Nov 14, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Data Accuracy  Source

[ AnalyticsWeek BYTES]

>> Solving Common Data Challenges by analyticsweek

>> Sisense AI – What it Really Takes to Build a Better Mousetrap by analyticsweek

>> Three Upcoming Talks on Big Data and Customer Experience Management by bobehayes

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]

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

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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]

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: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]

#BigData #BigOpportunity in Big #HR by @MarcRind #JobsOfFuture #Podcast

 #BigData #BigOpportunity in Big #HR by @MarcRind #JobsOfFuture #Podcast

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[ QUOTE OF THE WEEK]

Data really powers everything that we do. – Jeff Weiner

[ PODCAST OF THE WEEK]

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

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

Subscribe 

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[ FACT OF THE WEEK]

According to execs, the influx of data is putting a strain on IT infrastructure. 55 percent of respondents reporting a slowdown of IT systems and 47 percent citing data security problems, according to a global survey from Avanade.

Sourced from: Analytics.CLUB #WEB Newsletter

Nov 07, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Fake data  Source

[ AnalyticsWeek BYTES]

>> Does the Future Lie with Embedded BI? by analyticsweek

>> How can you reap the advantages of Big Data in your enterprise? Services you can expect from a Remote DBA Expert by thomassujain

>> Voices in AI – Episode 91: A Conversation with Mazin Gilbert by analyticsweekpick

Wanna write? Click Here

[ FEATURED COURSE]

CS229 – Machine Learning

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This course provides a broad introduction to machine learning and statistical pattern recognition. … 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]

Finding a success in your data science ? Find a mentor
Yes, most of us dont feel a need but most of us really could use one. As most of data science professionals work in their own isolations, getting an unbiased perspective is not easy. Many times, it is also not easy to understand how the data science progression is going to be. Getting a network of mentors address these issues easily, it gives data professionals an outside perspective and unbiased ally. It’s extremely important for successful data science professionals to build a mentor network and use it through their success.

[ DATA SCIENCE Q&A]

Q:What is POC (proof of concept)?
A: * A realization of a certain method to demonstrate its feasibility
* In engineering: a rough prototype of a new idea is often constructed as a proof of concept

Source

[ VIDEO OF THE WEEK]

Solving #FutureOfWork with #Detonate mindset (by @steven_goldbach & @geofftuff) #JobsOfFuture #Podcast

 Solving #FutureOfWork with #Detonate mindset (by @steven_goldbach & @geofftuff) #JobsOfFuture #Podcast

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]

@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 

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[ FACT OF THE WEEK]

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

Sourced from: Analytics.CLUB #WEB Newsletter

A Single Customer View : The Secret Weapon Everyone Must Use

Insurers have a lot of customer data across different systems peppered throughout their enterprise. Customer data would typically live in multiple systems like CRM, Billing, Policy Administration, and so on. This approach however suffers from multiple challenges:

 

  1. Duplicate data across multiple systems
  2. Multiple Versions of the same data point
  3. No Single source of truth
  4. No correlation between cause and action
  5. Completely under utilized customer interaction data

 

Imagine a structure so flexible and scalable that, that it could bring all your data sources together, irrespective of the data formats, tie in with the customer key result areas (KRAs) and at the same time deliver predictable insights at the point of decision. In real time.

 

Enter single customer view. Or as we call it – Customer OneView.

 

CRUX OneView

OneView in Action

 

Customer OneView is part of Aureus data analytics platform called CRUX. OneView is not anything like a CRM. While a CRM would show only static information, OneView delivers intelligent, usable and real time insights that can be put to use immediately.

OneView can integrate with (atleast) four broad event data streams:

  1. Customer
  2. Relationship
  3. Transactions
  4. Interaction

 

Nitin had written about stream based data integration on his insightful post titled “Cheers to Stream Based Integration“

 

These data streams could originate across multiple data systems – Policy Admin, CRM, Billing, etc.. Between them, these four cover some of the most critical customer data, that often lies under utilized. OneView not only brings these data streams together, but it also helps build a comprehensive customer life journey showing important milestones, critical customer interactions, sentiment at each interaction or transaction level as well as at a relationship level.  While OneView is a powerful insights delivery framework, it also helps to deliver the output of predictive analytics models in a form that is usable by the business users. OneView can help translate the output of the analytical models into usable insights. Imagine a customer sales representative talking to a customer, or a field sales agent going to meet a customer. OneView will give them unambiguous insight into the customers history, sentiment and even potential  action to take, without burdening them with the Hows and Whys.

 

Imagine a typical customer cross sell scenario. Most organizations tend to throw (figuratively speaking) the entire product catalog at the customer without any consideration for their lifestage needs, portfolio, demographics etc… Not only is this a highly ineffective cross sell approach, but it is a terrible customer experience approach. With OneView the customer service representative or the field service agents knows exactly what the customers latest and overall sentiment is, what her product portfolio looks like and which product the customer is most likely to buy.

