across web and mobile – because of the complexity of the issue, we’ve only provided an introduction here. So if you have any questions, don’t hesitate to drop us a line. We’ll be more than happy to fill you in on all the details.

Contact us

The post Why On-Premises App Analytics Is the Way to Go appeared first on Piwik PRO.

Source: Why On-Premises App Analytics Is the Way to Go by analyticsweek

How Predictive Analytics Is Fueling Subscription-Based Businesses

These days, you can’t go 10 minutes without hearing about a subscription service. We have video streaming services (Netflix, Hulu), meal delivery services (Blue Apron, Hello Fresh), movie passes (MoviePass), and subscription “boxes” for everything from beauty items (Birchbox) to pet supplies (BarkBox). Subscription-based business models are everywhere—and they’re tapping into an increasing stream of data. In today’s digital world, information on people’s buying habits is readily available. And thanks to predictive analytics, that data can be used to grow a business.

>> Related: Predictive Analytics 101 <<

Today’s most successful subscription network businesses are those that keep user acquisition costs low and quickly scale in order to negotiate premiums with retail establishments. As an example, let’s look at MoviePass, a $9.95-a-month subscription service that lets users watch up to three movies per month in a variety of theaters.

Leveraging Predictive Data

MoviePass collects demographics on its subscribers during signup, and then collects even more information when a subscriber uses the app to buy a ticket (time, location, theater, movie, etc.). Once they’ve scaled to a sizable number of subscribers, MoviePass can employ predictive analytics to answer questions such as:

  • What kind of movie does this person typically see?
  • Will this person see this movie this Friday at 7:00 pm?
  • How many people will see this movie this Friday at 7:00 pm at this specific location?

Armed with this predictive information, MoviePass has the power to navigate users to a specific theater—and therefore can more easily negotiate lucrative deals. For example, if there are a three theaters within a five-mile radius, the theater that gives the best deal to MoviePass could be the lucky one at the top of MoviePass subscribers’ lists to see a particular movie at 7:00 pm.

Expanding Offerings

Why stop at movies? Many moviegoers may also go to a local restaurant for dinner. MoviePass could again use the power of numbers in their ecosystem to negotiate deals with retail businesses and rapidly expand its offerings. Predictive data can answer questions such as:

  • Which subscribers go to a movie and have dinner at a nearby restaurant?
  • Which subscribers travel to a movie using Uber?

MoviePass then essentially becomes “RetailPass,” where any related retail service—dinner, transportation, etc.—can be packaged as part of the subscription for an additional one-time or monthly cost. In kind, these transactions would provide even more datathat could be used to tweak the offers that make the most sense for consumers. 

We might see more subscription businesses cropping up soon—maybe an UberPass, DinnerPass, LunchPass, and so on.Every service industry is a target for the subscription model as companies use the power of predictive analytics to drive user adoption and future business.

See how Logi can help with your predictive analytics needs. Sign up for a free demo of Logi Predict today >

 

Originally Posted at: How Predictive Analytics Is Fueling Subscription-Based Businesses by analyticsweek

Dec 12, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

[  COVER OF THE WEEK ]

image
Complex data  Source

[ AnalyticsWeek BYTES]

>> Clarifying Employee Engagement: A Review of Four Employee Engagement Measures by bobehayes

>> Do Attitudes Predict Behavior? by analyticsweek

>> Using Analytics To Counter Early Claim Fraud by analyticsweek

Wanna write? Click Here

[ FEATURED COURSE]

Python for Beginners with Examples

image

A practical Python course for beginners with examples and exercises…. more

[ FEATURED READ]

Antifragile: Things That Gain from Disorder

image

Antifragile is a standalone book in Nassim Nicholas Taleb’s landmark Incerto series, an investigation of opacity, luck, uncertainty, probability, human error, risk, and decision-making in a world we don’t understand. The… 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:What do you think about the idea of injecting noise in your data set to test the sensitivity of your models?
A: * Effect would be similar to regularization: avoid overfitting
* Used to increase robustness

Source

[ VIDEO OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with @DavidRose, @DittoLabs

 #BigData @AnalyticsWeek #FutureOfData #Podcast with @DavidRose, @DittoLabs

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

The data fabric is the next middleware. – Todd Papaioannou

[ PODCAST OF THE WEEK]

Nick Howe (@Area9Nick @Area9Learning) talks about fabric of learning organization to bring #JobsOfFuture #Podcast

 Nick Howe (@Area9Nick @Area9Learning) talks about fabric of learning organization to bring #JobsOfFuture #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

Facebook stores, accesses, and analyzes 30+ Petabytes of user generated data.

