Month: November 2017

Data scientists are not dime a dozen and they are not in abundance as well. Buzz around bigdata has produced a job category that is not only confusing but has been costing companies a lot in their stride to look through the talent pool to dig for a so called data scientist. So, what exactly is the problem and why are we suddenly seeing a lot of data scientist emerging from nowhere with very different skill sets? To understand this we need to understand the bigdata phenomena.

With emergence of big data user companies like Google, Facebook, yahoo etc. and their amazing contribution to open source, new platforms have been developed to process too much data using commodity hardware in fast and yet, cost efficient ways. Now with that phenomenon, every company wants to get savvier when it comes to managing data to gain insights and ultimately building competitive edge over their competitors. But companies are used to understanding small pieces of data using their business analysts. But talk about more data and more tools. Who will fit in? So, they started on lookout for special breed of professional that have the capability to deal with big data and it’s hidden insights.

So, where is the problem here? The problem lies in the fact that only one job title emerged from this phenomenon- data scientist. The professionals who are currently practicing some data science via business analysis, data warehousing or data designing jumped on the bandwagon grabbing the title of the data scientist. What is interesting here is that data scientist job as explained above does not deserve a single job description so it should be handled accordingly. It was never a magical job title that has all the answers for any data curious organization, to be able to understand, develop and manage a data project.

Before we go into what companies should do, let’s reiterate what is a data scientist. As the name suggest, it is something to do with data and scientist. Which means it should include job description that has done some data engineering, data automation, and scientific computing with a hint of business capabilities. If we extrapolate, we are looking at a professional with computer science degree, doctorate in statistical computing and MBA in business. What would be luck in finding that candidate and by-the-way, they should have some industry domain expertise as well. What is the likelihood that such a talent exists? Rare. But, even if they are in abundance, companies should tackle this problem at much granular and sustainable scale. And one more thing to note here is that no two data scientist job requirements are the same. This means that your data scientist requirement could be extremely different from what anyone else is looking for in a data scientist. So, why should we have one title to cater to such a diverse category?

So, what should companies do? First it is important to understand that companies are building data scientists’ capabilities and should not be hiring the herd of data scientists. This means that companies/ hiring managers should understand that they are not looking for a particular individual but a team as a solution. It is important for businesses to clearly articulate those magic skillsets that their so-called data scientist should carry. Following this drill, companies should split the skillset into categories, Data analytics, Business analyst, data warehousing professionals, software developer, and data engineers to name a few. Finding a common island where business analysts, statistical computing modelers and data engineers work in harmony to address a system that handles big data is a great start. Think of it as putting together a central data office. Huh! another buzz word. Don’t worry; I will go into more details in the follow-up blogs. Think of it as a department where business, engineering and statistician work together on a common objective. Data science is nothing but an art to find value in lots of data. So, big-data is to build capability to parse/analyze lots of data. So, business should work through their laundry list of skillset. First identify internal resources that could accommodate that list. Following this, companies should form a hard matrix structure to prove the idea of set of people working together as a data scientist. BTW I am not saying that you need one individual from each category, but, together the team should have all the skills mentioned above

One important take away for companies is to understand that the moment they came across a so called data scientist, it is important to understand which side of data scientist the talent represents. Placing that talent in their respective silo will help provide a clearer vision when it comes to understanding the talent and understanding the void that could stay intact if the resources are not filled accordingly. So, living in this convoluted world of data scientist is hard and tricky. Having some chops into understanding data science as a talent, companies could really play the big data talent game to their advantage and lure some cutting edge people and grow sustainably.

Originally Posted at: The convoluted world of data scientist by v1shal

When you encounter apparently unmanageable and insurmountable Big Data sets, it seems practically impossible to get easy access to the correct data. The fact is, Big Data management could prove to be highly tricky and challenging and may come up with a few issues. However, effective data access could still be attained. Here are a few strategies for effectively achieving superlative data connectivity.

