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Data Quality: A Survival Guide for Marketing
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- Executive Summary
- Typical Data Problems in a Marketing Campaign
- Duplicate Account Records
- Incomplete Data
- The Wrong Data
- How Marketing Benefits from Quality Data
- But How Do I Find My Data Quality Problems?
- Attacking the Data Quality Problem at the Source
- Transactional Updates
- Operational Feeds
- Purchased Data
- Legacy Migration
- Regular Maintenance
- Mapping the Opportunities to Cleanse
- Data Quality Functions in a Marketing Environment
- Delivering Data Quality Functionality
- On-Premise Software
- Internal Hosting via Web Services
- Service Bureaus
- Tying It All Together
Direct marketing is about communicating a message to a specific prospect or customer. The success of that communication, measured in terms of a qualified lead that generates a sale, depends on accurately identifying the prospect. Once you’ve identified the prospect, you need to contact and convince them your message is worth their time. There is nothing like misspelling their name, using the wrong title, mailing to an old address, or mailing multiple copies to instantly lose credibility. But ensuring data quality can be a significant challenge if you have 500,000 or a million or 10 million prospect records in your Customer Relationship Management (CRM) system and you are trying to target the right prospect. The challenge becomes matching the target audience of your upcoming marketing campaign to the records in your database. How do you select the correct prospects? Data quality, data integration, and other functions of enterprise information management (EIM) are crucial to this endeavor.
Demographics of the prospects are certainly key to mapping your CRM records to the campaign selection, but have you applied the right demographic codes to the right prospects? In past eras, when mass marketing was in vogue, it was standard practice to just mail out the 10 million marketing flyers and hope for the best. In those days, marketing budgets were constructed to support the printing and postage fees of massive mailing campaigns. Not so today, where marketing is compelled to be significantly more efficient and cost-effective at reaching the right customers.
Ultimately, even with the finest marketing organizations, the success of marketing comes down to the data. Surrounding the data and storage application are people, processes, standards, and technologies used to manage that data. This white paper from Business Objects, an SAP company, focuses on these concepts as they pertain to marketing, and particularly as they are supported by data quality functions inside of the broader EIM framework.
TYPICAL DATA PROBLEMS IN A MARKETING CAMPAIGN
Perhaps you recognize or have suffered at the hands of some of these problems. We list and discuss them briefly here to establish a common understanding of what we face. One thing that becomes apparent is data quality problems exacerbate each other. For example, if you have duplicate records, some of the duplicates will not be reconciled if crucial data elements such as addresses are incorrect or not standardized.
DUPLICATE ACCOUNT RECORDS
Which record do you choose? I have often seen an 8:1 duplication ratio between customer records. That is, for every one Ford Motor customer record that you think is the master, you could have seven more records in the same file, as well as others across your enterprise in disparate systems. How does this happen? In many ways–and those of us who capture, create, and store customer data are still inventing new ways.
Mergers and acquisitions (M&A) is certainly one large contributor to duplicate records. I’ve seen it over and over where IT is saddled with merging two versions of accounts payable, billing, shipping, fulfillment, order entry, and CRM systems, and is either given too little time, has its original scheduled shortened, or lacks adequate resources to merge the systems without creating duplicates. In truth, even the best M&A integration projects still create some duplicate records, but more often overburdened IT shops–in order to make their deadlines–are forced to just merge two systems together and let operations clean up the mess after the deadline has passed.
Account managers are another popular source of duplicate account records. For myriad reasons, some of them illogical, when creating a customer record an account manager sees an existing record that matches a customer’s, but ignores it and creates a new record. Often this duplicate effort is driven by data control or even compensation issues.
A third source for duplicate records–and there are more–is poor visibility and linkage across systems. This happens in highly segmented enterprises where each business unit, function, or department has its own customer data repository. In this case, if the account manager for hoists and lifts wants to add new data to the existing derricks and cranes customer record, they can’t because they don’t have access to it.
Incomplete data–such as blank data fields–causes a variety of problems for the marketer. First, and most obvious, when the address, email, or phone number field is blank, your ability to deliver the message is impacted. Second, if a field such as title, salutation, job code, or ethnicity is blank, your ability to segment prospects into the correct categories or demographics is impacted. And third, if any of those fields–and others like social security, account number, log-in ID, or account name–are blank, your ability to identify similar or related records across systems, or even within the same repository, is impacted–accentuating your duplicate record problem because you needed those fields to match against to identify similarities.
