FEMA’s recent flood maps incorrectly identified some residents’ homes in a hilly area north of Austin as being located in a high-risk flood zone. Unfortunately, attempts to correct these errors cost the Texan homeowners both time and money. Since FEMA’s flood maps plays a key role in determining the premiums homeowners pay for their home insurance, the fact that these maps often use inaccurate, outdated data is alarming.
In the case of the FEMA flood maps, the common issue was that mapmakers did not correctly integrate new elevation data with historical maps. FEMA is not the only organization that experiences historical data problems. These obstacles also occur frequently with individual insurers, especially with insurers in the midst of a legacy system migration.
The fast-paced business landscape demands a move away from legacy systems in order for companies to increase efficiency (faster transactions speed, real-time processing, new product rollout, etc.) and flexibility (rule-based versus code-based). However, historical data conversion – a vital component of legacy system migrations –is a complex, time-consuming and cost-prohibitive process. Incomplete historical data returns erroneous analyses and reports, leading to frustrations for all parties involved.
To get into production quickly and avoid the time and cost of converting historical data, companies often kept both the new core system and the legacy system running. While this reduces short-term challenges, disparate data sources can cause long-term inefficiencies. iPartners recommends an approach based on the utilization of a robust data warehouse. This method leads to immediate enhanced reporting on legacy and a better opportunity to sunset the legacy system.
Regardless, any approach to historical data conversion during a legacy system migration should consider the companies’ individual needs. Executives need to answer questions such as: What role does historical data play in the decision-making process? Can management live with disparate reporting systems? Are there technical resources and time available to combine the two environments? A historical data conversion project can be successful with the right answers, resources and approach.
By: David Caro, COO
A seamless implementation is critical to business intelligence (BI) adoption among an insurer’s business users. Users are eager to start using the solution and analyzing the organization’s data. If the process is too long and complex, end-user interests may wane.
A BI SaaS solution can help firms achieve the streamlined deployment they seek. The SaaS vendor brings in a team for deployment; the team completes most backend infrastructure setup and front end customizations. This relieves the client of hardware and licensing selection and installation duties. Furthermore, post-implementation infrastructure upkeep by the client is minimized and maintenance, management and support is handled by the vendor.
Nonetheless, the common misconception is that the client organization is mostly hands-off during the implementation process. The client hands over the keys to the kingdom to the SaaS vendor’s implementation team. Then, the client sits back and weeks later, they are ushered into a room for the big reveal by the vendor. The implementation team pulls back the curtain and it’s business intelligence, delivered exactly as the client envisioned.
While iPartners can deliver a solution to clients in a matter of weeks, a rapid, effective BI SaaS deployment is difficult to achieve without the client’s involvement. We can provide the technology and help lay the foundation for BI best practices, but the continuous inputs from you – the client – helps us tailor the solution to your needs and exceed your expectations.
Even if the out-of-the-box solution already includes everything you’re looking for, consider other implications of implementations in which your contribution is significant. For example, iPartners’ BI solution can generate the dashboards, reports and calculations you need, but is the data behind those analyses accurate? Data values vary from client to client. While results can still be produced without a confirmation on data accuracy, the value of the BI solution is decreased.
Optimizing the full value of BI requires efficient coordination and collaboration between the client and the vendor. A BI SaaS solution can give clients a smooth ride through implementation, but without feedback and client participation, the end results may not deliver on the true promise of BI.
By: Tom DiMarco, CIO
Earlier this month, at the annual IASA Conference in Washington D.C., Robert Hyle of Tech Decisions and PropertyCasualty360.com noted a rising trend among insurers: they are looking more to the cloud for solutions to current challenges.
The cloud is no longer a new concept. Thus, slow adoption by insurers is surprising. The common perception of the cloud has been that it lacks security. But the cloud has grown up. So are insurers finally coming around?
According to the Tech Decision article, most vendors believe that it’s more of a trade-off for insurers and not true acceptance. Insurers find that the benefits of costs and complexity reductions the cloud offers outweigh concerns around cloud security.
This may account for some movement to the cloud, since as-a-service BI providers alleviate the complexity of implementation, management and maintenance for customers. Oftentimes, their customizable user interface is also highly intuitive and comprehensive, allowing for quick results.The predictable monthly subscription model isn’t too bad for the OpEx, either.