Understanding Hadoop

Hadoop is actually an ecosystem that has been designed for helping organizations in storing mammoth quantities of Big Data. It is important for you to have a sound understanding of ways to successfully bring your Big Data into and take it out of Hadoop so that you could effectively move ahead, as companies are involved in handling challenges of integrating Big Data within already existing data-infrastructure.

Integrating Cloud Data with Already Existing Reporting Applications

Integrating Cloud Data with already existing reporting applications such as Salesforce Dx has totally transformed the way you perceive and work with your customer data. These systems would, however, could face certain complications in acquiring the real-time reports. You would be relying on these reports for perfect business decision-making, thus generating the demand for an effective solution that allows such kind of real-time reporting.

Do Not Let the Sheer Scale of Big Data Get to You

Big Data could be hugely advantageous for businesses but if your organization is not ready to effectively handle it, you may have to do without the business value Big Data actually has on offer for you. Some organizations have the necessary scalable, flexible data infrastructure required for exploiting Big Data in order to achieve crucial business insight.

Access Salesforce Data via SQL

Salesforce data actually provides great value for numerous organizations; however, access issues could prove to be major obstacles in the way of organizations from reaping the fullest possible advantage. However, now businesses could effectively have an easy access to Salesforce data through ODBC and SQL. These smart drivers would be allowing you to create a connection and start implementing your queries in just a few minutes.

Do Accurate Analysis of Big Data

You could get a greater accuracy of big data depending on the technology utilized. There are several Big Data platforms that could be chosen by a company such as Apache Spark and Hadoop could come up with unique and accurate analysis of Big Data sets. More cutting-edge Big Data technology would be successfully generating more state-of-the-art Big Data models. Many organizations would be opting for a reliable Big Data provider. There are a great variety of options open to them and so businesses today could easily locate a Big Data provider that is suitable for their specific requirements and that comes up with accurate results or precise outcomes.

Conclusion

Business organizations must take extra initiative in assessing and analyzing the data collected by them. They must make sure that the data is collected from an authentic and reliable source. They must identify the context behind data generation. Every move involved in the analysis process requires being observed carefully right from the proper data ingestion to its enrichment and preparation. Data protection from external interference is essential.

Author Bio:

Sujain Thomas is a Salesforce consultant and discusses the benefits of Salesforce Dx in her blog posts. She is an avid blogger and has an impressive fan base.

Source: Important Strategies to Enhance Big Data Access

Everybody has an opinion about Steve Jobs. Please tell me what you think of him and how he has impacted your life in this brief survey.

Steve Jobs, co-founder of Apple, passed away earlier this week at the age of 56. In the process of writing about how he impacted my life in my blog, I created an image of him. To make this image, I collected quotes and articles that were written about him in the day following his passing. The quotes were from such notables like President Obama, Mark Zuckerberg, Guy Kawasaki, and Bill Gates, to name a few. Using these descriptive words of Steve Jobs, I created a word cloud in the form of his soon-to-be iconic image on Apple.com.

In the world cloud, the font size of the words is related to the frequency of usage of the words; the larger the font size, the more frequently that word is used to describe Steve Jobs. This picture essentially represents how these people define him, remember him.

I now want to be more purposeful in creating the same image using words from people who never met him but whose lives may have been impacted by him. Could you please complete my one-minute survey about Steve Jobs? I am also going to conduct sentiment analysis on your comments to understand the sentiment behind them. So… your survey responses help to create art and advance science. In addition to feeling good about yourself, I will notify you when this project is completed (if you provide your email address in the survey).

Bob E. Hayes, Ph.D.

Source: Please share your thoughts about Steve Jobs by bobehayes

Patient experience (PX) has become an important topic for US hospitals. The Centers for Medicare & Medicaid Services (CMS) will be using patient feedback about their care as part of their reimbursement plan for acute care hospitals (see Hospital Value-Based Purchasing Program). Not surprisingly, hospitals are focusing on improving the patient experience to ensure they receive the maximum of their incentive payments. Additionally, US hospitals track other types of metrics (e.g., process of care and mortality rates) as measures of quality of care.