THE WRONG DATA
Wrong data is simply that–the data is incorrect. There are a number of ways that wrong data grows within your system. Age is one. People move (change addresses), change phone numbers, change jobs, and so on. Over time, these changes accumulate. Managers experienced with using data are often skeptical of data that has not been updated for six months or a year or more because they understand the compounding effect of data aging. System migrations are another source of wrong data. Incorrect field mappings from source to target, or trying to merge a larger field into a shorter field, can result in cryptic abbreviations that are understandable only to the person doing the migration and are easily misinterpreted by the end user. And then, of course, poor data capture procedures may allow the placement of the third phone number for an office in the second email address field. To the account manager who recorded the data and manages the account today, everything is understandable and known, but for the account manager who takes over tomorrow, nothing is known and these data shortcuts become problems.
A more insidious form of wrong data is fraud, where the supplier of information purposefully enters the wrong data to mislead the business–for example, when a terminated customer with a delinquent mobile phone account opens a new account by supplying fictitious information.
HOW MARKETING BENEFITS FROM QUALITY DATA
Data quality is crucial to knowing who your customers are and reaching them in an effective manner. In order for marketing efforts to gain the greatest benefits, a clear and single view of the customer is necessary. Without this view, contacts are made with the wrong prospects and the right prospects either are missed or have multiple touches that are confusing and costly. A goal of marketing is to cross-sell and up-sell existing customer accounts, which is not achievable if the multiple accounts for the same customer are not matched and consolidated into a single view. This is where data quality plays a direct role in delivering value through marketing efforts.
Usually, the way an organization first experiences the need for data quality and other EIM capabilities is when they build a CRM or customer data integration (CDI) system, and find that the data they’ve loaded into the system is far less than expectations. Throughout this paper, we use CRM as the surrogate for marketing data repositories in general. Somewhere, somehow, customer and prospect data must be stored and accessed, and CRM/CDI systems, whether homegrown or vendor-supplied, are the common repositories for this data.
BUT HOW DO I FIND MY DATA QUALITY PROBLEMS?
Your position within the marketing organization determines your visibility into the data and the perceptions of the quality of that data. The higher in the organization, the more removed a manager is from the data that drives their operations. A chief marketing officer (CMO), for example, may be the person to ask the question, "How do I know my data is defective?" The field-marketing specialist is most likely to wonder, "I know the types of problems, I need the counts." Fortunately, no matter who is asking, the solution to both situations and other data integrity questions is the same: Conduct a data quality assessment. Without the findings from an assessment, you’ll have a number of issues to deal with:
- You won’t know the scope and depth of your problems. For example, are they systemic or superficial?
- You won’t know the cause of the defects. Without knowing the types of problems, you can’t track back in the process to isolate the source.
- You won’t know how effective the resulting cleansing operation was.
- The cleansing operation may very well miss whole categories of problems.
- You won’t be able to report on progress because no baseline was established. That means no ROI calculation for the cleansing.
- You won’t be able to conduct trend analysis over time to see how your data is regressing or progressing.
Many data quality problems are processed-based. That is, the problems result from non-standard practices, no validation of data entry procedures, or just faulty application design. The results of a data quality assessment often uncover process issues as you work through the cause and effect. The assessment exposes the effect, and it’s a relatively simple matter for the marketing manager to backtrack through the data distribution chain to, for example, the account management system and verify field edits are being used or enforced.
A data quality assessment is something marketing managers can do themselves, especially if they have an assessment tool suitable for business users. Another alternative is for the marketing manager to engage IT to conduct the assessment, or even contract with a third-party information management-consulting firm. Regardless, there is little mystery to conducting a data quality assessment. The hardest part belongs with the business–that is, marketing–to articulate the business rules that define good or bad. What are the rules that govern a specific field, such as product name? For example, is it a mandatory field, can the field have abbreviations, is there a maximum length, are special characters allowed, how many generations of products are allowed to be listed, and what is the relationship with the other fields, such as SKU code? At the very least, these rules rest in the minds of the marketing specialists, also known as subject matter experts (SMEs). SMEs need to compose the rules, agree on them, and then load them into whatever tool is used, be it a commercial profiling tool or custom SQL code that is used to explore the data.
Probably the most important part of an assessment, other than the initial rules gathering, is the reporting of the findings. Graphical or tabular reports are crucial to delivering and communicating the impact of the defects. Once armed with this information, marketing management can now make an informed decision as to what fields of what dimensions or tables need to be cleansed. There is no need to "boil the ocean," that is, cleanse every field of every table. At any given time, certain fields of specific tables will be of primary concern, and those are the fields that should be first assessed and then cleansed, because they are the ones that will deliver the greatest value the soonest.
ATTACKING THE DATA QUALITY PROBLEM AT THE SOURCE
The best place for direct marketers to cleanse their data is as close to the point of creation as possible. Consider if you will an information supply chain where at the very beginning the data is captured from the prospect, perhaps at a trade show or from a Web site registration form. See Figure 1.
