Print this articleAvaya Group Safeguards Internal Information Quality
Avaya maintains more than 100 terabytes of customer, vendor, service, financial, and pricing data. To ensure that the stockpile complies with internal data standards, the company, which provides telecom equipment and services, last year established its Data Quality Center of Excellence.
The center’s two dozen employees are responsible for implementing data quality management practices, such as avoiding the creation of duplicate records. Championed by Guy Lardieri, VP of strategic initiatives and business architecture, the center was created in April 2005 as a spin-off from a project to replace an aging system with enterprise applications from SAP and Siebel Systems.
The legacy system was hamstrung by defective data that drove up expenses and cut into revenue. In some cases, Avaya serviced customers’ telecom equipment but didn’t bill them for gear that was erroneously left out of service agreements. Other customers who paid only for standard service were getting premium service because of database errors, says Rich Trapp, Avaya’s global data quality director.
Avaya’s Data Quality Center of Excellence provides the tools for improving and maintaining data quality, including Business Objects’ IQ Insight profiling tool for identifying bad data. The Data Quality Executive Council, made up of top company executives, "provides the teeth for the data quality efforts," Trapp says. But it’s the business units that have ultimate responsibility for data quality.
Can Tools Help Redefine the Role of the DBA?
Venkat Devraj and Rainer L. Luistro were expert Oracle DBAs when they observed that much of their time and effort was spent performing complex but repeatable tasks. Moreover, different DBAs would solve the same problem in very different ways. Similar problems had no uniform resolution. They had the idea of abstracting data administration best practices and putting them into standard operating procedures that both experienced and inexperienced DBAs could call forth.
Last month, their company, StrataVia, launched its first commercial product — Data Palette. DBTA talked with company president and CEO Thor Culverhouse about how tools could help redefine the DBA’s role.
DBTA: What is the market opportunity you see?
Culverhouse: A lot of DBA tasks are complex but fairly repeatable. There was no uniform approach or automation built into the tools out there today. There would be huge efficiency gains if there were such a tool.
DBTA: You recently launched your first product - Data Palette. What is your strategy?
Culverhouse: We see ourselves changing the DBA paradigm. Database administrators in the future will be database architects. We need to give them a toolset that allows them to be much more strategic and move up in the technology stack. They can only do that if we free them from the repeatable, mundane tasks they do every day.
DBTA: There is no shortage of tool vendors. What is unique about Data Palette?
Culverhouse: We standardize, automate and audit database administration best practices. Most of the other tool vendors enable human interaction. There are not strong automation capabilities in most of those tools. And there is not the strong audit and analytics capabilities that are in Data Palette. For most of the tools out there, the core functionality is still around monitoring and troubleshooting. We abstract intellectual property into the toolset so processes become more repeatable.
DBTA: How will this interact with tools that companies already have?
Culverhouse: We recognize that there will be many folks who will use those tools to do what we call the table stakes — functionality around monitoring and alerting.
We are complementary to that framework. Our focus is about taking DBA best practices, which are very complex, and building them into standard operating procedures that can be evoked manually or automatically based on a certain set of criteria.
DBTA: Who do you see as your prime target market?
Culverhouse: Our offering resonates with people who are in a fairly complex environment. We support heterogeneous environments. With the first release, we support DB2, Oracle and SQL Server. In the longer term, we are looking at open source frameworks as well.
DBTA: Who will make the decision to bring this tool in?
Culverhouse: Many of the organizations we talk to are looking to push best practices, uniform technology and smaller technology footprints — standardization across the organization. Our tool plays well to that customer. But at the DBA level, we allow the DBA to look at many tasks that are complex but repeatable in nature. If we can automate those for them, the DBAs can focus more on architectural issues and how to optimize the environment. That is where they would like to spend their time and it is where their core competency is.
DBTA: What are the “headline” processes you are automating?
Culverhouse: Backups, refreshes and restores and maintenance functions. We can go into an environment and build SOPs particular to a customer environment with our professional services offering. And the product has an SOP wizard built in so we can teach DBAs how to build SOPs.
DBTA: You automate the development of SOPs?
Culverhouse: And then you can go back and audit the SOPs that were completed, which plays into the compliance concerns.
DBTA: What is your product roadmap?
Culverhouse: We are building a core competency to develop SOPs. Those are modules that sit on top of the framework. We also have an expert engine that sits on a metadata repository collecting information from these heterogeneous database environments. Over time, we will deliver complex analytics that will look at the entire environment that will say you are going to have a problem in the future and here is a SOP to fix it. The system will become selfhealing — which we call autonomics.
Driving Sales
Saab revs up its customer satisfaction efforts to speed its revenue growth.
Saab car owners have a reputation for being some of the most loyal in the industry. Why, then, would executives at Saab Cars USA feel the need to create a 360-degree view of customers and prospects? Two words: sales and service.
Saab Cars USA, a wholly owned subsidiary of Saab Automobile AB (owned by General Motors), imports and distributes Saab automobiles. Its approximately 220 U.S. dealers sold about 38,000 new cars in 2002; the goal is to sell more than 45,000 cars in 2003. “Our CRM initiatives will play a big role in helping us get there,” says Robert Henry, manager, eCommerce and
CRM solutions of Saab Cars USA, in Norcross, GA.
Saab Cars USA rolled out its enterprisewide CRM solution and strategy, dubbed TouchPoint, beginning in January 2002. Saab is using TouchPoint to improve customer service efforts, as well as to support customers and dealers. The initiative focuses on the customer interaction center, marketing, lead management, and data quality.
Prior to TouchPoint Saab Cars USA had about five systems in place, but they weren’t integrated. “One thing we wanted to do was have a consolidated view of existing customers. We didn’t have a system or solution in place that tracked our customers,” Henry says. “We had to go outside to purchase our own customer names.”
Creating a 360-degree view of its customers would allow Saab to use more sophisticated, multistage marketing campaigns, to improve the efficiency and functionality of the call center, and to share data across the organization. “The homegrown system we had worked fairly well for what we needed it to do, but wasn’t enterprisewide,” Henry says. “Data stayed in there and couldn’t be used in other parts of the organization.”
First Gear: Buy-In
Many CRM project leaders struggle to convince upper management of the value of CRM, but Henry and then-Director of CRM Dan David (now vice president of parts and service; currently Patrik Riese is director of CRM) had no problem convincing top brass to buy in. The trick was convincing them to go with Siebel to serve a company that has a staff of only 143. “Executive buy-in is critical, especially for solutions of this magnitude. Siebel isn’t cheap and integration costs aren’t either,” Henry says. “It’s a big commitment to do enterprise CRM — and a big expense.”
Henry and David put together a comprehensive business case and delivered presentations to then-President Dan Chasins (Debra Kelly-Ennis, from GM’s Oldsmobile division, is now president), CFO Ken Adams, and Vice President of Marketing Hans Krondahl. Fortunately, parent company GM had standardized on Siebel 6.3, “which helped our argument,” Henry says. “Plus, the implementation was part of a global CRM initiative.”
One of the key selling points was the marketing initiative, for two reasons: 1) Saab had no way to track leads faxed to its dealers; 2) it had to buy its own customer names from Polk to conduct marketing campaigns. Using Siebel to define and run direct marketing activities in-house would save money, even though Saab would still purchase some data from Polk. “Marketing is where significant ROI comes in,” Henry says.
