Report sets research priorities for Biden’s cancer moonshot

Big Data and data analytics are major agenda items of the moonshot program.

… The moonshot report recommends creating a national network to give more patients around the country access to tumor profiling. Those patients also would be able to share their genetic data with researchers, and volunteer for cutting-edge clinical trials of treatments that match their genetics.

Reference: Report sets research priorities for Biden’s cancer moonshot

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Firms may violate workers’ medical privacy with big data

It may be time to take a step back and re-evaluate how U.S. companies are using big data gathered in employee wellness and other health care analytics programs.

… In an editorial posted Tuesday in JAMA Internal Medicine, some Texas researchers argue that use of big data to predict the “risk” of a woman getting pregnant may be crossing a line. It could exacerbate long-standing patterns of employment discrimination and paint pregnancy as something to be discouraged.

… The concern arose after reports of how one health care analytics company launched a product that can track, for example, if a woman has stopped filling birth-control prescriptions or has searched for fertility information on the company’s app. And women may not even be aware that such data is being collected.

Reference: Firms may violate workers’ medical privacy with big data

A Tale of Two Disciplines: Data Scientist and Business Analyst

The ability to use data to achieve enterprise goals requires advanced skills that many organizations don’t yet have. But they are looking to add them – and fast. The question is, what type of big data expert is needed? Does an organization need a data scientist or does it need a business analyst? Maybe it even needs both. These two titles are often used interchangeably, and confusion abounds.

Business analysts typically have educational backgrounds in business and humanities. They find and extract valuable information from a variety of sources to evaluate past, present, and future business performance – and then determine which analytical models and approaches will help explain solutions to the end users who need them.

With educational backgrounds in computer science, mathematics, and technology, data scientists are digital builders. They use statistical programming to actually construct the framework for gathering and using the data by creating and implementing algorithms to do it. Such algorithms help businesses with decision making, data management, and the creation of data visualizations to help explain the data that they gather.

Reference: A Tale of Two Disciplines: Data Scientist and Business Analyst

Gartner positions Microsoft as a leader in the Magic Quadrant for Operational Database Management Systems

This is the new Gartner magic quadrant for operational (transnational, OLTP) DBMS (Oct., 2015). It appears that Microsoft’s hard work and licensing structure pay off to beat Oracle. I was hoping to see the rankings of big data vendors, such as MapR, Cloudera, Hortonworks, and others, to be a lot higher but they are not. Maybe, big data applications are still limited to a few big name companies such as Google, Amazon, or Netflix, but not the majority of businesses. And, those big data businesses are more related to streaming and internet searches, less about point-of-sales and logistics operations. Or, maybe, traditional DBMS is already sufficient to meet most business needs with reasonable cost.

Reference: Gartner positions Microsoft as a leader in the Magic Quadrant for Operational Database Management Systems

Virtual Reality Space Lets Students Experience Big Data

A new facility at Virginia Tech uses large-scale visuals and sound to immerse users in vast amounts of data. Very good presentation.

Reference: Virtual Reality Space Lets Students Experience Big Data

Six signs that your Big Data expert, isn’t

This is so far the best article that I have been reading about the Big Data. It is what I have been advocating to people.

1. They talk about “bigness” and “data,” rather than “new questions”

… It seems most of the tech industry is completely drunk on “Big Data.”

… most companies are spending vast amounts of money on more hardware and software yet they are getting little, if any, positive business value.

… “Big Data” is a terrible name for the revolution going on all around us. It’s not about Bigness, and it’s not about the Data. Rather, it’s about “new questions,” being facilitated by ubiquitous access to massive amounts of data.

… If all you’re doing is asking the same old questions of bigger amounts of the same old data, you’re not doing “Big Data,” you’re doing “Big Business Intelligence,” which is itself becoming an oxymoron.

2. They talk about technology, rather than business

… You may end up with the world’s largest server cluster, but other than bragging rights, who cares? START with a business issue, figure out how to better-characterize that issue with data, THEN start working on a technical solution.

3. They focus on insights, rather than actions

Most of the organizations that I work with are so focused upon analytics as an end-result they completely miss the whole point of this Big Data exercise: better actions. … If, after all of this effort, you haven’t changed how your organization acts, what your product or service does for your customers, or how you subsequently respond to the world around you, you’ve failed, utterly.

… Insight is great, but action is what brings home the bacon. If your “Big Data Expert” is focused on gaining insight rather than generating new business outcomes, you’re running a science experiment.

4. They talk about conclusions, rather than correlations

… Many of this new wave of Big Data experts don’t understand the nuance between correlation and causation. … Correlation means that there is the appearance of a relationship between things. Such relationships may indicate that certain inputs MAY lead to certain outputs. But, with correlation, there is no certainty.

… This is sort of a bummer to business people, who like to work with absolutes, or at least the appearance of absolutes. Well, there’s no such thing in data analytics. Your data may represent a vast collection of facts, but analytics and statistics are theater. What you see isn’t always what you get. Indeed, many “data scientists” are more “data manipulators,” generating politically acceptable outputs that support a given agenda.

… Correlation does not guarantee causation. Any Big Data expert who tells you they found causation should be immediately suspect until proven otherwise.

5. They talk about data quality, rather than data validity

… While data quality matters, it’s far more important to focus on data validity: Do I even have the right data to answer the questions I’m asking? … New analyses require VALID data, but determining whether or not data is clean before asking questions of it makes no sense whatsoever.

6. They sound like everyone else who is talking Big Data

… We are being drowned in all of the noise surrounding Big Data. … If your “Big Data Experts” don’t get this, then they’re not getting it. And neither are you.

Reference: Six signs that your Big Data expert, isn’t

The Big Data Iceberg

I like the picture of this file.

If there’s one area of analytics that people get really passionate about, it’s visualization. But as a new generation of people discover the joys of analytics, it’s worth remembering that pretty dashboards and charts are the easy part. The real challenge is getting data that’s worth viewing in the first place.

Reference: The Big Data Iceberg