Big data analytics and NLP: How health plans can make more money -- and keep it

Natural language processing is an emerging area that can help unlock value from the vast stores of unstructured data that account for as much as 80% of all clinical data. UPMC Health Plan does just that.

Big data analytics in healthcare has largely been about looking at claims, electronic health records (EHR) and other forms of structured data. Natural language processing (NLP) is an emerging area that can help unlock value from the vast amounts of unstructured data that are pervasive in healthcare. In the emerging era of value-based payments, risk adjustments may well determine the difference between profit and loss for the health insurance industry.

UPMC Health Plan, the health insurance arm of the University of Pittsburgh Medical Center (UPMC), has deployed NLP-based technology and big data analytics to efficiently process millions of pieces of documentation to accurately identify risk adjustment possibilities and capture incremental revenue.

Can IT keep up with big data?

Though IT and its functions and responsibilities have changed over the years, there's one area that remains consistent: IT primarily focuses on major enterprise applications and on large machines—whether they are mainframes or super servers.

When IT deals with big data, the primary arena for it is, once again, large servers that are parallel processing in a Hadoop environment. Thankfully for the company at large, IT also focuses on reliability, security, governance, failover, and performance of data and apps—because if it didn't, there would be nobody else internally to do the job that is required. Within this environment, IT's job is most heavily focused on the structured transactions that come in daily from order, manufacturing, purchasing, service, and administrative systems that keep the enterprise running. In this environment, analytics, unstructured data and smaller servers in end user departments are still secondary.

Enterprises turn to outsourcings as data analytics market booms

The world data analytics outsourcing market is expected to reach $US5.9 billion by 2020, registering a CAGR of 29.1 percent during 2015 to 2020.

“Over the years, there has been an exponential increase in the data generated by enterprises,” says Gunjan Malani, Research Analyst, Allied Market Research.

“This data is now being outsourced to data analytics service providers enabling the enterprises to make effective insights-driven business decisions, offer enhanced services to customers, and avoid risks and losses.

“Most of the enterprises do not have in-house analytics capabilities or skilled workforce, thereby accelerating the growth of data analytics outsourcing market.”

Allied Market Research findings suggest that organisations are increasingly turning towards data analytics outsourcing due to its “numerous benefits” such as strategic decision making, operational efficiency, reduced operational costs, and enhanced customer service among others.

Read More: http://www.reseller.co.nz/article/593765/enterprises-turn-outsourcings-data-analytics-market-booms/

Data analytics outsourcing to grow

The market for data analytics outsourcing is set to grow to $5.9bn by 2020, according to a report from Allied Market Research.

According to the report, entitled “World Data Analytics Outsourcing – Market Opportunities and Forecasts, 2014-2020”, the market’s CAGR will be 5.9% per annum.

The reason is that enterprises are generating more and more data and the Big Data market is growing as a result. Mobile devices are fueling this data explosion further as individuals put comments and data out into the world from wherever they are. Unfortunately – unless you’re one of the specialists selling services – the tools to make this data into something useful are not easy to master and are often unavailable in house.

So outsourcing is the answer for many enterprises wanting to take full advantage of strategic decision making and of course reduced costs as a result.

The report also notes a shift from descriptive analytics to more advanced models such as predictive analytics.

For more details: http://www.professionaloutsourcingmagazine.net/newsitems/data-analytics-outsourcing-to-grow

Data analytics is at the juncture of man and machine

The network, in my mind’s eye, feels like a complex labyrinth with winding passages leading to opened and closed ports and firewalls exploding. Interestingly, when I did a little Google search to make sure I had my Greek mythology correct, I stumbled across this nugget of wisdom.

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In reference to Ovid’s Metamorphoses, and the labyrinth built by Daedalus, Wikipedia warns, “This story thus encourages others to consider the long-term consequences of their own inventions with great care, lest those inventions do more harm than good.”

The hidden costs of NoSQL

The NoSQL industry was developed quickly on the promise of schema-free design, infinitely scalable clusters and breakthrough performance. But there are hidden costs, including the added complexity of an endless choice of datastores (now numbering 225), the realization that analytics without SQL is painful, high query latencies require you to pre-compute results, and the inefficient use of hardware leads to server sprawl.

