Oct 17, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Convincing  Source

[ AnalyticsWeek BYTES]

>> Dec 06, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

>> BI Implementation Insights: Clear and Easy Starting Points by analyticsweek

>> The Data Driven Road Less Traveled by d3eksha

Wanna write? 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]

Antifragile: Things That Gain from Disorder

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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]

Keeping Biases Checked during the last mile of decision making
Today a data driven leader, a data scientist or a data driven expert is always put to test by helping his team solve a problem using his skills and expertise. Believe it or not but a part of that decision tree is derived from the intuition that adds a bias in our judgement that makes the suggestions tainted. Most skilled professionals do understand and handle the biases well, but in few cases, we give into tiny traps and could find ourselves trapped in those biases which impairs the judgement. So, it is important that we keep the intuition bias in check when working on a data problem.

[ DATA SCIENCE Q&A]

Q:Give examples of bad and good visualizations?
A: Bad visualization:
– Pie charts: difficult to make comparisons between items when area is used, especially when there are lots of items
– Color choice for classes: abundant use of red, orange and blue. Readers can think that the colors could mean good (blue) versus bad (orange and red) whereas these are just associated with a specific segment
– 3D charts: can distort perception and therefore skew data
– Using a solid line in a line chart: dashed and dotted lines can be distracting

Good visualization:
– Heat map with a single color: some colors stand out more than others, giving more weight to that data. A single color with varying shades show the intensity better
– Adding a trend line (regression line) to a scatter plot help the reader highlighting trends

Source

[ VIDEO OF THE WEEK]

@ReshanRichards on creating a learning startup for preparing for #FutureOfWork #JobsOfFuture #Podcast

 @ReshanRichards on creating a learning startup for preparing for #FutureOfWork #JobsOfFuture #Podcast

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

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

[ PODCAST OF THE WEEK]

#DataScience Approach to Reducing #Employee #Attrition

 #DataScience Approach to Reducing #Employee #Attrition

Subscribe 

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[ FACT OF THE WEEK]

Within five years there will be over 50 billion smart connected devices in the world, all developed to collect, analyze and share data.

Sourced from: Analytics.CLUB #WEB Newsletter

Creating Value from Analytics: The Nine Levers of Business Success

IBM just released the results of a global study on how businesses can get the most value from Big Data and analytics. They found nine areas that are critical to creating value from analytics. You can download the entire study here.

IBM Institute for Business Value surveyed 900 IT and business executives from 70 countries from June through August 2013. The 50+ survey questions were designed to help translate concepts relating to generating value from analytics into actions.

Nine Levers to Value Creation

Figure 1. Nine Levers to Value Creation from Analytics
Figure 1. Nine Levers to Value Creation from Analytics. Click image to enlarge.

The researchers identified nine levers that help organizations create value from data. They compared leaders (those who identified their organization as substantially outperforming their industry peers) with the rest of the sample. They found that the leaders (19% of the sample) implement the nine levers to a greater degree than the non-leaders. These nine levers are:

  1. Source of value: Actions and decisions that generate results. Leaders tend to focus primarily on their ability to increase revenue and less so on cost reduction.
  2. Measurement: Evaluating the impact on business outcomes. Leaders ensure they know how their analytics impact business outcomes.
  3. Platform: Integrated capabilities delivered by hardware and software. Sixty percent of Leaders have predictive analytic capabilities, as well as simulation (55%) and optimization (67%) capabilities.
  4. Culture: Availability and use of data and analytics within an organization. Leaders make more than half of their decisions based on data and analytics.
  5. Data: Structure and formality of the organization’s data governance process and the security of its data. Two-thirds of Leaders trust the quality of their data and analytics. A majority of leaders (57%) adopt enterprise-level standards, policies and practices to integrate data across the organization.
  6. Trust: Organizational confidence. Leaders demonstrate a high degree of trust between individual employees (60% between executives, 53% between business and IT executives)
  7. Sponsorship: Executive support and involvement. Leaders (56%) oversee the use of data and analytics within their own departments, guided by an enterprise-level strategy, common policies and metrics, and standardized methodologies compared to the rest (20%).
  8. Funding: Financial rigor in the analytics funding process. Nearly two-thirds of Leaders pool resources to fund analytic investments. They evaluate these investments through pilot testing, cost/benefit analysis and forecasting KPIs.
  9. Expertise: Development of and access to data management and analytic skills and capabilities. Leaders share advanced analytics subject matter experts across projects, where analytics employees have formalized roles, clearly defined career paths and experience investments to develop their skills.