Once the executives were on board it was time to get the users — contact center agents and dealers —to buy in, too. Saab used both software and instructor-led training for its agents (who are actually employed by EDS), and over a two-month period before the launch sent out about five newsletters that described the new solution, discussed the differences between it and the previous system, and explained the more important role agents would be playing by being at the core of the customer database. “The system was significantly more robust than what they had been using. You have to know what you’re doing or you can get lost,” Henry says. “But right from the beginning everything went surprisingly smooth.”
Call center manager Dick Rommich agrees. “The agents have adapted extremely well to the Siebel system,” he says. “And we have several people who have gone above and beyond to identify issues and offer solutions to various challenges in the system.”
The dealers, however, were a different story. Saab piloted with 40 dealers in January through July 2002, but waited for a full rollout, because instructor-led training wasn’t necessary and the e-learning course the CRM project team put together for the dealers wasn’t ready until July. By October 90 percent of the dealers had completed the training and had started receiving leads through TouchPoint.
Although the training went smoothly, the uptake wasn’t immediate. “When we went live in July we did a campaign to win a PDA: If you signed up, in August we would distribute leads; the first ten to use the system would win a PDA. We got about eighty-five dealers,” Henry says.
So the CRM team ran another campaign. The company had received about 40,000 leads for the new 9-3 sports sedan. “We said, ‘This is how we’ll be distributing the new leads,’” Henry says. Saab also offered a $50 American Express gift card to any dealer who completed the training and then received a password to the system. Those who did were entered into a raffle for two round-trip plane tickets. All but 20 small dealers signed on.
Second Gear: Ramping Up the Contact Center
The cornerstone of TouchPoint is Saab Cars USA’s customer interaction center (CIC). The first phase of the TouchPoint began in January 2002 with the CIC, customer service, lead management, and dealer component of the solution. Saab implemented Siebel eAutomotive 6.3 in its CIC, and gave each of its dealers Siebel eDealer for lead management.
“The central application for dealing with customers is the call center application. That is the most up-to-date customer data. So it was the best place to start,” Henry says. “For marketing to work properly you need data quality. That’s a huge factor.”
Saab previously had a customer assistance center and an outsourced lead generation center. “With Siebel we were able to bring this in-house in the CIC. So agents are cross-trained and are part of Saab,” Henry says. “The lead generation partners were answering phones for thirty or forty other vendors, so they didn’t have much brand understanding. Now that it’s in-house, people are more excited about the brand.” Saab Cars USA has about 45 employees in the CIC: five agents for lead management, about 30 for service—the rest are managers.
Not surprisingly, customer satisfaction is important to Saab. And TouchPoint is already generating results in that area. “We’ve seen customer satisfaction ratings going up already, from 69 percent to 75 percent,” Henry says. “Siebel is only one part of why that’s gone up. We have some excellent employees and managers.”
Call monitoring software from Witness Systems that Saab began using in January “has had as much influence on the success of our center as Siebel has,” Henry says. “Agents love it. They were nervous at first, but are getting used to it. Before, managers would use a tape recorder to listen in on calls. It’s a significant addition to our center.”
Although Witness itself was “pretty much a turnkey solution,” Saab had to make adjustments to its Avaya switch at a significant cost, in the tens of thousands, Henry says. The cost was worth the return. Managers can now monitor such things as voice and movement around the system. “Through listening and watching, managers could tell how well agents are using the Siebel system,” he says. “It’s been tremendous.”
Saab also wants to improve communication with its customers, so it uses Siebel’s email management system. Email comes into Siebel, creates a contact record, and agents can respond thru Siebel. Saab previously used KANA, which Henry says was excellent. “Our main reason for moving to Siebel was to have all customer contacts in one location,” he says. “It was just one part of collecting all this information.”
As far as communicating with dealers, any lead generation activities go through Siebel. Even if dealers are not on eDealer, they will receive an email about any lead. All leads go to the fulfillment center, and Saab tracks and uses that information for marketing.
Once Saab sends the leads electronically, the goal is to have the dealers update the system with follow-up information. “We’d like to know when they make the initial contact and if a test drive was taken, because if they test drive they’re more likely to buy—then the final disposition: Did they buy the car? If not, why not?” Henry says.
The response from dealers so far is not as high as the marketing team would like it to be, so Saab is getting its field sales force more involved. “The dealers are not updating the system enough, so we’re working diligently on getting them to.” TouchPoint generates bimonthly reports that list dealers, leads, and follow-up rates. District managers then use that information to discuss the status of leads with dealers in their territories.
Part of the problem was a miscommunication about the types of leads dealers were receiving. “Back in October we were sending out leads, but they were not all hot leads,” Henry says. “We communicated this to the dealers, but not well enough. We hoped the dealers would treat [these somewhat interested customers] differently. But the dealers approached them in more of a hard sell approach, which pissed off customers.
We’ve worked since then to improve the quality of the leads. It adds to the costs, but by leveraging the lead management tool we call people to verify their interest, then pass on to dealers only leads that would be valuable in their eyes.”
According to Henry, the ability to qualify leads has been another advantage of pulling the lead management team in-house. The team also has the time to call customers and dealers to follow up on leads. “Maximizing their time is a cost savings for us,” Henry says.
Third Gear: It’s All About The Data
In May 2002 Saab Cars USA implemented Firstlogic ACE data quality software. “For our first phase back in January, we implemented the Siebel connector for Firstlogic. It checks, validates, and standardizes data as is comes in, in real time.” This is important, Henry says, because as Saab increases the number of lead sources and data integration points, it needs to eliminate duplicates at the point of entry.
Cost was a significant factor in choosing Firstlogic, Henry says. “For our money it’s doing what we need it to do. We can go in and weigh the different values on different fields, for example, how important is last name, zip code, etc.? Depending on the weight of each is the result,” he says. “The ability to match data is very complex. We have a good solution in place.” Using Firstlogic Saab was able to reduce its database size by 50,000 records. The company currently has 300,000 customer records in its database, with another 500,000 prospect names contained in the system.
The next phase was implementing Siebel Marketing. Saab’s marketing team uses the consolidated data from Firstlogic in conjunction with the Siebel campaign management software to automate and run highly targeted, multistage marketing campaigns.
One basic program that Saab has put in place is an outbound telemarketing campaign to new owners to verify their contact information and ask about their satisfaction with their car. Agents update the customer’s record with information from the call.
Saab now also has a long term—lease loyalty campaign in place. Depending on such selection criteria as whether customers are 12, nine, six, or three months away from the end of their lease, the system creates a mail file that sends information to fulfillment, which sends the appropriate materials to each customer. The system also creates a campaign record so agents can see which customers were contacted, what the contact was, and if the customer responded (e.g., redeemed a certificate or called in).
Cruising Speed
It’s too early in the initiative to give specific ROI numbers, Henry says. But Saab Cars USA has seen its share of positive results so far.
“The biggest impact has been to consolidate all our customer and prospect data into one place where it is accessible to multiple parts of the organization, and all the benefits that brings,” says Director of CRM Patrik Riese.
It’s the first time in Saab’s history that it has had a consolidated database. According to Henry, there are still some data quality issues, but without an employee dedicated to data quality, this is not unexpected. “I don’t know if you ever have clean data, but it’s more pronounced now, because we have the marketing tools in place,” he says.
Saab expects to see cost savings, especially in marketing. “We will use our data to do better and more effective targeted marketing to maintain and improve our customer relationships,” Riese says.
“Learning from campaigns to see if what you’re doing is adding value or just adding cost will be a great benefit,” Henry adds. “That’s where you see the improved efficiency and costs savings.”
Sending leads electronically is one significant improvement. “Once we receive feedback consistently, that will help us make the proper marketing decisions,” Henry says. “Dealers are on the frontlines. If they give us feedback we could refine campaigns to bring in better-qualified and meaningful prospects.”
