Mar 22, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Data Accuracy  Source

[ NEWS BYTES]

>>
 Turning the Internet of Things into the “Internet of Secure Things” – CIO Under  Internet Of Things

>>
 With Rs 100 Cr, three CAs and a data scientist are fixing loopholes in the SME lending market – YourStory.com Under  Data Scientist

>>
 AWS is partnering with Cerner on cloud deal for HealtheIntent – CNBC Under  Cloud

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[ 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]

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]

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 do you control for biases?
A: * Choose a representative sample, preferably by a random method
* Choose an adequate size of sample
* Identify all confounding factors if possible
* Identify sources of bias and include them as additional predictors in statistical analyses
* Use randomization: by randomly recruiting or assigning subjects in a study, all our experimental groups have an equal chance of being influenced by the same bias

Notes:
– Randomization: in randomized control trials, research participants are assigned by chance, rather than by choice to either the experimental group or the control group.
– Random sampling: obtaining data that is representative of the population of interest

Source

[ VIDEO OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with @MichOConnell, @Tibco

 #BigData @AnalyticsWeek #FutureOfData #Podcast with @MichOConnell, @Tibco

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[ QUOTE OF THE WEEK]

Data really powers everything that we do. – Jeff Weiner

[ PODCAST OF THE WEEK]

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

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

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[ FACT OF THE WEEK]

In that same survey, by a small but noticeable margin, executives at small companies (fewer than 1,000 employees) are nearly 10 percent more likely to view data as a strategic differentiator than their counterparts at large enterprises.

Sourced from: Analytics.CLUB #WEB Newsletter

How Google Understands You [Infographic]

How Google Understands You [Infographic]
How Google Understands You [Infographic]

You thought the human brain was complex? With its ability to retrieve stored memories from years past and forge connections from seemingly disparate topics, it truly seems like the brain is a miraculous organ that rules our everyday lives. But what about the Google brain? Just as intricate and just as ever-changing as a human’s brain, the Google search engine works to make associations, recommendations, and analysis based upon your search phrases.

However, the question remains: how does Google understand what we want from it? When we ask it a question, how do those millions of results show up for us effortlessly, ranked in terms of relevancy and authority? Every one of us takes this process for granted so in this infographic, we’ll look at the inner mechanics of the Google search engine that produces the results you see on your screen.

How Google Understands You [INFOGRAPHIC]
Infographic by Vertical Measures

Originally Posted at: How Google Understands You [Infographic] by v1shal

Mar 15, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Productivity  Source

[ AnalyticsWeek BYTES]

>> Why Is Big Data Is So Big In Health Care? by analyticsweek

>> SAS enlarges its palette for big data analysis by analyticsweekpick

>> The ultimate customer experience [infographic] by v1shal

Wanna write? Click Here

[ NEWS BYTES]

>>
 Rival IQ Provides Free Social Media Analytics to HubSpot Customers with New Integration PartnershipHubSpot … – Markets Insider Under  Social Analytics

>>
 Deloitte: 5 Trends That Will Drive Machine Learning Adoption – InformationWeek Under  Machine Learning

>>
 What Data Science Can Tell Us About Our World – Yale News Under  Data Science

More NEWS ? Click Here

[ FEATURED COURSE]

Learning from data: Machine learning course

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This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applicati… more

[ FEATURED READ]

The Industries of the Future

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The New York Times bestseller, from leading innovation expert Alec Ross, a “fascinating vision” (Forbes) of what’s next for the world and how to navigate the changes the future will bring…. 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:What is A/B testing?
A: * Two-sample hypothesis testing
* Randomized experiments with two variants: A and B
* A: control; B: variation
* User-experience design: identify changes to web pages that increase clicks on a banner
* Current website: control; NULL hypothesis
* New version: variation; alternative hypothesis

Source

[ VIDEO OF THE WEEK]

#FutureOfData with @theClaymethod, @TiVo discussing running analytics in media industry

 #FutureOfData with @theClaymethod, @TiVo discussing running analytics in media industry

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[ QUOTE OF THE WEEK]

Getting information off the Internet is like taking a drink from a firehose. – Mitchell Kapor

[ PODCAST OF THE WEEK]

#FutureOfData with @CharlieDataMine, @Oracle discussing running analytics in an enterprise

 #FutureOfData with @CharlieDataMine, @Oracle discussing running analytics in an enterprise

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[ FACT OF THE WEEK]

Poor data across businesses and the government costs the U.S. economy $3.1 trillion dollars a year.

Sourced from: Analytics.CLUB #WEB Newsletter

Using Big Data to Kick-start Your Career

Gordon Square Communications and WAAT offers tips about how to make the most of online resources to land a dream job – all without spending a penny.

