“If I use master data technology to create a 360-degree view of my client and I have a data breach, then someone could steal all the information about my client.”
Um, wait, what? Insurance companies take personally identifiable information very seriously. The statement is flawed in the relationship between client master data and securing your client data. Let’s dissect the statement and see what master data and data security really mean for insurers. We’ll start by level setting a few concepts.
What is your Master Client Record?
Your master client record is your 360-degree view of your client. It represents everything about your client. It uses Master Data Management technology to virtually integrate and syndicate all of that data into a single view. It leverages identifiers to ensure integrity in the view of the client record. And finally it makes an effort through identifiers to correlate client records for a network effect.
There are benefits to understanding everything about your client. The shape and view of each client is specific to your business. As an insurer looks at their policyholders, the view of “client” is based on relationships and context that the client has to the insurer. This are policies, claims, family relationships, history of activities and relationships with agency channels.
And what about security?
Naturally there is private data in a client record. But there is nothing about the consolidated client record that contains any more or less personally identifiable information. In fact, most of the data that a malicious party would be searching for can likely be found in just a handful of database locations. Additionally breaches happen “on the wire”. Policy numbers, credit card info, social security numbers, and birth dates can be found in less than five database tables. And they can be found without a whole lot of intelligence or analysis.
That data should be secured. That means that the data should be encrypted or masked so that any breach will protect the data. Informatica’s data masking technology allows this data to be secured in whatever location. It provides access control so that only the right people and applications can see the data in an unsecured format. You could even go so far as to secure ALL of your client record data fields. That’s a business and application choice. Do not confuse field or database level security with a decision to NOT assemble your golden policyholder record.
What to worry about? And what not to worry about?
Do not succumb to fear of mastering your policyholder data. Master Data Management technology can provide a 360-degree view. But it is only meaningful within your enterprise and applications. The view of “client” is very contextual and coupled with your business practices, products and workflows. Even if someone breaches your defenses and grabs data, they’re looking for the simple PII and financial data. Then they’re grabbing it and getting out. If the attacker could see your 360-degree view of a client, they wouldn’t understand it. So don’t over complicate the security of your golden policyholder record. As long as you have secured the necessary data elements, you’re good to go. The business opportunity cost of NOT mastering your policyholder data far outweighs any imagined risk to PII breach.
So what does your Master Policyholder Data allow you to do?
Imagine knowing more about your policyholders. Let that soak in for a bit. It feels good to think that you can make it happen. And you can do it. For an insurer, Master Data Management provides powerful opportunities across everything from sales, marketing, product development, claims and agency engagement. Each channel and activity has discreet ROI. It also has direct line impact on revenue, policyholder satisfaction and market share. Let’s look at just a few very real examples that insurers are attempting to tackle today.
- For a policyholder of a certain demographic with an auto and home policy, what is the next product my agent should discuss?
- How many people live in a certain policyholder’s household? Are there any upcoming teenage drivers?
- Does this personal lines policyholder own a small business? Are they a candidate for a business packaged policy?
- What is your policyholder claims history? What about prior carriers and network of suppliers?
- How many touch points have your agents and had with your policyholders? Were they meaningful?
- How can you connect with you policyholders in social media settings and make an impact?
- What is your policyholder mobility usage and what are they doing online that might interest your Marketing team?
These are just some of the examples of very streamlined connections that you can make with your policyholders once you have your 360-degree view. Imagine the heavy lifting required to do these things without a Master Policyholder record.
Fear is the enemy of innovation. In mastering policyholder data it is important to have two distinct work streams. First, secure the necessary data elements using data masking technology. Once that is secure, gain understanding through the mastering of your policyholder record. Only then will you truly be able to take your clients’ experience to the next level. When that happens watch your revenue grow in leaps and bounds.
Did I really compare data quality to flushing toilet paper? Yeah, I think I did. Makes me laugh when I read that, but still true. And yes, I am still playing with more data. This time it’s a location schedule for earthquake risk. I see a 26-story structure with a building value of only $136,000 built in who knows what year. I’d pull my hair out if it weren’t already shaved off.
So let’s talk about the six steps for data quality competency in underwriting. These six steps are standard in the enterprise. But, what we will discuss is how to tackle these in insurance underwriting. And more importantly, what is the business impact to effective adoption of the competency. It’s a repeating self-reinforcing cycle. And when done correctly can be intelligent and adaptive to changing business needs.
