Monthly Archives: May 2017

Former IBM employee pleads guilty to ‘economic espionage’ after stealing trade secrets for China

 Former IBM employee pleads guilty to ‘economic espionage’ after stealing trade secrets for China

A former developer for IBM pled guilty on Friday to economic espionage and to stealing trade secrets related to a type of software known as a clustered file system, which IBM sells to customers around the world.

Xu Jiaqiang stole the secrets during his stint at IBM from 2010 to 2014 “to benefit the National Health and Family Planning Commission of the People’s Republic of China,” according to the U.S. Justice Department.

In a press release describing the criminal charges, the Justice Department also stated that Xu tried to sell secret IBM source code to undercover FBI agents posing as tech investors. (The agency does not explain if Xu’s scheme to sell to tech investors was to benefit China or to line his own pockets.)

Part of the sting involved Xu demonstrating the stolen software, which speeds computer performance by distributing works across multiple servers, on a sample network. The former employee acknowledged that others would know the software had been taken from IBM, but said he could create extra computer script to help mask his origins.

Xu, who is a Chinese national who studied computer science at the University of Delaware, will be sentenced on October 13.

The Justice Department’s press release does not identify IBM, but instead refers to “the Victim Company.” But other news outlets name IBM as the target of the theft, while a LinkedIn page with Xu’s name shows he worked at IBM as a file system developer during the relevant dates.

IBM did not immediately respond to request for comment on Sunday.

This isn’t the first time that Chinese nationals have carried out economic espionage against American companies. In 2014, the Justice Department charged five Chinese hackers for targeting U.S. nuclear and solar energy firms. And late last year, the agency charged three others for hacking U.S. law firms with the goal of trading on insider information that they obtained.

This story originally appeared on Fortune.com. Copyright 2017

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Thoughts on the 2017 KDNuggets Poll on Data Science Tools

toast Thoughts on the 2017 KDNuggets Poll on Data Science Tools

Are you a data scientist and do not know KDNuggets.com?  How is this possible?  Ok, go there right now, add a bookmark, and make this part of your daily reading list.  But don’t forget to come back here afterwards to read the rest of this post.

KDNuggets is one of the most popular portals for data science, and is a great source for news and information.  It probably will not be winning a design award any time soon.  But the rich, deep content is why you will go back over and over, and that’s what really matters.

I spend more time on KDNuggets than usual in May, because that’s when the annual KDNuggets poll What data science solution did you use in the past 12 months? comes out. Gregory Piatetsky-Shapiro, the editor of KDNuggets and one of the best-known data scientists in the world, has been doing this poll for 18 years.

Gregory just published the results for 2017, and about 2,900 people have shared their software preferences for data science tools. And as always, there is a lot to learn from those results.

What’s new in data science in 2017?

First things first: RapidMiner was again voted as the most popular general data science platform and this is all thanks to our user community!  33% of all voters said that they are using RapidMiner, which is an amazing result. Many thanks to all of you!

But we know that data scientists are using up to 6 different tools in parallel so besides RapidMiner, what other tools are people using?

Let’s start with the programming languages. It should not come as a surprise that R and Python are the two leading languages for data science.  This year, Python got slightly more votes than R which might not be a significant difference really.  But in general Python has shown the bigger growth rates in the previous years, and I would not be surprised to see Python to take over the leading position over R in the future.  And then there is of course SQL, which made the third place among the programming languages.  SQL will of course never die, so no surprise here.

Connected to Python growth is Anaconda, a Python distribution with package management. Big shout out to our friends at Anaconda for growing that quickly!

On an infrastructure level, Apache Spark was used by 23% of all data scientists but Hadoop only by 7%.  And while we are talking about big data, the library MLLib only was used by 5% and hence much less than many other options.  To be honest, this was a bit of a surprise to me.

Deep Learning is all the rage

Yes, I am guilty for not playing along with the crazy deep learning hype of the past few years.  After all, the technology is much less innovative than most people believe. But I will admit that there is a strong growth trend around deep learning in our field.

This year, more than 32% of all data scientists said that they are using deep learning, up from 18% in 2016 and 9% in 2015.  Doubling every year is impressive growth indeed.

There are now a dozen or so deep learning libraries.  The most widely used one of course is Google’s Tensorflow, now used by 20% of all data scientists.

RapidMiner’s history with the KDNuggets poll

I view this poll a bit like a sporting event. It won’t make or break a vendor, but I at least take it serious. I think all vendors should take it seriously, and it looked like more vendors did this year.

The history of RapidMiner in the poll is interesting as well.  In 2006, our co-founder Ralf Klinkenberg was already why YALE was not an option in the poll (YALE was the former name of RapidMiner, and an acronym for “Yet Another Learning Environment”).  Who could know that only 11 years later machine learning would be all the hype?

RapidMiner was first included in the poll in 2007, and YALE was the most widely used open source platform from the start.  But some of our commercial competitors like SAS and SPSS were ahead of us back then.  But thanks to our loyal community and user base this changed quite quickly.  In 2008, we ended up just shy of SPSS Clementine (which later became SPSS Modeler).  We remained in the top 3 for a couple of years, and during that time other open source solutions like R started to gain more traction in the poll.

Starting In 2011, RapidMiner took over first place among all data science platform tools, and we have been able to keep this position since then.  One of the great things, however, is that data scientists now have many different approaches and often mix and match the different solutions.  There are clearly leading data science platforms like RapidMiner and in addition we have two great programming languages for data science as well, namely R and Python.

And then there are dozens of libraries like MLLib or Tensorflow, most of them accessible through RapidMiner as well.  So, you will be able to find the right tool for your problem and this is a wonderful situation to be in for data scientists.  Compare this to software offerings in the earlier years of this poll (check out the links above).

It’s a great time to be a data scientist indeed!