Left to right: Vamory Traore, Sylvia Arthur and Grzegorz Gonciarz

You are probably familiar with Monster.com or Indeed.com, huge jobs websites where you can upload your CV together with other 150 million people every month.

The bad news is that it is unlikely that your CV will ever get seen on one of these websites, discovered attendees of London Technology Week event Using Tech to Find a Job at Home or Abroad.

“There are too many people looking for a small number of jobs,” says Sylvia Arthur, Communicator Consultant at Gordon Square Communications and author of the book Get Hired! out on 30th June.

“The problem is that only 20% of jobs are advertised, while 25% of people are seeking a new job. If you divide twenty by twenty-five, the result of the equation is that you lose,” explains Ms Arthur.

So, how can we use technology to effectively find a job?

The first step is to analyse the “Big Data” – all the information that tells us about trends or associations, especially relating to human behaviour.

For example, if we were looking for a job in IT, we could read in the news that a new IT company has opened in Shoreditch, and from there understand that there are new IT jobs available in East London.

Big Data also tells us about salaries and cost of living in different areas, or what skills are required.

“Read job boards not as much to find a job as to understand what are the growing sectors and the jobs of the future,” is Ms Arthur’s advice.

Once you know where to go with the skills you have, you need to bear in mind that most recruiters receive thousands of CVs for a single job and they would rather ask a colleague for a referral than scan through all of them.

So if you are not lucky enough to have connections, you need to be proactive and make yourself known in the industry. “Comment, publish, be active in your area, showcase your knowledge,” says Ms Arthur.

“And when you read about an interesting opportunity, be proactive and contact the CEO, tell them what you know and what you can do for them. LinkedIn Premium free trial is a great tool to get in touch with these people.”

Another good advice is to follow the key people in your sector on social media. Of all the jobs posted on social media, 51% are on Twitter, compared to only 23% on LinkedIn.

And for those looking for jobs in the EEA, it is worth checking out EURES, a free online platform where job seekers across Europe are connected with validated recruiters.

“In Europe there are some countries with shortage of skilled workforce and others with high unemployment,” explains Grzegorz Gonciarz and Vamory Traore from WAAT.

“The aim of EURES is to tackle this problem.”

Advisers with local knowledge also help jobseekers to find more information about working and living in another European country before they move.

As for recent graduates looking for experience, a new EURES program called Drop’pin will start next week.

The program aims to fill the skills gap that separates young people from recruitment through free training sessions both online and on location.

To read the original article on London Technology Week, click here.

Originally Posted at: Using Big Data to Kick-start Your Career

Mar 08, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Data Mining  Source

[ AnalyticsWeek BYTES]

>> February 13, 2017 Health and Biotech analytics news roundup by pstein

>> Genomics England exploits big data analytics to personalise cancer treatment by analyticsweekpick

>> Looking for Building Machine Learning Solution? Learn From a Bartender by v1shal

Wanna write? Click Here

[ NEWS BYTES]

>>
 Syngenta Signs Long-Term Licensing of NRGene’s Data Analytics Platform – CropLife Under  Big Data Analytics

>>
 IT managers view data security as biggest priority – LocalGov.co.uk … – LocalGov Under  Data Security

>>
 A mysterious radiation cloud spread over Europe in September. Russia finally acknowledged it. – Vox Under  Cloud

More NEWS ? Click Here

[ FEATURED COURSE]

Statistical Thinking and Data Analysis

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This course is an introduction to statistical data analysis. Topics are chosen from applied probability, sampling, estimation, hypothesis testing, linear regression, analysis of variance, categorical data analysis, and n… more

[ FEATURED READ]

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

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Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the “data-analytic thinking” necessary for e… more

[ TIPS & TRICKS OF THE WEEK]

Save yourself from zombie apocalypse from unscalable models
One living and breathing zombie in today’s analytical models is the pulsating absence of error bars. Not every model is scalable or holds ground with increasing data. Error bars that is tagged to almost every models should be duly calibrated. As business models rake in more data the error bars keep it sensible and in check. If error bars are not accounted for, we will make our models susceptible to failure leading us to halloween that we never wants to see.