Captured data is propagated through numerous data repositories, processes, and is perhaps even sold and purchased, then merged into a data warehouse where it is ultimately loaded into the CRM system from which the direct marketing campaign will be driven. This long chain of processes and migrations is an information supply chain. It should be the goal of the marketer to work with the IT department to institute cleansing functions in this supply chain as close to the point of creation as possible. The reason is the further upstream the data is validated, cleansed, and consolidated, the greater the number of downstream marketing and other operations benefit, and the fewer problems defective data will cause.
If we examine the information supply chain graphic in Figure 1, we see a declining level of data quality investment the further into the supply chain you progress. The reason is that upfront and early data cleansing benefits all downstream operations, and makes subsequent cleansing easier and less complicated. At the Web site, for example, a hypothetical $1,000 is spent on cleansing a set of records (in this case, ensuring the fields are correctly filled in, ZIP Codes are correct, as are the street address and city/state names). The benefit at that point in the supply chain is to the Web site order entry process. However, as the data is propagated beyond order entry, other functions (such as the operational data store or ODS) that use the data immediately benefit from the previous data quality work. Managers of the ODS still need to do their own cleansing, such as checking field formatting, matching the data to existing records, or consolidating like records, but they don’t have to do as much if they were starting from scratch, and the complexity of issues are less. This cascading data quality (DQ) benefit works its way through the entire supply chain and all the branches that derive value from the data. The benefit of that initial $1,000 is magnified at each link in the chain until at the very end where the mailroom sending out the promotional piece need only adjust the data, such as applying the right salutation and selecting the applicable address before mailing the piece.
Another reason to manage data quality at the point of creation is it is much easier to validate data and ask the contributor to confirm details as they provide them, rather than months or years later when you actually want to use the data. The picture below (Figure 2) shows an example of how Adobe Systems has implemented real-time data cleansing at the point of capture in its Web order entry process to both automatically validate the data and then to ask the customer which address they prefer.
However, we understand that the marketer is often the inheritor of data and may not have the opportunity to institute a function to cleanse the data as soon as it is captured. After all, the information supply chain can extend many steps and even years from where the data was originally created. Because of this, marketing managers must be prepared to improve data quality anywhere in their process that they have the availability. In general, there are five common opportunities to cleanse data that will occur in information management processes. They are as follows:
- Transactional updates–often at the point of creation
- Operational feeds–upstream and before the data enters your system
- Purchased data–if you’re buying it, demand that it is clean
- Legacy migration–data is in the enterprise, but not in your system yet
- Regular maintenance–as your data ages, you need to cleanse it
We discuss each in these next sections.
This opportunity fits well with organizations that take a proactive approach to data cleansing. Organizations can identify the entry points of information into the organization–in this case, during transactions, such as a new customer login or order entry–and where exposure to flawed data may occur. When a transaction is processed, organizations have an opening to validate the data before it is saved to an operational system. Transactional updating also affords the chance to validate data as it arrives in its information packet rich with contextual information. Since this contextual setting is lost as soon as the data is sent down stream, it is therefore important to leverage it.
By their very nature, transaction updates force organizations to handle individual information packets as they become available, which implies real-time processing, low volumes, and a potentially wide distribution of implementation. In other words, the cleansing functionality must be connected to or embedded in the transactional environment and be able to respond in milliseconds, and also be able to service multiple transactional applications.
The second opportunity to cleanse and consolidate data is during operational feeds. These are regular weekly, nightly, hourly, or even sub-hourly updates supplied from distributed sites to a central data store. A nightly upload from a subsidiary’s CRM system to the corporate data warehouse is just one example. Regular operational feeds allow an organization to implement batch-oriented data quality functions in the path of the data stream, and volumes can be from the few thousands of changed records, as in change data capture, or can be in the millions, even hundreds of millions. Operational cleansing utilizes a predefined cleansing job or project selected from a library of potentially hundreds of jobs. The appropriate job is usually triggered or scheduled to run automatically on a specific data flow (flat file, database table, input stream, and so forth) with an established mapping to the data model of the input stream.
The third opportunity to cleanse is when you purchase data from a third party. Many organizations erroneously assume data to be clean when purchased. Not so. Buying third-party data is in many ways like buying a used car. Do you really know what the previous owner has done to it? Of course not, that’s why you take the car to your mechanic to have him pop the hood and put it on the hoist. You should do the same thing with purchased data; otherwise, you are essentially abdicating your data quality standards to those of the vendor.
