Gregory Maciag

Big Data for Healthcare

The Institute of Medicine is calling for a “Knowledge Network of Disease”. By bringing together diverse data on health, new discoveries can be made. Healthcare can get smarter, faster, and more evenly distributed.

Reporting on developments in The Atlantic, David A. Shaywitz highlights the tension facing all data aggregators: whether it's possible to create a single, coherent and correct data model, or whether it's better to use a more ad hoc approach. He quotes system biologist Eric Schadt: “The grand data model of everything in biology has been tried over and over again and ultimately it comes crashing down because once you lock in a model, you constrain the types of questions you can ask of the data. The information half life is short, and we know and understand so little about what the data can actually tell us that we don't really understand the questions that ultimately will be the most useful.”

How do we build a knowledge structure when we don't know what we want to know? It's a problem we face in business too.   However, the problem can be attacked in an incremental way. There are levels of detail at which healthcare professionals will agree. They'll agree that there's such a thing as a patient, for example. They are also likely to agree on the major biological systems and organs. They will also probably agree on a core set of signs and symptoms.

There will be a point at which interested parties can no longer agree on definitions of items or relationships. But that doesn't mean the modeling approach should be abandoned. We have to tolerate fuzziness and ambiguity at the depths and fringes of our knowledge structures. That's how we learn.

As healthcare data sets become larger, researchers will use analytics to discover new types and constellations of facts. These discoveries will sharpen the resolution of the outlying fuzzy areas. So the model will become more detailed and more effective in response to its use in the field.

The idea that standards need to be all-or-nothing is one of the myths of information management. Standards exist to facilitate  action, not constrain it. You don't have to have a grand data model. But you need a model.   Instead of thinking that a model or standard “locks in” users, we need to manage standards as evolving tools that learn from users as well as supporting them. Standards must lock step with the communities they serve. Database for Medical Knowledge

Data Mining for Mining

The Australian agency responsible for collecting geoscientific data is making its vast collection open to the mining industry. Geoscience Australia (GA) manages masses of data generated by exploratory activities over the years. Processing and storage technology has caught up with the archive, meaning it's now possible for the mining companies to analyze the data stocks. It's a new form of prospecting, if you will.

This news prompts me to wonder where the uncharted stocks of the world's data riches might be. If the mining companies in Australia hadn't been obligated to send their geoscientific data to the government, would they have discarded it? I doubt it. They'd have stored it themselves. GA has done a good job as archivist but its greater value-add is as aggregator. The mining industry has an industry repository open to all members.

There must be countless other fields of activity where rich data deposits lie in corporate silos. Figuring out where this stuff is and how much of it is legitimately shareable are both considerable tasks. Agreeing standards for data formats would be almost trivial in comparison.

Maybe we will see a new type of entrepreneur arise: organizations that seek out data deposits and bring together communities of interest that can benefit from those resources. Data Miners

Insurance Data & Analytics Summit New York

ACORD and Reactions Magazine launched the new IDA Summit in New York City on April 25th. If you did not attend, please plan to do so next year.  We will keep you posted on the dates. Here is the agenda and copies of the presentations.

 

 

 

Download IDA Agenda 

Download Reinsurance eBusiness

Download Tom Paparatto RMS

Download Richard Hoehne IBM

Download Ajay Bhargava TATA

Download Forrester

 

Analytics Definitions

Meta Brown defines the key terms in the data analytics area in a short piece for Smart Data Collective. Want to know what the difference is between "analysis" and "analytics"? "Analytics" implies there's some math involved. And when is "data mining" more than just sales-speak? Read the article to find out.

 Data Mining Fundamentals

Rising Tech Expectations

Analytics is in fashion as underwriters start making better use of data to upsell and cross-sell. Al Slavin writes an interesting piece on the current state of play in Best's Review. I'd like to see a little more about silo-busting, but it's encouraging to see that technology expectations are rising right across the value chain.

 READ MORE

Cloudbursts

Reetika Joshi, an analyst at ValueNotes Sourcing Practice, wrote a piece last fall predicting that insurance analytics could be the a major market for business process outsourcing (BPO). Joshi's discussion of the analytics opportunity for carriers still holds, but I'm less sure that it points towards outsourcing in the traditional sense.

With the rapid evolution of cloud services, organizations looking for the storage and processing power to run large scale analytics have a growing range of on-demand services at their disposal. Analytics is a "bursty" activity, meaning that analysts focus on different data sets of different sizes and complexity on an unpredictable schedule.

Unless we're folding BPO in with the cloud services market, I see cloud operators picking up this kind of business. And I'd argue that BPO ought to be more than (and different than) the cloud. BPO providers offer expertise as well as sheer power. The question is, will those who run analytics in insurance companies believe there's unique analytics expertise to be found in BPO suppliers? Or will they see themselves as the experts in their data – experts who could use the pay-as-you-go added capacity delivered by the cloud?

It seems to me that both types of supplier need to prove their utility in the field of analytics, but that the cloud guys have the advantage. BPO providers need to be looking at how they can add value above and beyond the heavy lifting.

Insurance Analytics Outsourcing 

Correlation Nation: Analytics

“Progressive Insurance found that if it looked at people's credit scores, it could find who it should and should not insure - the higher the score, the less likely the potential customer would have a costly fender bender.”

Industry leaders like Progressive are discovering the power of analytics. Analytics is management information – but it incorporates data from outside the organization, and even, as in this case, from outside the organization's home domain.

This example of an analytics discovery is particularly appealing because it reminds us how central information has always been to insurance. You could say that policies and rates are the results of information analysis. In the insurance industry, these information entities don't just accompany a product or service – they are the product and the service. And insurance is hungry for data. The more we can know about the world in which our risks operate, the better service we can give, and the more value we can generate.   Analytics

New Uses for Data Standards

The Insurance Canada blog carries a post outlining the spin-off benefits ACORD and CSIO standards have delivered the industry over the years. The theme of the piece is how standards turn out to be the source of answers for each new wave of business technology challenge. The next additional benefit of standards is, according to the blog, Master Data Management. Industry data standards are right at the heart of the new age of analytics.

Intersection Canada

Download The Intersection Data Standards