Category Archives: Vertical
As I continue to counsel insurers about master data, they all agree immediately that it is something they need to get their hands around fast. If you ask participants in a workshop at any carrier; no matter if life, p&c, health or excess, they all raise their hands when I ask, “Do you have broadband bundle at home for internet, voice and TV as well as wireless voice and data?”, followed by “Would you want your company to be the insurance version of this?”
Now let me be clear; while communication service providers offer very sophisticated bundles, they are also still grappling with a comprehensive view of a client across all services (data, voice, text, residential, business, international, TV, mobile, etc.) each of their touch points (website, call center, local store). They are also miles away of including any sort of meaningful network data (jitter, dropped calls, failed call setups, etc.)
Similarly, my insurance investigations typically touch most of the frontline consumer (business and personal) contact points including agencies, marketing (incl. CEM & VOC) and the service center. On all these we typically see a significant lack of productivity given that policy, billing, payments and claims systems are service line specific, while supporting functions from developing leads and underwriting to claims adjucation often handle more than one type of claim.
This lack of performance is worsened even more by the fact that campaigns have sub-optimal campaign response and conversion rates. As touchpoint-enabling CRM applications also suffer from a lack of complete or consistent contact preference information, interactions may violate local privacy regulations. In addition, service centers may capture leads only to log them into a black box AS400 policy system to disappear.
Here again we often hear that the fix could just happen by scrubbing data before it goes into the data warehouse. However, the data typically does not sync back to the source systems so any interaction with a client via chat, phone or face-to-face will not have real time, accurate information to execute a flawless transaction.
On the insurance IT side we also see enormous overhead; from scrubbing every database from source via staging to the analytical reporting environment every month or quarter to one-off clean up projects for the next acquired book-of-business. For a mid-sized, regional carrier (ca. $6B net premiums written) we find an average of $13.1 million in annual benefits from a central customer hub. This figure results in a ROI of between 600-900% depending on requirement complexity, distribution model, IT infrastructure and service lines. This number includes some baseline revenue improvements, productivity gains and cost avoidance as well as reduction.
On the health insurance side, my clients have complained about regional data sources contributing incomplete (often driven by local process & law) and incorrect data (name, address, etc.) to untrusted reports from membership, claims and sales data warehouses. This makes budgeting of such items like medical advice lines staffed by nurses, sales compensation planning and even identifying high-risk members (now driven by the Affordable Care Act) a true mission impossible, which makes the life of the pricing teams challenging.
Over in the life insurers category, whole and universal life plans now encounter a situation where high value clients first faced lower than expected yields due to the low interest rate environment on top of front-loaded fees as well as the front loading of the cost of the term component. Now, as bonds are forecast to decrease in value in the near future, publicly traded carriers will likely be forced to sell bonds before maturity to make good on term life commitments and whole life minimum yield commitments to keep policies in force.
This means that insurers need a full profile of clients as they experience life changes like a move, loss of job, a promotion or birth. Such changes require the proper mitigation strategy, which can be employed to protect a baseline of coverage in order to maintain or improve the premium. This can range from splitting term from whole life to using managed investment portfolio yields to temporarily pad premium shortfalls.
Overall, without a true, timely and complete picture of a client and his/her personal and professional relationships over time and what strategies were presented, considered appealing and ultimately put in force, how will margins improve? Surely, social media data can help here but it should be a second step after mastering what is available in-house already. What are some of your experiences how carriers have tried to collect and use core customer data?
Recommendations and illustrations contained in this post are estimates only and are based entirely upon information provided by the prospective customer and on our observations. While we believe our recommendations and estimates to be sound, the degree of success achieved by the prospective customer is dependent upon a variety of factors, many of which are not under Informatica’s control and nothing in this post shall be relied upon as representative of the degree of success that may, in fact, be realized and no warrantee or representation of success, either express or implied, is made.