The researchers state that each of the nine levers have a different impact on the organization’s ability to deliver value from the data and analytics; that is, all nine levers distinguish Leaders from the rest but each Lever impacts value creation in different ways. Enable levers need to be in place before value can be seen through the Drive and Amplify levers. The nine levers are organized into three levels:

  1. Enable: These levers form the basis for big data and analytics.
  2. Drive: These levers are needed to realize value from data and analytics; lack of sophistication within these levers will impede value creation.
  3. Amplify: These levers boost value creation.

Recommendations: Creating an Analytic Blueprint

Figure 2. Analytics Blueprint for Creating Value from Data. Click image to enlarge
Figure 2. Analytics Blueprint for Creating Value from Data. Click image to enlarge

Next, the researchers offered a blueprint on how business leaders can translate the research findings into real changes for their own businesses. This operational blueprint consists of three areas: 1) Strategy, 2) Technology and 3) Organization.

1. Strategy

Strategy is about the deliberateness with which the organization approaches analytics. Businesses need to adopt practices around Sponsorship, Source of value and Funding to instill a sense of purpose to data and analytics that connects the strategic visions to the tactical activities.

2. Technology

Technology is about the enabling capabilities and resources an organization has available to manage, process, analyze, interpret and store data. Businesses need to adopt practices around Expertise, Data and Platform to create a foundation for analytic discovery to address today’s problems while planning for future data challenges.

3. Organization

Organization is about the actions taken to use data and analytics to create value. Businesses need to adopt practices around Culture, Measurement and Trust to enable the organization to be driven by fact-based decisions.

Summary

One way businesses are trying to outperform their competitors is through the use of analytics on their treasure trove of data. The IBM researchers were able to identify the necessary ingredients to extract value from analytics. The current research supports prior research on the benefits of analytics in business:

  1. Top-performing businesses are twice as likely to use analytics to guide future strategies and guide day-to-day operations compared to their low-performing counterparts.
  2. Analytic innovators 1) use analytics primarily to increase value to the customer rather than to decrease costs/allocate resources, 2) aggregate/integrate different business data silos and look for relationships among once-disparate metric and 3) secure executive support around the use of analytics that encourage sharing of best practices and data-driven insights throughout their company.

Businesses, to extract value from analytics, need to focus on improving strategic, technological and organizational aspects on how they treat data and analytics. The research identified nine area or levers executives can use to improve the value they generate from their data.

For the interested reader, I recently provided a case study (see: The Total Customer Experience: How Oracle Builds their Business Around the Customer) that illustrates how one company uses analytical best practices to help improve the customer experience and increase customer loyalty.

————————–

TCE Total Customer Experience

 

Buy TCE: Total Customer Experience at Amazon >>

In TCE: Total Customer Experience, learn more about how you can  integrate your business data around the customer and apply a customer-centric analytics approach to gain deeper customer insights.