The CIC is benefiting, too. In time Saab will be able to reduce head count with the efficiency of the Siebel system. Although Saab doesn’t have specific efficiency improvement numbers, as a result of using Witness “soft ROI is significant,” Henry says. Even the realization of being recorded has affected the behavior of the agents.
Customer satisfaction ratings have increased from 69 percent to 75 percent, and Saab expects that this will translate into increased sales. “That our customer satisfaction rating is high speaks well of our [CIC] managers, that they train and manage agents well.”
And it speaks well of the entire CRM project team that Saab hit its deadlines and budget. “We did factor in that through improved loyalty we would increase sales,” Henry says. “We’ll see those result after we see the learning from marketing. That will come in time as we improve.”
Saab Cars USA’s CRM initiative came in on time and under budget. And that was just the beginning. Saab has also:
• Created a 360-degree view of customers, because all customer data is in one, centralized location
• Increased customer satisfaction ratings from 69 percent to 75 percent
• Deleted 50,000 duplicate customer records
• Reduced costs significantly, because it no longer has to buy its own customer names
• Upgraded from faxing leads to sending them via email
• Improved in-house CIC agents’ efficiency and enthusiasm; they are more excited about the brand than outsourced agents were.
Hazardous Data
Allowing dirty data to populate your database can contaminate your business processes. You may not need a haz-mat team to clean up the mess, but until you scrub it down youre never going to realize its full potential.
Robert Regis Hyle
No business wants to look foolish in the eyes of its customers, yet for some companies it happens every day. Customers receive multiple copies of an insurers privacy statement or marketing material touting a new annuity product. Instead of feeling secure because they know their private information is safe, or excited about a new investment opportunity, customers are left laughing and shaking their heads. Claudia Imhoff, president of Intelligent Solutions, speaks with insurers all the time about this problem and sometimes feels more like a counselor than a consultant. Its kind of like being in an AA program, she says. You first have to recognize you have a problem.
Large corporations constantly are dealing with the age-old problem of internal communication. Insurers have silos of processes and silos of workflow and dont realize the data they create in these processes and workflows actually is used in other processes and workflows, says Imhoff. Not in the way they assume it will be used.
James Fridenberg, vice president applications development with Farmers Insurance Group, says there is one thing insurers need to remember: The data has to come first. When that happens, many problems are solved, and even more important, many opportunities are opened for carriers.
An Easy Sell
Its an easy story to sell to upper management, Fridenberg believes, especially when you can point to specific problems and potential problems that will arise from not having data-quality initiatives, checkpoints, and touchpoints. When you are dealing with 15 million customers and consider the volume of change and the number of transactions we do a day, the impact [of poor data quality] would be pretty severe, he says. Implementing data-quality initiatives can be costly, but the potential for savings is tremendous. Its not a hard sell when you can tell a story of what the potential could be by not having stringent data-control initiatives in place, says Fridenberg. I believe you could draw a good business case for ROI by putting in data-quality initiatives. The potential for creating impact is substantial when youre dealing with large systems such as ours, so the ROI is there through cost avoidance.
Farmers has worked hard on improving the quality of its ad hoc marketing data for over three years, according to Fridenberg. And the company has found poor quality has an impact on customers, agents, and service centers. This is a serious business, and we take our applications and our data very seriously, he says. The importance of data quality is ingrained in us.
Multi-line insurer Consecos decision to bring its data cleansing in-house was financial, asserts Tom Besancon. As assistant vice president of marketing information and technology, Besancon says, We turned to it to save us some moneyreduce costs in terms of standardizing and cleaning up some of our [customer] names for mailings.
The suite of tools Conseco purchased from software provider First Logic not only helped to clean up the companys data, it offered the opportunity to enhance the insurers data as well, particularly some of the prospect lists Conseco purchased for marketing its products. We were looking at the tools to improve some of our modeling efforts by using matching and consolidation to get a single view of the customer, he says. We are doing a lot of ad hoc merge purgespurging privacy mailings and previous campaigns to reduce costs and stay in compliance with the privacy stipulations and laws.
Fill in the Blanks
Cleaning up data is important, Imhoff believes, but improving data can be done easily if everyone dealing with the data understands the needs of other departments within the company. One of the problems I see all the time is the claims team will fill out forms so it can do its job, which is to process claims, she says. There are lots of fields in a claims form, though, which have nothing to do with paying for the claim. Some of those fields are useful down the road in analyzing the claimhow did it happen, what were the reasons, what were the dates of the claimand can be very useful in showing patterns of fraud.
The problem is those fields are meaningless to the people trying to close the file. They simply say, Is this a valid claim? If it is, they pay them, says Imhoff.
Administrative people entering data dont realize what they are entering into the system is being used elsewhere in the company, even if no one in that particular department is using it. Sometimes its just recognizing the problem, she says. Once its recognized you can start to say, What can we do to fix the problem?
The ROI
At that point, Imhoff suggests, a bigger problem comes into play: ROI. How do I change someones business processes without affecting the bottom line? she asks. If I make claims clerks look up codes or verify dates, I slow them down. If they are like most order-entry clerks, they are paid by the number of claims they enter in a day. Part of the quality problem is looking at the holistic view and saying, Is it worth the time its going to take and the money were going to lose because were not getting in claims as fast as we used to?
Most companies have the correct information about a customer, they just have too much information about the same person, and that leads to confusion. Imhoff says data-cleansing tools really shine in this environment. If it is the customer information youre worried about and you have multiple instances of the same customer, then the tools can handle that easily, she says. Using her own name as an example, she says Claudia Imhoff can appear on data as C. Imhoff, C.M. Imhoff, or Dr. Claudia Imhoff. All these are different versions of me, she says. Data-cleansing tools can clean all that up and consolidate it into a single record, which is what you want.
The (Almost) Perfect Data
There is no such thing as perfect data, according to Imhoff, but companies can get close, and the closer they get, the less money its going to cost them in the long run. You try to get the data to the point where it is as good as it possibly can be, she says. There are always subversive things that will cause the data to be maybe 99 percent perfect, instead of 100 percent, but thats better than what most organizations are dealing with today.
There is more than just customer information to contend with, however. Insurers have product and claims information, and health insurers have lists of providers. Are they in there multiple times, says Imhoff. You dont want to go in and fix the whole thing at once. You want to go in piece by piece and slowly work through the enterprise data.
That means establishing priorities, though. What most insurance companies do that Im familiar with is to prioritize the data and decide which pieces are most critical right now, says Imhoff.
Fridenberg describes it as peeling back the layers of an onion. The decision has to be made to start with the areas insurers feel will have the most immediate impact on their customers, the agents, and the service centers. Those things are typically anything that has to do with balancing, in the sense of accounting or GL, he says. Commissions are always top of mind, or anything fee related or premium related. Those are things that are touchpointscritical path items.
Cynthia Saccocia, senior analyst in the insurance practice for the research consultant TowerGroup, believes the uniqueness of insurance contributes to the quality of data it can collect on its customers. She believes that is one reason a number of companies are pushing to have their independent agents licensed in both the P&C field and with financial products.
Insurers have typically maintained a silo organization, she says. They are dealing with a long legacy of doing business a particular way. Now, convergence in the marketplace is pushing them into a space they havent been accustomed to working in, and they are yet to get really comfortable.
She believes data collected by insurers is superior to what financial services companies can get on their clients. Typically, a customer goes into a bank for episodic-type advice, says Saccocia. Something that is very oriented to point in time. Insurers have a wealth of data they can support in cross-selling opportunities and be very targeted.