That tag line got your attention – did it not? Last week I talked about how companies are trying to squeeze more value out of their asset data (e.g. equipment of any kind) and the systems that house it. I also highlighted the fact that IT departments in many companies with physical asset-heavy business models have tried (and often failed) to create a consistent view of asset data in a new ERP or data warehouse application. These environments are neither equipped to deal with all life cycle aspects of asset information, nor are they fixing the root of the data problem in the sources, i.e. where the stuff is and what it look like. It is like a teenager whose parents have spent thousands of dollars on buying him the latest garments but he always wears the same three outfits because he cannot find the other ones in the pile he hoardes under her bed. And now they bought him a smart phone to fix it. So before you buy him the next black designer shirt, maybe it would be good to find out how many of the same designer shirts he already has, what state they are in and where they are.
Recently, I had the chance to work on a like problem with a large overseas oil & gas company and a North American utility. Both are by definition asset heavy, very conservative in their business practices, highly regulated, very much dependent on outside market forces such as the oil price and geographically very dispersed; and thus, by default a classic system integration spaghetti dish.
My challenge was to find out where the biggest opportunities were in terms of harnessing data for financial benefit.
The initial sense in oil & gas was that most of the financial opportunity hidden in asset data was in G&G (geophysical & geological) and the least on the retail side (lubricants and gas for sale at operated gas stations). On the utility side, the go to area for opportunity appeared to be maintenance operations. Let’s say that I was about right with these assertions but that there were a lot more skeletons in the closet with diamond rings on their fingers than I anticipated.
After talking extensively with a number of department heads in the oil company; starting with the IT folks running half of the 400 G&G applications, the ERP instances (turns out there were 5, not 1) and the data warehouses (3), I queried the people in charge of lubricant and crude plant operations, hydrocarbon trading, finance (tax, insurance, treasury) as well as supply chain, production management, land management and HSE (health, safety, environmental).
The net-net was that the production management people said that there is no issue as they already cleaned up the ERP instance around customer and asset (well) information. The supply chain folks also indicated that they have used another vendor’s MDM application to clean up their vendor data, which funnily enough was not put back into the procurement system responsible for ordering parts. The data warehouse/BI team was comfortable that they cleaned up any information for supply chain, production and finance reports before dimension and fact tables were populated for any data marts.
All of this was pretty much a series of denial sessions on your 12-step road to recovery as the IT folks had very little interaction with the business to get any sense of how relevant, correct, timely and useful these actions are for the end consumer of the information. They also had to run and adjust fixes every month or quarter as source systems changed, new legislation dictated adjustments and new executive guidelines were announced.
While every department tried to run semi-automated and monthly clean up jobs with scripts and some off-the-shelve software to fix their particular situation, the corporate (holding) company and any downstream consumers had no consistency to make sensible decisions on where and how to invest without throwing another legion of bodies (by now over 100 FTEs in total) at the same problem.
So at every stage of the data flow from sources to the ERP to the operational BI and lastly the finance BI environment, people repeated the same tasks: profile, understand, move, aggregate, enrich, format and load.
Despite the departmental clean-up efforts, areas like production operations did not know with certainty (even after their clean up) how many well heads and bores they had, where they were downhole and who changed a characteristic as mundane as the well name last and why (governance, location match).
Marketing (Trading) was surprisingly open about their issues. They could not process incoming, anchored crude shipments into inventory or assess who the counterparty they sold to was owned by and what payment terms were appropriate given the credit or concentration risk associated (reference data, hierarchy mgmt.). As a consequence, operating cash accuracy was low despite ongoing improvements in the process and thus, incurred opportunity cost.
Operational assets like rig equipment had excess insurance coverage (location, operational data linkage) and fines paid to local governments for incorrectly filing or not renewing work visas was not returned for up to two years incurring opportunity cost (employee reference data).
A big chunk of savings was locked up in unplanned NPT (non-production time) because inconsistent, incorrect well data triggered incorrect maintenance intervals. Similarly, OEM specific DCS (drill control system) component software was lacking a central reference data store, which did not trigger alerts before components failed. If you add on top a lack of linkage of data served by thousands of sensors via well logs and Pi historians and their ever changing roll-up for operations and finance, the resulting chaos is complete.
