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#Walmart: The Big Data Skills Crisis And Recruiting #BusinessAnalytics Talent

As the amount of digital information generated by businesses and organizations continues to grow exponentially, a challenge –or as some have put it, a crisis–has developed.

There just aren’t enough people with the required skills to analyze and interpret this information–transforming it from raw numerical (or other) data into actionable insights – the ultimate aim of any Big Data-driven initiative.

One survey recently carried out by researchers at Gartner IT +0.93% found that more than half of the business leaders they queried felt their ability to carry out analytics was restricted by the difficulty in finding the right talent.

Overcoming this problem is a challenge that all companies will have to face, and market leaders–aware that they have more to lose than many by falling behind in the race to keep up with technology–have come up with some innovative solutions.

Walmart decided to apply one of the fundamental weapons in the Big Data arsenal–crowdsourcing–to the problem, with positive results.

Last year, they turned to crowdsourced analytics competition platform Kaggle. At Kaggle, an army of “armchair data scientists” apply their skills to analytical problems submitted by companies, with the designer of the best solution being rewarded – sometimes financially, in this case with a job.

To continue reading click the source link below.

 

Source: Walmart: The Big Data Skills Crisis And Recruiting Analytics Talent

 

 

#DataForAnalytics

#DataAndAnalytics

#BigData

 

 

 

 

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#DataAndAnalytics:  From #BusinessInsights To Value

D&A is rising up the agenda – for executives, for business managers, for customers and, increasingly, for equity markets and investors. And now, more than ever before, organizations are starting to sharpen their focus on driving value from their D&A investments. And as organizations move along the journey from data to insights and from insights to value, key themes are emerging.

Go to the article (link below) to read a couple of good reports regarding data & analytics.

Source: Data & Analytics: From insights to value

 

 

#DataForAnalytics

#DataAndAnalytics

 

 

 

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27 Free Books on #DataMining

 

Part of our mission statement is to make CPG reports that strive for a higher level of insights. Consider the 8 levels of analytics below as coined from SAS. Where does your organization stand on this list?

1) Standard reports – Standard reports provide summary statistics and answer questions like “What happened?” and “When did it happen?” said Davis. “That’s analytics, but not enough.”

2) Ad hoc reports – Ad-hoc reports answer questions like, “How many? How often? Where?” he said. They provide a level of independence on desktops that allow an individual, for example, to see sales in a particular region or at a particular point in time without needing to go to an IT governance counsel and wait three months for the result.

3) Query drill-downs – Also referred to as OLAP, query drill-downs answer questions like, “Where exactly is the problem?” and “How do I find the answers?” said Davis. This is for when an organization wants to see not only the results, but what the results mean and what backs it up, he explained.

4) Alerts – Alerts answer questions like, “When should I react?” and “What actions are needed now?” said Davis. “This is when you reach a particular threshold … something changes from green to red, so you do something about it.”

5) Statistical analysis – Statistical analysis answers the questions, “Why is this happening?” and “What opportunities am I missing?” he said. “You begin to take the data … and you begin to understand why things are happening.”

6) Forecasting – A popular level, forecasting answers questions like, “What if these trends continue? How much is needed? When will it be needed?” he said.

7) Predictive modeling – Predictive modeling tells users what will happen next and how it will affect the business, Davis said.

8) Optimization – Optimization answers the questions, “How do we do things better?” and “What is the best decision for a complex problem?

 

Want to read up on how to make it to level 8? We’ve found a list of 27 free machine learning (think artificial intelligence, data mining, statistical inference, predictive modeling) books to whet your appetite.

Don’t pull a muscle! For a free consultation on your business intelligence strategy contact us.