 

Source

Oct 10, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Conditional Risk  Source

[ AnalyticsWeek BYTES]

>> Can Hadoop be Apple easy? by analyticsweekpick

>> How Harmful Is the Net Promoter Score? by analyticsweek

>> #FutureOfData Podcast: Conversation With Sean Naismith, Enova Decisions – Playcast – Data Analytics Leadership Playbook Podcast by v1shal

Wanna write? Click Here

[ FEATURED COURSE]

R, ggplot, and Simple Linear Regression

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Begin to use R and ggplot while learning the basics of linear regression… 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]

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

Source

[ VIDEO OF THE WEEK]

@RCKashyap @Cylance on State of Security & Technologist Mindset #FutureOfData #Podcast

 @RCKashyap @Cylance on State of Security & Technologist Mindset #FutureOfData #Podcast

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]

@SidProbstein / @AIFoundry on Leading #DataDriven Technology Transformation #FutureOfData #Podcast

 @SidProbstein / @AIFoundry on Leading #DataDriven Technology Transformation #FutureOfData #Podcast

Subscribe 

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[ FACT OF THE WEEK]

By 2020, at least a third of all data will pass through the cloud (a network of servers connected over the Internet).

Sourced from: Analytics.CLUB #WEB Newsletter

How to Price Your Predictive Application

In a recent survey of 500 application teams, predictive analytics was the number one feature being added to product roadmaps. It’s clear why: Predictive analytics solves critical business challenges and adds tremendous value to applications.

>> Related: How to Package and Price Embedded Analytics <<

As application vendors begin to add predictive insights into their applications, the question becomes: How should they price predictive capabilities? Let’s look at the best (and worst) ways to price and package predictive analytics with your product.

The Wrong Way to Price Predictive Analytics

Say you have a cloud-based customer churn reporting application, and your customers pay a $500 per month subscription to use it. Then you add predictive features as a new module. How will you price that module?

A common predictive analytics pricing strategy is to choose a price point—such as 30 to 50 percent—and add that on top of the current monthly license. If we pick 50 percent, then the new module will be priced at $250 per month. But is that a good way to price your product?

Your price should be based on supply and demand as well as the value your product offers. A typical dashboard application is already a commodity, so it is difficult to charge a premium since many vendors offer similar application functionality. On the other hand, predictive analytics is a hot technology. It provides unique insights into the future, and few vendors have incorporated it in their products. Those who roll out predictive analytics features first will have a head start in capturing the market.

So, even though it may seem like a 50 percent markup is reasonable, that doesn’t get to the heart of the value of predictive analytics. The answer to our earlier question is no: Basing the price of a new premium feature (predictive analytics) off of your commodity features (embedded analytics) is not a good way to price your product.

The Right Way to Price Predictive

If the traditional pricing strategy is out, how do we price your predictive application? Since our new module can predict who will churn, let’s identify what value the module offers for a customer. Say a customer is losing one million dollars annually due to churn and is looking to reduce that by 20 percent a year. By identifying who is likely to churn and taking a proactive approach, the predictive module can help your customer save $200,000 a year—that’s a $200,000 value!

So, how much can you charge a customer for that type of value? Fifteen to 30 percent is reasonable, but let’s be very conservative and say you only charge 10 percent. That’s a price of $20,000 per year (or $1,667 per month) for the predictive module.

Remember, your current churn dashboard costs customers $500 per month. We’ve given the new predict module a starting price of $1,667 per month—that’s three times higher than your current commodity dashboard pricing. This may seem like a lot, but it’s your marketing department’s job to create a strong sales pitch that clearly conveys the value to your customers.

The Best Way to Price Predictive: Think Outside the Box

Is there something we can do to make the predictive analytics pricing (and the total application package) more appealing to customers? Yes, there is!

Since the new predict module is adding much more value than your dashboard reporting application, your product manager should lead all sales with it. It doesn’t make sense to sell your customers on the old application and then mention the optional predictive module later on. The new predictive application is going to be the most valuable to customers, so make it the focus of the application. The best way to price predictive analytics is to create a new package for, say, $2,000 a month. This should include everything: your commodity dashboard application and your new predictive capabilities.

In summary: Push yourself to calculate the value your product offers. Challenge your marketing team to figure out innovative ways to articulate that value. And finally, come up with forward-thinking packages that lead with your predictive functionality.

