Aug 02, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Pacman  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Better than Master Data Management: Building the Ultimate Customer 360 with Artificial Intelligence by jelaniharper

>> Eradicating Silos Forever with Linked Enterprise Data by jelaniharper

>> December 26, 2016 Health and Biotech analytics news roundup by pstein

Wanna write? Click Here

[ NEWS BYTES]

>>
 Collision Course: Foreign Influence Operations, Data Security and Privacy – Lexology Under  Data Security

>>
 euNetworks brings data center interconnect services to Dublin and Hilversum – LightWave Online Under  Data Center

>>
 Syllabus for a course on Data Science Ethics – Boing Boing Under  Data Science

More NEWS ? Click Here

[ FEATURED COURSE]

Pattern Discovery in Data Mining

image

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]

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

image

“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:What is the maximal margin classifier? How this margin can be achieved?
A: * When the data can be perfectly separated using a hyperplane, there actually exists an infinite number of these hyperplanes
* Intuition: a hyperplane can usually be shifted a tiny bit up, or down, or rotated, without coming into contact with any of the observations
* Large margin classifier: choosing the hyperplance that is farthest from the training observations
* This margin can be achieved using support vectors

Source

[ VIDEO OF THE WEEK]

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

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

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

It’s easy to lie with statistics. It’s hard to tell the truth without statistics. – Andrejs Dunkels

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with Joe DeCosmo, @Enova

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Joe DeCosmo, @Enova

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

94% of Hadoop users perform analytics on large volumes of data not possible before; 88% analyze data in greater detail; while 82% can now retain more of their data.

Sourced from: Analytics.CLUB #WEB Newsletter

May 8, 2017 Health and Biotech analytics news roundup

HHS’s Price Affirms Commitment to Health Data Innovation: Secretary Price emphasized the need to decrease the burden on physicians.

Mayo Clinic uses analytics to optimize laboratory testing: The company Viewics makes software for the facility, which uses it to look for patterns and increase efficiency.

Nearly 10,000 Global Problem Solvers Yield Winning Formulas to Improve Detection of Lung Cancer in Third Annual Data Science Bowl: The winners of the competition, which challenged contestants to accurately diagnose lung scans, were announced.

Gene sequencing at Yale finding personalized root of disease; new center opens in West Haven: The Center for Genomic Analysis at Yale opened and is intended to help diagnose patients.

Source

7 Limitations Of Big Data In Marketing Analytics

Big data — the cutting edge of modern marketing or an overhyped buzzword? Columnist Kohki Yamaguchi dives in to some of the limitations of user-centered data.

As everyone knows, “big data” is all the rage in digital marketing nowadays. Marketing organizations across the globe are trying to find ways to collect and analyze user-level or touchpoint-level data in order to uncover insights about how marketing activity affects consumer purchase decisions and drives loyalty.

In fact, the buzz around big data in marketing has risen to the point where one could easily get the illusion that utilizing user-level data is synonymous with modern marketing.

This is far from the truth. Case in point, Gartner’s hype cycle as of last August placed “big data” for digital marketing near the apex of inflated expectations, about to descend into the trough of disillusionment.

It is important for marketers and marketing analysts to understand that user-level data is not the end-all be-all of marketing: as with any type of data, it is suitable for some applications and analyses but unsuitable for others.

Following is a list describing some of the limitations of user-level data and the implications for marketing analytics.

1. User Data Is Fundamentally Biased

The user-level data that marketers have access to is only of individuals who have visited your owned digital properties or viewed your online ads, which is typically not representative of the total target consumer base.

Even within the pool of trackable cookies, the accuracy of the customer journey is dubious: many consumers now operate across devices, and it is impossible to tell for any given touchpoint sequence how fragmented the path actually is. Furthermore, those that operate across multiple devices is likely to be from a different demographic compared to those who only use a single device, and so on.

User-level data is far from being accurate or complete, which means that there is inherent danger in assuming that insights from user-level data applies to your consumer base at large.

2. User-Level Execution Only Exists In Select Channels

Certain marketing channels are well suited for applying user-level data: website personalization, email automation, dynamic creatives, and RTB spring to mind.