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RapidMiner

Simultaneous Auto-growth in Multiple Files

SQL Server 2016 has a new configuration to control the auto-growth of multiple files in the same filegroup. When we create several files in the same filegroup SQL Server does a round robin across the files, writing a piece of data in each of them until all the data is finally on the files.

However, the amount of data written in each file may not be always the same. The algorithm SQL Server uses for the round robin takes into account the amount of free space in each file. Due to that, to ensure an even data distribution across the files, we need to keep the files with the same size.

If the auto-growth happens, one file will be bigger than the other, therefore the data distribution across the files will be unbalanced.

Starting in SQL Server 2016 we have a solution for this problem: The filegroups now have the “autogrow_all_files” attribute.  This attribute ensures that all files will grow together, keeping the same size.

Let’s execute a demo, step by step.

1) Create a new database. The statement below creates the database with two filegroups, the Primary and another one called FG1. You need to correct the path of the files before execute this statement.

CREATE DATABASE sales 

ON PRIMARY 

        (NAME = sales_dat, filename =
                ‘C:\MyFolder\Sales.mdf’, size = 8mb, maxsize = 500mb, filegrowth = 20% ),
filegroup fg1 — Default
        (NAME = sales_dat2, filename = ‘C:\MyFolder\Sales2.ndf’, size = 8mb, maxsize =
                 500mb, filegrowth = 20% ),

        (NAME = sales_dat3, filename =
                 ‘C:\MyFolder\Sales3.ndf’, size = 8mb, maxsize = 500mb, filegrowth = 20% )

log ON
        (NAME = sales_log, filename = ‘C:\MyFolder\Sales.ldf’, size = 20mb, maxsize =
                 unlimited, filegrowth = 10mb );
go 

2) Check the filegroups configuration. The result, in the image below, shows the default value of the attribute autogrow_all_files, disabled.

USE sales
go
SELECT NAME,is_autogrow_all_files
FROM   sys.filegroups 

AutogrowthAll05 Simultaneous Auto growth in Multiple Files

3) Let’s create a table in filegroup FG1, insert 2000 records and check the database files. The autogrowth didn’t happen yet.

CREATE TABLE test 
  (
     id    INT IDENTITY(1, 1) PRIMARY KEY,
     texto CHAR(8000)
  )
ON fg1
go
INSERT INTO test 
VALUES      (‘x’)
go 2000
EXEC Sp_helpfile
go 

AutogrowthAll02 Simultaneous Auto growth in Multiple Files

4) Using the DMV ‘sys.dm_db_database_page_allocations’ we can identify the data distribution across the files.

select extent_file_id,count(*)
 from
sys.dm_db_database_page_allocations
     (DB_ID(‘Sales’),OBJECT_ID(‘test’),1,1,‘DETAILED’)
group by extent_file_id

go

AutogrowthAll03 Simultaneous Auto growth in Multiple Files

5) Let’s insert more 20 records and check the files again. The auto-growth already happened in only one of the files.

insert into test values (‘x’)

go 20

exec sp_helpfile

AutogrowthAll04 Simultaneous Auto growth in Multiple Files

This result will unbalance the round-robin, reducing any advantage it’s creating for the environment. Let’s try the same demonstration again, this time changing the autogrow_all_files attribute of FG1 filegroup.

1) Drop and re-create the database, changing the autogrow_all_files and checking the change. Again, you need to correct the path of the files.

use master

go

drop databaseif exists Sales;

go

CREATE DATABASE Sales
 on Primary
  (NAME = Sales_dat, FILENAME = ‘C:\MyFolder\Sales.mdf’, SIZE = 8MB, MAXSIZE = 500MB, FILEGROWTH = 20% ),
 Filegroup FG1 — Default
  (NAME = Sales_dat2, FILENAME = ‘C:\MyFolder\Sales2.ndf’, SIZE = 8MB, MAXSIZE = 500MB, FILEGROWTH = 20% ),
  (NAME = Sales_dat3, FILENAME = ‘C:\MyFolder\Sales3.ndf’, SIZE = 8MB, MAXSIZE = 500MB, FILEGROWTH = 20% )
LOG ON
  (NAME = Sales_log, FILENAME = ‘C:\MyFolder\Sales.ldf’, SIZE = 20MB, MAXSIZE = UNLIMITED, FILEGROWTH = 10MB );

go

alter database Sales modify filegroup [FG1] AutoGrow_All_Files
      With Rollback Immediate

go

use sales

go

select name,is_autogrow_all_files
 from sys.filegroup

AutogrowthAll05 Simultaneous Auto growth in Multiple Files

2) Create the table, insert 2000 records and check the files.

create table test 
( id int identity(1,1) primary key,
  texto char(8000))
  on FG1

go

insert into test values (‘x’)

go 2000

exec sp_helpfile

go

AutogrowthAll06 Simultaneous Auto growth in Multiple Files

3) Insert 20 more records and check the files again. Now the auto-growth happened in both files, keeping the data distribution even across the files.

insert into test values (‘x’)

go 20 

exec sp_helpfile

AutogrowthAll07 Simultaneous Auto growth in Multiple Files

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SQL – Simple Talk

Genesys Launches G-Nine CX Framework

Genesys on Monday introduced G-Nine as the innovation framework underlying all of its product offerings.

genesys Genesys Launches G Nine CX Framework

“Think of G-Nine as the Genesys innovation framework that guides many aspects of our business — product strategy being one of those,” said Genesys CMO Merijn te Booij.

“Within the G-Nine innovation framework, we have defined our themes related to technology and consumer trends that we’ll focus on in the next two years,” he told CRM Buyer.