One approach we employed around NPT improvements was to take the revenue from production figure from their 10k and combine it with the industry benchmark related to number of NPT days per 100 day of production (typically about 30% across avg depth on & offshore types). Then you overlay it with a benchmark (if they don’t know) how many of these NPT days were due to bad data, not equipment failure or alike, and just fix a portion of that, you are getting big numbers.
When I sat back and looked at all the potential it came to more than $200 million in savings over 5 years and this before any sensor data from rig equipment, like the myriad of siloed applications running within a drill control system, are integrated and leveraged via a Hadoop cluster to influence operational decisions like drill string configuration or asmyth.
Next time I’ll share some insight into the results of my most recent utility engagement but I would love to hear from you what your experience is in these two or other similar industries.
Recommendations contained in this post are estimates only and are based entirely upon information provided by the prospective customer and on our observations. While we believe our recommendations and estimates to be sound, the degree of success achieved by the prospective customer is dependent upon a variety of factors, many of which are not under Informatica’s control and nothing in this post shall be relied upon as representative of the degree of success that may, in fact, be realized and no warrantee or representation of success, either express or implied, is made.
I believe that most in the software business believe that it is tough enough to calculate and hence financially justify the purchase or build of an application - especially middleware – to a business leader or even a CIO. Most of business-centric IT initiatives involve improving processes (order, billing, service) and visualization (scorecarding, trending) for end users to be more efficient in engaging accounts. Some of these have actually migrated to targeting improvements towards customers rather than their logical placeholders like accounts. Similar strides have been made in the realm of other party-type (vendor, employee) as well as product data. They also tackle analyzing larger or smaller data sets and providing a visual set of clues on how to interpret historical or predictive trends on orders, bills, usage, clicks, conversions, etc.
If you think this is a tough enough proposition in itself, imagine the challenge of quantifying the financial benefit derived from understanding where your “hardware” is physically located, how it is configured, who maintained it, when and how. Depending on the business model you may even have to figure out who built it or owns it. All of this has bottom-line effects on how, who and when expenses are paid and revenues get realized and recognized. And then there is the added complication that these dimensions of hardware are often fairly dynamic as they can also change ownership and/or physical location and hence, tax treatment, insurance risk, etc.
Such hardware could be a pump, a valve, a compressor, a substation, a cell tower, a truck or components within these assets. Over time, with new technologies and acquisitions coming about, the systems that plan for, install and maintain these assets become very departmentalized in terms of scope and specialized in terms of function. The same application that designs an asset for department A or region B, is not the same as the one accounting for its value, which is not the same as the one reading its operational status, which is not the one scheduling maintenance, which is not the same as the one billing for any repairs or replacement. The same folks who said the Data Warehouse is the “Golden Copy” now say the “new ERP system” is the new central source for everything. Practitioners know that this is either naiveté or maliciousness. And then there are manual adjustments….
Moreover, to truly take squeeze value out of these assets being installed and upgraded, the massive amounts of data they generate in a myriad of formats and intervals need to be understood, moved, formatted, fixed, interpreted at the right time and stored for future use in a cost-sensitive, easy-to-access and contextual meaningful way.
I wish I could tell you one application does it all but the unsurprising reality is that it takes a concoction of multiple. None or very few asset life cycle-supporting legacy applications will be retired as they often house data in formats commensurate with the age of the assets they were built for. It makes little financial sense to shut down these systems in a big bang approach but rather migrate region after region and process after process to the new system. After all, some of the assets have been in service for 50 or more years and the institutional knowledge tied to them is becoming nearly as old. Also, it is probably easier to engage in often required manual data fixes (hopefully only outliers) bit-by-bit, especially to accommodate imminent audits.