In many channels however, it is difficult or impossible to apply user data directly to execution except via segment-level aggregation and whatever other targeting information is provided by the platform or publisher. Social channels, paid search, and even most programmatic display is based on segment-level or attribute-level targeting at best. For offline channels and premium display, user-level data cannot be applied to execution at all.

3. User-Level Results Cannot Be Presented Directly

More accurately, it can be presented via a few visualizations such as a flow diagram, but these tend to be incomprehensible to all but domain experts. This means that user-level data needs to be aggregated up to a daily segment-level or property-level at the very least in order for the results to be consumable at large.

4. User-Level Algorithms Have Difficulty Answering “Why”

Largely speaking, there are only two ways to analyze user-level data: one is to aggregate it into a “smaller” data set in some way and then apply statistical or heuristic analysis; the other is to analyze the data set directly using algorithmic methods.

Both can result in predictions and recommendations (e.g. move spend from campaign A to B), but algorithmic analyses tend to have difficulty answering “why” questions (e.g. why should we move spend) in a manner comprehensible to the average marketer. Certain types of algorithms such as neural networks are black boxes even to the data scientists who designed it. Which leads to the next limitation:

5. User Data Is Not Suited For Producing Learnings

This will probably strike you as counter-intuitive. Big data = big insights = big learnings, right?

Wrong! For example, let’s say you apply big data to personalize your website, increasing overall conversion rates by 20%. While certainly a fantastic result, the only learning you get from the exercise is that you should indeed personalize your website. While this result certainly raises the bar on marketing, but it does nothing to raise the bar formarketers.

Actionable learnings that require user-level data – for instance, applying a look-alike model to discover previously untapped customer segments – are relatively few and far in between, and require tons of effort to uncover. Boring, ol’ small data remains far more efficient at producing practical real-world learnings that you can apply to execution today.

6. User-Level Data Is Subject To More Noise

If you have analyzed regular daily time series data, you know that a single outlier can completely throw off analysis results. The situation is similar with user-level data, but worse.

In analyzing touchpoint data, you will run into situations where, for example, a particular cookie received – for whatever reason – a hundred display impressions in a row from the same website within an hour (happens much more often than you might think). Should this be treated as a hundred impressions or just one, and how will it affect your analysis results?

Even more so than “smaller” data, user-level data tends to be filled with so much noise and potentially misleading artifacts, that it can take forever just to clean up the data set in order to get reasonably accurate results.

7. User Data Is Not Easily Accessible Or Transferable

Because of security concerns, user data cannot be made accessible to just anyone, and requires care in transferring from machine to machine, server to server.

Because of scale concerns, not everyone has the technical know-how to query big data in an efficient manner, which causes database admins to limit the number of people who has access in the first place.

Because of the high amount of effort required, whatever insights that are mined from big data tend to remain a one-off exercise, making it difficult for team members to conduct follow-up analyses and validation.

All of these factors limit agility of analysis and ability to collaborate.

So What Role Does Big Data Play?

So, given all of these limitations, is user-level data worth spending time on? Absolutely — its potential to transform marketing is nothing short of incredible, both for insight generation as well as execution.

But when it comes to marketing analytics, I am a big proponent of picking the lowest-hanging fruit first: prioritizing analyses with the fastest time to insight and largest potential value. Analyses of user-level data falls squarely in the high-effort and slow-delivery camp, with variable and difficult-to-predict value.

Big data may have the potential to yield more insights than smaller data, but it will take much more time, consideration, and technical ability in order to extract them. Meanwhile, there should be plenty of room to gain learnings and improve campaign results using less granular data. I have yet to see such a thing as a perfectly managed account, or a perfectly executed campaign.

So yes, definitely start investing in big data capabilities. Meanwhile, let’s focus as much if not more in maximizing value from smaller data.

Note: In this article I treated “big data” and “user-level data” synonymously for simplicity’s sake, but the definition of big data can extend to less granular but more complex and varied data sets.