Among G-Nine’s initial capabilities:

  • Smart App Automation, with more than 80 predefined micro-applications for voice and digital self-service;
  • Asynchronous messaging, through Genesys Hub, in often-used channels such as Facebook Messenger Slack, Skype and WeChat;
  • Next-generation engagement, which extends the customer experience into the Internet of Things to determine in real time the best course of action given user context, resource availibility, customer profile and business attributes;
  • Bring-Your-Own Bot, which lets corporations bring their own bots, such as those powered by IBM Watson, to the Genesys customer experience platform; and
  • Kate, which is customer service-specific artificial intelligence in a personified AI ecosystem.

“Depending upon the platform, some capabilities, like smart app automation and asynchronous messaging, are currently available to customers,” te Booij said. “Others will be rolled out in the coming months.”

Extending Genesys to Users’ Current Systems

Kate brings together the capabilities of blended AI, such as using Salesforce Einstein for CRM and IBM Watson for big data. It has its own micro apps and natural language understanding.

The capabilities of the bots customers choose to bring in will blend seamlessly with native Genesys AI and machine learning systems to offer a deeper understanding of customer interactions across channels, te Booij noted.

“The Genesys platform is extremely open, so it can be extended to any AI or machine learning-based bot,” he added.

What G-Nine Is

G-Nine “is [Genesys’] experience platform leveraging their past framework and now being called a ‘platform,'” said Ray Wang, principal analyst at Constellation Research. “It’s predictive analysis and machine learning meets customer journeys.”

Next-generation engagement, which aims to extend the customer experience into the IoT, can be used “to bring in contextual IoT data to help with the customer experience,” Wang told CRM Buyer.

The advantage of next-generation engagement “is in the digital channels blended back to traditional ones,” he suggested. “Think chatbots, virtual assistance and contact centers.”

On the other hand, “it’s not completely clear how much G-Nine is a suite of offerings versus a development toolkit,” said Rebecca Wettemann, VP of research at Nucleus Research.

“It will be interring to see how G-Nine innovations can be plucked and integrated from Genesys to other platforms,” she told CRM Buyer.

Plugging in the AI Stuff

“AI and virtual assistants are all the rage in the customer experience, and Genesys is no exception,” Wettemann remarked.

Genesys last year completed the acquisition of Interactive Intelligence, and this “both gave Genesys a modern cloud platform and a customer base that was already innovating at cloud pace,” she noted.

AI needs seven key components in order to work, according to Constellation’s Wang. It requires lots of data; compute power; great math talent and algorithms; time compression; domain expertise; great interactive UX-like chat, vision and speech; and a good recommendation engine.

“Genesys is providing the algorithms and leveraging the data in the contact center and knowledge repositories,” Wang said.

Kate “is a good start, but it needs much work. It’s more advanced than Cortana but not as good as TensorFlow,” he observed.

“Given the flurry of AI and intelligent agent announcements in the field over the past few quarters,” said Wettemann, “Genesys will have to show both technically and from a time-to-value perspective how Kate — and the other new innovations — deliver, compared with the competition.”
end enn Genesys Launches G Nine CX Framework


Richard%20Adhikari Genesys Launches G Nine CX FrameworkRichard Adhikari has been an ECT News Network reporter since 2008. His areas of focus include cybersecurity, mobile technologies, CRM, databases, software development, mainframe and mid-range computing, and application development. He has written and edited for numerous publications, including Information Week and Computerworld. He is the author of two books on client/server technology.
Email Richard.

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WATCH: New Trailer Debut Of Comedy ‘The Hitman’s Bodyguard’ Starring Samuel L. Jackson And Ryan Reynolds

 WATCH: New Trailer Debut Of Comedy ‘The Hitman’s Bodyguard’ Starring Samuel L. Jackson And Ryan Reynolds

The world’s top protection agent [Ryan Reynolds] is called upon to guard the life of his mortal enemy, one of the world’s most notorious hitmen [Samuel L. Jackson]. The relentless bodyguard and manipulative assassin have been on the opposite end of the bullet for years and are thrown together for a wildly outrageous 24 hours. During their raucous and hilarious adventure from England to the Hague, they encounter high-speed car chases, outlandish boat escapades and a merciless Eastern European dictator [Gary Oldman] who is out for blood. Salma Hayek joins the mayhem as Jackson’s equally notorious wife. 

The film will be “hitting” your theatres on August 18. Watch the trailer below;

Tyler Perry Moves Forward With ‘Boo 2! A Madea Halloween’ This October!

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Alipay Reaches U.S. Payments Deal

Ant Financial’s Alipay service has inked a deal with U.S. payment processing services provider First Data Corp to allow Alipay users to purchase from more than four million American vendors.

Souheil Badran, Alipay’s North America president, said this will make Alipay ubiquitous in the U.S. and enable it to enter more countries. Instead of operating independently in the U.S., they prefer to cooperate with ecosystems with certain scale.

In China, Alipay and WeChat payment hold combined market share of over 90% in the Chinese mobile payment market. At present, Alipay hopes to provide a wider range of services to Chinese tourists who go overseas. Alipay’s mobile wallet already supports credit cards of American Express, Visa, and MasterCard. So far, over 100,000 retailers in 70 international markets accept payments via Alipay.

By cooperating with First Data, Alipay will be able to lift its penetration level in the U.S. to the same as Apple Pay. Apple’s chief executive officer Tim Cook recently revealed on a conference call that Apple’s mobile payment service is now available at 4.5 million places in the U.S.

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Registration is Open – CRMUG Summit 2017

CRMUG Summit 2017 300x225 Registration is Open – CRMUG Summit 2017

If you’ve been part of the PowerObjects community for any time at all, you’re bound to know that we’re huge fans of CRMUG! No only do we participate in the online CRMUG community, we also love CRMUG’s many in-person events. That said, it’s no surprise that we go BIG for CRMUG Summit each year!

CRMUG Summit 2017 will be held in Nashville at the iconic Gaylord Opryland Hotel on October 10-13… and we can’t wait! CRMUG Summit will include electrifying general sessions, essential peer-to-peer knowledge exchanges, product insight forums, and authentic networking opportunities. With the largest annual gathering of CRM users, CRMUG Summit is designed, led, and attended by CRM users, partners, and experts from around the world.

