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.”

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[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

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

[  COVER OF THE WEEK ]

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Accuracy  Source

[ NEWS BYTES]

>>
 HP stealthily installs new spyware called HP Touchpoint Analytics Client – Computerworld Under  Analytics

>>
 Customer segmentation with big data at hand – Business MattersBusiness Matters Under  Prescriptive Analytics

>>
 5 ways analytics can help health systems optimize their collection strategies – Becker’s Hospital Review Under  Analytics

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[ TIPS & TRICKS OF THE WEEK]

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

[ DATA SCIENCE Q&A]

Q:Is it better to spend 5 days developing a 90% accurate solution, or 10 days for 100% accuracy? Depends on the context?
A: * “premature optimization is the root of all evils”
* At the beginning: quick-and-dirty model is better
* Optimization later
Other answer:
– Depends on the context
– Is error acceptable? Fraud detection, quality assurance

Source

[ VIDEO OF THE WEEK]

Rethinking classical approaches to analysis and predictive modeling

 Rethinking classical approaches to analysis and predictive modeling

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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]

Venu Vasudevan @VenuV62 (@ProcterGamble) on creating a rockstar data science team #FutureOfData #Podcast

 Venu Vasudevan @VenuV62 (@ProcterGamble) on creating a rockstar data science team #FutureOfData #Podcast

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[ FACT OF THE WEEK]

100 terabytes of data uploaded daily to Facebook.

Sourced from: Analytics.CLUB #WEB Newsletter

2018 Trends in Data Governance: Heightened Expectations

Organizations will contend with an abundance of trends impacting data governance in the coming year. The data landscape has effectively become decentralized, producing more data, quicker, than it ever has before. Ventures in the Internet of Things and Artificial Intelligence are reinforcing these trends, escalating the need for consistent data governance. Increasing regulatory mandates such as the General Data Protection Regulation (GDPR) compound this reality.

Other than regulations, the most dominant trend affecting data governance in the new year involves customer experience. The demand to reassure consumers that organizations have effective, secure protocols in place to safely govern their data has never been higher in the wake of numerous security breaches.

According to Stibo Systems Chief Marketing Officer Prashant Bhatia, “Our expectations, both as individuals as well as from a B2B standpoint, are only getting higher. In order for companies to keep up, they’ve got to have [governance] policies in place. And, consumers want to know that whatever data they share with a third party is trusted and secure.”

The distributed nature of consumer experience—and the heightened expectations predicated on it—is just one of the many drivers for homogeneous governance throughout a heterogeneous data environment. Governing that data in a centralized fashion may be the best way of satisfying the decentralized necessities of contemporary data processes because, according to Bhatia:

“Now you’re able to look at all of those different types of data and data attributes across domains and be able to centralize that, cleanse it, get it to the point where it’s usable for the rest of the enterprise, and then share that data out across the systems that need it regardless of where they are.”

Metadata Management Best Practices
The three preeminent aspects of a centralized approach to governing data are the deployment of a common data model, common taxonomies, and “how you communicate that data for…integration,” Bhatia added. Whether integrating (or aggregating) data between different sources either within or outside of the enterprise, metatdata management plays a crucial role in doing so effectually. The primary advantage metadata yields in this regards is in contextualizing the underlying data to clarify both their meaning and utility. “Metadata is a critical set of attributes that helps provide that overall context as to why a piece of data matters, and how it may or may not be used,” Bhatia acknowledged. Thus, in instances in which organizations need to map to a global taxonomy—such as for inter-organizational transmissions between supply chain partners or to receive data from global repositories established between companies—involving metadata is of considerable benefit.

According to Bhatia, metadata “has to be accounted for in the overall mapping because ultimately it needs to be used or associated with throughout any other business process that happens within the enterprise. It’s absolutely critical because metadata just gives you that much more information for contextualization.” When attempting to integrate or aggregate various decentralized sources, such an approach is also useful. Mapping between varying taxonomies and data models becomes essential when utilizing sources from decentralized environments into a centralized one, as does involving metadata in these efforts. Mapping metadata is so advantageous because “the more data you can have, the more context you can have, the more accurate it is, [and] the better you’re going to be able to use it within a… business process going forward,” Bhatia mentioned.

Regulatory Austerity
Forrester’s 2018 predictions identify the GDPR as one of the fundamental challenges organizations will contend with in the coming year. The GDPR issue is so prominent because it exists at the juncture between a number of data governance trends. It represents the greater need to satisfy consumer expectations as part of governance, alludes to the nexus between governance and security for privacy concerns, and illustrates the overarching importance of regulations in governance programs. The European Union’s GDPR creates stringent mandates about how consumer information is stored and what rights people have regarding data about them. Its penalties are some of the more convincing drivers for formalizing governance practices.