Sourced from: Analytics.CLUB #WEB Newsletter

True Test of Loyalty – Article in Quality Progress

Read the study by Bob E. Hayes, Ph.D. in the June 2008 edition of Quality Progress magazine titled The True Test of Loyalty. This Quality Progress article discusses the measurement of customer loyalty. Despite its importance in increasing profitability, customer loyalty measurement hasn’t kept pace with its technology. Using advocacy, purchasing and retention indexes to manage loyalty is statistically superior to using any single question alone. These indexes helped predict the growth potential of wireless service providers and PC manufacturers. You can download the article here.

Originally Posted at: True Test of Loyalty – Article in Quality Progress

Dec 05, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

[  COVER OF THE WEEK ]

image
Data Storage  Source

[ AnalyticsWeek BYTES]

>> Making the Bet on Open Source by analyticsweekpick

>> Tutorial: Azure Data Lake analytics with R by analyticsweek

>> Accelerating Discovery with a Unified Analytics Platform for Genomics by analyticsweek

Wanna write? Click Here

[ FEATURED COURSE]

CS109 Data Science

image

Learning from data in order to gain useful predictions and insights. This course introduces methods for five key facets of an investigation: data wrangling, cleaning, and sampling to get a suitable data set; data managem… more

[ FEATURED READ]

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

image

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]

Winter is coming, warm your Analytics Club
Yes and yes! As we are heading into winter what better way but to talk about our increasing dependence on data analytics to help with our decision making. Data and analytics driven decision making is rapidly sneaking its way into our core corporate DNA and we are not churning practice ground to test those models fast enough. Such snugly looking models have hidden nails which could induce unchartered pain if go unchecked. This is the right time to start thinking about putting Analytics Club[Data Analytics CoE] in your work place to help Lab out the best practices and provide test environment for those models.

[ DATA SCIENCE Q&A]

Q:Explain what a false positive and a false negative are. Why is it important these from each other? Provide examples when false positives are more important than false negatives, false negatives are more important than false positives and when these two types of errors are equally important
A: * False positive
Improperly reporting the presence of a condition when it’s not in reality. Example: HIV positive test when the patient is actually HIV negative

* False negative
Improperly reporting the absence of a condition when in reality it’s the case. Example: not detecting a disease when the patient has this disease.

When false positives are more important than false negatives:
– In a non-contagious disease, where treatment delay doesn’t have any long-term consequences but the treatment itself is grueling
– HIV test: psychological impact

When false negatives are more important than false positives:
– If early treatment is important for good outcomes
– In quality control: a defective item passes through the cracks!
– Software testing: a test to catch a virus has failed

Source

[ VIDEO OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with Eloy Sasot, News Corp

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Eloy Sasot, News Corp

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

What we have is a data glut. – Vernon Vinge

[ PODCAST OF THE WEEK]

George (@RedPointCTO / @RedPointGlobal) on becoming an unbiased #Technologist in #DataDriven World #FutureOfData #Podcast

 George (@RedPointCTO / @RedPointGlobal) on becoming an unbiased #Technologist in #DataDriven World #FutureOfData #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

By 2020, we will have over 6.1 billion smartphone users globally (overtaking basic fixed phone subscriptions).

Sourced from: Analytics.CLUB #WEB Newsletter

QlikSense Set Analysis – Creating and Using Variables

Patrick McCaffrey
Practice Director, Business Analytics
John Daniel Associates, Inc. 
Patrick’s Profile

The majority of Qlik application developers will tell you that a large portion of their time is spent doing rework for customers who consistently change the scope of work. This can be very frustrating, especially after hours of work have been spent placing business logic in numerous complex expressions and the customer comes to you and asks you for something as simple as the addition of a “Product Type.” To the customer this may not seem like a lot of work, but developers, even at a beginner level, know that the addition of a Product Type may require hours of additional work depending on how many expressions need updated to reflect this change. The truth of the matter is that the customer is correct – this should not be a lot of work! Throughout the remainder of this blog I’d like to give you an experienced developer tip that can save you tremendous amounts of time and headaches while creating or updating your set analysis expressions.

An experienced developer should plan ahead and predict what variables may frequently change throughout the development process based on their previous experiences during customer engagements. They use these past experiences to take a proactive approach when developing their set analysis expressions, which they do by implementing variables. The use of variables in set analysis expressions diminishes the need to constantly go back and modify each and every expression every time a change is requested.