If you’re a member of CRMUG, you can save up to $ 400 when you register before the Early Bird pricing deadline on June 29 . If you’re not a member but are interested in becoming one, you might want to consider bundling your membership and Summit registration to save. In addition to these great discounts, you can use the PowerObjects promo code: PRPPO to save 10%. For more pricing details, click here.

Check out the recap video from CRMUG EMEA Summit, earlier this year. Happy CRM’ing!

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PowerObjects- Bringing Focus to Dynamics CRM

5 Actionable Tips To Update Your SEO Strategy Right Now

Last August, a woman arrived at a Reno, Nevada, hospital and told the attending doctors that she had recently returned from an extended trip to India, where she had broken her right thighbone two years ago. The woman, who was in her 70s, had subsequently developed an infection in her thigh and hip for which she was hospitalized in India several times. The Reno doctors recognized that the infection was serious—and the visit to India, where antibiotic-resistant bacteria runs rampant, raised red flags.

When none of the 14 antibiotics the physicians used to treat the woman worked, they sent a sample of the bacterium to the U.S. Centers for Disease Control (CDC) for testing. The CDC confirmed the doctors’ worst fears: the woman had a class of microbe called carbapenem-resistant Enterobacteriaceae (CRE). Carbapenems are a powerful class of antibiotics used as last-resort treatment for multidrug-resistant infections. The CDC further found that, in this patient’s case, the pathogen was impervious to all 26 antibiotics approved by the U.S. Food and Drug Administration (FDA).

In other words, there was no cure.

This is just the latest alarming development signaling the end of the road for antibiotics as we know them. In September, the woman died from septic shock, in which an infection takes over and shuts down the body’s systems, according to the CDC’s Morbidity and Mortality Weekly Report.

Other antibiotic options, had they been available, might have saved the Nevada woman. But the solution to the larger problem won’t be a new drug. It will have to be an entirely new approach to the diagnosis of infectious disease, to the use of antibiotics, and to the monitoring of antimicrobial resistance (AMR)—all enabled by new technology.

sap Q217 digital double feature2 images2 5 Actionable Tips To Update Your SEO Strategy Right NowBut that new technology is not being implemented fast enough to prevent what former CDC director Tom Frieden has nicknamed nightmare bacteria. And the nightmare is becoming scarier by the year. A 2014 British study calculated that 700,000 people die globally each year because of AMR. By 2050, the global cost of antibiotic resistance could grow to 10 million deaths and US$ 100 trillion a year, according to a 2014 estimate. And the rate of AMR is growing exponentially, thanks to the speed with which humans serving as hosts for these nasty bugs can move among healthcare facilities—or countries. In the United States, for example, CRE had been seen only in North Carolina in 2000; today it’s nationwide.

Abuse and overuse of antibiotics in healthcare and livestock production have enabled bacteria to both mutate and acquire resistant genes from other organisms, resulting in truly pan-drug resistant organisms. As ever-more powerful superbugs continue to proliferate, we are potentially facing the deadliest and most costly human-made catastrophe in modern times.

“Without urgent, coordinated action by many stakeholders, the world is headed for a post-antibiotic era, in which common infections and minor injuries which have been treatable for decades can once again kill,” said Dr. Keiji Fukuda, assistant director-general for health security for the World Health Organization (WHO).

Even if new antibiotics could solve the problem, there are obstacles to their development. For one thing, antibiotics have complex molecular structures, which slows the discovery process. Further, they aren’t terribly lucrative for pharmaceutical manufacturers: public health concerns call for new antimicrobials to be financially accessible to patients and used conservatively precisely because of the AMR issue, which reduces the financial incentives to create new compounds. The last entirely new class of antibiotic was introduced 30 year ago. Finally, bacteria will develop resistance to new antibiotics as well if we don’t adopt new approaches to using them.

Technology can play the lead role in heading off this disaster. Vast amounts of data from multiple sources are required for better decision making at all points in the process, from tracking or predicting antibiotic-resistant disease outbreaks to speeding the potential discovery of new antibiotic compounds. However, microbes will quickly adapt and resist new medications, too, if we don’t also employ systems that help doctors diagnose and treat infection in a more targeted and judicious way.

Indeed, digital tools can help in all four actions that the CDC recommends for combating AMR: preventing infections and their spread, tracking resistance patterns, improving antibiotic use, and developing new diagnostics and treatment.

Meanwhile, individuals who understand both the complexities of AMR and the value of technologies like machine learning, human-computer interaction (HCI), and mobile applications are working to develop and advocate for solutions that could save millions of lives.

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Keeping an Eye Out for Outbreaks

Like others who are leading the fight against AMR, Dr. Steven Solomon has no illusions about the difficulty of the challenge. “It is the single most complex problem in all of medicine and public health—far outpacing the complexity and the difficulty of any other problem that we face,” says Solomon, who is a global health consultant and former director of the CDC’s Office of Antimicrobial Resistance.

Solomon wants to take the battle against AMR beyond the laboratory. In his view, surveillance—tracking and analyzing various data on AMR—is critical, particularly given how quickly and widely it spreads. But surveillance efforts are currently fraught with shortcomings. The available data is fragmented and often not comparable. Hospitals fail to collect the representative samples necessary for surveillance analytics, collecting data only on those patients who experience resistance and not on those who get better. Laboratories use a wide variety of testing methods, and reporting is not always consistent or complete.

Surveillance can serve as an early warning system. But weaknesses in these systems have caused public health officials to consistently underestimate the impact of AMR in loss of lives and financial costs. That’s why improving surveillance must be a top priority, says Solomon, who previously served as chair of the U.S. Federal Interagency Task Force on AMR and has been tracking the advance of AMR since he joined the U.S. Public Health Service in 1981.