“Once the regulation is in place, you no longer have a choice,” Bhatia remarked about the GDPR. “Whether you are a European company or you have European interactions, the fact of the matter is you’ve got to put governance in place because the integrity of what you’re sending, what you’re receiving, when you’re doing it, and how you’re doing it…All those things no longer becomes a ‘do I need to’, but now ‘I have to’.” Furthermore, the spring 2018 implementation of GDPR highlights the ascending trend towards regulatory compliance—and stiff penalties—associated with numerous vertical industries. Centralized governance measures are a solution for providing greater utility for the data stewardship and data lineage required for compliance.

Data Stewardship
The focus on regulations and distributed computing environments only serves to swell the overall complexity of data stewardship in 2018. However, dealing with decentralized data sources in a centralized manner abets the role of a data steward in a number of ways. Stewards primarily exist to implement and maintain the policies begat from governance councils. Centralizing data management and its governance via the plethora of means available for doing so today (including Master Data Management, data lakes, enterprise data fabrics and others) enable the enterprise to “cultivate the data stewardship aspect into something that’s executable,” Bhatia said. “If you don’t have the tools to actually execute and formalize a governance process, then all you have is a process.” Conversely, the stewardship role is so pivotal because it supervises those processes at the point in which they converge with technological action. “If you don’t have the process and the rules of engagement to allow the tools to do what they need to do, all you have is the technology,” Bhatia reflected. “You don’t have a solution.”

Data Lineage
One of the foremost ways in which data stewards can positively impact centralized data governance—as opposed to parochial, business unit or use case-based governance—is by facilitating data provenance. Doing so may actually be the most valuable part of data stewardship, especially when one considers the impact of data provenance on regulatory compliance. According to Bhatia, provenance factors into “ensuring that what was expected to happen did happen” in accordance to governance mandates. Tracing how data was used, stored, transformed, and analyzed can deliver insight vital to regulatory reporting. Evaluating data lineage is a facet of stewardship that “measures the results and the accuracy [of governance measures] by which we can determine have we remained compliant and have we followed the letter of the law,” commented Bhatia. Without this information gleaned from data provenance capabilities, organizations “have a flawed process in place,” Bhatia observed.

As such, there is a triad between regulations, stewardship, and data provenance. Addressing one of these realms of governance will have significant effects on the other two, especially when leveraging centralized means of effecting the governance of distributed resources. “The ability to have a history of where data came from, where it might have been cleansed and how it might emerge, who it was shared with and when it was shared, all these different transactions and engagements are absolutely critical from a governance and compliance standpoint,” Bhatia revealed.

Governance Complexities
The complexities attending data governance in the next couple of years show few signs of decreasing. Organizations are encountering more data than ever before from a decentralized paradigm characterized by an array of on-premise and cloud architectures that complicate various facets of governance hallmarks such as data modeling, data quality, metadata management, and data lineage. Moreover, data is produced much more celeritously than before with an assortment of machine-generated streaming options. When one considers the expanding list of regulatory demands and soaring consumer expectations for governance accountability, the pressures on this element of data management become even more pronounced. Turning to a holistic, centralized means of mitigating the complexities of today’s data sphere may be the most viable means of effecting data governance.

“As more data gets created the need, which was already high, for having a centralized platform to share data and push it back out, only becomes more important,” Bhatia said.

And, with an assortment of consumers, regulators, and C-level executives evincing a vested interest in this process, organizations won’t have many chances to do so correctly.

Originally Posted at: 2018 Trends in Data Governance: Heightened Expectations by jelaniharper

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

[  COVER OF THE WEEK ]

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Data security  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Big big love, how big data’s influencing the future of the online dating scene by analyticsweekpick

>> From Data Scientist to Diplomat by tony

>> Wrapping my head around Big-data problem by v1shal

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[ NEWS BYTES]

>>
 Equinix Agrees to Buy Australian Data Center Firm Metronode for … – Data Center Knowledge Under  Data Center

>>
 TIBCO Named a Leader in Streaming Analytics by Top Independent Research Firm – CSO Australia Under  Streaming Analytics

>>
 Twistlock Ties Container and Serverless Security Into a Single Platform – SDxCentral Under  Cloud Security

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Data Science from Scratch: First Principles with Python

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[ TIPS & TRICKS OF THE WEEK]