  1. An Introduction to Statistical Learning: with Applications in R
    Overview of statistical learning based on large datasets of information. The exploratory techniques of the data are discussed using the R programming language.
  2. Data Science for Business: What you need to know about data mining and data-analytic thinking
    An introduction to data sciences principles and theory, explaining the necessary analytical thinking to approach these kind of problems. It discusses various data mining techniques to explore information.
  3. Modeling With Data
    This book focus some processes to solve analytical problems applied to data. In particular explains you the theory to create tools for exploring big datasets of information.
  4. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners
    On this resource the reality of big data is explored, and its benefits, from the marketing point of view. It also explains how to store these kind of data and algorithms to process it, based on data mining and machine learning.
  5. Data Mining: Practical Machine Learning Tools and Techniques
    Full of real world situations where machine learning tools are applied, this is a practical book which provides you the knowledge and hability to master the whole process of machine learning.
  6. Machine Learning – Wikipedia Guide
    A great resource provided by Wikipedia assembling a lot of machine learning in a simple, yet very useful and complete guide.
  7. Data Mining and Analysis: Fundamental Concepts and Algorithms
    A great cover of the data mining exploratory algorithms and machine learning processes. These explanations are complemented by some statistical analysis.
  8. Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More
    The exploration of social web data is explained on this book. Data capture from the social media apps, it’s manipulation and the final visualization tools are the focus of this resource.
  9. Probabilistic Programming & Bayesian Methods for Hackers
    A book about bayesian networks that provide capabilities to solve very complex problems. Also discusses programming implementations on the Python language.
  10. Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
    A data mining book oriented specifically to marketing and business management. With great case studies in order to understand how to apply these techniques on the real world.
  11. Inductive Logic Programming Techniques and Applications
    An old book about inductive logic programming with great theoretical and practical information, referencing some important tools.
  12. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition
    This is a conceptual book in terms of data mining and prediction from a statistical point of view. Covers many machine learning subjects too.
  13. An Introduction to Data Science
    An introductory level resource developed by a american university that presents a overview of the most important data science’s notions.
  14. Mining of Massive Datasets
    The main focus of this book is to provide the necessary tools and knowledge to manage, manipulate and consume large chunks of information into databases.
  15. A Programmer’s Guide to Data Mining
    A guide through data mining concepts in a programming point of view. It provides several hands-on problems to practice and test the subjects taught on this online book.
  16. Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery
    The objective of this book is to provide you lots of information  on data manipulation. It focus on the Rattle toolkit and the R language to demonstrate the implementation of these techniques.
  17. Reinforcement Learning: An introduction
    A solid approach to the reinforcement learning thematic providing solution methods. It describes also some very important case studies.
  18. Pattern Recognition and Machine Learning (Information Science and Statistics)
    This book presents you a lot of pattern recognition stuff based on the bayesian networks perspective. Many machine learning concepts are approached and exemplified.
  19. Machine Learning, Neural and Statistical Classification
    A good old book about statistical methodology, learning techniques and another important issues related to machine learning.
  20. Information Theory, Inference, and Learning Algorithms
    An interesting approach to information theory merged with the inference and learning concepts. This book taughts a lot of data mining techniques creating a bridge between it and information theory.
  21. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die [Broken Link] A great predictive analytics book providing an insight about the concept, alongside with case studies to consolidate the theory.
  22. Introduction to Machine Learning
    A simple, yet very important book, to introduce everyone to the machine learning subject.
  23. Data Mining and Business Analytics with R
    Another R based book describing all processes and implementations to explore, transform and store information. It also focus on the concept of Business Analytics.
  24. Machine Learning
    A very complete book about the machine learning subject approching several specific, and very useful techniques.
  25. Think Bayes, Bayesian Statistics Made Simple
    A Python programming language approach to the bayesian statistical methods, where these techniques are applied to solve real-world problems and simulations.
  26. Bayesian Reasoning and Machine Learning
    Another bayesian book reference, this one focusing on applying it to machine learning algorithms and processes.  It is a hands-on resource, great to absorb all the knowledge in the book.
  27. Gaussian Processes for Machine Learning
    This is a theoretical book approaching learning algortihms based on probabilistic gaussian processes. It’s about supervised learning problems, describing models and solutions related to machine learning.

 

Original Source: http://www.dataonfocus.com/21-free-data-mining-books/

 

#DataMining

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Bigger Is Not Always Better – Why #Amazon Is Worth More Than #Walmart – #Retailing

Over the weekend a lot has been made about Amazon topping Walmart in market value.  This is the second time in two months an online based company has surpassed Walmart.  Last month Facebook moved above Walmart and pushed the retailer out of the Top 10 list of the Forbes 500.

While Walmart has a much larger revenue and profit than these online centric companies, the concern is the slowing revenue and profit growth with the large fixed cost of the huge scale infrastructure.