A Collaborative Diagnosis

Ineffective surveillance has also contributed to huge growth in the use of antibiotics when they aren’t warranted. Strong patient demand and financial incentives for prescribing physicians are blamed for antibiotics abuse in China. India has become the largest consumer of antibiotics on the planet, in part because they are prescribed or sold for diarrheal diseases and upper respiratory infections for which they have limited value. And many countries allow individuals to purchase antibiotics over the counter, exacerbating misuse and overuse.

In the United States, antibiotics are improperly prescribed 50% of the time, according to CDC estimates. One study of adult patients visiting U.S. doctors to treat respiratory problems found that more than two-thirds of antibiotics were prescribed for conditions that were not infections at all or for infections caused by viruses—for which an antibiotic would do nothing. That’s 27 million courses of antibiotics wasted a year—just for respiratory problems—in the United States alone.

And even in countries where there are national guidelines for prescribing antibiotics, those guidelines aren’t always followed. A study published in medical journal Family Practice showed that Swedish doctors, both those trained in Sweden and those trained abroad, inconsistently followed rules for prescribing antibiotics.

Solomon strongly believes that, worldwide, doctors need to expand their use of technology in their offices or at the bedside to guide them through a more rational approach to antibiotic use. Doctors have traditionally been reluctant to adopt digital technologies, but Solomon thinks that the AMR crisis could change that. New digital tools could help doctors and hospitals integrate guidelines for optimal antibiotic prescribing into their everyday treatment routines.

“Human-computer interactions are critical, as the amount of information available on antibiotic resistance far exceeds the ability of humans to process it,” says Solomon. “It offers the possibility of greatly enhancing the utility of computer-assisted physician order entry (CPOE), combined with clinical decision support.” Healthcare facilities could embed relevant information and protocols at the point of care, guiding the physician through diagnosis and prescription and, as a byproduct, facilitating the collection and reporting of antibiotic use.

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Cincinnati Children’s Hospital’s antibiotic stewardship division has deployed a software program that gathers information from electronic medical records, order entries, computerized laboratory and pathology reports, and more. The system measures baseline antimicrobial use, dosing, duration, costs, and use patterns. It also analyzes bacteria and trends in their susceptibilities and helps with clinical decision making and prescription choices. The goal, says Dr. David Haslam, who heads the program, is to decrease the use of “big gun” super antibiotics in favor of more targeted treatment.

While this approach is not yet widespread, there is consensus that incorporating such clinical-decision support into electronic health records will help improve quality of care, contain costs, and reduce overtreatment in healthcare overall—not just in AMR. A 2013 randomized clinical trial finds that doctors who used decision-support tools were significantly less likely to order antibiotics than those in the control group and prescribed 50% fewer broad-spectrum antibiotics.

Putting mobile devices into doctors’ hands could also help them accept decision support, believes Solomon. Last summer, Scotland’s National Health Service developed an antimicrobial companion app to give practitioners nationwide mobile access to clinical guidance, as well as an audit tool to support boards in gathering data for local and national use.

“The immediacy and the consistency of the input to physicians at the time of ordering antibiotics may significantly help address the problem of overprescribing in ways that less-immediate interventions have failed to do,” Solomon says. In addition, handheld devices with so-called lab-on-a-chip  technology could be used to test clinical specimens at the bedside and transmit the data across cellular or satellite networks in areas where infrastructure is more limited.

Artificial intelligence (AI) and machine learning can also become invaluable technology collaborators to help doctors more precisely diagnose and treat infection. In such a system, “the physician and the AI program are really ‘co-prescribing,’” says Solomon. “The AI can handle so much more information than the physician and make recommendations that can incorporate more input on the type of infection, the patient’s physiologic status and history, and resistance patterns of recent isolates in that ward, in that hospital, and in the community.”

Speed Is Everything

Growing bacteria in a dish has never appealed to Dr. James Davis, a computational biologist with joint appointments at Argonne National Laboratory and the University of Chicago Computation Institute. The first of a growing breed of computational biologists, Davis chose a PhD advisor in 2004 who was steeped in bioinformatics technology “because you could see that things were starting to change,” he says. He was one of the first in his microbiology department to submit a completely “dry” dissertation—that is, one that was all digital with nothing grown in a lab.

Upon graduation, Davis wanted to see if it was possible to predict whether an organism would be susceptible or resistant to a given antibiotic, leading him to explore the potential of machine learning to predict AMR.

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As the availability of cheap computing power has gone up and the cost of genome sequencing has gone down, it has become possible to sequence a pathogen sample in order to detect its AMR resistance mechanisms. This could allow doctors to identify the nature of an infection in minutes instead of hours or days, says Davis.

Davis is part of a team creating a giant database of bacterial genomes with AMR metadata for the Pathosystems Resource Integration Center (PATRIC), funded by the U.S. National Institute of Allergy and Infectious Diseases to collect data on priority pathogens, such as tuberculosis and gonorrhea.

Because the current inability to identify microbes quickly is one of the biggest roadblocks to making an accurate diagnosis, the team’s work is critically important. The standard method for identifying drug resistance is to take a sample from a wound, blood, or urine and expose the resident bacteria to various antibiotics. If the bacterial colony continues to divide and thrive despite the presence of a normally effective drug, it indicates resistance. The process typically takes between 16 and 20 hours, itself an inordinate amount of time in matters of life and death. For certain strains of antibiotic-resistant tuberculosis, though, such testing can take a week. While physicians are waiting for test results, they often prescribe broad-spectrum antibiotics or make a best guess about what drug will work based on their knowledge of what’s happening in their hospital, “and in the meantime, you either get better,” says Davis, “or you don’t.”