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

[ DATA SCIENCE Q&A]

Q:How do you know if one algorithm is better than other?
A: * In terms of performance on a given data set?
* In terms of performance on several data sets?
* In terms of efficiency?
In terms of performance on several data sets:

– ‘Does learning algorithm A have a higher chance of producing a better predictor than learning algorithm B in the given context?”
– ‘Bayesian Comparison of Machine Learning Algorithms on Single and Multiple Datasets”, A. Lacoste and F. Laviolette
– ‘Statistical Comparisons of Classifiers over Multiple Data Sets”, Janez Demsar

In terms of performance on a given data set:
– One wants to choose between two learning algorithms
– Need to compare their performances and assess the statistical significance

One approach (Not preferred in the literature):
– Multiple k-fold cross validation: run CV multiple times and take the mean and sd
– You have: algorithm A (mean and sd) and algorithm B (mean and sd)
– Is the difference meaningful? (Paired t-test)

Sign-test (classification context):
Simply counts the number of times A has a better metrics than B and assumes this comes from a binomial distribution. Then we can obtain a p-value of the HoHo test: A and B are equal in terms of performance.

Wilcoxon signed rank test (classification context):
Like the sign-test, but the wins (A is better than B) are weighted and assumed coming from a symmetric distribution around a common median. Then, we obtain a p-value of the HoHo test.

Other (without hypothesis testing):
– AUC
– F-Score

Source

[ VIDEO OF THE WEEK]

Andrea Gallego(@risenthink) / @BCG on Managing Analytics Practice #FutureOfData #Podcast

 Andrea Gallego(@risenthink) / @BCG on Managing Analytics Practice #FutureOfData #Podcast

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[ 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]

#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]

Distributed computing (performing computing tasks using a network of computers in the cloud) is very real. Google GOOGL -0.53% uses it every day to involve about 1,000 computers in answering a single search query, which takes no more than 0.2 seconds to complete.

Sourced from: Analytics.CLUB #WEB Newsletter

Know your Enterprise Feedback Management Provider


Provider 1 Word Cloud - TOP WORDS: CUSTOMER, EXPERIENCE, MANAGEMENT, SOLUTIONS, SERVICES
Provider 2 Word Cloud - TOP WORDS: CUSTOMER, CONTACT, CENTER, SERVICES, PARTNERS
Provider 3 Word Cloud - TOP WORDS: SURVEY, ONLINE, FEEDBACK, CUSTOMER, MANAGEMENT
Provider 4 Word Cloud - TOP WORDS: CUSTOMER, SURVEYS, ENGAGE, SOFTWARE, FEEDBACK
Provider 5 Word Cloud - TOP WORDS: CUSTOMER, SURVEYS, FEEDBACK, MANAGEMENT, MEASURE
Provider 6 Word Cloud - TOP WORDS: BUSINESS, SERVICES, CUSTOMER, SUPPORT, EXPERIENCE
Provider 7 Word Cloud - TOP WORDS: RESEARCH, CUSTOMER, MANAGEMENT, MARKET, FEEDBACK

I recently wrote about the value of Enterprise Feedback Management vendors. EFM is the process of collecting, managing, analyzing and disseminating different sources (e.g., customers, employees, partners) of feedback.  EFM vendors help companies facilitate their customer experience management (CEM)  and voice of the customer (VoC) efforts, hoping to improve the customer experience and increase customer loyalty.  This week, I take a non-scientific approach to understanding the EFM space and wondered how EFM/CEM vendors try to differentiate themselves from each other.

Using a word cloud-generating site, tagxedo.com, I created word clouds for 7  EFM/CEM vendors based on content from their respective Web sites. Word clouds are used to visualize free form text. I generated word clouds by simply inputting that vendor’s url into the tagxedo.com site (done on 7/15/2011 – prior to the announcement of the Vovici acquisition by Verint). I used the same tagxedo.com parameters when generating each vendor’s word cloud. For each word cloud, I manually removed company/proper names and trademarked words (e.g., Net Promoter Score) that would easily identify the vendor. The resulting word clouds appear to the right (labeled Provider 1 thorugh 7). These word clouds represent the key words each vendor uses to convey their solutions to the world. The seven vendors I used in this exercise are (in alphabetical order):

  • Allegiance
  • Attensity
  • MarketTools
  • Medallia
  • Mindshare
  • Satmetrix
  • Vovici

Can you match the vendors to their word cloud? Can you even identify the vendor your company uses (given it’s in the list, of course)? Answers to the word cloud matching exercise appear at the end of this post.