Given that hospitals have a variety of metrics at their disposal, it would be interesting to understand how these different metrics are related with each other. Do hospitals that receive higher PX ratings (e.g., more satisfied patients) also have better scores on other metrics (lower mortality rates, better process of care measures) than hospitals with lower PX ratings? In this week’s post, I will use the following hospital quality metrics:

1. Patient Experience
2. Health Outcomes (mortality rates, re-admission rates)
3. Process of Care

I will briefly cover each of these metrics below.

1. Patient Experience

Patient experience (PX) reflects the patients’ perceptions about their recent inpatient experience. PX is collected by a survey known as HCAHPS (Hospital Consumer Assessment of Healthcare Providers and Systems). HCAHPS (pronounced “H-caps“) is a national, standardized survey of hospital patients and was developed by a partnership of public and private organizations and was created to publicly report the patient’s perspective of hospital care.

The survey asks a random sample of recently discharged patients about important aspects of their hospital experience. The data set includes patient survey results for over 3800 US hospitals on ten measures of patients’ perspectives of care (e.g., nurse communication, pain well controlled). I combined two general questions (Overall hospital rating and recommend) to create a patient advocacy metric. Thus, a total of 9 PX metrics were used. Across all 9 metrics, hospital scores can range from 0 (bad) to 100 (good). You can see the PX measures for different US hospital here.

2. Process of Care

Process of care measures show, in percentage form or as a rate, how often a health care provider gives recommended care; that is, the treatment known to give the best results for most patients with a particular condition. The process of care metric is based on medical information from patient records that reflects the rate or percentage across 12 procedures related to surgical care.  Some of these procedures are related to antibiotics being given/stopped at the right times and treatments to prevent blood clots.  These percentages were translated into scores that ranged from 0 (worse) to 100 (best).  Higher scores indicate that the hospital has a higher rate of following best practices in surgical care. Details of how these metrics were calculated appear below the map.

I calculated an overall Process of Care Metric by averaging each of the 12 process of care scores. The process of care metric was used because it has good measurement properties (internal consistency was .75) and, thus reflects a good overall measure of process of care. You can see the process of care measures for different US hospital here.

3. Health Outcomes

Measures that tell what happened after patients with certain conditions received hospital care are called “Outcome Measures.” We use two general types of outcome measures: 1) 30-day Mortality Rate and 2) 30-day Readmission Rate. The 30-day risk-standardized mortality and 30-day risk-standardized readmission measures for heart attack, heart failure, and pneumonia are produced from Medicare claims and enrollment data using sophisticated statistical modeling techniques that adjust for patient-level risk factors and account for the clustering of patients within hospitals.

The death rates focus on whether patients died within 30 days of their hospitalization. The readmission rates focus on whether patients were hospitalized again within 30 days.

Three mortality rate and readmission rate measures were included in the healthcare dataset for each hospital. These were:

1. 30-Day Mortality Rate / Readmission Rate from Heart Attack
2. 30-Day Mortality Rate / Readmission Rate from Heart Failure
3. 30-Day Mortality Rate / Readmission Rate from Pneumonia

Mortality/Readmission rate is measured in units of 1000 patients. So, if a hospital has a heart attack mortality rate of 15, that means that for every 1000 heart attack patients, 15 of them die get readmitted. You can see the health outcome measures for different US hospital here.

Results

The three types of metrics (e.g., PX, Health Outcomes, Process of Care) were housed in separate databases on the data.medicare.gov site. As explained elsewhere in my post on Big Data, I linked these three data sets together by hospital name. Basically, I federated the necessary metrics from their respective database and combined them into a single data set.

Descriptive statistics for each variable are located in Table 1. The correlations of each of the PX measures with each of the Health Outcome and Process of Care Measures is located in Table 2. As you can see, the correlations of PX with other hospital metrics is very low, suggesting that PX measures are assessing something quite different than the Health Outcome Measures and Process of Care Measures.