At PATRIC, researchers are using machine-learning classifiers to identify regions of the genome involved in antibiotic resistance that could form the foundation for a “laboratory free” process for predicting resistance. Being able to identify the genetic mechanisms of AMR and predict the behavior of bacterial pathogens without petri dishes could inform clinical decision making and improve reaction time. Thus far, the researchers have developed machine-learning classifiers for identifying antibiotic resistance in Acinetobacter baumannii (a big player in hospital-acquired infection), methicillin-resistant Staphylococcus aureus (a.k.a. MRSA, a worldwide problem), and Streptococcus pneumoniae (a leading cause of bacterial meningitis), with accuracies ranging from 88% to 99%.

Houston Methodist Hospital, which uses the PATRIC database, is researching multidrug-resistant bacteria, specifically MRSA. Not only does resistance increase the cost of care, but people with MRSA are 64% more likely to die than people with a nonresistant form of the infection, according to WHO. Houston Methodist is investigating the molecular genetic causes of drug resistance in MRSA in order to identify new treatment approaches and help develop novel antimicrobial agents.

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The Hunt for a New Class of Antibiotics

There are antibiotic-resistant bacteria, and then there’s Clostridium difficile—a.k.a. C. difficile—a bacterium that attacks the intestines even in young and healthy patients in hospitals after the use of antibiotics.

It is because of C. difficile that Dr. L. Clifford McDonald jumped into the AMR fight. The epidemiologist was finishing his work analyzing the spread of SARS in Toronto hospitals in 2004 when he turned his attention to C. difficile, convinced that the bacteria would become more common and more deadly. He was right, and today he’s at the forefront of treating the infection and preventing the spread of AMR as senior advisor for science and integrity in the CDC’s Division of Healthcare Quality Promotion. “[AMR] is an area that we’re funding heavily…insofar as the CDC budget can fund anything heavily,” says McDonald, whose group has awarded $ 14 million in contracts for innovative anti-AMR approaches.

Developing new antibiotics is a major part of the AMR battle. The majority of new antibiotics developed in recent years have been variations of existing drug classes. It’s been three decades since the last new class of antibiotics was introduced. Less than 5% of venture capital in pharmaceutical R&D is focused on antimicrobial development. A 2008 study found that less than 10% of the 167 antibiotics in development at the time had a new “mechanism of action” to deal with multidrug resistance. “The low-hanging fruit [of antibiotic development] has been picked,” noted a WHO report.

Researchers will have to dig much deeper to develop novel medicines. Machine learning could help drug developers sort through much larger data sets and go about the capital-intensive drug development process in a more prescriptive fashion, synthesizing those molecules most likely to have an impact.

McDonald believes that it will become easier to find new antibiotics if we gain a better understanding of the communities of bacteria living in each of us—as many as 1,000 different types of microbes live in our intestines, for example. Disruption to those microbial communities—our “microbiome”—can herald AMR. McDonald says that Big Data and machine learning will be needed to unlock our microbiomes, and that’s where much of the medical community’s investment is going.

He predicts that within five years, hospitals will take fecal samples or skin swabs and sequence the microorganisms in them as a kind of pulse check on antibiotic resistance. “Just doing the bioinformatics to sort out what’s there and the types of antibiotic resistance that might be in that microbiome is a Big Data challenge,” McDonald says. “The only way to make sense of it, going forward, will be advanced analytic techniques, which will no doubt include machine learning.”

Reducing Resistance on the Farm

Bringing information closer to where it’s needed could also help reduce agriculture’s contribution to the antibiotic resistance problem. Antibiotics are widely given to livestock to promote growth or prevent disease. In the United States, more kilograms of antibiotics are administered to animals than to people, according to data from the FDA.

One company has developed a rapid, on-farm diagnostics tool to provide livestock producers with more accurate disease detection to make more informed management and treatment decisions, which it says has demonstrated a 47% to 59% reduction in antibiotic usage. Such systems, combined with pressure or regulations to reduce antibiotic use in meat production, could also help turn the AMR tide.

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Breaking Down Data Silos Is the First Step

Adding to the complexity of the fight against AMR is the structure and culture of the global healthcare system itself. Historically, healthcare has been a siloed industry, notorious for its scattered approach focused on transactions rather than healthy outcomes or the true value of treatment. There’s no definitive data on the impact of AMR worldwide; the best we can do is infer estimates from the information that does exist.

The biggest issue is the availability of good data to share through mobile solutions, to drive HCI clinical-decision support tools, and to feed supercomputers and machine-learning platforms. “We have a fragmented healthcare delivery system and therefore we have fragmented information. Getting these sources of data all into one place and then enabling them all to talk to each other has been problematic,” McDonald says.

Collecting, integrating, and sharing AMR-related data on a national and ultimately global scale will be necessary to better understand the issue. HCI and mobile tools can help doctors, hospitals, and public health authorities collect more information while advanced analytics, machine learning, and in-memory computing can enable them to analyze that data in close to real time. As a result, we’ll better understand patterns of resistance from the bedside to the community and up to national and international levels, says Solomon. The good news is that new technology capabilities like AI and new potential streams of data are coming online as an era of data sharing in healthcare is beginning to dawn, adds McDonald.

The ideal goal is a digitally enabled virtuous cycle of information and treatment that could save millions of dollars, lives, and perhaps even civilization if we can get there. D!

Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.


About the Authors:

Dr. David Delaney is Chief Medical Officer for SAP.

Joseph Miles is Global Vice President, Life Sciences, for SAP.

Walt Ellenberger is Senior Director Business Development, Healthcare Transformation and Innovation, for SAP.

Saravana Chandran is Senior Director, Advanced Analytics, for SAP.

Stephanie Overby is an independent writer and editor focused on the intersection of business and technology.

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Individual Excellence vs. Organizational Impact: Know the Difference!

image thumb 4 Individual Excellence vs. Organizational Impact: Know the Difference!