[ DATA SCIENCE Q&A]

Q:Provide examples of machine-to-machine communications?
A: Telemedicine
– Heart patients wear specialized monitor which gather information regarding heart state
– The collected data is sent to an electronic implanted device which sends back electric shocks to the patient for correcting incorrect rhythms

Product restocking
– Vending machines are capable of messaging the distributor whenever an item is running out of stock

Source

[ VIDEO OF THE WEEK]

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

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

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[ QUOTE OF THE WEEK]

Data is not information, information is not knowledge, knowledge is not understanding, understanding is not wisdom. – Clifford Stoll

[ PODCAST OF THE WEEK]

#FutureOfData with @CharlieDataMine, @Oracle discussing running analytics in an enterprise

 #FutureOfData with @CharlieDataMine, @Oracle discussing running analytics in an enterprise

Subscribe 

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[ 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

Benchmarking the share of voice of Coca-Cola, Red Bull and Pepsi

Today we’re comparing three soft drink brands: Coca Cola, Pepsi and Red Bull. All are big names in the beverages industry. We’ll use BuzzTalk’s benchmark tool to find out which brand is talked about the most and how people feel about this brand. As you probably know it’s not enough if people talk about your brand. You want them to be positive and enthusiastic.

Coca Cola has the largest Share of Voice

In order to benchmark these brands we’ve created three Media Reports in BuzzTalk. These are all set-up the same way. We include news sites, blogs, journals and Twitter for the time period starting at 23 September 2013. In these reports we didn’t include printed media.

softdrinks share of buzzAs you can see Coca Cola (blue) is the dominant brand online. Nearly 45% of the publications mention Coca Cola. Red Bull (green) and Pepsi Cola (red) follow close to each other at 29 and 26%.

Benchmarking the Buzz as not all buzz is created equal

Coca Cola doesn’t dominate everywhere on the web. If we take a closer look the dominance of Coca Cola is predominantly caused by it’s share of tweets. When we zoom in on news sites we notice it’s Red Bull who’s got the biggest piece of the pie. On blogs (not shown) Coca Cola and Red Bull match up.

buzz by content type

Is Coca Cola’s dominance on Twitter due to Beliebers?

About 99,6% of Coca Cola related publications is on Twitter. Most of these tweets relate to the Coca-Cola.FM radio station in South America in relation with Justin Bieber. On 12th November Coca Cola streamed the concert of this young pop star and what we’re seeing here is the effect of ‘Beliebers’ on the share of voice.

coca cola hashtag justin bieber

The Coca Cola Christmas effect can still be detected

The Bieber effect is even stronger than christmas (42884 versus 2764 tweets).

coca cola hashtag xmas

Last year we demonstrated what’s marking the countdown to the holidays: it’s the release of the new Coca Cola TV-commercial. What we noticed then was a sudden increase in the mood state ‘tension’. In the following graph you can see it’s still there (Coca Cola is still in blue).

coca cola tension time novemberThe mood state ‘tension’ relates to both anxiety and excitement. It’s the emotion we pick up during large product releases. If this is the first time you’re reading about mood states we recommend reading this blogpost as an introduction. Mood states are an interesting add-on to sentiment to be used in predictions about human behavior. The ways in which actual predictions can be made are subject of ongoing research.

How do we feel about these brands?

Let’s examine some more mood states and see whether we can find a mood state that’s clearly associated with a brand. As you can see in the graphs below each soft drink brand gets it fair share of mood state tension. Tension not specific for Coca Cola, though it is more prominent during the countdown towards christmas.

mood states by brandPepsi Cola evokes the most ‘confusion’ and slightly more ‘anger’. The feelings of confusion are often related to feeling quilty after drinking (too much) Pepsi.

how do we feel

Red Bull generates the most mood states as it’s dominating not only for fatigue, but also – to a lesser extend – for depression, tension and vigor.

 

Striking is the amount of publications for Red Bull in which the mood state fatigue can be detected. They say “Red Bull gives you wings” and this tag line has become famous. People now associated tiredness with the desire for Red Bull. But people also blame Red Bull for (still) feeling tired or more tired. At least it’s good to see Red Bull also has it’s share in the ‘vigor’ mood state department.

To read the original article on BuzzTalk, click here.

Originally Posted at: Benchmarking the share of voice of Coca-Cola, Red Bull and Pepsi by analyticsweekpick

Mar 01, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Pacman  Source

[ NEWS BYTES]

>>
 How Business Schools Can Integrate Data Analytics into the … – The CPA Journal Under  Business Analytics

>>
 The Next Phase Of Machine Learning – SemiEngineering Under  Machine Learning

>>
 Senior Analytics Analyst – Enova | Built In Chicago – Built In Chicago Under  Analytics

More NEWS ? Click Here

[ FEATURED COURSE]

Master Statistics with R

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In this Specialization, you will learn to analyze and visualize data in R and created reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform fre… more

[ FEATURED READ]

Storytelling with Data: A Data Visualization Guide for Business Professionals

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Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You’ll discover the power of storytelling and the way to make data a pivotal point in your story. Th… more

[ TIPS & TRICKS OF THE WEEK]

Strong business case could save your project
Like anything in corporate culture, the project is oftentimes about the business, not the technology. With data analysis, the same type of thinking goes. It’s not always about the technicality but about the business implications. Data science project success criteria should include project management success criteria as well. This will ensure smooth adoption, easy buy-ins, room for wins and co-operating stakeholders. So, a good data scientist should also possess some qualities of a good project manager.