See how Logi can help with your next predictive analytics project. Watch a free demo of Logi Predict today >

 

Originally Posted at: How to Price Your Predictive Application by analyticsweek

Oct 03, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Convincing  Source

[ AnalyticsWeek BYTES]

>> Secret Sauce to Sustain Business through Technology Driven Era by d3eksha

>> Call Centers Are Here to Stay [Infographics] by v1shal

>> Paul Ballew(@Ford) on running global data science group #FutureOfData #Podcast by v1shal

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]

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

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Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored f… more

[ TIPS & TRICKS OF THE WEEK]

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

[ DATA SCIENCE Q&A]

Q:Examples of NoSQL architecture?
A: * Key-value: in a key-value NoSQL database, all of the data within consists of an indexed key and a value. Cassandra, DynamoDB
* Column-based: designed for storing data tables as sections of columns of data rather than as rows of data. HBase, SAP HANA
* Document Database: map a key to some document that contains structured information. The key is used to retrieve the document. MongoDB, CouchDB
* Graph Database: designed for data whose relations are well-represented as a graph and has elements which are interconnected, with an undetermined number of relations between them. Polyglot Neo4J

Source

[ VIDEO OF THE WEEK]

@AnalyticsWeek Panel Discussion: Health Informatics Analytics

 @AnalyticsWeek Panel Discussion: Health Informatics Analytics

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

I keep saying that the sexy job in the next 10 years will be statisticians. And I’m not kidding. – Hal Varian

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with Nathaniel Lin (@analytics123), @NFPA

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Nathaniel Lin (@analytics123), @NFPA

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

YouTube users upload 48 hours of new video every minute of the day.

Sourced from: Analytics.CLUB #WEB Newsletter

Data Driven Innovation: A Primer

Data Driven Innovation A Primer
Data Driven Innovation A Primer

We are hearing all the hoopla about Big-data. How it is radically changing the way we look at company data and provide data driven reasoning for better and less risky decision making. Innovation is one such area. Big-data could provide a real lift to DDI. Having a data driven approach will help in better, targeted and relevant innovations that are craved by the clients/ customers. This bottom-up approach at its most effective form could be easily conceived by a good data driven innovation strategy.

Now let’s get into the primer on DDI- What is entails and how could one leverage that. Here is the Who, What, Where, Why and When of Data Driven innovation.

Why use DDI?
Let us address why do we actually need to use DDI- what will it buy us. Consider a situation where you have to come up with your next best product/feature/innovation. Where would that come from? From your gut based on some hunch or from hardcore actual data from right sources. Hunch based discoveries are great but their failure rate is higher. Also, they are difficult to validate as the implementer has to do various focus groups which in themselves are flawed to certain extent. Now consider a case where your product-customer interactions, operations fill you up on what is relevant and you can use data to understand its impact on the organizations. This helps you identify what matters most and helps you choose the idea with substantial data to back up the theory. So, there is no need for spending money on focus groups, but there is a need to leverage real interactions with the real customers/people leading to real results. This ultimately means lesser chance of failure and cost effective way to find the next big thing that is most craved by your customer or organization. This reduces the risk of failure substantially and puts you at ease. So, DDI is important and it could provide a sustainable and continuous way to innovate, iterate and improve.

What is DDI?
Data driven innovation, as name suggests is the way through which data is used for learning about new features, modifications, product ideas that is most cherished by your current customers, market landscape. However, its usage and manifestation in an organization could be different based on its structure, maturity, usage and implementation. Its definition could very well incorporate the application and purpose it is set to achieve. For some organization DDI is a way for finding process improvements, for others it is way to learn from customers and how they use products to learn about next features and/or products, for some it is a sustained source of learning about people, process and technology. But, I would put it in generic and call it “A method to innovate/iterate/improve using sustainable & continuous ways using data based decision process, where data is sourced to help learn about people, process and technology critical to your organization”.