Before you read the answers, here is some help. It is clear that there is much similarity among these EFM vendors. They all do similar things; they use technology to capture, analyze and disseminate feedback. Beyond there core solutions, how do they try to differentiate themselves? Giving the word clouds the standard inter-ocular test (aka eye-balling the data), I noticed that, although “Customer” appears as a top word for all vendors, there are top words that are unique to a particular vendor:

  • Provider 1: Experience and Solutions
  • Provider 2: Contact, Center and Partners
  • Provider 3: Online
  • Provider 4: Engage and Software
  • Provider 5: Measure
  • Provider 6: Business
  • Provider 7: Market and Research

Maybe this differentiation, however subtle, can help you with the matching exercise. Let me know how you did. If you have thoughts on how EFM/CEM vendors can better differentiate themselves from the pack, please share your thoughts. More importantly, how can these vendors provide more value to their customers? One way is to help their clients integrate their technology solutions into their VoC program. Those EFM vendors who can do that will be more likely to succeed than those who simply want to sell technology as a solution (remember the CRM problem?).

—–

Answers to the EFM/CEM vendor word clouds:  Medallia (Provider 1); Attensity (Provider 2); Vovici (Provider 3); Allegiance (Provider 4); Mindshare (Provider 5); Satmetrix (Provider 6); MarketTools (Provider 7)

Source: Know your Enterprise Feedback Management Provider by bobehayes

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

[  COVER OF THE WEEK ]

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Statistically Significant  Source

[ AnalyticsWeek BYTES]

>> The Big List: 80 Of The Hottest SEO, Social Media & Digital Analytics Tools For Marketers by analyticsweekpick

>> Big Data – What it Really Means for VoC and Customer Experience Professionals by bobehayes

>> Your Relative Performance: A Better Predictor of Employee Turnover by bobehayes

Wanna write? Click Here

[ NEWS BYTES]

>>
 An Inconvenient Truth: 93% of Customer Experience Initiatives are Failing… – Customer Think Under  Customer Experience

>>
 Logility acquires Halo Business Intelligence to expand advanced … – Logistics Management Under  Prescriptive Analytics

>>
 Apache Hadoop 3.0 goes GA, adds hooks for cloud and GPUs – TechTarget Under  Hadoop

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[ FEATURED COURSE]

Pattern Discovery in Data Mining

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Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods, and applications of pattern disc… more

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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]

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:What is better: good data or good models? And how do you define ‘good”? Is there a universal good model? Are there any models that are definitely not so good?
A: * Good data is definitely more important than good models
* If quality of the data wasn’t of importance, organizations wouldn’t spend so much time cleaning and preprocessing it!
* Even for scientific purpose: good data (reflected by the design of experiments) is very important

How do you define good?
– good data: data relevant regarding the project/task to be handled
– good model: model relevant regarding the project/task
– good model: a model that generalizes on external data sets

Is there a universal good model?
– No, otherwise there wouldn’t be the overfitting problem!
– Algorithm can be universal but not the model
– Model built on a specific data set in a specific organization could be ineffective in other data set of the same organization
– Models have to be updated on a somewhat regular basis

Are there any models that are definitely not so good?
– ‘all models are wrong but some are useful” George E.P. Box
– It depends on what you want: predictive models or explanatory power
– If both are bad: bad model

Source

[ VIDEO OF THE WEEK]

@AnalyticsWeek: Big Data at Work: Paul Sonderegger

 @AnalyticsWeek: Big Data at Work: Paul Sonderegger

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[ QUOTE OF THE WEEK]

It is a capital mistake to theorize before one has data. Insensibly, one begins to twist the facts to suit theories, instead of theories to

[ PODCAST OF THE WEEK]

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

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

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[ FACT OF THE WEEK]

According to estimates, the volume of business data worldwide, across all companies, doubles every 1.2 years.

Sourced from: Analytics.CLUB #WEB Newsletter

Big data analytics are the future of cyber security

LAS VEGAS: Developments in hacking culture and enterprise technology mean that big data-led intelligence defences are the future of the security industry, according to EMC.

David Goulden, CEO of information infrastructure at EMC, made the claim during a press session at EMC World attended by V3.

“The security industry is changing dramatically. If you look to the past, firewall and antivirus technologies came up as the main solution in the second platform,” he said.

“But at this stage in IT the apps were generally used within the enterprise so it was manageable. But this is no longer the case in the third platform.”

Goulden explained that in today’s threat landscape there is no way that companies can keep determined adversaries out of their systems.