All of these costs add up to a picture far less rosy than initially presented. However, the data model for NoSQL does make sense for certain workloads, across key-value and document data types. Fortunately, those are now incorporated into multi-mode and multi-model databases representing a simplified and consolidated approach to data management.

Let’s take a closer look at the impetus for the NoSQL movement and the true impact of abandoning SQL.

Dawn and decline of the NoSQL movement

The popularity of NoSQL grew from the need to scale beyond what traditional disk-based relational databases could handle, and because high performance solutions from large database companies get very expensive very quickly. Coupled with data growth, developers needed a better way for the growing use of simple data structures like users and profile information associated with mobile applications. NoSQL promised an easy path to performance.

Another explanation for NoSQL popularity comes from the perception that SQL can be hard to learn. But Michael Stahnke, director of engineering at Puppet Labs, claims that is an early, and invalid argument, noting that, “instead you must learn one query language for each tool you use.”

Read More: http://www.networkworld.com/article/3019122/tech-primers/the-hidden-costs-of-nosql.html

How to measure the value of big data

Data itself is quite often inconsequential in its own right. Measuring the value of data is a boundless process with endless options and approaches – whether structured or unstructured, data is only as valuable as the business outcomes it makes possible.

It is how we make use of data that allows us to fully recognise its true value and potential to improve our decision making capabilities and, from a business stand point, measure it against the result of positive business outcomes.

There are multiple approaches to improving a business’s decision-making process and to determine the ultimate value of data, including data warehouses, business intelligence systems, and analytics sandboxes and solutions.

These approaches place high emphasis on the importance of every individual data item that goes into these systems and, as a result, highlight the importance of every single outcome linking to business impacts delivered.

Big data characteristics are defined popularly through the four Vs: volume, velocity, variety and veracity. Adapting these four characteristics provides multiple dimensions to the value of data at hand.

Essentially, there is an assumption that the data has great potential, but no one has explored where that might be. Unlike a business intelligence system, where analysts know what information they are seeking, the possibilities of exploring big data are all linked to identifying connections between things we don’t know. It is all about designing the system to decipher this information.

Read More: http://www.information-age.com/it-management/strategy-and-innovation/123460041/how-measure-value-big-data

IBM vs. Intel Corporation: The Data Center Battle Escalates

IBM (NYSE:IBM) used to sell Intel (NASDAQ:INTC) -powered low to mid-range servers. But last year, IBM sold that unit to Lenovo to focus on selling high-end servers and mainframes powered by its own Power processors instead.

Since then, IBM has positioned its remaining data center businesses directly against Intel, which holds a formidable 99% market share in server chips. In July, it announced the creation of a 7nm chip, which seemingly targeted Intel's plans to launch 10nm chips in 2017. It also expanded its "Open Power" initiative, which shares processor specs, firmware, and software with partners to fuel the third-party production of Power-based servers.

That's why it wasn't surprising when IBM recently partnered with Xilinx (NASDAQ:XLNX), a maker of FPGAs (field-programmable gate arrays), to counter Intel's acquisition of FPGA maker Altera. FPGAs are less powerful than IBM's or Intel's server chips, but they can be reprogrammed, making them well-suited for custom uses in connected cars, consumer devices, and airplanes. IBM and Intel are both using FPGAs to complement their server chips by accelerating workloads.

What Big Blue wants
IBM's revenue has fallen for 14 consecutive quarters, due to sluggish demand for its core IT services, software, and hardware. Big Blue wants investors to focus on the growth of its "strategic imperatives" -- cloud, analytics, mobile, social, and security -- which posted 17% annual sales growth last quarter, or 27% on a constant-currency basis excluding its divested System x (Intel-powered server) business. That growth was solid, but it wasn't enough to offset the steep declines in its other aging businesses.

Last quarter, IBM's hardware revenues plunged 39% annually and accounted for less than 8% of its top line. Power Systems revenues slipped 3% and system storage revenues fell 19%, but z Systems mainframe revenues rose 15%. The decline in Power Systems revenue was expected, due to Intel's dominance of the data center market. But the growth in mainframes was surprising, since analysts had predicted the death of fridge-sized mainframes for decades.

Read More: http://www.fool.com/investing/general/2015/11/24/ibm-vs-intel-corporation-the-data-center-battle-es.aspx?source=eptfxblnk0000004