Patient Loyalty and Health Outcomes and Process of Care

Patient loyalty/advocacy (as measured by the Patient Advocacy Index) is logically correlated with the other measures (except for Death Rate from Heart Failure). Hospitals that have higher patient loyalty ratings have lower death rates, readmission rates and higher levels of process of care. The degree of relationship, however, is quite small (the percent of variance explained by patient advocacy is only 3%).

Patient Experience and Health Outcomes and Process of Care

Patient experience (PX) shows a complex relationship with health outcome and process of care measures. It appears that hospitals that have higher PX ratings also report higher death rates. However, as expected, hospitals that have higher PX ratings report lower readmission rates. Although statistically significant, all of the correlations of PX metrics with other hospitals metrics are low.

The PX dimension that had the highest correlation with readmission rates and process of care measures was “Given Information about my Recovery upon discharge“.  Hospitals who received high scores on this dimensions also experienced lower readmission rates and higher process of care scores.

Summary

Hospitals are tracking different types of quality metrics, metrics being used to evaluate each hospital’s performance. Three different metrics for US hospitals were examined to understand how well they are related to each other (there are many other metrics on which hospitals can be compared). Results show that the patient experience and patient loyalty are only weakly related to other hospital metrics, suggesting that improving the patient experience will have little impact on other hospital measures (health outcomes, process of care).

 

Originally Posted at: Evaluating Hospital Quality using Patient Experience, Health Outcomes and Process of Care Measures by bobehayes

By Jack Nicas

While the increasing use of encryption helps smartphone users protect their data, another sometime related technology, cloud computing, can undermine those protections.

The reason: encryption can keep certain smartphone data outside the reach of law enforcement. But once the data is uploaded to companies’ computers connected to the Internet–referred to as “the cloud”–it may be available to authorities with court orders.
“The safest place to keep your data is on a device that you have next to you,” said Marc Rotenberg, head of the Electronic Privacy Information Center. “You take a bit of a risk when you back up your device. Once you do that it’s on another server.”

Encryption and cloud computing “are two competing trends,” Mr. Rotenberg said. “The movement to the cloud has created new privacy risks for users and businesses. Encryption does offer the possibility of restoring those safeguards, but it has to be very strong and it has to be under the control of the user.”

Apple is fighting a government request that it help the Federal Bureau of Investigation unlock the iPhone of Syed Rizwan Farook, the shooter in the December terrorist attack in San Bernardino, Calif.

The FBI believes the phone could contain photos, videos and records of text messages that Mr. Farook generated in the final weeks of his life.

The data produced before then? Apple already provided it to investigators, under a court search warrant. Mr. Farook last backed up his phone to Apple’s cloud service, iCloud, on Oct. 19.

Encryption scrambles data to make it unreadable until accessed with the help of a unique key. The most recent iPhones and Android phones come encrypted by default, with a user’s passcode activating the unique encryption key stored on the device itself. That means a user’s contacts, photos, videos, calendars, notes and, in some cases, text messages are protected from anyone who doesn’t have the phone’s passcode. The list includes hackers, law enforcement and even the companies that make the phones’ software: Apple and Google.

However, Apple and Google software prompt users to back up their devices on the cloud. Doing so puts that data on the companies’ servers, where it is more accessible to law enforcement with court orders.

Apple says it encrypts data stored on its servers, though it holds the encryption key. The exception is so-called iCloud Keychain data that stores users’ passwords and credit-card information; Apple says it can’t access or read that data.

Officials appear to be asking for user data more often. Google said that it received nearly 35,000 government requests for data in 2014 and that it complies with the requests in about 65% of cases. Apple’s data doesn’t allow for a similar comparison since the company reported the number of requests from U.S. authorities in ranges in 2013.

Whether they back up their smartphones to the cloud, most users generate an enormous amount of data that is stored outside their devices, and thus more accessible to law enforcement.