[ DATA SCIENCE Q&A]

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

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

Source

[ VIDEO OF THE WEEK]

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

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

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[ QUOTE OF THE WEEK]

The world is one big data problem. – Andrew McAfee

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData with Jon Gibs(@jonathangibs) @L2_Digital

 #BigData @AnalyticsWeek #FutureOfData with Jon Gibs(@jonathangibs) @L2_Digital

Subscribe 

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[ FACT OF THE WEEK]

In late 2011, IDC Digital Universe published a report indicating that some 1.8 zettabytes of data will be created that year.

Sourced from: Analytics.CLUB #WEB Newsletter

What Crying Baby Could Teach Big Data Discovery Solution Seekers?

What Crying Baby Could Teach Big Data Discovery Solution Seekers?
What Crying Baby Could Teach Big Data Discovery Solution Seekers?

Yes, you read it right. It is a light title for a serious problem. I spoke with big-data scientists in some fortune 100 companies and tried to poke them to learn their strategy on how they want to tackle big data & how they are figuring out the method/tool that works best for them. It was interesting to hear their story, to learn all the options that are available to them and how they ended up picking the tool. I was trying to understand/resolve the problem and then, one night I saw my 2 year daughter cry non-stop. We all huddled to find what is troubling her. Then it occurred to me that, it is the similar situation that companies are facing today.

First, let me explain what happened, and then I will try to make the connection on why and how it is relevant. On one blue moon, my daughter who has just turned two, started acting fussy compared to her normal state. There were some guests at home, so as a normal parent we started figuring out what is bothering her to calm her down, but nothing seems to be working. One of guest put forward some suggestion for the reason for her fussiness, and then there were other theories that got added. All of us were trying to find the right reason for her fussiness from our individual experience and soon, a collaboration of various tricks worked and she found her peace. Not sure if the reason for the fussiness is any important here but the good part is that she became relaxed.

Now, this is the problem that most of the companies are facing today. Like my daughter they all are fussy as they all have a big-data problem, they have lot of unknowns hiding in their data. They all can barely understand how to find them, let alone the way to put them to use. And if we compare visualization tool to guests, parent and everybody around my daughter trying to figure out their own version of what is happening- It’s a chaos. If you let one of the many figure out their version of what it is, they may be off for quite some time that could be painful, discomforting and wrong for some time. On the other hand, a model of collective wisdom worked best as everyone gave their quick thoughts which helped us collaborate and iterate on the information and figure out the best path.

Now consider companies’ using multiple tools on their problem, and babysitting for days/months/years costing time, money and resources. These tools could end up becoming the best nanny there is or the worst one. Outcome is anyone’s guess, but if you get a good tool, will you ever find out if there is a better or best tool out there. That is the problem big-data industry is facing today. Unlike their other traditional appliances/tools, big-data tool requires considerable cash influx and time/resource commitment, so going through long sales cycle and marrying a single tool should not be high on their charts.
Before you get onto your hunting, make sure to create a small data set that best defines your business chaos. The data should contain almost every aspect of your business in a way that it could work as a good recruiting tool for data discovery platform. I will go a bit deeper into what entails some good preparatory steps before you go shopping. But for this blog, let’s make sure we have our basic data set ready for testing the tools.

Now, the best approach in recruiting best visualization framework should go through one of the three ways:
1. Hiring an independent consulting, like we consult pediatrics for their expertise in dealing with baby problems, we could hire a specialized shop that could work closely with your business, and other data visualizations vendors. These consultants could help companies recruit those tools by acting as a mediation layer to help you filter out any bias, or technological challenge that restricts your decision making capabilities. These consultants could sit with your organizations, understand it’s requirements and go for tool fishing recommending the best tool that suits your needs.

2. Maximizing the use of trial periods for platform. Just as we quickly turn around things and validate which method could pacify the kids quickly and not get into long cycle of failures, we could treat It is the same. This technique is painful but still does relatively less damage than going full throttle with one tool on long journey of failure. This approach prepares you to have a mindset, tactical and strategic agenda to hire/fire tool fast and pick the best tool that is delivering maximum value per dataset. This technique is relatively expensive among the three and it could introduce some bias in the decision making.