Imagine that your customer, a sporting goods store named ‘XYZ Sports’, has just told you that they want an application that shows the sales of their current outdoor sports inventory, but they only want to see the sales that relate to basketball, football, soccer, and baseball. The image below shows a general set analysis expression you would use in this situation. In the image you can see that we are summing sales of ‘Basketball’, ‘Football’, ‘Soccer’, and ‘Baseball’ for the month of ‘July’ and the year ‘2016’.

XYZ Sports then tells you that they want to see the sales of these same Product Types, but they would like to see the sales over the entire year of 2015, the entire year of 2016, and each quarter of 2016, all of which need to be in a different chart. You would now have a total of 7 expressions that include the Product Types field in your application. A few examples of these expressions are below.

A week after you have the application built and all expressions working for these Product Types, the Project Manager at XYZ Sports comes to you and explains that they are now considering ‘Hiking’, ‘Fishing’, and ‘Camping’ as outdoor sports and this inventory will also need to be counted towards the sales in the charts you have just created. The first thought from many novice developers is to go back into each expression and make the requested changes to the ‘Product Type’ field as shown below.

Updating all 7 charts with the new “Product Type” fields as shown above is a solution, but it is not the most effective solution. However, the use of variables will allow for this change to be made in one central location, which would apply the changes to all expressions containing that variable. In order to do this you would need to follow the steps below:

Step 1:

Open the Variable Overview and create a Product Type variable.

  • In QlikView press Ctrl+Alt+V
  • In Qlik Sense Hit Edit then click  at the bottom left of the screenIn the variable, place the entire string that is expected between your two Product Type curly brackets, or braces.

Step 2:

In the variable, place the entire string that is expected between your two Product Type curly brackets, or braces.

Product Type = {  }

Do not include an ‘=’ in front of the variable, as it will cause issues with the format in the next step (Step 3). Depending on your variable format, it may look as if you have spelling errors due to the red underscores, but you can ignore this warning.

Step 3: 

Place the newly created variable in your set analysis expressions in the format shown below. This variable will input the entire string you created in step 2 above.

Now each time you need to change, add, or remove a ‘Product Type’ you simply have to go to the Variable Overview screen and make the modification in that one central location. Once the change is made in the Variable Overview screen all expressions containing that variable will be updated. You no longer have to go into each expression and make the changes one at a time! This same approach can be taken for ‘Sales Month’, ‘Sales Year’, or any other field that you feel will be consistently changing throughout the front end development process.

  • It’s important to note that variable usage is much more practical when you have numerous expressions using similar fields and values throughout your application. Because of this you should evaluate your set analysis approach on a case-by-case basis.
  • Sometimes the need for a variable is evident from the onset of the application build which makes it easier to plan ahead and use variable implementation from the start. Other times you may not see the need for a variable until you are well into the development process. Identifying these scenarios will come with practice and experience.

I can already hear some of you say, “But, Master Measures in Qlik Sense….” Yes, Qlik Sense has helped to alleviate some of this pressure when utilizing the same expression over multiple charts with the introduction of Master Measures, but many Master Measures may still have some Set Analysis in common. The use of variables in those cases will accomplish the same thing. If that Set Analysis ever changes, you can simply update a variable for all, rather than adjust each one individually.

Front end application development can be tricky, cumbersome, and time consuming, and while variable usage can be a tremendous help in remedying these issues you will learn that there are many other tricks and tips that will help you become a more advanced and efficient developer. Developers should consistently be looking for ways to sharpen their skills and make their own work more efficient. No matter what level developer you are, I suggest that you do the same. Taking past and current experiences, learning from them, and building upon your current skill set is a great way to increase your efficiency in future endeavors. Knowing and understanding how customers operate can keep you a step ahead of the game while saving invaluable amounts of time, as evidenced above by the use of a simple variable. Process improvement skills, such as effective variable use, are highly valuable in the business intelligence field and will be acquired over time as you gain more experience.

 

 

Patrick is our Business Analytics Practice Director, a valued member of our John Daniel Associates Leadership Team in Pittsburgh, PA.

Save

The post QlikSense Set Analysis – Creating and Using Variables appeared first on John Daniel Associates, Inc..

Source: QlikSense Set Analysis – Creating and Using Variables by analyticsweek

Setting Metric Targets in UX Benchmark Studies

benchmark-target

benchmark-targetIn Benchmarking the User Experience, I write about the importance of a regular plan for quantifying the user experience of your websites, apps, or devices.

This involves collecting metrics, usually at both task and study levels.

But the point of benchmarking isn’t just to collect metrics to put on a dashboard, it’s to ultimately improve them.

