“ ‘BIG DATA’ REPRESENTS A
UNIQUE SET OF HARDWARE
AND SOFTWARE TECHNOLOGIES
THAT PROCESSES DATA
USING A SPECIAL TECHNIQUE
CALLED MASSIVELY PARALLEL
PROCESSING.”
It also helps provide a standardized method for
gathering and storing all data and driving legacy
modernization initiatives.
A TECHNICIAN tests a computer on a rack of computer servers. So much data is generated every day around the world, from shopping habits
to medical records, that management experts say we have entered the era of “big data.”
Q: Who owns this vast universe of data?
A: Ownership is really about accessibility
and the ability to respond to questions when
asked about the data pedigree. Many master
data management projects look to establish a
“data custodian” who can tell you the nuances
of one policy administration from the other. One
person or team needs to take ownership of the
information, its meaning and pedigree, as well
as change management. Access and subscription
involves engineering turning data into actionable
information. This is where data architects and
business intelligence analysts provide the feed,
and end users use their tool of choice to view and
analyze data.
Going Big with
Q: Is “big data” more expensive to manage than
regular data?
A: “Big data” represents a unique set of hardware
and software technologies that processes data
using a special technique called massively parallel
processing (MPP). If a carrier has 1 billion database
rows, query processing taking 24 hours – that
may not be an acceptable response time for the
actuaries. MPP enables “big data” by giving data
architects the ability to split the data into equal,
discrete chunks and reading them all at the same
time – resulting in executing that same query in
about 20 minutes. It’s not the size of the data that’s
crucial; it’s how quickly you want it to be accessible.
Costs today range from $17,000 to $50,000 per
terabyte including hardware, software and possibly
even a technician with a high-end platform.
; RISK MANAGEMENT INFORMATION SYSTEMS
the Data
Companies need master data management strategies in this new era
of massive data, where actuaries want to be able to query processing
among a billion rows of information in less than 24 hours.
In February, Risk & Insurance® Managing Editor
Cyril Tuohy conducted a question-and-answer
session on the role of data in the insurance
industry with Don Canning, vice president of
SunGard’s insurance business. The era of vast
amounts of data is upon us and Canning explains
what that means for the industry.
Q: How are insurers leveraging data? What does
“leveraging” data mean?
A: The actuaries typically use the largest amount
of data and require the longest history – especially
in life and annuity type policies. Leveraging data
means having the data accessible at the business
user’s finger tips in time to make the best business
decision – no matter the broker, TPA or anyone else
in the supply chain. He who has access to the most
accurate data the quickest will win the business
every time.
Q: The expression “big data” is cropping up more
frequently in the mainstream. What does that
mean and why is it called “big”?
A: “Big data” pertains to the legacy of data
warehousing. Many data warehouse projects did
not pay off for carriers over the past 10 years,
when millions of dollars were spent to deliver
only delayed fractional benefits. Moreover, they
delivered results slowly, causing other teams to
spring up, fractioning into departmental solutions
that now have several, unlinked mini-data
warehouses – a rather expensive mess. Today,
data warehouse technologies have gone the way of
attempting to create a “turn-key appliance,” like a
black-box processing machine able to handle data
across departments and business lines.;
A: Risk management in the insurance business
can be thought of as a layer cake, starting with a
foundation where actuaries need data to perform
asset liability management, stochastic modeling and
seriatim processing. While most actuaries require
at least seven years of history, many would like
10 years to 15 years, and others would go back 30
years if they could. The next layer is rolling up all
the product types within a given division or line of
business. This is primarily the aggregate data business
line executives want to be able to drill down into the
transaction level. Next, the chief actuary will want
to gather actuary results from every department and
division creating key risk indicators overseeing the
risk appetite of the business. Having a bottom-up
enterprise master data management strategy is key
to giving executives line of sight over risk controls.
The more data they have, the more granular risk
monitoring the chief actuary can provide to the board.
Q: How will all this data affect the ability of
corporate risk management to analyze enterprise
risks?
A: If the chief actuary can build that layer cake,
he will have line of sight on the carrier’s overall
risk. The data provides the baseline to build “shock
tests” or balance sheet stress testing. Big data can
be viewed the same way a greens keeper looks
at the overall landscape of golf course sand traps
and greens – they look at the overall landscape of
the rolling hills taking in the overall pattern and
flow of the course. He’ll look at the blades of grass
Q: Who is responsible for generating this data,
managing it, and where is it stored?
A: Ideally, the chief information officer should
have a master data management strategy as a
strategic initiative. This can also provide a new
fabric of infrastructure that breaks the habit of
drilling into policy systems for operational data.
Summary
Q: In the context of risk management, what does
“big data,” refer to?
• “Big data” pertains to the legacy of data
warehousing.
• The chief information officer should have a master
data management strategy.