Originally posted via “7 Limitations Of Big Data In Marketing Analytics”


 

Originally Posted at: 7 Limitations Of Big Data In Marketing Analytics by analyticsweekpick

Jul 26, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Tour of Accounting  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> 66 job interview questions for data scientists by analyticsweekpick

>> @JohnNives on ways to demystify AI for enterprise #FutureOfData by admin

>> The Value of Enterprise Feedback Management Vendors by bobehayes

Wanna write? Click Here

[ NEWS BYTES]

>>
 7 Things Lawyers Should Know About Artificial Intelligence – Above the Law Under  Artificial Intelligence

>>
 Cyber security shorts: Daniel Schatz, Perform Group – IBC365 Under  cyber security

>>
 Cloud is not a commodity – avoiding the ‘Cloud Trap’ – ITProPortal Under  Cloud

More NEWS ? Click Here

[ FEATURED COURSE]

R Basics – R Programming Language Introduction

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Learn the essentials of R Programming – R Beginner Level!… more

[ FEATURED READ]

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

image

“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]

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:What are confounding variables?
A: * Extraneous variable in a statistical model that correlates directly or inversely with both the dependent and the independent variable
* A spurious relationship is a perceived relationship between an independent variable and a dependent variable that has been estimated incorrectly
* The estimate fails to account for the confounding factor

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]

The most valuable commodity I know of is information. – Gordon Gekko

[ PODCAST OF THE WEEK]

@TimothyChou on World of #IOT & Its #Future Part 1 #FutureOfData #Podcast

 @TimothyChou on World of #IOT & Its #Future Part 1 #FutureOfData #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

140,000 to 190,000. Too few people with deep analytical skills to fill the demand of Big Data jobs in the U.S. by 2018.

Sourced from: Analytics.CLUB #WEB Newsletter

Jul 19, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Complex data  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ NEWS BYTES]

>>
 Protecting data in a hybrid cloud environment | Network World – Network World Under  Hybrid Cloud

>>
 Big Four Vs of Big Data – BW Businessworld Under  Big Data

>>
 Sr. Marketing Analyst – United States – Built In Austin Under  Marketing Analytics

More NEWS ? Click Here

[ FEATURED COURSE]

CS109 Data Science

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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]

On Intelligence

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Jeff Hawkins, the man who created the PalmPilot, Treo smart phone, and other handheld devices, has reshaped our relationship to computers. Now he stands ready to revolutionize both neuroscience and computing in one strok… 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 Tufte’s concept of ‘chart junk’?
A: All visuals elements in charts and graphs that are not necessary to comprehend the information represented, or that distract the viewer from this information

Examples of unnecessary elements include:
– Unnecessary text
– Heavy or dark grid lines
– Ornamented chart axes
– Pictures
– Background
– Unnecessary dimensions
– Elements depicted out of scale to one another
– 3-D simulations in line or bar charts

Source

[ VIDEO OF THE WEEK]

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

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

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Without big data, you are blind and deaf and in the middle of a freeway. – Geoffrey Moore

[ PODCAST OF THE WEEK]

@TimothyChou on World of #IOT & Its #Future Part 1 #FutureOfData #Podcast

 @TimothyChou on World of #IOT & Its #Future Part 1 #FutureOfData #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

A quarter of decision-makers surveyed predict that data volumes in their companies will rise by more than 60 per cent by the end of 2014, with the average of all respondents anticipating a growth of no less than 42 per cent.

Sourced from: Analytics.CLUB #WEB Newsletter

A Timeline of Future Technologies 2019-2055

Our friends at Futurism uses the data from National Academy of Sciences, SmartThings Future Living reports, Scientific American, University of Bristol and several other sources to create this fascinating infographics.

A Timeline of Future Technologies 2019-2055
A Timeline of Future Technologies 2019-2055

originally posted @ https://futurism.com/images/things-to-come-a-timeline-of-future-technology-infographic/

Source by v1shal

Jul 12, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

image
Statistically Significant  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> 4 Ways Big Data Will Change Every Business by analyticsweekpick

>> 3 Emerging Big Data Careers in an IoT-Focused World by kmartin

>> Analyzing Big Data: A Customer-Centric Approach by bobehayes

Wanna write? Click Here

[ NEWS BYTES]

>>
 Global Data Analytics Outsourcing Market- By Key Players, Type, Application, Region, and Forecast 2018-2025 – City Councilor Under  Financial Analytics

>>
 Briggs And Stratton Hiring For 65 Jobs In Wauwatosa – Patch.com Under  Sales Analytics

>>
 Daunting challenges of data security and privacy & how AI comes to rescue – Analytics India Magazine Under  Data Security

More NEWS ? Click Here

[ FEATURED COURSE]

Pattern Discovery in Data Mining

image

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]

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]

Data aids, not replace judgement
Data is a tool and means to help build a consensus to facilitate human decision-making but not replace it. Analysis converts data into information, information via context leads to insight. Insights lead to decision making which ultimately leads to outcomes that brings value. So, data is just the start, context and intuition plays a role.