Below is an interesting article highlighting the shift in retailing by comparing Amazon to Walmart now that the Amazon market value exceeds that of Walmart.

Source:  Bigger Is Not Always Better – Why Amazon Is Worth More Than Walmart

#retailing

#Walmart

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#DataAnalyticsTechnology: Turn insight into action with #PredictiveAnalytics

 

Good Saturday morning – hope you are enjoying your cup of coffee.  Now enjoy a -not too bad- geeky article on predictive analytics and how to make use of it.

See you again next week at the office.

 

Source: Turn insight into action with predictive analytics

 

 

#DataAnalyticsTechnology

#PredictiveAnalytics

#BigData

 

 

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Demystifying #SelfServiceData and the Future of #BusinessIntelligence

Imagine that you wanted to see a review of the latest weekend blockbuster, but you couldn’t just Google its Rotten Tomatoes score. Instead, you had to submit a written request to an information technology department, wait five days, then sift through a binder-sized report that breaks down the reviews by publication type, readership size, and reviewer age.

As fascinating as that information might be, it took five days too long — you went to the movie days ago on a hunch it would be good.

Welcome to the world of traditional business information.  This is the beginning of a look at self-service data analytics which is the future of business intelligence.

Continue reading the article from the link below and contact us for a free consultation on how to put self-service BI into the hands of those who need it the most – your category managers and sales analysts.
Source: Demystifying Self-Service Data and the Future of Business Intelligence

 

#BusinessIntelligence
#DataAsAService
#BigData

 

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#DataForAnalytics: #PredictiveAnalytics tools point to better business actions

From recommending additional purchases based on the items that customers place in online shopping carts to pinpointing hospital patients who have a greater risk of readmission, the use of predictive analytics tools and techniques is enabling organizations to tap their collections of data to predict future business outcomes — if the process is managed properly.

Using predictive analytics tools lets organizations look ahead in an effort to optimize business strategies. But there has to be a purpose to the analytics efforts, and a solid plan behind them.

Predictive analytics has become an increasingly hot topic in analytics circles as more people realize that predictive modeling of customer behavior and business scenarios is “the big way to get big value out of data,” said Mike Gualtieri, an analyst at Forrester Research Inc. As a result, predictive analytics deployments are gaining momentum, according to Gualtieri, who said that he has seen an increase in adoption levels from about 20% in 2012 to “the mid- to high-30% range” now.

That’s still relatively low — which creates even bigger potential business benefits for organizations that have invested in predictive analytics software. If a company’s competitors aren’t doing predictive analytics, it has “a great opportunity to get ahead,” Gualtieri said.

Predictive analytics projects can also provide those benefits across various industries, said Eric King, president and founder of The Modeling Agency LLC, an analytics consulting and training services firm based in Pittsburgh. “Everyone is overwhelmed with data and starving for information,” King noted.

But that doesn’t mean it’s just a matter of rolling out the technology and letting analytics teams play around with data. When predictive analytics is done well, the business benefits can be substantial — but there are “some mainly strategic pitfalls” to watch out for, King said. “Many companies are doing analytics to do analytics, and they aren’t pursuing analytics that are measurable, purposeful, accountable and understandable by leadership.”

Data scientists don’t know it all

Plan ahead on predictive analytics

For a free consultation on your business intelligence strategy contact us.

 

 

Source: Predictive analytics tools point to better business actions

 

 

#DataForAnalytics

#PredictiveAnalytics

#BigData

 

 

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If you don’t really understand #bigdata how about tackling medium data first?

Excellent article about how big data is affecting all areas of our lives – link to the source is below.

The lifeblood of the information age is data and the prevailing wisdom is that the companies that can extract insights from data have an advantage over those that don’t.

INNOVATION STATIONS

The term “big data” refers to the huge quantities of raw data from outside the organization that can be commingled with internal data and mined for intelligence.

Analysis company Gartner says big data is

“high-volume, high-velocity and high-variety information assets that require cost-effective, innovative forms of information processing for enhanced insight and decision-making”.

Not necessarily, says Matt Kuperholz, a partner in PwC’s modelling and analytics group.

“Big data is simply using different tools and techniques to extract the full value from data.”

WHAT DO YOU WANT TO KNOW?

Businesses should tackle big data by asking themselves a business question, says Sally Wood, professor of business analytics at the University of Sydney Business School.