Profile – Effectively profile and discover data from multiple sources
We’ll start at the beginning, a very good place to start. First you need to understand your data. Where is it from and in what shape does it come? Whether internal or external sources, the profile step will help identify the problem areas. In underwriting, this will involve a lot of external submission data from brokers and MGAs. This is then combined with internal and service bureau data to get a full picture of the risk. Identify you key data points for underwriting and a desired state for that data. Once the data is profiled, you’ll get a very good sense of where your troubles are. And continually profile as you bring other sources online using the same standards of measurement. As a side, this will also help in remediating brokers that are not meeting the standard.
Measure – Establish data quality metrics and targets
As an underwriter you will need to determine what is the quality bar for the data you use. Usually this means flagging your most critical data fields for meeting underwriting guidelines. See where you are and where you want to be. Determine how you will measure the quality of the data as well as desired state. And by the way, actuarial and risk will likely do the same thing on the same or similar data. Over time it all comes together as a team.
Design – Quickly build comprehensive data quality rules
This is the meaty part of the cycle, and fun to boot. First look to your desired future state and your critical underwriting fields. For each one, determine the rules by which you normally fix errant data. Like what you do when you see a 30-story wood frame structure? How do you validate, cleanse and remediate that discrepancy? This may involve fuzzy logic or supporting data lookups, and can easily be captured. Do this, write it down, and catalog it to be codified in your data quality tool. As you go along you will see a growing library of data quality rules being compiled for broad use.
Deploy – Native data quality services across the enterprise
Once these rules are compiled and tested, they can be deployed for reuse in the organization. This is the beautiful magical thing that happens. Your institutional knowledge of your underwriting criteria can be captured and reused. This doesn’t mean just once, but reused to cleanse existing data, new data and everything going forward. Your analysts will love you, your actuaries and risk modelers will love you; you will be a hero.
Review – Assess performance against goals
Remember those goals you set for your quality when you started? Check and see how you’re doing. After a few weeks and months, you should be able to profile the data, run the reports and see that the needle will have moved. Remember that as part of the self-reinforcing cycle, you can now identify new issues to tackle and adjust those that aren’t working. One metric that you’ll want to measure over time is the increase of higher quote flow, better productivity and more competitive premium pricing.
Monitor – Proactively address critical issues
Now monitor constantly. As you bring new MGAs online, receive new underwriting guidelines or launch into new lines of business you will repeat this cycle. You will also utilize the same rule set as portfolios are acquired. It becomes a good way to sanity check the acquisition of business against your quality standards.
In case it wasn’t apparent your data quality plan is now more automated. With few manual exceptions you should not have to be remediating data the way you were in the past. In each of these steps there is obvious business value. In the end, it all adds up to better risk/cat modeling, more accurate risk pricing, cleaner data (for everyone in the organization) and more time doing the core business of underwriting. Imagine if you can increase your quote volume simply by not needing to muck around in data. Imagine if you can improve your quote to bind ratio through better quality data and pricing. The last time I checked, that’s just good insurance business.
And now for something completely different…cats on pianos. No, just kidding. But check here to learn more about Informatica’s insurance initiatives.
I was just looking at some data I found. Yes, real data, not fake demo stuff. Real hurricane location analysis with modeled loss numbers. At first glance, I thought it looked good. There are addresses, latitudes/longitudes, values, loss numbers and other goodies like year built and construction codes. Yes, just the sort of data that an underwriter would look at when writing a risk. But after skimming through the schedule of locations a few things start jumping out at me. So I dig deeper. I see a multi-million dollar structure in Palm Beach, Florida with $0 in modeled loss. That’s strange. And wait, some of these geocode resolutions look a little coarse. Are they tier one or tier two counties? Who would know? At least all of the construction and occupancy codes have values, albeit they look like defaults. Perhaps it’s time to talk about data quality.
This whole concept of data quality is a tricky one. As cost in acquiring good data is weighed against speed of underwriting/quoting and model correctness I’m sure some tradeoffs are made. But the impact can be huge. First, incomplete data will either force defaults in risk models and pricing or add mathematical uncertainty. Second, massively incomplete data chews up personnel resources to cleanse and enhance. And third, if not corrected, the risk profile will be wrong with potential impact to pricing and portfolio shape. And that’s just to name a few.