Guess Which One Grows Your Career (or Company) More? (Hint: It’s the One on the Right)
(But Individual Excellence is a Prerequisite for Org-Wide Impact)

  -Marshall Mathers

image thumb 10 Individual Excellence vs. Organizational Impact: Know the Difference!Last week’s post on learning DAX before learning M mostly met with positive reviews, but also drew some fire.  A few staunch M supporters showed up and voiced their disagreement – including the one and only Ken Puls.  Now I know from experience not to mess with Ken…  OK OK, I confess…  messing with Ken is a barrel of monkeys actually.  Put it on your bucket list.  That said, I have immense respect for his skills and perspective, and only enjoy messing with him out of friendship.  He’s an amazing human being and learns more things in a year than I could ever squeeze into my leaky head over a lifetime.

But I still firmly believe in what I said.  I’m not here to offer apologies – only clarification and justification.  There very much IS an olive branch in all of this, but again, that comes merely from clarification.

First clarification:  I love love LOVE Power Query and M!  They are a godsend!  I never said what some people thought I was saying, which was “meh, you can ignore/neglect that part of the platform.”  Nope, you absolutely benefit tremendously from both.

The minor tension from last week raised a MASSIVELY important point, one that transcends any technical debate and puts things in their proper perspective.  So I’m grateful for the opportunity that the misunderstanding provides us.  Let’s begin with…

image thumb 11 Individual Excellence vs. Organizational Impact: Know the Difference!

At first this sounds like an incredibly abstract question.  I mean, how can you put a dollar figure on massive gains in personal efficiency?  Sounds impossible, right?

But I’ve got a card up my sleeve:  how much is your salary?  It’s not terribly outlandish to say that the best you could EVER do, in terms of speeding up the tasks in your workday, is to completely make yourself redundant.

And the market has already put a dollar value on THAT, right?  It’s called your salary.  No, it’s not a perfect number, not at all.  Many of you reading this are criminally underpaid in some very real sense, for instance.  But this is what the market is saying today, and that’s a very real quantity.  Furthermore it’s not like it’s really possible to automate 100% of your duties using ANY of the tools available today (thankfully!), so it’s somewhat “gracious” to set the max at 100% of salary.

The folks applying for our Principal Consultant role lately have ranged in current salary from a definitely-criminal $ 40k on the low end to a damn-near-executive $ 160k on the high end.

So let’s continue with the “gracious” theme and go with the high end:  $ 160k per year is a serviceable maximum ROI for “making my current job run faster.”

You might be thinking, at this point, that I’m still not being Gracious Enough.  It’s possible, after all, for a single hyper-efficient individual to suddenly replace MULTIPLE other individuals right?  Setting aside the distasteful notion of those lost jobs for a moment, I still think my $ 160k figure isn’t that bad, given that it’s 100% of an entire individual on the high end of the range.  But fine, if you want to multiply it by 3 and make it $ 480k, I don’t think that necessarily undermines any of the points I want to make.  I’m in the business of adding more zeroes to the productivity multiplier, not linear multiplications.

que01 thumb Individual Excellence vs. Organizational Impact: Know the Difference!At first, nothing.  Both DAX* and M, in the early going, are BOTH very much “speed up my current workflow” kind of things.  And that’s perfectly natural – what you’re currently doing is ALWAYS the best place to start, the best place to learn.

(* Remember, when I say “DAX,” I use that as shorthand for “DAX and Modeling,” where “Modeling” is best described as “figuring out how many tables you should have, what they should look like, and how they are related).

And that “improve what I’m currently doing” lens is why M/Power Query steals the show in the earliest demos – it’s easier to see how it’s going to change your life, because it automates/accelerates a larger percentage of what Excel Pros have traditionally done.

Hold this thought for a moment while I introduce something else…

image thumb 8 Individual Excellence vs. Organizational Impact: Know the Difference!This is a real thing, it belongs to one of our clients, and we helped them build it.  To call it a “workbook” is a bit of an insult of course, because it’s a modern marvel – an industrial-strength DAX model with a suite of complementary scorecards as a frontend.  But all built in Excel.  (Power Pivot, specifically).

And this model has provably returned about $ 25M a year to the bottom line for this client.  As in, profit.  Not revenue.  Pure sweet earnings.  This workbook is visible on their quarterly earnings reports to Wall Street.

And this wonder of the modern world is well into its fourth year of service now, bringing its lifetime “winnings” into the $ 100 Million range.  No lie.  This happened, and continues to happen.  This “workbook” is a tireless machine that makes it rain money.

Let’s do some math:  $ 25M per year vs. $ 160k per year is… 150x.

In other words, the ROI of this project went FAR beyond any amount of “accelerating what we already did.”  It was, instead, a MASSIVE dose of “doing something we’ve NEVER done before.”

image thumb 12 Individual Excellence vs. Organizational Impact: Know the Difference!This may sound like a cheap verbal trick, but I sincerely think it is a weighty truth that everyone should internalize.  The workbook above, which now in some sense runs the show at this large client, had no predecessor whatsoever (its “forerunners” were a scattered collection of hundreds of distinct reports, each of which was just burying readers in borderline-raw data).  For an even bigger example, consider that Instagram started as a hybrid of Foursquare and Mafia Wars before deciding to go “all-in” on their most popular feature, photo sharing.  The blank canvas has no ceiling, if you permit me to mash-up some metaphors, and both of these success stories are rooted in a combination of analytics and courage.

What we’ve been doing traditionally, in both the traditional Excel and traditional BI worlds, is nothing to brag about.  Most of our reporting and analysis output has been, traditionally-speaking, designed by the path of least resistance – as opposed to defined by careful and creative thinking about what truly matters.  The president of one of our clients/partners’ told us last week, “people tend to measure what they can easily count, as opposed to what they SHOULD measure,” and I just about leapt out of my chair screaming “YES!  PRECISELY!”

If you want to learn more about this topic, start with Ten Things Data Can Do For You and We Have a “Crush” on Verblike Reports.  For now, it’s time to move on to Leverage.

image thumb 13 Individual Excellence vs. Organizational Impact: Know the Difference!