Ive Got (Algo)rithms
The software tools are incredibly sophisticated today, Imhoff points out. A program is made up of hundreds or thousands of algorithms that comb the data. Once you set [the program] free, it is off and running and can do whatever you want it to do, she says. Its just a matter of getting to that point. Imhoff advises insurers not to try to build their own tools, though. You would have to write those hundreds if not thousands of algorithms, she says. You dont want to do that. You might as well go into the [data-cleansing] business if youre going to do that.
Insurers should consider one example, she explains: What if you wanted to send a mailer and you realized 20 percent of [the names and addresses] were dupes? she asks. How much money would you save if you didnt spend the money [for the duplicates] on the postage and so forth. I imagine [the savings] would more than pay for the tools, especially if you have millions of insurance policies. Now youre looking at a customer instead of five.
Finding the Right Tools
Insurers shouldnt complicate things from the outset, Imhoff warns. I would look for a tool, first of all, that is easy to use, easy to set up, easy to understand, and easy to maintain, she says. Dont expect all data-cleansing tools to be simple, though. Some of them can be difficult, she adds. They are very esoteric in some respects, so I look for ease of use.
The second step in the selection process is the level of sophistication required by the insurer.
A third step is determining whether the system maps to the current technology the insurer has in place. Can you use it on all your databases, or are you limited to mainframes? she asks. You need to look for a map in terms of the technology.
The fourth step often is overlooked. Thats support from the software company itself, she says. I dont think most people think about that. She believes the vendor has to be fully supportive of the project. Will they help me, not just working the tool, but in analyzing the results from what the tool gives us, she says. What is this data telling me? What do I need to do? What needs to change?
Besancon says Conseco had a specific need when it began its product search. We were looking for something that could sit on a UNIX box and process loads of data quickly, he says. When shopping around, insurers will find a wide array of products. There are products out there that are PC based and larger products as well, he says.
There also are plenty of outsourcing options, Besancon mentions. Although Conseco has purchased its own tool, it is keeping its options open. The First Logic product has a suite of tools, which will allow Conseco to add on over time. But the company also kept some of its outsourcing options open. Were not putting all our eggs in one basket, he says.
The historical problem of data for insurers deals with the expensive process of building a data warehouse, trying to cleanse the data, and defining it, according to Saccocia. Insurers have had a lot of starts and stops with those types of activities, she says. Now theyre at a point where they have to step back and say, Are we doing a good job in building this? What are our core objectives to achieve it? That is when they need to look for vendor support to move to the next level.
She believes the typical approach to data solutions for insurers was to build a system because insurers felt they had unique needs. In our conversations, were finding many vendors that may be horizontal in nature and are trying to become more vertical for the insurance industry, says Saccocia. The vendors can provide specific tools for an individual industry. The data is there, she says. It just needs to be better managed. This will become more important as insurance leaders change their view of what an insurance company is. They are becoming more brokerage oriented, and they view themselves as competitors in the financial services marketplace, says Saccocia.
Worth the Effort
Conseco is saving a great deal of money today in printing costs and postage because it has eliminated many of the duplicate names and addresses from its database. But from a marketing standpoint, Besancon believes the ability to track the success of a mailer will be invaluable. We go back to the tool for response analysis, he says. If we do a mailing of, say, 50,000 people, we actually can determine if they bought a product by checking the database. He claims the company had ways of gauging its marketing success prior to purchasing the software, but the cost was just way out of hand.
Besancon says Conseco has surpassed all expectations of the project. Our success rate has been better than imagined, he says. We didnt realize how much money we could save by bringing this in-house.
The Industry's Dirty Secret
By Therese Rutkowski, Managing Editor
October 1, 2003 – “Garbage in, garbage everywhere.” That’s a twist on the old adage, “garbage in, garbage out,” courtesy of Firstlogic Corp., a La Crosse, Wis.-based data quality software provider. “We say, ‘garbage in, garbage everywhere’ because so many systems share data that bad data in one spot can easily propagate across the entire organization,” says Chris Colbert, industry marketing director, at Firstlogic.
Bad data can also spread across organizations, as David Jokinen discovered when J.P. Morgan Chase & Co. identified him as deceased in its systems-instead of his mother, who passed away in April 2001.
For more than two years-as reported recently in The Wall Street Journal-Jokinen has been trying to correct J.P. Morgan’s error and convince mortgage brokers, credit card issuers, car dealers and insurers that he is very much alive and deserving of their products and services.
Indeed, the financial services industry is converging. And as it does, insurers, banks, brokerages and their business partners are exchanging information electronically in real-time to process transactions more quickly, to improve customer service and to reduce costs associated with manual workflow.
“We share data with agents, we share it with people who do claims processing, with banks that offer mortgages, and with risk managers,” says Mele Fuller, interface architect, at Seattle-based Safeco Corp. “There are many organizations with whom we share our data-and it’s growing.” (See “The Industry Standard for Consistent Data,” page 24.)
The industry also is implementing customer relationship management (CRM), data warehousing and business intelligence solutions. The intention is to share data enterprisewide, and analyze it to make more informed decisions more quickly in response to market changes and competitive pressures.
Therein lies the conundrum: The financial services industry, which always has been built on data, is becoming even more dependent on data. And as it becomes more dependent on sharing data-often in real time-managing that data effectively becomes not only important-but absolutely necessary.
“Detroit manufactures cars. You can go to the dealership. You can touch them. You can smell them. You can drive them. You can see the deliverable,” says George Jablonski, P&C enterprise data architect at The Hartford, Hartford, Conn.
“Insurance, on the other hand, sells promises,” he says. “The promises are documented in contracts. And contracts turn into data. And data is stored. Our asset is that data versus a car that you can see. If you understand this analogy, you understand the importance of information as an asset to an insurance company.”
A big problem
Yet, despite the fact that data management is the linchpin of insurers’ operations, a lot of dirty data lurks in their systems. Mr. Jokinen’s tale is one of many stories of a data error gone awry in the financial services world; most go untold.
“The problem is bigger than anyone fully realizes, or is willing to acknowledge,” says Ron Barker, insurance practice area leader at Chicago-based Knightsbridge Solutions LLC, a data management consulting firm.
In fact, The Data Warehousing Institute, Seattle, estimates that poor quality customer data costs U.S. businesses a staggering $611 billion per year. This figure doesn’t even include the cost of losing customer loyalty by incorrectly addressing letters or failing to recognize a customer who calls or visits a company’s Web site (see “The Cost of Dirty Data,” page 23).
“People are getting fed up with getting mail with their names scrambled,” says Jack Hermansen, CEO of Language Analysis Systems Inc., a Herndon, Va.-based multicultural name recognition software provider. “I received a letter that read, ‘Dear Mr. Inc.’ I’m sure everybody has stories like that. A lot of people just throw the mail in the garbage and say, ‘If this is how much this company cares about treating names, what luck will I have calling them and being treated like an individual?’”
Customers are getting fed up, and the government is putting pressure on companies to manage their data better. The Gramm-Leach Bliley Act (GLBA) and the Health Insurance Portability and Accountability Act (HIPAA) both require insurers to protect the privacy of customer data. Similarly, the recently passed Sarbanes-Oxley law mandates that public companies report accurate financial data-with hefty fines and imprisonment as penalties.
Data quality is a hot topic again, says Tracy Spadola, senior industry consultant at Teradata, a division of NCR Corp., Dayton, Ohio. “I’ve been working in the field for 20 years. It had its heyday in the 1980s, and it dipped. But it’s coming around.” With so much more information being captured, shared and scrutinized, companies are asking, “How do we manage it?” she says.