[ DATA SCIENCE Q&A]

Q:How do you take millions of users with 100’s transactions each, amongst 10k’s of products and group the users together in meaningful segments?
A: 1. Some exploratory data analysis (get a first insight)

* Transactions by date
* Count of customers Vs number of items bought
* Total items Vs total basket per customer
* Total items Vs total basket per area

2.Create new features (per customer):

Counts:

* Total baskets (unique days)
* Total items
* Total spent
* Unique product id

Distributions:

* Items per basket
* Spent per basket
* Product id per basket
* Duration between visits
* Product preferences: proportion of items per product cat per basket

3. Too many features, dimension-reduction? PCA?

4. Clustering:

* PCA

5. Interpreting model fit
* View the clustering by principal component axis pairs PC1 Vs PC2, PC2 Vs PC1.
* Interpret each principal component regarding the linear combination it’s obtained from; example: PC1=spendy axis (proportion of baskets containing spendy items, raw counts of items and visits)

Source

[ VIDEO OF THE WEEK]

@JohnTLangton from @Wolters_Kluwer discussed his #AI Lead Startup Journey #FutureOfData #Podcast

 @JohnTLangton from @Wolters_Kluwer discussed his #AI Lead Startup Journey #FutureOfData #Podcast

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]

@CRGutowski from @GE_Digital on Using #Analytics to #Transform Sales #FutureOfData #Podcast

 @CRGutowski from @GE_Digital on Using #Analytics to #Transform Sales #FutureOfData #Podcast

Subscribe 

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[ FACT OF THE WEEK]

Big data is a top business priority and drives enormous opportunity for business improvement. Wikibon’s own study projects that big data will be a $50 billion business by 2017.

Sourced from: Analytics.CLUB #WEB Newsletter

AtScale opens Hadoop’s big-data vaults to nonexpert business users

When it comes to business intelligence, most enterprise users are intimately acquainted with tools such as Microsoft Excel. They tend to feel less comfortable with data-management technologies like Hadoop—despite the considerable insights such tools could offer.

Enter AtScale, a startup that on Tuesday emerged from stealth with a new offering designed to designed to put those capabilities within closer reach. The AtScale Intelligence Platform is designed to enable interactive, multidimensional analyses on Hadoop from within standard BI tools such as Microsoft Excel, Tableau Software or QlikView, without the need for any data movement, custom drivers or a separate cluster.

“Today, millions of information workers could derive value from Hadoop, but their organizations have not been able to empower them to do so, either because their current toolset doesn’t work natively with Hadoop or because IT doesn’t have the tools to provision them with secure, self-service access,” said Dave Mariani, AtScale’s founder and CEO.

In essence, AtScale’s platform aims to give business users the ability to analyze in real time the entirety of their Hadoop data—tapping Hadoop SQL engines like Hive, Impala and Spark SQL—using the BI tools they are already familiar with. In that way, its intent is similar in many ways to that of Oracle, which recently unveiled new big-data tools of its own for nonexperts.

AtScale’s software strives to make big-data analytics accessible in several ways. Its cube designer, for instance, converts Hadoop into interactive OLAP cubes with full support for arrays, structs and non-scalars, enabling complex data to be converted into measures and dimensions that anyone can understand and manage, the company says.

“We have a community of more than 110 million users and a massive amount of data about how people play our games,” said Craig Fryar, head of business intelligence at Wargaming, creator of online game World of Tanks. “Our cluster stores billions of events that we can now easily explore in just a few clicks.

Originally posted via “AtScale opens Hadoop’s big-data vaults to nonexpert business users”

Source by analyticsweekpick