3. Go with platform plays: Similar to pediatric clinic, you could find almost everything that could help pacify the situation. Similarly, vendors that provide you with platform system to help you experiment all those methodologies and let you pick the best combination that will work for your system. These vendors are not stuck to any visualization techniques but they make everything available to clients and help them get stuck with best package out there. Having locked at such system you could make sure that your business interest should get the highest precedence and not any specific visualization/discovery technique. For keeping the blog clean from any shout outs, I would keep the company name out of the text, but do let me know if you are interested to know which all companies provide platform play for you to experiment with.

And by that you could make the baby stop crying in fastest, most cost effective and business responsive manner.

Originally Posted at: What Crying Baby Could Teach Big Data Discovery Solution Seekers?

Lavastorm Democratizing Big Data Analytics in Face of Skills Shortage

Democratizing Big Data refers to the growing movement of making products and services more accessible to other staffers, such as business analysts, along the lines of “self-service business intelligence” (BI).

In this case, the democratized solution is “the all-in-one Lavastorm Analytics Engineplatform,” the Boston company said in an announcement today announcing product improvements. It “provides an easy-to-use, drag-and-drop data preparation environment to provide business analysts a self-serve predictive analytics solution that gives them more power and a step-by-step validation for their visualization tools.”

It addresses one of the main challenges to successful Big Data deployments, as listed in study after study: lack of specialized talent.

“Business analysts typically encounter a host of core problems when trying to utilize predictive analytics,” Lavastorm said. “They lack the necessary skills and training of data scientists to work in complex programming environments like R. Additionally, many existing BI tools are not tailored to enable self-service data assembly for business analysts to marry rich data sets with their essential business knowledge.”

XXX
[Click on image for larger view.]The Lavastorm Analytics Engine (source: Lavastorm Analytics)

That affirmation has been confirmed many times. For example, a recent report by Capgemini Consulting, “Cracking the Data Conundrum: How Successful Companies Make Big Data Operational,” says that lack of Big Data and analytics skills was reported by 25 percent of respondents as a key challenge to successful deployments. “The Big Data talent gap is something that organizations are increasingly coming face-to-face with,” Capgemini said.

Other studies indicate they haven’t been doing such a good job facing the issue, as the self-service BI promises remain unfulfilled.

Enterprises are trying many different approaches to solving the problem. Capgemini noted that some companies are investing more in training, while others try more unconventional techniques, such as partnering with other companies in employee exchange programs that share more skilled workers or teaming up with or outright acquiring startup Big Data companies to bring skills in-house.

Others, such as Altiscale Inc., offer Hadoop-as-a-Service solutions, or, like BlueData, provide self-service, on-premises private clouds with simplified analysis tools.

Lavastorm, meanwhile, uses the strategy of making the solutions simpler and easier to use. “Demand for advanced analytic capabilities from companies across the globe is growing exponentially, but data scientists or those with specialized backgrounds around predictive analytics are in short supply,” said CEO Drew Rockwell. “Business analysts have a wealth of valuable data and valuable business knowledge, and with the Lavastorm Analytics Engine, are perfectly positioned to move beyond their current expertise in descriptive analytics to focus on the future, predicting what will happen, helping their companies compete and win on analytics.”

The Lavastorm Analytics Engine comes in individual desktop editions or in server editions for use in larger workgroups or enterprise-wide.

New predictive analytics features added to the product as listed today by Lavastorm include:

  • Linear Regression: Calculate a line of best fit to estimate the values of a variable of interest.
  • Logistic Regression: Calculate probabilities of binary outcomes.
  • K-Means Clustering: Form a user-specified number of clusters out of data sets based on user-defined criteria.
  • Hierarchical Clustering: Form a user-specified number of clusters out of data sets by using an iterative process of cluster merging.
  • Decision Tree: Predict outcomes by identifying patterns from an existing data set.

These and other new features are available today, Lavastorm said, with more analytical component enhancements to the library on tap.

The company said its approach to democratizing predictive analytics gives business analysts drag-and-drop capabilities specifically designed to help them master predictive analytics.

“The addition of this capability within the Lavastorm Analytics Engine’s visual, data flow-driven approach enables a fundamentally new method for authoring advanced analyses by providing a single shared canvas upon which users with complementary skill sets can collaborate to rapidly produce robust, trusted analytical applications,” the company said.

About the Author- David Ramel is an editor and writer for 1105 Media.

Originally posted via “Lavastorm Democratizing Big Data Analytics in Face of Skills Shortage”

Source: Lavastorm Democratizing Big Data Analytics in Face of Skills Shortage