So what do you do in the meantime until all the relevant data is in a single system to get an enterprise-level way to fix your asset tower of Babel and leverage the data volume rather than treat it like an unwanted step child? Most companies, which operate in asset, fixed-cost heavy business models do not want to create a disruption but a steady tuning effect (squeezing the data orange), something rather unsexy in this internet day and age. This is especially true in “older” industries where data is still considered a necessary evil, not an opportunity ready to exploit. Fact is though; that in order to improve the bottom line, we better get going, even if it is with baby steps.
If you are aware of business models and their difficulties to leverage data, write to me. If you even know about an annoying, peculiar or esoteric data “domain”, which does not lend itself to be easily leveraged, share your thoughts. Next time, I will share some examples on how certain industries try to work in this environment, what they envision and how they go about getting there.
The need to be more customer-centric in financial services is more important than ever as banks and insurance companies look for ways to reduce churn as those in the industry know that loyal customers spend more on higher margin products and are likely to refer additional customers. Bankers and insurers who understand this, and get this right, are in a better position to maintain profitable and lasting customer loyalty and reap significant financial rewards. The current market conditions remain significant and will be difficult to overcome without the right information management architecture to help companies be truly customer centric. Here’s why:
- Customer satisfaction with retail banks has decreased for four consecutive years, with particularly low scores in customer service. Thirty-seven percent of customers who switched primary relationships cited in an industry survey showed poor customer service as the main reasons.
- The commoditization of traditional banking and insurance products has rapidly increased client attrition and decreased acquisition rates. Industry reports estimate that banks are losing customers at an average rate of 12.5% per year, while average acquisition rates are at 13.5%, making acquisitions nearly a zero-sum game. Further, the cost of acquiring new customers is estimated at five times the rate of retaining existing ones.
- Switching is easier than ever before. Customer churn is at an all-time high in most European countries. According to an industry survey, 42 percent of German banking customers had been with their main bank for less than a year. As customer acquisition costs running between of €200 to €400, bankers and insurers need to keep their clients at least 5 to 7 years to simply break even.
- Mergers and acquisitions impact even further the complexity and risks of maintaining customer relationships. According to a recent study, 17 percent of respondents who had gone through a merger or acquisition had switched at least one of their accounts to another institution after their bank was acquired, while an additional 31 percent said they were at least somewhat likely to switch over the next year.
Financial services professionals have long recognized the need to manage customer relationships vs. account relationships by shifting away from a product-centric culture toward a customer-centric model to maintain client loyalty and grow their bottom lines organically. Here are some reasons why:
- A 5% increase in customer retention can increase profitability by 35% in banking, 50% in brokerage, and 125% in the consumer credit card market.
- Banks can add more than $1 million to the profitability of their commercial banking business line by simply extending 16 of these large corporate relationships by one year, or by saving two such clients from defecting. In the insurance sector, a one percent increase in customer retention results in $1M in revenue.
- The average company has between a 60% and 70% probability of success selling more services to a current customer, a 20% to 40% probability of selling to a former customer, and a 5% to 20% probability of making a sale to a prospect.
- Up to 66% of current users of financial institutions’ social media sites engage in receiving information about financial services, 32% use it to retrieve information about offers or promotions and 30% to conduct customer service related activities.
So what does it take to become more Customer-centric?
Companies who have successful customer centric business models share similar cultures of placing the customer first, people who are willing to go that extra mile, business processes designed with the customer’s needs in mind, product and marketing strategy that is designed to meet a customer’s needs, and technology solutions that helps access and deliver trusted, timely, and comprehensive information and intelligence across the business. These technologies include
Why is data integration important? Customer centricity begins with the ability to access and integrate your data regardless of format, source system, structure, volume, latency, from any location including the cloud and social media sites. The data business needs originates from many different systems across the organization and outside including new Software as a Service solutions and cloud based technologies. Traditional hand coded methods and one off tools and open source data integration tools are not able to scale and perform to effectively and efficiently access, manage, and deliver the right data to the systems and applications in the front lined. A the same time, we live in the Big Data era with increasing transaction volumes, new channel adoption including mobile devices and social media combined generating petabytes of data of which to support a capable and sustainable customer centric business model, requires technology that can handle this complexity, scale with the business, while reducing costs and improving productivity.