“We’re hearing more and more about data quality and data management because it’s like a pressure cooker,” says William Sinn, vice president of insurance and healthcare marketing at Teradata. “People realize they can’t embark on a lot of business initiatives unless they’ve got good data quality.” (See “Cleaning Your Data-And Keeping It Clean,” page 38.)
Indeed, insurers are investing in initiatives such as business intelligence, data mining and analytical tools to help them correlate policy, claims, demographic, geographic, and other customer and operational data-and respond more quickly to market pressures.
360-degree view
Allstate Insurance Co., for example, is developing an enterprise CRM program which involves infrastructure modifications, an enterprise customer database, analytics, business rules software and change management.
The objective is to create a 360-degree profile of Allstate’s customers-and their households-to assist the Northbrook, Ill.-based company in cross-selling and retaining those policyholders across distribution channels, according to Kimberly Harris, research director, Gartner Inc., Stamford, Conn.
U.S. Risk Insurance Group, a Dallas-based managing general agency (MGA) that distributes excess and surplus lines, also is investing in analytical technology to understand and run its business better. “There’s an increasing need to articulate our business plans and to understand our book of business better,” says Monte Stringer, executive vice president and CIO of U.S. Risk Insurance Group.
“For an MGA to be successful in this current hard market, that MGA has to have an almost fanatical focus on underwriting,” he says.
To that end, U.S. Risk is implementing business intelligence technology from Thazar Inc., a Skywire Software company located in Frisco, Texas. Thazar’s software will enable U.S. Risk to determine what business they’re producing, where the business is coming from geographically, and from what producers. “The more we know about our business, the better we can perform in the marketplace,” Stringer says.
The insurance industry currently is focusing on underwriting results more than in the recent soft market cycle, but the infrastructure in most companies does not support the granularity and level of analysis companies need to truly understand the relationship between risk and costs, according to Tom Chesbrough, executive vice president and founder of Thazar.
Insurers need detailed data about demographics, driving records, vehicles, geography and premium and loss characteristics, he says.
They also need clean data. Data mapping and cleansing is by far the most challenging part of any data mastery project, according to Matthew Josefowicz, senior analyst, at Celent Communications Inc., a Boston-based research and advisory firm. This process typically consumes 80% of the implementation time and resources, and 40% of the overall project from planning to training and maintenance, he notes in a recent Celent report titled, “Insurance Data Mastery Strategies.”
A significant portion of U.S. Risk’s business intelligence implementation involves testing data quality, Stringer explains. Initially, U.S. Risk Group is creating manual reports, and calculating certain known variables. Then, the team is plugging the same data into the business intelligence system to ensure the data is clean and the results are accurate. “Bad data is worse than no data,” he says.
In fact, many insurers became aware of just how dirty their data is when they implemented CRM systems and data warehouses back in the 1990s, says Teradata’s Spadola.
“Once insurers began pulling all their data together, chances are, it was the first time they were seeing it all linked,” she says. “Instead of their marketing data here and their underwriting data there, it was all pulled together-and that’s when many companies realized they had some quality issues.”
It’s also why many executives are now reluctant to invest in data management solutions, according Knightsbridge’s Barker. “A data warehouse alone can cost millions of dollars,” he says. “And there are enough data warehouse train wrecks and CRM train wrecks out there that CIOs are reluctant to pony up the money to support these efforts now.”
With credibility risks, compliance requirements, and competitive pressures mounting, however, insurance executives realize they can’t ignore data quality and data management much longer.
“This is a strategic issue,” says Teradata’s Spadola. “It’s all well and good to say, ‘We know we have data problems, and we need to fix them.’ But it really requires setting up a formal data stewardship role and putting policies and procedures in place that say, ‘We are going to treat our data as a resource, and we’re going to manage it effectively.’”
That’s precisely what’s happening at The Hartford, according to Jablonski. This year, the carrier established an enterprise data unit. And “information/data” is a category unto itself in the company’s information technology investment portfolio.
“Establishing this unit signifies that the business folks recognize the importance of data, and that it’s a good idea for the management of data to be centralized,” Jablonski says. “It will help us in the future to make sure we treat data consistently across the organization.”
Such initiatives have come and gone in the past, he says, but this time it’s different. “This is a very strong effort. The recognition is there that we want to treat information as an asset-and folks here are doing something about it.”
Still, it’s not uncommon for companies to view improving data quality as a one-time project. When they bring in a new system, they see that as an opportunity to clean up their data, Firstlogic’s Colbert says. “But data quality is an all-the-time thing. Data degrades over time. People move. People get married. Obviously, in the insurance business, people die. These changes have to be dealt with on a consistent basis.”
One tool Knightbridge’s Barker promotes is a metadata repository. “Metadata is data about data,” he says. It describes: What is the data? Where did it come from? What transformations did it go through? What happened to it from the time it was pulled from the source system into the data warehouse? How did it change? “Metadata becomes the key element associated with data quality,” he says.
An information architecture approach to data management is also essential, according to Thazar’s Chesbrough. His company promotes a centralized data warehouse-rather than having many data marts-to ensure there is “one version of the truth.”
“Store once, use many,” is a mantra spoken by proponents of centralized data warehouses. “The idea is to start with the data in a single place and build from there,” Teradata’s Sinn says. “You can keep reusing the data, but why store it in 20 different systems when you can have it in one place and pull it from there?”
One bite at a time
It’s important to remember only 10% to 15% of an organization’s data is “enterprise” data-data that it relevant across the organization, The Hartford’s Jablonski notes. “The other 85% lives in the business ‘siloes.’
“Siloes aren’t bad,” Jablonski says. “Many organizations have been set up with smaller units to be flexible and react to business changes. That’s just the nature of the beast.” With an enterprise view of data assets, siloes can still operate as they always have. “We want to provide an enterprise view of information without being disruptive to the business areas.”
At The Hartford, for instance, there are approximately 50,000 total data elements, and only 500 are likely to be “enterprise” data elements, he says. But the pitfall for many companies is “they try to bake the whole cake. They try to tackle mastering all their data in one huge initiative. That’s overwhelming. It’s staggering, and people fumble on it.”
Companies are wise to “think big, but start small” when implementing data quality solutions, sources say. “You’ve got to start someplace, so start at a place you think is the worst, or at least an area that you can clean up, and build out from there,” says Teradata’s Sinn. “It’s like the old adage: How do you eat an elephant? One bite at a time.”
A few years ago, companies built huge data warehouses from scratch, says Thazar’s Chesbrough. “That was very expensive. Now, we’re able to implement systems in components-certain lines of business or certain areas such as claims, in phases.” This way, an insurer can build confidence in the technology, and prove its worth with short-term benefits and return on investment, he says.
In addition, some relatively inexpensive methods of improving data quality can produce ROI quickly. For example, using an address verification tool can cut costs associated with duplicate mailings almost immediately.
“You can narrow down thousands of data records by simply verifying that an address is valid,” says Tho Nguyen, program director in data management strategy for SAS Institute Inc., a Cary, N.C.-based business intelligence and analytics software provider.
“When we compare mailing campaigns after addresses have been verified with previous mailings, we’ve seen as many as 33% of the names dropped because they were invalid,” he says. That translates into significant printing and mailing cost reduction.
Kathy Armstrong, a data quality coordinator at Republic Mortgage Insurance Co., Winston-Salem, N.C., says an automated data auditing tool, which her company purchased from Firstlogic about six months ago, has already doubled her efficiency. Plus, she’ll be able to produce more professional management reports, rather than Excel spreadsheets.