He added that to overcome the challenge businesses will have to adopt big data analytics solutions that identify and combat malicious activity on the network.

“The two big challenges in security are big data challenges and based on intelligence. The first is how to securely authenticate who is coming into the network,” he said.

“The second big challenge in security is the identification of anomalies occurring in your network in real time.”

EMC is one of many firms to cite analytics as the future of cyber security.

UK firm Darktrace said during an interview with V3 that businesses’ reliance on perimeter-based defences is a key reason why hackers can spend months at a time in companies’ systems undetected.

A lack of employee awareness regarding security best practice is another common problem at many businesses. Verizon said in March that poor security awareness results in the success of one in four phishing scams.

Taking a swipe at competitors, Goulden boasted that the sweep of data breaches resulting from businesses security practices has led to a rise in the number of companies migrating to RSA analytics solutions.

“We’re a generation ahead of people at this [big data analytics-based security]. Every public breach that has occurred in recent memory has been at companies using our competitors and many have since moved to use RSA technologies,” he said.

He added that the firm has monopolised on this trend, claiming: “75 percent of RSA’s work is aimed at improving security in the future.”

Despite Goulden’s bold claims RSA reported just $4m year-on-year growth in revenue in itsQ1 2015 financials.

RSA took in a total $248m in revenue, marking an overall $165m gross profit during the first three months of 2015.

Goulden’s comments follow the launch of EMC’s Data Domain DD9500 solution and updates for its ProtectPoint and CloudBoost services.

Originally posted via “Big data analytics are the future of cyber security”

Originally Posted at: Big data analytics are the future of cyber security by analyticsweekpick

March 6, 2017 Health and Biotech analytics news roundup

Here’s the latest in health and biotech analytics:

Mathematical Analysis Reveals Prognostic Signature for Prostate Cancer: University of East Anglia researchers used an unsupervised technique to categorize cancers based on gene expression levels. Their method was better able than current supervised methods to identify patients with more harmful variants of the disease.

Assisting Pathologists in Detecting Cancer with Deep Learning: Scientists at Google have trained deep learning models to detect tumors in images of tissue samples. These models beat pathologists’ diagnoses by one metric.

Patient expectations for health data sharing exceed reality, study says: The Humana study shows that, among other beliefs, most patients think doctors share more information than they actually do. They also expect information from digital devices will be beneficial.

NHS accused of covering up huge data loss that put thousands at risk: The UK’s national health service failed to deliver half a million medically relevant documents between 2011 and 2016. They had previously briefed Parliament about the failure, but not the scale of it.

Entire operating system written into DNA at 215 Pbytes/gram: Yaniv Erlich and Dina Zielinski (New York Genome Center) used a “fountain code” to translate a 2.1 MB archive into DNA. They were able to retrieve the data by sequencing the resulting fragments, a process that was robust to mutations and loss of sequences.

Source by pstein

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

[  COVER OF THE WEEK ]

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Extrapolating  Source

[ AnalyticsWeek BYTES]

>> Focus on success, not perfection: Look at this data science algorithm for inspiration by analyticsweekpick

>> Which Machine Learning to use? A #cheatsheet by v1shal

>> #FutureOfData with @CharlieDataMine, @Oracle discussing running analytics in an enterprise by v1shal

Wanna write? Click Here

[ NEWS BYTES]

>>
 Relx Group’s trading update is reassuringly relaxed – The Times Under  Business Analytics

>>
 Global Big Data Security Market 2017- IBM, Microsoft, Oracle, Amazon Web Services – News of Columnist: Research News By Market.Biz Under  Big Data Security

>>
 Solving the ‘Last Mile’ Problem in Data Science – Datanami Under  Data Scientist

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[ FEATURED COURSE]

Tackle Real Data Challenges

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Learn scalable data management, evaluate big data technologies, and design effective visualizations…. more

[ FEATURED READ]

Data Science from Scratch: First Principles with Python

image

Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn … more

[ TIPS & TRICKS OF THE WEEK]

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

[ DATA SCIENCE Q&A]

Q: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]

Discussing #InfoSec with @travturn, @hrbrmstr(@rapid7) @thebearconomist(@boozallen) @yaxa_io

 Discussing #InfoSec with @travturn, @hrbrmstr(@rapid7) @thebearconomist(@boozallen) @yaxa_io

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[ 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]

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

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

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[ FACT OF THE WEEK]

39 percent of marketers say that their data is collected ‘too infrequently or not real-time enough.’

Sourced from: Analytics.CLUB #WEB Newsletter