“I always say to businesses, ‘Tell me what you would do with the data if you had all the data in the world. What is the research question you want to answer?’ ”

Wood believes it’s the nature of the question that determines whether big data is the solution. So what sorts of questions require a big data solution?

Big data is also useful far beyond the realm of sales. “Some companies want to understand what factors affect leadership qualities,” Wood says. “All these things that were thought of as fluffy and non-rigorous suddenly can become much more evidence based.”

COMPLEX PROCESSES

PwC’s Kuperholz says big data comes into its own when a business has a large number of customers who are serviced via multiple channels with different costs. Throw in serious competition to win those customers away and retain their loyalty and the case to use big data grows stronger.

“Then you have complex supply chains or complex processes that can be optimized,” he says. “How does [global freight and logistics company] UPS get a jump on lower cost of delivery? Because they optimize routes.”

In many US states, drivers can turn right through a red traffic light, so UPS puts more right-hand turns in its drivers’ routes. This has saved hundreds of millions of dollars. “That’s a clever use of analytics,” Kuperholz says.

ANALYTICS BEGINS AT HOME

Getting more from existing data wasn’t a reason for avoiding external big data among survey respondents. Of the 58 per cent of respondents not using big data, the reasons largely fell into two categories: cost and lack of understanding about its nature and benefits.

In fact, the advice is for businesses to make absolutely sure, before embracing big data, that they have a fantastic handle on the data they already generate.

“Most organizations have more than enough data to get started, but more need to know how to use it to drive commercial value,” says Sahil Merchant, head of McKinsey Digital Australia.

The challenge for companies is to develop their internal capabilities with their own data. “Rather than focusing on big data, companies can start with medium data and use what they have got.

INSTINCTS PUT TO THE TEST

Are we moving into an age when human instinct is redundant? The majority of those using big data still see greater value in the expertise of people (70 per cent rated the team as most important) over data (30 per cent). But that mindset is increasingly insufficient.

DOING IT RIGHT

Start learning what insights you can gain from big data by contacting us for a free consultation.

 

Source: If you don’t really understand big data how about tackling medium data first?

 

 

#BigData

 

 

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#Retailing News:  #Walmart Canada Chooses Ottawa For Online Grocery Shopping Launch

The retail giant announced Friday that it has launched its own e-commerce grocery shopping service at all its Ottawa locations and in some outlying stores as well.

 

“For us, when we talk to our customers, we realize that absolutely, people are time-starved today. An hour today is worth way more than it was 10 years ago,” said Simon Rodrigue, Wal-Mart Canada’s senior vice-president, e-commerce.

 

This is Wal-Mart’s first foray into grocery shopping online in Canada. Mr. Rodrigue said the retailer chose Ottawa because it has found the city to be very receptive to previous e-commerce initiatives.

 

“I think second, Ottawa is that perfect blend of education with the universities, it has a high-tech base and from a demographic perspective, there are a lot of families,” he added.

 

Mr. Rodrigue said online shopping is the future of grocery retailing.

 

“Our customers have been telling us they wanted it,” he said. “Wal-Mart is really targeting for our customers to buy what they want, when they want, where they want, and I think the grocery home shopping is a key part of that. It’s one of the key initiatives.”

 

 

#Retailing

#Walmart

#OnlineShopping

 

Source: Wal-Mart Canada chooses Ottawa for online grocery shopping launch

 

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Saturday Talk: How Independent #Retailers Can Leverage the #InternetOfThings to End the Checkout Line

Enjoying your Saturday morning cup of coffee?  Well here’s an interesting piece discussing how to use the Internet of Everything to enhance shopping.

See you back at the office next Tuesday.

 

Waiting in checkout lines is one of a consumer’s greatest frustrations when it comes to shopping. In fact, 52 percent of consumers report that they would actually take their business elsewhere if checkout wait times exceed five to 10 minutes. Independent retailers can end the frustration of waiting in line by using Internet of Everything (IoE) technologies such as mobile applications, Wi-Fi, sensors and predictive analytics to optimize the checkout or even eliminate it completely.

 

Check out the online article by clicking on the link below.

Source: How Independent Retailers Can Leverage the Internet of Everything to End the Checkout Line

 

#RetailAnalytics

#PredictiveAnalyticsRetail