Standing apart from competitors is about presentation, consistency and conforming to standards, according to U.S. Risk Group’s Stringer.
Much of U.S. Risk’s business is written with Lloyds of London, he says. “Every year, when we go to renegotiate our contract with Lloyds, they’re looking at our results. They look to us for data. So the more we bring data that is actuarially sound and consistent with ACORD standards, the more credible that data is to them.
“If it’s not consistent and it doesn’t follow actuarial standards, they don’t pay a lot of attention to it,” he says.
Piecing Together the Data Picture
Data quality translates into companies having the right information at the right time to make decisions.
August 11, 2003 (Computerworld) — Poor data quality can confuse your customers, undermine your applications or even put you out of business — and there’s everything in the world you can do about it. More than simple data-cleansing, which involves correcting a misspelled name or changing “Avenue” to “Street,” a data quality initiative addresses more complex and subtle problems.
For example, one New York bank that had a 3% to 5% bad-debt ratio on its credit card operation acquired another bank, says Aaron Zornes, a San Francisco-based analyst at Meta Group Inc. “It turns out that the acquired bank had a 15% bad-debt ratio. The New York bank took over, and the bad debt nearly put them out of business,” he says.
If the acquiring bank had had a data quality initiative to run large database-comparison jobs off-line, the problem could have been averted, says Zornes. Bank managers could have predicted the loan default rate by comparing the outstanding debt, incomes and even partial ZIP codes of the acquired bank’s credit card customers against a historical database of similar customer profiles.
”They would have been able to tell that this company wasn’t a good buy,” Zornes says. “Enterprises cannot afford to wait on data quality efforts.”
Data quality initiatives are critical to enterprise applications such as CRM and ERP systems, Zornes notes. And according to The Data Warehousing Institute in Seattle, data quality problems cost U.S. businesses more than $600 billion per year.
”The basis of any CRM system is the integrity of the data,” says Steve Deeb, vice president for CRM at Monster Worldwide Inc. in Maynard, Mass. “Any and all processes are driven by that data.”
In addition to business needs, there are now regulatory pressures to maintain better data, Zornes says. “If someone has bought a large amount of ammonia-based fertilizer, then rents a car,” the U.S. Department of Homeland Security wants to know about it, he says. “And this isn’t information you can wait months or even a week to find out.”
The tools to to improve data quality exist, says Zornes, but although “businesses give lip service to the need for data quality, too often they don’t do anything about it.”
James Eardley, a managing director of CRM at FleetBoston Financial Corp., agrees. “Data quality gets short shrift too often. It’s not important until you need it,” he says.
Although in dissimilar industries, FleetBoston and Monster both use CRM software from Siebel Systems Inc. in San Mateo, Calif., and faced similar data quality problems. Duplicate records in customer and contact databases meant one department didn’t know what another was doing.
”What we were missing was a total picture of the customer relationship. We have multiple business sales forces following a single customer. It’s hard enough to get one business unit’s data clean. We now have 24,” Eardley says.
”There’s no consistency with how users enter customer and contact records,” he continues. “Some people use upper- and lowercase; others use all uppercase.” Today FleetBoston’s system standardizes the data elements and does ZIP code lookups.
The company opted for data quality software from FirstLogic Inc. in La Crosse, Wis. Those tools, coupled with the Siebel software, “seemed to do exactly what we needed,” Eardley says.
To prevent duplicate entries, when a user enters a record, the FirstLogic system generates a token, which it compares to others to see if the database has similar tokens. If it finds any, it shows them to the user to determine whether the record is a duplicate.
”We had to work a little bit to get the tokens to our liking, and then it worked fine,” Eardley says. “We also run batch jobs monthly to identify and fix any duplicates.” Any records that the system can’t resolve go to the business side for review.
Monster Problem
Similar data inconsistencies undermined confidence in Monster’s system, says Deeb. Duplicates and unidentified accounts in the Siebel system made it difficult to know which database to use for ordering or invoicing, he says. And the sales staff wasn’t getting the support it needed.
Initially, Deeb says, “we didn’t see a product that mapped directly into what we were doing.” But after building its own address-matching application, the company found that it needed a more strategic tool and more sophisticated analysis than its in-house application could offer.
About a year and a half ago, Monster took another look at the field and chose the Trillium Siebel connector from Trillium Software, a division of Harte-Hanks Inc. in Billerica, Mass.
”When we were looking at the ROI, the ease with which the Trillium product could be integrated into our systems was attractive,” Deeb says. “We leveraged the strength of the Trillium core product — such as the way name and address databases from around the world can be plugged in — and integrated it into our processes in a way that made sense to the way we do business.”
Now, when a record is entered, the system evaluates in real time whether it’s new or a modification of an existing record. The company also runs data quality checks in batches to ensure that duplicates aren’t introduced when it incorporates a new mailing list into its existing database. They’re also performed at regular intervals to minimize data degradation. In addition to the IT resources dedicated to maintaining data quality, business staffers are also assigned to monitor the system and resolve anomalies.
It’s the essence of analytical CRM, Deeb says. “Real-time analysis to determine the right offer to the right customer at the right time in a predictable manner is driven by the quality of customer data supporting that analysis,” he says.
But most companies believe that their data is cleaner and more accurate than it is, says Wayne Eckerson, The Data Warehousing Institute’s education and research director. He cites as one example an insurance company that each month gets 2 million claims, each with 377 data elements. At an error rate of 0.1% for all claims data, that’s more than 754,000 errors monthly, which amounts to 9.04 million errors annually. If 10% of data elements are critical to its business decisions, the company each year must correct more than 1 million errors that could damage its ability to conduct business. Estimating the risk cost at $10 per error, poor data quality costs the company $10 million annually in erroneous payouts.
”It’s bewildering,” says Eckerson, “but almost half of all companies have no plan for managing data quality.” Responsibility for data quality often rests with IT staffers, who make their decisions based on the tools available.
Data Quality Means Business
”First and foremost, data quality is a business issue,” says Ted Friedman, an analyst at Gartner Inc. in Stamford, Conn. “But the solution is the proverbial three-legged stool: people, process and technology.”
The first step in a data quality initiative is to analyze what the data is and how it’s used, Friedman says.
GMAC Mortgage Corp. in Horsham, Pa., followed this measured course in its data quality initiative. When interest rates went into free-fall a year and a half ago, the first thing the company’s CEO wanted employees to do “was cope with a 300% to 400% increase in daily business of people refinancing mortgages,” says David Adams, GMAC’s enterprise data access manager.
Tuning the Oracle database that supported application processing improved performance, he says, “but it also opened our eyes to the need to go further and address the quality of the data itself.” And with GMAC beginning a major overhaul of its data warehouse—”actually, it was more a large tank of data than a data warehouse,” says Adams—the timing was right to launch a data quality initiative.
”To compete on the other side of the refinancing boom, we were going to have to have better, cleaner data to get the accurate analyses that the CEO wanted and that we needed to make the most of our operation,” he says.
Adams brought in a data quality consultant to explain to the executive council what the project would entail. Adams and his team researched the data quality tools, ran two pilots and then selected software from Ascential Software Corp. in Westboro, Mass. The Ascential product was more expensive and took more work to get going than some less sophisticated tools, he says. But Adams was sold on the software’s heuristic logic, which let it adapt to GMAC’s operation.
”The ETL [extract, transform and load] technology is pretty mature, and it works well,” says Adams. “But it’s the data quality and metadata stuff that’s going to give you the great advances.”
Physically merging databases would have required that every division agree on a single definition for each data element, which was “probably impossible,” Adams says.