Still, I’d like to offer another reason behind the cloud movement: insurers now recognize that reliable cloud vendors, in reality, often deploy rigorous best practices security measures. Their reputation and business depend on it. In addition to complying with numerous security regulations mandated by governing bodies, cloud solutions providers also invest heavily in security protocols such as data encryption, penetration testing and 24x7x365 monitoring.
Cloud solution providers often take it a step further by delivering a solution that is fully replicated, enabling a robust DR strategy that can provide return to operation times (RTO) of less than two hours.
Hence, by turning to the cloud, insurers can cut upfront capital, decrease the strain on internal resources to deploy and maintain a solution, utilize a system of support for the solution post-implementation and benefit from a high level of security and availability for their valuable data. So is it really a trade-off or an overall win-win?
By: Robert Lasher, CEO
The finished picture looks great. But when you rip the package apart and the countless pieces scatter in front of you, putting together the puzzle pieces suddenly become a daunting task. Even worse – what if you were down the last piece and it was missing?
Unfortunately, many executives in the P&C Insurance industry today routinely experience this frustration. They are regularly tasked with delivering complete analyses of the current business operations, forecasts, pricing and product developments. Yet, the critical information they need to complete these tasks are often difficult to find.
While P&C Insurance executives understand there is value in their data, the obstacle lies in the most effective way to extract this value. Excel is a common default location for data. But gathering all the Excel spreadsheets and then determining the correct versions is a time-consuming process, leaving little time left in the schedule for in-depth analyses.
Disparate sources of data and a lack of an efficient data modeling/data warehousing structure all contribute to convoluted views of the growing information P&C Insurance firms are amassing. The feedback I’ve received from executives is that it would be more efficient if they could merge all the data sources together, then search one and done.
This initiative is easier said than done. Gathering meaningful data into one place can’t be completed with just a push of the “Easy” button. Infrastructures must be built, software must be implemented and permissions must be assigned. Now another puzzle needs to be pieced together.
To simplify this part of the equation, organizations are turning to as-a-service business intelligence solutions provider. The cloud is no longer a mysterious concept. A new survey of 1,200 respondents by Dresner Advisory Services notes that about 75%of the organizations surveyed stated that cloud or SaaS business intelligence solution is a key innovation and will implement some sort of cloud-based BI solution. Are you in?
Tell us what you think about Cloud Business Intelligence. Is it here to stay? Is it a solution your own organization is exploring?
How accurate was your latest Excel report? Did the formulas copy correctly to adjacent cells? Did you lock the proper inputs? The recent discovery of multiple errors in a widely referenced Harvard economics study has definitely made me double-check (and triple) my inputs and formulas. Unfortunately, Reinhart and Rogoff’s case was hardly news or unique. Remember The London Whale debacle at J.P. Morgan?
Spreadsheet errors have been identified as far back as four years ago when a study noted that nearly 90% of all Excel spreadsheets contain some kind of error, especially in formula cells. The root of the issue here is the manual labor spreadsheets require. With close to one billion Excel users worldwide, human errors are impossible to avoid. A second – or even fourth – look can still miss mistakes. We’re only human.
Mistakes are costly, especially in the insurance industry. While they cannot be completely eliminated, there are ways to reduce the chance of errors. It’s simple: reduce as much manual computation and analyses as possible.
This is definitely easier said than done. Meticulous users who are accustomed to entering their own formulas may be hesitant to adopt new automated processes. Some may even feel as if they are relinquishing control. However, the purpose of an analytics tool is for users to streamline processes, reduce manual labor and take advantage of the built-in expertise.
An entire library BI analytics and reporting tools eliminates the need for manual formula entry and cell references. The data just needs to be entered and several clicks of the mouse later, results are generated. Granted, the manual labor of data entry may still create errors, but the chances of making a second round of errors with manual formula entry is eliminated with BI solutions.
Furthermore, utilizing BI analytics and reporting tools can help end-users define what information is needed. For example, if an insurance organization’s ultimate goal is to calculate equitable pricing for future insurance products, where would they start? What historical statistics would they need? Selecting a set of data for analysis only to find out down the road that it’s the wrong set means wasted time and resources.
Are you staying ahead of the Excel curve with BI solutions? What measures has your firm taken to reduce user errors?
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