“Your phone is an incredibly intricate surveillance device. It knows everyone you talk to, where you are, where you live and where you work,” said Bruce Schneier, chief technology officer at cybersecurity firm Resilient Systems Inc. “If you were required to carry one by law, you would rebel.”

Google, Yahoo Inc. and others store users’ emails on their servers. Telecom companies keep records of calls and some standard text messages.
Facebook
Inc. and Twitter Inc. store users’ posts, tweets and connections.

Even Snapchat Inc., the messaging service known for photo and video messages that quickly disappear, stores some messages. The company says in its privacy policy that “in many cases” it automatically deletes messages after they are viewed or expire. But it also says that “we may also retain certain information in backup for a limited period or as required by law” and that law enforcement sometimes requires it “to suspend our ordinary server-deletion practices for specific information.”

Snapchat didn’t respond to a request for comment.

Write to Jack Nicas at jack.nicas@wsj.com
(END) Dow Jones Newswires
02-18-161938ET
Copyright (c) 2016 Dow Jones & Company, Inc.

Source: Why Using the ‘Cloud’ Can Undermine Data Protections by analyticsweekpick

Big data that can be immediately actionable in business decisions is transforming corporate thinking. One expert cautions that a mindset change is needed to get the most from these analytics.

Gartner reported in September 2014 that 73% of respondents in a third quarter 2014 survey had already invested or planned to invest in big data in the next 24 months. This was an increase from 64% in 2013.

The big data surge has fueled the adoption of Hadoop and other big data batch processing engines, but it is also moving beyond batch and into a real-time big data analytics approach.

Organizations want real-time big data and analytics capability because of an emerging need for big data that can be immediately actionable in business decisions. An example is the use of big data in online advertising, which immediately personalizes ads for viewers when they visit websites based on their customer profiles that big data analytics have captured.

“Customers now expect personalization when they visit websites,” said Jeff Kelley, a big data analytics analyst from Wikibon, a big data research and analytics company. “There are also other real-time big data needs in specific industry verticals that want real-time analytics capabilities.”

The financial services industry is a prime example. “Financial institutions want to cut down on fraud, and they also want to provide excellent service to their customers,” said Kelley. “Several years ago, if a customer tried to use his debit card in another country, he was often denied because of fears of fraud in the system processing the transaction. Now these systems better understand each customer’s habits and the places that he is likely to travel to, so they do a better job at preventing fraud, but also at enabling customers to use their debit cards without these cards being locked down for use when they travel abroad.”

Kelly believes that in the longer term this ability to apply real-time analytics to business problems will grow as the Internet of Things (IoT) becomes a bigger factor in daily life.

“The Internet of Things will enable sensor tacking of consumer type products in businesses and homes,” he said. “You will be collect and analyze data from various pieces of equipment and appliances and optimize performance.”

The process of harnessing IoT data is highly complex, and companies like GE are now investigating the possibilities. If this IoT data can be captured in real time and acted upon, preventive maintenance analytics can be developed to preempt performance problems on equipment and appliances, and it might also be possible for companies to deliver more rigorous sets of service level agreements (SLAs) to their customers.

Kelly is excited at the prospects, but he also cautions that companies have to change the way they view themselves and their data to get the most out of IoT advancement.

“There is a fundamental change of mindset,” he explained, “and it will require different ways of approaching application development and how you look at the business. For example, a company might have to redefine itself from thinking that it only makes ‘makes trains,’ to a company that also ‘services trains with data.'”

The service element, warranties, service contracts, how you interact with the customer, and what you learn from these customer interactions that could be forwarded into predictive selling are all areas that companies might need to rethink and realign in their business as more IoT analytics come online. The end result could be a reformation of customer relationship management (CRM) to a strictly customer-centric model that takes into account every aspect of the customer’s “life cycle” with the company — from initial product purchases, to servicing, to end of product life considerations and a new beginning of the sales cycle.

Originally posted via “Surge in real-time big data and IoT analytics is changing corporate thinking”

Originally Posted at: Surge in real-time big data and IoT analytics is changing corporate thinking by analyticsweekpick