Who could use DDI?
Data driven innovation is not everyone’s play. Not that it is too difficult to implement or it requires too much investment, but it requires certain maturity in your data handling capability before getting started. If you are diligent about using data to learn about your processes and its effectiveness, it will be easier. If you are not yet focused towards using data around your product and processes, you still might have some distance to travel before you delve into data driven methodologies. It is never late to start planning and executing strategies to introduce and leverage data points that go beyond your traditional direct customer & product data. So, in short, DDI could be used by any organization that is serious about learning from data. In Fact, smaller the firm, the better it could be implemented and lesser it would cost. The more silos, more complicated product/process structure, the more it is going to cost, to execute. In short, you could safely tag your DDI initiative on your management, the more selling your management requires for a data driven project, the farther you are from pursuing a full scale DDI. So, first get the leadership buyin on its value and then start shaping your organization to implement DDI.

Where will DDI take place?
Yes, you could figure out that DDI is a system that runs on data driven insights and data is everywhere in an organization so, it could show up anywhere. But, it is a bit trickier than that. The toughest part is not when data driven decision making is running in an organization’s DNA but the time when organizations decides to get started. DDI requires some careful understanding of how data works and how it could be used to get insights. Therefore, place where it should start is important. The best starting place for DDI could be around project managers, or if your organization is big enough to accommodate project management office, for agile companies, it should be around group leads. In short, DDI should start from a place that is not a stranger to data and understands how to handle it. So, in short, it could exist everywhere but it should start from a place that provides the most amiable surroundings required by a data driven project. Project managers, supervisors, PMOs are meant to keep a tap on the progress, so they possess some basic skills to function as data driven professionals and therefore, could help the best in understanding and executing a good DDI strategy.

When is the time to delve into DDI?
In short, the sooner the better. DDI requires substantial amount of preplanning and dedication. The sooner organizations delve into data driven innovation, the better will be its execution and value to the organization. A good data driven innovation implementation requires some practice and iterations on data models, validations, analysis and reporting. So, a successful implementation will rarely emerge from first implementation and would require some iteration. Also, the sooner the organization will start in direction of implementing DDI, the better it is because organizations will start acting in ways to facilitate smart data handling, which will have its own benefits. But, one caveat is that organization should have data to play with. Doing DDI sooner when data handling capabilities are not established could confuse the processes and steer the implementation in wrong directions. So, we could reword our sooner as “the sooner the organizations have started embracing data based decision making process, the better”.

To summarize, DDI is important and beneficial to any organization. It has the tendency to make any organization grow sustainably without having to invest too much into research and development. It support continuous improvements and that too without investing too much money and it could re-utilize the same infrastructure for sustainable leanings.

As a treat, here is a video on Big-Data and Innovation:

Originally Posted at: Data Driven Innovation: A Primer by v1shal

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

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[  COVER OF THE WEEK ]

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Conditional Risk  Source

[ AnalyticsWeek BYTES]

>> Big data solves mystery: Why humans have no more genes than worms by analyticsweekpick

>> Making Big Data Work: Supply Chain Management by analyticsweekpick

>> @Schmarzo @DellEMC on Ingredients of healthy #DataScience practice #FutureOfData #Podcast by v1shal

Wanna write? Click Here

[ FEATURED COURSE]

Probability & Statistics

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This course introduces students to the basic concepts and logic of statistical reasoning and gives the students introductory-level practical ability to choose, generate, and properly interpret appropriate descriptive and… more

[ FEATURED READ]

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

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In the world’s top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Mast… 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:How frequently an algorithm must be updated?
A: You want to update an algorithm when:
– You want the model to evolve as data streams through infrastructure
– The underlying data source is changing
– Example: a retail store model that remains accurate as the business grows
– Dealing with non-stationarity