I’ll admit it’s daunting to think about. Imagine tens of thousands of submissions a month. Schedules of thousands of locations received every day. Can there even be a way out of this cave? The answer is yes, and that answer is a robust enterprise data quality infrastructure. But wait, you say, enterprise data quality is an IT problem. Yeah, I guess, just like trying to flush an entire roll of toilet paper in one go is the plumber’s problem. Data quality in underwriting is a business problem, a business opportunity and has real business impacts.
Join me in Part 2 as I outline the six steps for data quality competency in underwriting with tangible business benefits and enterprise impact. And now that I have you on the edge of your seats, get smart about the basics of enterprise data quality.
Eighteen months ago, I was sitting in a conference room, nothing remarkable except for the great view down 6th Avenue toward the Empire State Building. The pre-sales consultant sitting across from me had just given a visually appealing demonstration to the CIO of a multinational insurance corporation. There were fancy graphics and colorful charts sharply displayed on an iPad and refreshing every few seconds. The CIO asked how long it had taken to put the presentation together. The consultant excitedly shared that it only took him four to five hours, to which the CIO responded, “Well, if that took you less than five hours, we should be able to get a production version in about two to three weeks, right?”
The facts of the matter were completely different however. The demo, while running with the firm’s own data, had been running from a spreadsheet, housed on the laptop of the consultant and procured after several weeks of scrubbing, formatting, and aggregating data from the CIO’s team; this does not even mention the preceding data procurement process. And so, as the expert in the room, the voice of reason, the CIO turned to me wanting to know how long it would take to implement the solution. At least six months, was my assessment. I had seen their data, and it was a mess. I had seen the flow, not a model architecture and the sheer volume of data was daunting. If it was not architected correctly, the pretty colors and graphs would take much longer to refresh; this was not the answer he wanted to hear.
The advancement of social media, new web experiences and cutting edge mobile technology have driven users to expect more of their applications. As enterprises push to drive value and unlock more potential in their data, insurers of all sizes have attempted to implement analytical and business intelligence systems. But here’s the truth: by and large most insurance enterprises are not in a place with their data to make effective use of the new technologies in BI, mobile or social. The reality is that data cleanliness, fit for purpose, movement and aggregation is being done in a BI when it should be done lower down so that all applications can take advantage of it.
Let’s face it – quality data is important. Movement and shaping of data in the enterprise is important. Identification of master data and metadata in the enterprise is important and data governance is important. It brings to mind episode 165, “The Apology”, of the mega-hit show Seinfeld. Therein George Costanza accuses erstwhile friend Jason Hanky of being a “step skipper”. What I have seen in enterprise data is “step skipping” as users clamor for new and better experiences, but the underlying infrastructure and data is less than ready for consumption. So the enterprise bootstraps, duct tapes and otherwise creates customizations where it doesn’t architecturally belong.
Clearly this calls for a better solution; A more robust and architecturally sustainable data ecosystem, which shepherds the data from acquisition through to consumption and all points in between. It also must be attainable by even modestly sized insurance firms.
First, you need to bring the data under your control. That may mean external data integration, or just moving it from transactional, web, or client-server systems into warehouses, marts or other large data storage schemes and back again. But remember, the data is in various stages of readiness. This means that through out of the box or custom cleansing steps the data needs to be processed, enhanced and stored in a way that is more in line with corporate goals for governing the quality of that data. And this says nothing of the need to change a data normalization factor between source and target. When implemented as a “factory” approach, the ability to bring new data streams online, integrate them quickly and maintain high standards become small incremental changes and not a ground up monumental task. Move your data shaping, cleansing, standardization and aggregation further down in the stack and many applications will benefit from the architecture.
Critical to this process is that insurance enterprises need to ensure the data remains secure, private and is managed in accordance with rules and regulations. They must also govern the archival, retention and other portions of the data lifecycle.
At any point in the life of your information, you are likely sending or receiving data from an agent, broker, MGA or service provider, which needs to be processed using the robust ecosystem, described above. Once an effective data exchange infrastructure is implemented, the steps to process the data can nicely complement your setup as information flows to and from your trading partners.
Finally, as your enterprise determines “how” to implement these solutions, you may look to a cloud based system for speed to market and cost effectiveness compared to on-premises solutions.
And don’t forget to register for Informatica World 2014 in Las Vegas, where you can take part in sessions and networking tailored specifically for insurers.