 

The end goal of any activity is to make the end customers experience epic. By knowing how a customer is likely to behave, modeled on her previous behavior, insurance companies can ensure that the customer experience is always moving to the right.

 

OneView

Source: A Single Customer View : The Secret Weapon Everyone Must Use by analyticsweek

The Mainstream Adoption of Blockchain: Internal and External Enterprise Applications

The surging interest in blockchain initially pertained to its utility as the underlying architecture for the cryptocurrency phenomenon. Nonetheless, its core attributes (its distributed ledger system, immutability, and requisite permissions) are rapidly gaining credence in an assortment of verticals for numerous deployments.

Blockchain techniques are routinely used in several facets of supply chain management, insurance, and finance. In order to realize the widespread adoption rates many believe this technology is capable of, however, blockchain must enhance the very means of managing data-driven processes, similar to how applications of Artificial Intelligence are attempting to do so.

Today, there are myriad options for the enterprise to improve operations by embedding blockchain into fundamental aspects of data management. If properly architected, this technology can substantially impact facets of Master Data Management, data governance, and security. Additionally, it can provide these advantages not only between organizations, but also within them, operating as what Franz CEO Jans Aasman termed “a usability layer on top” of any number of IT systems.

Customer Domain Management
A particularly persuasive use case for the horizontal adoption of blockchain is deploying it to improve customer relations. Because blockchain essentially functions as a distributed database in which transactions between parties must be validated for approval (via a consensus approach bereft of centralized authority), it’s ideal for preserving the integrity of interactions between the enterprise and valued customers. In this respect it can “create trusted ledgers for customers that are completely invisible to the end user,” Aasman stated. An estimable example of this use case involves P2P networks, in which “people just use peer-to-peer databases that record transactions,” Aasman mentioned. “But these peer-to-peer transactions are checked by the blockchain to make sure people aren’t cheating.” Blockchain is used to manage transactions between parties in supply chains in much the same way. Blockchain aids organizations with this P2P customer use case because without it, “it’s very, very complicated for normal people to get it done,” Aasman said about traditional approaches to inter-organization ledger systems. With each party operating on a single blockchain, however, transactions become indisputable once they are sanctioned between the participants.

Internal Governance and Security
Perhaps the most distinguishable feature of the foregoing use case is the fact that in most instances, end users won’t even know they’re working with blockchain. What Aasman called an “invisible” characteristic of the blockchain ledger system is ideal for internal use to monitor employees in accordance with data governance and security procedures. Although blockchain supports internal intelligence or compliance for security and governance purposes, it’s most applicable to external transactions between organizations. In finance—just like in supply chain or in certain insurance transactions—“you could have multiple institutions that do financial transactions between each other, and each of them will have a version of that database,” Aasman explained. Those using these databases won’t necessarily realize they’re fortified by blockchain, and will simply use them as they would any other transactional system. In this case, “an accountant, a bookkeeper or a person that pays the bills won’t even know there’s a blockchain,” commented Aasman. “He will just send money or receive money, but in the background there’s blockchain making sure that no one can fool with the transactions.”

Master Data Management
Despite the fact that individual end users may be ignorant of the deployment of blockchain in the above use cases, it’s necessary for underlying IT systems to be fully aware of which clusters are part of this ledger system. According to Aasman, users will remain unaware of blockchain’s involvement “unless, of course, someone was trying to steal money, or trying to delete intermediate transactions, or deny that he sent money, or sent the same money twice. Then the system will say hey, user X has engaged in a ‘confusing’ activity.” In doing so, the system will help preserve adherence to company policies related to security or data governance issues.

Since organizations will likely employ other IT systems without blockchain, Master Data Management hubs will be important for “deciding for which transactions this applies,” Aasman said. “It’s going to be a feature of MDM.” Mastering the data from blockchain transactions with centralized MDM approaches can help align this data with others vital to a particular business domain, such as customer interactions. Aasman revealed that “the people that make master data management have to specify for which table this actually is true. Not the end users: the architects, the database people, the DBAs.” Implementing the MDM schema for which to optimize such internal applications of blockchain alongside those for additional databases and sources can quickly become complex with traditional methods, and may be simplified via smart data approaches.

Overall Value
The rapidity of blockchain’s rise will ultimately be determined by the utility the enterprise can derive from its technologies, as opposed to simply limiting its value to financial services and cryptocurrency. There are just as many telling examples of applying blockchain’s immutability to various facets of government and healthcare, or leveraging smart contracts to simplify interactions between business parties. By using this technology to better customer relations, reinforce data governance and security, and assist specific domains of MDM, organizations get a plethora of benefits from incorporating blockchain into their daily operations. The business value reaped in each of these areas could contribute to the overall adoption of this technology in both professional and private spheres of life. Moreover, it could help normalize blockchain as a commonplace technology for the contemporary enterprise.

Source: The Mainstream Adoption of Blockchain: Internal and External Enterprise Applications