Instead, metadata resides in Ascential DataStage and links divisional databases at the logical level, with “pointers” indicating the source of the data. Each division’s database remains inviolate.
Each division can decide what data can be shared and with whom, which is important for adhering to government regulations. Other tools couldn’t deliver that granularity of control, says Adams.
The team installed the software in January and, working with the data warehousing team, went live in May with a relatively small application for new credit policy reporting. The first large data mart, to support all reporting for GMAC’s wholesale operations, will go live Aug. 15.
”Information is a critical asset,” says Meta’s Zornes. “We need to change the way we think about it. It may sound like science fiction now, but in the future, companies will certify information the way we certify works of art and financial instruments, i.e., by assigning that information asset’s value and origination.”
Lais is a Computerworld contributing writer in Takoma Park, Md.
Start-Ups Mine Database Field
Nimble Software Helps Make Sense Of Information Tide
Nov, 18th, 2007
By Don Clark | Wall Street Journal
Most databases are based on technology that originated 30 years ago. But change is in the air.
A mob of start-ups have been developing variants of the software, which provides the equivalent of filing cabinets for corporate information. Customers say the offerings are generating faster answers to questions that require sifting through huge volumes of business information. Established suppliers aren’t conceding much to the newcomers, but industry executives agree the pace of progress is accelerating.
“The database market is going to be an exciting place to be in the next decade,” said Michael Stonebraker, an adjunct professor at the Massachusetts Institute of Technology and chief technology officer of a new entrant called Vertica Systems Inc.
His opinions carry some weight. Mr. Stonebraker, during a 25-year stint at the University of California, Berkeley, was a major force in the 1970s behind relational databases &mdash the strain of technology in products from companies such as Oracle Corp., International Business Machines Corp., Microsoft Corp. and Sybase Inc. Besides his initial product, called Ingres, he helped develop another database called Postgres that many companies use today.
One reason for the latest activity is the need to make sense of a flood of business information. Web services, for example, generate a stream of information about the activities of visitors to the sites. Companies use “business-intelligence” software to analyze such data, a reason for a takeover wave that includes IBM’s deal yesterday to buy Cognos Inc. for $5 billion.
Corporate-transaction data is typically transferred to software repositories, called data warehouses, where it can be studied using business-intelligence programs. A buyer for Wal-Mart Stores Inc., for example, might want to plan for storm season by sifting through cash-register records of what people in Florida bought just before and after a major hurricane, Mr. Stonebraker said.
Depending on their complexity, such queries can take many hours using standard databases. So companies have developed a range of techniques to speed up the job.
Teradata Corp., a pioneer in data warehouses that recently was spun off from NCR Corp., developed technology to pass information quickly between server systems that come packaged with its software. Netezza Corp., a start-up in Framingham, Mass., that went public this year, helped popularize the idea of “analytic appliances” &mdash a combination of software and servers that are accelerated with the aid of certain chips.
Other start-ups, such as Greenplum, of San Mateo Calif., and Dataupia Corp., of Cambridge, Mass., have developed their own hardware ideas. One of their techniques is to divide up data-warehouse jobs over many inexpensive servers so that adding more computers gets answers more quickly.
One user is iCrossing Inc., of Scottsdale, Ariz., which provides analytical services to companies that operate Web sites. Analyzing a day’s worth of some types of data once took 20 to 22 hours, said Tony Wasson, the company’s vice president of engineering. With Greenplum’s technology, and some modifications to its own software, the job now takes about an hour, he said.
Others are using a different style of software. Relational databases typically store records in rows with multiple columns of transaction informatio. Sifting through all those columns can create delays in getting answers.
Another approach, pioneered by Sybase, accelerates the process by searching only through specific columns that are the focus of a query. Some users of these “columnar” databases rave about them.
Investment Technology Group Inc., a New York firm that provides brokerage and technology services to institutional investors, said its data warehouse has swelled with the heavy volume of electronic trades and associated message traffic. One standard query, which analyzes transaction data over 30 days, once took about five hours, said Michael Dearinger, an ITG senior vice president. Using the columnar software Sybase IQ, the firm gets answers in about 13 minutes, he said.
The columnar approach also is used by Vertica, the Andover, Mass., company co-founded in 2005 by Mr. Stonebraker. Its executive chairman is Jerry Held, an Oracle veteran who worked with Mr. Stonebraker at UC Berkeley. Another start-up that uses a similar technique to narrow searches is ParAccel Inc. of San Diego.
“With columnar databases you are searching only through the relevant haystack,” said Barry Zane, a former Netezza executive who is ParAccel’s chief technology officer.
Some predict specialized products will find a niche. “One kind of database is not going to suit all of the different applications we are coming up with,” said Donald Feinberg, an analyst at market researcher Gartner Inc.
ParAccel Touts Columnar Analytic Database
11/14/2007
By Stephen Swoyer
Speed, especially vastly improved query performance, is the Holy Grail of the high-end data warehousing segment. It doesn’t matter that today’s enterprise data warehouses or orders of magnitude faster than their predecessors, nor that — in just the last 30 months — query performance (as recorded by the Transaction Processing Performance Council’s TPC-H benchmark, among others) has exploded exponentially. No, some customers can’t ever have enough speed.
For a long time, high-end query performance was the staple special sauce of Teradata, the soon-to-be-erstwhile subsidiary of NCR Corp.
Over the last five years, however, a number of vendors — including data warehouse appliance pioneer Netezza Inc., relational database stalwart Sybase Inc., and Unix king Sun Microsystems Inc., along with several others — ventured into the large-volume, high-performance data warehousing segment, promising improved performance and mind-boggling scalability.
More recently, this segment has grudgingly accommodated a number of new aspirants, including next-generation appliance vendor Dataupia Corp., analytic data warehouse specialist InfoBright Inc. (which applies “rough set” mathematics to analytic query issues), and — most recently — ParAccel Inc., a columnar database specialist that, with the launch of its ParAccel Analytic Database, vaulted to first place in the TPC’s TPC-H decision support benchmark.
More precisely, ParAccel vaulted to first in the 100 GB, 300 GB, 1 TB, and 3 TB segments of the TPC-H rankings, beating out — in both price and performance — familiar players Hewlett-Packard Co. and Dell Computer Corp.
Talk about making a splash. ParAccel officially launched its flagship product at the TDWI World Conference, held late last month in Orlando. Officials describe the ParAccel Analytic Database as a high-speed, CDBMS — or columnar database management system — that accelerates processing for demanding query-intensive BI and data warehousing applications.
ParAccel is designed be implemented on its own — i.e., as a standalone analytic DBMS — and can plug right into an existing data mart or operational data store. That’s what ParAccel calls the “Maverick” implementation scheme. There’s also an “Amigo” implementation whereby the ParAccel Analytic Database acts as a kind of drop-in acceleration platform for SQL Server (right now) and Oracle (forthcoming) RDBMS environments.
In this configuration, officials say, ParAccel plugs right into an existing SQL Server RDBMS instance and provides high-performance query routing, synchronization, and syntax offloading services.
Like rival Dataupia — which launched earlier this year with Netezza veteran Foster Hinshaw at the helm — ParAccel’s executive team is also pedigreed: founder and CTO Barry Zane is a Netezza veteran, too. ParAccel Analytic Database is a different animal than Netezza’s RDBMS-based DW appliances, however: it’s a columnar database that’s capable of running entirely in-memory.
That gives it a competitive leg-up over Netezza and other DW appliance vendors, argues Kim Stanick, vice-president of marketing with ParAccel.