A common question we receive when conducting benchmark studies is what to set the metric targets to. That is, what values should organizations aim for in their next benchmark? While it depends a bit on the context and consequences of the experience (and the metric itself), here are the target options I discuss that provide reasonable goals.

Above Average

A logical target to start with is having all metrics be at least above average. Let’s call this the Lake Wobegon target. We’ve collected data for the most common UX metrics to provide context and define what “average” is, at least for a broad set of contexts and products. Here are five common averages to use:

  • Completion rate average: 78%
  • Single Ease Question average: ~5.1
  • SUS average: 68
  • SUPR-Q average: 50%
  • Net Promoter Score for consumer software: 21%

Above Industry Average

To get more specific with using an average, narrow in on a relevant industry. Industry averages can be found from external reports or your own benchmarking analysis. We report many industry averages for websites and software. Here are a few averages by industry for SUPR-Q and SUS scores:

  • Hotel average SUPR-Q: 76%
  • Airline & aggregator website SUPR-Q: 83%
  • Retail websites SUPR-Q: 78%
  • Consumer software SUS: 75
  • Business software SUS: 66

Above a Competitor

If your website or product has a clear competitor, you should strive to at least meet or exceed it on key metrics (like the metrics listed above). Competitor data can be collected from conducting your own competitive benchmark or from published reports or databases. For example, Dropbox has a SUS score of 78 in our consumer software report and Netflix has a SUPR-Q score at the 95th percentile. A competitor, more generally speaking, can also be an old or existing product and any new version should at least meet or exceed the benchmark metrics of the legacy experience.

Above a Percentile Rank

For some measures that have enough data points, raw scores can be converted into percentiles ranks. Percentiles tell you where a score falls relative to all the scores in a database (often after a transformation to make the data normally distributed). This is a key characteristic of the SUPR-Q (hence the percentile rank in its name). A SUPR-Q percentile rank of 75% means the score is higher than 75% of the websites in the database.

For high-traffic retail websites, setting a target of above 90% makes sense as a target because it’s both above average for the retail website industry and a superior UX experience here has a closer association to revenue (and ROI)—meaning you want to aim high. Even if you aren’t a retail website, it makes sense to be at least above the 50th percentile (average) as a target for any metric.

Above a Grade

Percentile ranks can themselves be translated into letter grades (like the ones you got in school), which may help in interpretation and set additional target thresholds. Jim Lewis and I did this with the System Usability Scale as shown in Table 1. To achieve an “A” grade, you need to obtain a SUS score in the 90th percentile or above, which translates to at least a raw SUS score of 81. To have at least a “passing grade” you should target at minimum a SUS score of 52. But like a “D” in school, it’s hardly anything to be proud of!

Grade SUS Percentile Range
A+ 84.1-100 96-100
A 80.8-84.0 90-95
A- 78.9-80.7 85-89
B+ 77.2-78.8 80-84
B 74.1-77.1 70-79
B- 72.6-74.0 65-69
C+ 71.1-72.5 60-64
C 65.0-71.0 41-59
C- 62.7-64.9 35-40
D 51.7-62.6 15-34
F 0-51.6 0-14

Table 1: SUS scores, grades, and percentile ranks.

Use Norms, Context, and Competitors Together to Set Targets

Averages, competitors, percentiles, and grades provide comparison points to set targets, but the target your organization settles on usually involves a combination of them all and often starts with the context of the interface. For example, certain business-to-business software applications— like accounting software—are inherently more complicated than, say, a consumer website. We see this difference between our consumer and business software benchmarks too. For example, the average SUS score is 66 for B2B software products (a “C” grade) and 75 for B2C software products (a “B” grade). While it would be good to set a target of 90% for a SUS score (an A grade), it might be unachievable for enterprise accounting software compared to a search engine (although the users would appreciate it!).

This importance of product type and context is also supported by research from Kortum and Bangor. They conducted a large retrospective benchmark of everyday products (for example, microwaves, search engines, and Excel spreadsheets) using the SUS. The SUS for Excel in their dataset was 56 (a “D”) while web browsers (for example, Google Chrome) scored 88 (A+). While their Excel SUS was much lower than our Excel SUS (likely because they used a within- rather than between-subjects approach and users had low experiences), it does illustrate the different standards between classes of products. Excel is by many measures a very commercially successful product even though it scores much lower than Google Chrome.

When setting a target for your benchmark, aim high (higher than average, higher than your competitors and previous versions) but not so high as to set unrealistic targets.

Originally Posted at: Setting Metric Targets in UX Benchmark Studies by analyticsweek