Data quality issues must be dealt with proactively and managed by both business and technology stakeholders. Though technology itself cannot prevent all data quality errors from happening, it is a critical part of your customer information management process to ensure any issues that exist are identified and dealt with in an expeditious manner. Specifically, a Data Quality solution that can help detect data quality errors in any source, allow business users to define data quality rules, support seamless consumption of those rules by developers to execute, dashboards and reports for business stakeholders, and ongoing quality monitoring to deal with time and business sensitive exceptions. Data quality management can only scale and deliver value if an organization believes and manages data as an asset. It also helps to have a data governance framework consisting of processes, policies, standards, and people from business and IT working together in the process.
Lastly, growing your business, improving wallet share, retaining profitable relationships, and lowering the cost of managing customer relationships requires a single, trusted, holistic, and authoritative source of customer information. Managing customer information has historically been in applications across traditional business silos that lacked any common processes to reconcile duplicate and conflicting information across business systems. Master Data Management solutions are purposely designed to help breakdown the traditional application and business silos and helps deliver that single view of the truth for all systems to benefit. Master Data Management allows banks and insurance companies to access, identity unique customer entities, relate accounts to each customer, and extend that relationship view across other customers and employees including relationship bankers, financial advisors, to existing agents and brokers.
The need to attract and retain customers is a continuous journey for the financial industry however that need is greater than ever before. The foundation for successful customer centricity requires technology that can help access and deliver trusted, timely, consistent, and comprehensive customer information and insight across all channels and avoid the mistakes of the past, allow you to stay ahead of your competition, and maximize value for your shareholders.
 2010 UK Retail Banking Satisfaction Study, J.D. Power and Associates, October 2010.
 “Customer Winback”
 Mortgage Servicing News
I had the privilege to be invited to testify to the Health I.T. Policy Committee workgroup on the topic of data quality back in November. I’ve been an advocate for the work of the committee for years and am constantly impressed with the considerable insight and genuine passion they bring to their work. The opportunity to testify, however, was my first opportunity to actually participate in the policy-making process and it certainly was both a learning opportunity for me, as well as a chance to share my thoughts on the important topic of data quality. (more…)
Dennis Moore, senior vice president and general manager, Master Data Management, Informatica, discusses how MDM can help companies improve customer service.
During this video, Dennis answers the following questions:
- What did you discuss at the 2012 European MDM product advisory council?
- With the holiday season upon us, how can companies use MDM to best serve their customers?
- What have other industries learned from Retail? How can they apply what you have shared with us today?
In this video, Peter Ku, director of solution marketing, Global Financial Services, Informatica, discusses the data challenges associated with FATCA compliance.
The Foreign Account Tax Compliance Act (FATCA) was signed into U.S law in March 2010 and is coming into effect on January 1, 2014. The new law will require Foreign Financial Institutions to report the names of U.S. persons and owners of companies who have bank accounts in these banks for tax reporting and withholding purposes.
Peter answers the following questions:
1) What is FATCA and what are the requirements financial services companies must comply with?
2) What must financial institutions do to successfully meet these requirements?
3) What do financial institutions need to address the data-related challenges and comply with FATCA?
Like most Americans last week, I was glued to the news several days prior to Hurricane Sandy hitting landfall on the East Coast of the United States, hoping it would pass with minimal damage. Having lived in Hawaii and Florida for most of my life, I personally experienced three hurricanes and know how devastating these natural disasters can be during the storm and the hardships people go through afterwards. My thoughts are with all those who lost their lives and their belongings due to this disaster.