You wanna know why Data is so “Hot” these days?  It’s because of Leverage.  Data is hot precisely because proper application of data can impact the behavior and productivity of MANY people simultaneously.  You can’t typically save or make millions of incremental dollars as an individual, but it’s “easy” to do if you can magnify benefits across dozens of other people – or hundreds, or even thousands (as is the case with the $ 100M workbook).

In fact, it’s worth considering that the $ 100M Workbook actually offers only modest benefit!  On a per-person basis, on a single day, you wouldn’t even notice the difference.  But multiply that modest, say, 3% benefit across tens of thousands of people and 365 days…  and you get $ 25M per year.  When you have the power of Leverage, you don’t even have to find something “big,” like the Instagram “pivot” from one mission to another, to get something BIG.

We are all, everyone reading this, INCREDIBY FORTUNATE to be working in data, because of its somewhat-unique capacity for leverage.  So many jobs, whether white- or blue-collar, are essentially cogs in the machine, and the top-end benefits they provide are limited to the “just you” size.  But WE have hit the jackpot.  WE have a job that is “unfairly” capable of leverage.  It just fell into our laps.  But then, the mind-numbing dosages of VLOOKUP (in the traditional Excel world) and endless documentation and miscommunication of requirements (in the traditional BI world) deflected us off into a relatively un-ambitious mindset.

Data has ALWAYS had the advantage of Leverage, but the traditional methodologies and tools brought tremendous friction and inertia to the table.  They wore us down – in terms of time, money, and psychic energy.  They “chokepointed” our potential.  They enforced a terribly-linear culture of thinking.  In short, the traditional tools took the potential 100x or even 1,000x leverage possibilities of Data and tamped them down to about 10x – still good!  But so much less than what we COULD do.

Well guess what?  No more chokepoint.  Whatever you want to call it – Power BI, Power Pivot, Modern Excel – the next-generation toolset from Microsoft gives us those extra zeroes of potential.

Note the quotation marks in that heading, because the next section is more conciliatory, but there IS something very important to bring home here.

If you MADE me choose one or the other, I’d definitely choose DAX, because I think it offers us the virtually-unlimited twin powers of  WWNDB and Leverage.  In fact, I don’t think that, I know that – I (and my companies) have been blowing people’s doors off with this new toolset since 2010.  We didn’t even get Power Query until what, 2014?  Fully half the lifetime of this revolution pre-dates M.  Even the $ 100M Workbook predates M!  Heck, until Power Update came along, you couldn’t even schedule refreshes of models that relied on M, which almost by definition “funneled” M usage down the “just for me” path – and to this day, Microsoft still hasn’t finally released a server that natively runs M.

I just don’t think it’s nearly as easy to explore/exploit WWNDB or Leverage via the M path.  Not impossible, because there are plenty of exceptions that prove the rule.  And to be clear, I think most of the exceptions will be in the WWNDB category, not the Leverage category.

And that was kinda my whole point in last week’s article – Power Query dramatically captures the attention of new converts to Modern Excel precisely because of how well it fits and improves What We’ve Already Been Doing, as Individuals.  This is a Good Thing!  No caveats needed.  I just don’t want anyone to become so distracted with it that we miss the Big Wins of WWNDB and Leverage.

image thumb 14 Individual Excellence vs. Organizational Impact: Know the Difference!

This is What We Can Do With “Just” DAX and Modeling

The picture above illustrates how a single individual (you, or a member of your team) can achieve wins MUCH bigger than just them.  And it’s my experience-powered belief that you cannot get a Win of that size without leveraging DAX and Modeling.

But what if you THEN take that single individual’s newfound powers of WWNDB and Leverage, provided by DAX and Modeling, and now make THAT person more efficient?  “Holy Additional Multiplier, Batman!”

image thumb 15 Individual Excellence vs. Organizational Impact: Know the Difference!

If Our DAX Modeler Superhero “Levels Up” with the Efficiency Gains of Power Query / M…  Look Out!

Yeah, if you take THAT person, and make THEM more efficient, WOW, you can do EVEN MORE of the amazing, transformational, WWNB-and-Leverage style work.

Which we can all agree…  is a Very Good Thing.  One of my favorite personal sayings is “the length of a rectangle is not more ‘responsible’ for the area of the rectangle than the width.”  Double either one, and you double the area.  But that’s essentially my point in a nutshell – you can 100x with DAX and modeling, AND you can double with M.  If you had to choose one, choose 100x.  But we don’t have to choose.  Adding Power Query and M to your org-wide-impact powers, even if it’s “just” 2x or 3x, delivers JUST AS MUCH, or more, incremental Big Win as the original 100x.

We can have our flagons full of mead and drink them too, as Lothar once said.

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SuiteWorld 2017 Retail Keynote: Evolving to Next with Omnichannel

websitelogo SuiteWorld 2017 Retail Keynote: Evolving to Next with Omnichannel

Posted by Barney Beal, Content Director

In the SuiteWorld 2017 Retail keynote, Andy Lloyd, NetSuite’s Vice President of Products, discusses current trends in retail, what’s on the horizon and how NetSuite is helping some of the most innovative retailers in the industry achieve their goals including the new SuiteSuccess for retail industry solution.

He is joined on stage by Brandon Jenkins, NetSuite Regional Vice President of Retail; Nick Thomas, Managing Director of /BUILT, a new company changing the building supply market in the UK; Kyle Pretsch, director of integrations and Omnichannel for Lucky Brand, a retail apparel company; Chris McNabb, CEO of Dell Boomi, a cloud integration company; Matt Rhodus, NetSuite Director and Industry Principal for Retail; and Kelly Milazzo, Vice president of Operations for Toad & Co. and Fidan Kasra, Senior Product Manager for Bonobos, two retail apparel companies.


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