The In-Memory Advantage
“Given the fact that we are both in-memory capable and we also have a disk-based capability, people who want to run extremely fast systems can benefit,” she indicates. The timing is right, too, Stanick and other ParAccel officials argue. Thanks to a number of drivers — including in particular the push for real-time or near-real-time analysis of operational data — they believe ParAccel’s in-memory columnar database technology will generate quite a bit of buzz.
“If you’re looking for the really, really high-performance, all-in-memory scenario, if you’re looking to support real-time or near-real-time, you’re typically going to have a smaller set of data — you’re not going to be looking across a big history —it really makes sense for you to run in-memory,” Stanick points out.
ParAccel’s in-memory capabilities give it a clear performance advantage, she continues — but its columnar design amplifies that edge. “We take the core data, the data itself [i.e., straight from the source repositories]. We don’t require indexes or summaries or aggregation tables. That’s the advantage of columnar and compressed data: we can get really great performance just against the raw data using whatever schema you give us,” Stanick argues. “You don’t have to build a data warehouse-compressed schema if you don’t’ want to.”
Moreover, ParAccel officials argue, a large data warehouse footprint isn’t necessarily an impediment to running in-memory. “Most analysts say it’s about a 4:1 [core] data to blown-out ratio, so we only require you to load the core data; we’re saving you 4:1 there, and if you add compression on top of that — which is also about a 4:1 [reduction factor] — you now can compress 1 TB of data down into 125 GB of memory,” she indicates.
“What we use as a rule of thumb when you’re going to run in-memory is that about 40 GB of user data will fit into about 16 GB of memory, which is a very standard server size.”
Its in-memory value proposition notwithstanding, ParAccel’s drop-in-place Amigo configuration will likely resound with customers, too. Like rival Dataupia — which promises to work more or less out-of-the-box with a customer’s existing RDBMS assets — ParAccel Amigo is designed to complement existing SQL Server implementations (Oracle support is promised for next year.)
“This allows you to grow the system and provide the ‘queryability’ with the syntax coverage of your native SQL environment, so you don’t have to rewrite your applications,” Stanick explains. “One cluster can actually mirror multiple databases of record. You can get economies of scale and scale out as needed. The real point is that you offload the systems that are struggling with performance — you offload the heavy, complex, ugly queries so that those systems can do what they’re designed to do.”
This helps accelerate vanilla SQL Server performance, too, according to Stanick: “SQL Server is a very nice operational database … and what clients don’t realize is that they’re actually causing themselves a lot of pain by running these [complex] queries on it.”
ParAccel touted two prominent launch customers. One — telecommunications specialist LatiNode, which provides least-cost-routing services for calls placed to Latin America — used a Maverick implementation of ParAccel Analytic Database to cut its processing time from 60 hours to 2.5 minutes, topping out at six (mostly commodity) Sunfire 4100 servers. LatiNode plans to deploy its production implementation on top of HP DL380 systems.
“They took a look at that and decided that they didn’t really need to be able to run the query that fast, so they actually scaled it back a bit,” Stanick says.
She declined to identify ParAccel’s other prominent client — although she did describe it as a Fortune 500 information services provider for the legal profession. That company tapped a ParAccel Amigo implementation to run its queries an average of 50 times faster, Stanick notes. From this customer’s perspective, implementing ParAccel Amigo made a lot more sense than building a complementary data warehouse from scratch.
“They basically said [that] for us to turn around and have to build a whole ETL process, a data warehouse schema, all of the data warehouse project and design, just to get slightly faster performance — it wasn’t worth it,” she says. “With Amigo, they can literally bring in their operational system … and [plug right into] that system. It’s the best of both worlds. [Their operational system is] tuned for OLTP, but it’s also tuned for decision support [with ParAccel], so the apps aren’t built specifically for a platform any longer.”
Stephen Swoyer is a technology writer based in Athens, Ga. You can contact Stephen via E-mail at stephen.swoyer@spinkle.net.
ParAccel targets midmarket with new database
Startup presents database software that uses columnar orientation, which it claims is better for companies with business intelligence needs
By Chris Kanaracus, IDG News Service
October 30, 2007
ParAccel, a new company based in San Diego, this week released a database for midsize companies that combines a number of BI-oriented features at a low price point.
The company is targeting the middle of the midmarket, not smaller businesses, according to vice president of marketing Kim Stanick. “You have to have a certain amount of data before our product becomes compelling for you,” she said.
The software does not provide radical new technology, but ParAccel is touting its database’s columnar orientation and MPP (massively parallel processing) capabilities. The company argues that columnar databases are more effective for BI applications because users can query just the desired columns of a given row, saving bandwidth.
“That itself is a nice product set, especially with the price point,” Stanick said.
ParAccel is offering the database as a software package or incorporated into a hardware appliance. It can also be deployed as a “drop in” accelerator for existing SQL Server and Oracle databases. The company said the database is compatible with all major brands of hardware.
A number of licensing options are available. All-in-memory systems cost $1,000 per gigabyte, beginning at 100GB. Disk-based systems cost $40,000 per node plus $10,000 per terabyte, starting at five nodes. Subscriptions cost $5,000 a month and up.
ParAccel counts some notable names among its management team. One of Oracle’s co-founders, Bruce Scott, is vice president of engineering.
The startup originally positioned its product as an accelerator for SQL Server, but in response to market interest widened its focus to include data warehousing and Oracle’s platform. This week’s announcement doubled as the official company launch for ParAccel, which joins an increasingly crowded market for data warehousing.
ParAccel also announced benchmarking figures for its database running on Sun Fire X4100 and Blade 6000 servers. According to the company, the combination set TPC-H (ad-hoc, decision support) records for performance in the 1TB category. Sun and ParAccel are also partnering on a data warehousing product.
Company’s first product is a database management system capable of of all types of decision processing, from traditional data warehousing and analytics to operational business intelligence, online analytical processing, and high-speed query processing.
By Antone Gonsalves
November 7, 2007
Startup ParAccel has launched an analytic database that at least one expert sees as a potentially disruptive technology in the data warehouse and database management system markets.
The San Diego-based company officially launched itself and its first product last week by announcing the general availability of the ParaAccel Analytic Database, a DBMS capable of all types of decision processing, from traditional data warehousing and analytics to operational business intelligence, online analytical processing, and high-speed query processing.
In addition, ParAccel announced a partnership in which Sun Microsystems would offer a DBMS appliance with ParAccel software, which is also available as a standalone database or as a drop-in database accelerator.
In addition, the software can be configured for all-in-memory analytical processing, or for traditional disk-based database-execution deployments, James Kobielus, analyst for Current Analysis said in a recent research note. “It can run on a single massively parallel processing-capable compute node or on multiple distributed nodes with scale-out and high availability.”
Kobielus said the potential impact of the ParaAccel Analytic Database is high on the DBMS and DW markets because of its innovation, flexibility and scalability. “This new release could prove truly disruptive to established segments in which rivals offer point solutions rather than flexible, appliance-ready, analytics-processing solutions,” Kobielus said.
Nevertheless, ParAccel has its shortcomings. For one, it can operate as a drop-in accelerator only with Microsoft SQL Server, and not with the top two enterprise databases: Oracle and IBM DB2, the analyst said. In addition, ParaAccel’s offering competes with products in several market niches, and the startup has yet to prove that its technology is truly the best of breed in any of those segments.
However, ParAccel’s announcement “sends a signal that innovation is alive and well in the DBMS arena,” Kobielus said.
“Rival DBMS/DW vendors should rethink their go-to-market strategies in light of the release of ParAccel Analytic Database,” the analyst said. “This radically flexible new release could prove truly disruptive to many established market segments.”