Some options:
– Incremental algorithms: the model is updated every time it sees a new training example
Note: simple, you always have an up-to-date model but you can’t incorporate data to different degrees.
Sometimes mandatory: when data must be discarded once seen (privacy)
– Periodic re-training in “batch” mode: simply buffer the relevant data and update the model every-so-often
Note: more decisions and more complex implementations

How frequently?
– Is the sacrifice worth it?
– Data horizon: how quickly do you need the most recent training example to be part of your model?
– Data obsolescence: how long does it take before data is irrelevant to the model? Are some older instances
more relevant than the newer ones?
Economics: generally, newer instances are more relevant than older ones. However, data from the same month, quarter or year of the last year can be more relevant than the same periods of the current year. In a recession period: data from previous recessions can be more relevant than newer data from different economic cycles.

Source

[ VIDEO OF THE WEEK]

Discussing Forecasting with Brett McLaughlin (@akabret), @Akamai

 Discussing Forecasting with Brett McLaughlin (@akabret), @Akamai

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

You can use all the quantitative data you can get, but you still have to distrust it and use your own intelligence and judgment. – Alvin Tof

[ PODCAST 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 

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

What Your Social Data Knows About You – @SethS_D Author #NYTBestSeller Everybody Lies

What Your Social Data Knows About You – @SethS_D Author #NYTBestSeller Everybody Lies #FutureJobs#JobsOfFuture #Podcast

In this podcast Seth Stephens-Davidowitz (@SethD_S), author of New York Times Bestseller Everybody Lies, discussed what our social data knows about us. He shares some critical insights into human psyche on how humans behaves differently to machines then fellow humans. This shed some interesting light on how #JobsOfFuture would use our social and technology interactions to create experience that best represent and benefit us. He shared some insights into what future of work would look like. He sheds some insights into how businesses could use data to create a great experience for our employees, workers, clients, and partners. This is a great podcast for anyone looking to understand the depth of insights that data could create.

Seth’s Book:
Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are by Seth Stephens-Davidowitz amzn.to/2OA0YBs

Seth’s Recommended Read:
Enlightenment Now: The Case for Reason, Science, Humanism, and Progress by Steven Pinker amzn.to/2Kl2nsr

Podcast Link:
iTunes: math.im/jofitunes
Youtube: math.im/jofyoutube

Seth’s BIO:
Seth Stephens-Davidowitz has used data from the internet — particularly Google searches — to get new insights into the human psyche.

Seth has used Google searches to measure racism, self-induced abortion, depression, child abuse, hateful mobs, the science of humor, sexual preference, anxiety, son preference, and sexual insecurity, among many other topics.

His 2017 book, Everybody Lies, published by HarperCollins, was a New York Times bestseller; a PBS NewsHour Book of the Year; and an Economist Book of the Year.

Seth worked for one-and-a-half years as a data scientist at Google and is currently a contributing op-ed writer for the New York Times. He is a former visiting lecturer at the Wharton School at the University of Pennsylvania.
He received his BA in philosophy, Phi Beta Kappa, from Stanford, and his PhD in economics from Harvard.

In high school, Seth wrote obituaries for the local newspaper, the Bergen Record, and was a juggler in theatrical shows. He now lives in Brooklyn and is a passionate fan of the Mets, Knicks, Jets, Stanford football, and Leonard Cohen.

About #Podcast:
#JobsOfFuture is created to spark the conversation around the future of work, worker and workplace. This podcast invite movers and shakers in the industry who are shaping or helping us understand the transformation in work.

Wanna Join?
If you or any you know wants to join in,
Register your interest @ play.analyticsweek.com/guest/

Want to sponsor?
Email us @ info@analyticsweek.com

Keywords:
#JobsOfFuture #FutureOfWork #FutureOfWorker #FutuerOfWorkplace #Work #Worker #Workplace

Originally Posted at: What Your Social Data Knows About You – @SethS_D Author #NYTBestSeller Everybody Lies