Hurricane Sandy has been described as one of the largest storms both in size and in property damage to homes and businesses. According to the New York Times, the total economic damage from Hurricane Sandy will range between $10 to $20 billion with insurance companies paying for $5 to $10 billion in insurance claims. At the high end of that range, Sandy would become the third-most expensive storm for insurers in U.S. history. As property, casualty and flood insurance companies prepare to face a significant wave of calls and claims requests from policyholders, I wonder what the implications and costs will be for these companies who lack reliable, trusted and accurate data which has plagued the industry industry for years.
Reliable, trusted, and accurate data is critical in helping insurance companies manage their business from satisfying regulatory requirements, maintaining and growing customer relationships, combating fraud, to reducing the cost of doing business. Unfortunately, many insurance companies, large and small, have long operated on paper-based processes to onboard new customers, manage policy changes and process claim requests. Though some firms have invested in data quality and governance practices in recent years, the majority of today’s insurance industry has ignored the importance of managing and governing good quality data and dealing with the root causes to bad data including:
- Inadequate verification of data stored in legacy systems
- Non-validated data leaks and data entry errors made by human beings
- Inadequate or manual integration of data between systems
- Redundant data sources/stores that cause data corruption to dependent applications
- Direct back-end updates with little to no data verification and impact analysis
Because of this, the data in core insurance systems can contain serious data quality errors including:
- Invalid property addresses
- Policyholder contact details (Name, Address, Phone numbers)
- Policy codes and descriptions (e.g. motor or home property)
- Risk rating codes
- Flood zone information
- Property assessment values and codes
- Loss ratios
- Claims adjuster estimates and contact information
- Lack of a comprehensive view of existing policyholder information across different policy coverage categories and lines of business
The cost of bad data can be measured in the following areas as firms gear up to deal with the fallout of Hurricane Sandy:
- Number of claims errors multiplied by the time and cost to resolve these errors
- Number of phone calls and emails concerning claims processing delays multiplied by the time per phone call and the cost per Customer Service Rep or field agents handling those requests
- Number of fraudulent claims and the loss of funds from those criminal activities
- Number of policy cancellations caused by poor customer service experienced by existing policy holders
- Not to mention the reputational damage caused by poor customer service
Having a sound data quality practice requires a well-defined data governance framework consisting of the following elements:
- Data quality policies that spell out what data are required, how they should be used, managed, updated and retired. More importantly, these policies should be aligned to the company’s goals, defined and maintained by the business, not IT.
- Data quality processes that involve documented steps to implement and enforce the policies described above.
- Specific roles including data stewards that represent business organizations, core systems (i.e. Underwriting Data Steward), or Data Category stewards who understand the business definition, requirements and usage of key data assets by the business.
Finally, in addition to the points listed above, firms must not discount or ignore the importance of having industry leading data quality software solutions to enable an effective and sustainable data quality practice including:
- Data profiling and auditing to identify existing data errors in source systems, during data entry processes and as data is extracted and shared between systems.
- Data quality and cleansing to build and execute data quality rules to enforce the policies set forth by the business.
- Address Validation solutions to ensure accurate address information for flood zone mapping and loss analysis
- Data Quality dashboards and monitoring solutions to analyze the performance and quality levels of data and escalate data errors that require immediate attention.
As cleanup activities progress and people get back on their feet from Hurricane Sandy, insurance companies should take the time to measure how well they are managing their data quality challenges and start looking at addressing them in preparation for these inevitable events caused by Mother Nature.
History is the likely final arbiter of the effectiveness of the American Recovery and Reinvestment Act (ARRA), commonly called the Stimulus Bill. But, the legacy of that piece of legislation may forever change the way people view government spending. As never before, the ARRA called for and implemented transparency requirements associated with spending. In theory, citizens were able to track how stimulus dollars were being spent directly in their communities and the government was able to more easily identify improper payments, waste, fraud, and abuse. One of the lasting results is that the stimulus has done more to increase the need and imperative for government transparency than almost any other trigger in history. Further, there has been a large increase in demand for transparency at all levels of government. (more…)