In the case of a purchased list for a marketing campaign, you can ask for a random sample from the prospect list and conduct your own data quality assessment. Rudimentary tests for field completion and validation are simple to run. Validating purchased data extends to matching the purchased data against your current data set. The merging of two clean data sets is the equivalent of pouring a gallon of red paint into blue. A merge will not equate to 1+1 = 2, but is more like 1.5. The reason being duplication between data sets, and the duplication may not be easily reconciled. Two records may appear the same, but one record might have a crucial field that is different. The merged data sets must be matched and consolidated as one new, entirely different set to ensure continuity. A hidden danger with purchased data is it occurs as an ad hoc event, which means no regular process (a cleansing job with business rules) exists to incorporate the data into an existing system. The lack of regularly occurring processes raises the specter that in the rush to get the file loaded, "expedient" shortcuts may be taken.
The fourth opportunity to improve the quality of data is during legacy migration. Any time data from an existing system is exported to a new system, the data must be robustly checked and validated. A common problem to look for is legacy fields plagued with overuse, such as the field used by a leasing firm. Over the years the definition of "contract type" had changed and fallen out of favor. Yet sometimes it held important data such as the previous lease duration–important data to migrate, but obscured in an antiquated data model. Another problem was uncovered when a manufacturing company during a data quality assessment discovered it had three types of addresses (site location, billing address, and corporate headquarters) but only one address record per account. In order to capture all three addresses, the CRM analysts were duplicating account records. What they needed to do was extend their data model to hold three separate address records for each account, which impacted the data model of the new system being built. Had it not assessed its data beforehand, the manufacturer would not have discovered this problem until after the initial migration.
In resolving legacy migrations, an interesting relationship appears between data modeling and data quality. As the previous examples show, you can’t have good data quality with a deficient data model, and you can have a good data model with bad data quality. They are like the yin and the yang of data management. The two are inseparable.
When conducting a legacy migration, the data quality job takes on the form of an operational data cleanse project except the project is usually run just once. The same amount of initial thought is dedicated to mapping the source to target fields and defining the rules governing those target data elements. Test runs can be conducted to see how well the job conforms to the desired output.
The fifth opportunity is during regular maintenance. Even if an organization starts with perfect data today, tomorrow it will be flawed. Data ages–and ages more quickly that most expect. For example, 17% of U.S. households move each year, and in some years, as many as 60% of phone records change in some way. Moreover, every day people get married, divorced, have children, have birthdays, get new jobs, get promoted, and change titles. And if that wasn’t enough, the companies we work for start up, go bankrupt, merge, acquire, rename, and spin-off. To account for this irrevocable aging process, organizations must implement regular data cleansing and consolidation processes, be they nightly, weekly or monthly. The longer the interval between regular data quality activities, the lower the overall value of your data.
Of the five opportunities to cleanse data, regular maintenance is perhaps the easiest and most important to perform. It’s easier in that there are no real time constraints as in transactional processing. The data is in a single place, the host repository, and it’s staying there–that is, it’s not being moved at the time of cleansing, and you have schedule flexibility. Data such as prices can be checked and updated in place. What makes maintenance the most important of the five is that you know the data will age, and hence defects will grow. Also, consider that there are numerous connections to the data repository and any number of them can be supplying defective data. A regular maintenance process is your insurance, your backstop if you will, that any defect data that leaks into the system will be caught in the next maintenance sweep.
MAPPING THE OPPORTUNITIES TO CLEANSE
In the following diagram, a typical lead-generation process is used to map each of the five cleansing opportunities and highlight how they relate to each other. Most occur multiple times, as they will in any marketing operation. Each of the opportunities sits astride a data flow where the data moves from one function or repository to another. We call them opportunities because in order to move the data, a software program, IT process, marketing task, or all three must occur, and within that action a data quality function can be readily inserted. These are opportunities. What immediately becomes obvious is in this relatively simple lead-generation operation there are numerous locations to invoke a DQ process. These locations offer substantial flexibility to marketing managers when planning their data integrity strategy. Depending on span of control (who owns what tasks or data stores), budget, availability of resources, and time frame, the marketing manager can choose to implement DQ checks in all possible locations or just one and plan to build from there. The manager’s IT department will be crucial in guiding the strategy and even implementing the final plan. IT will be able to provide feedback, for example, on the complexity of implementing DQ maintenance procedures in a CRM system, and how long it would take to prepare for the legacy migration of data from the obsolete call center that is being replaced by the CRM system.
DATA QUALITY FUNCTIONS IN A MARKETING ENVIRONMENT
There are nine data quality functions marketers call upon to cleanse their data. As shown below and depicted in Figure 4, in order of their occurrence in a data quality project, those functions are:
- Identify (Parse)
These functions will usually be conducted in this order because they support each other. For example, to standardize the elements of a customer record, those elements–such as title, salutation, or phone number–need to be identified or parsed out from the contact data. Many marketing campaigns will receive data that comes straight from a mainframe in a multiline record with no fielding, as in the following example:
Director of Cybernetics, Formalux
Acetera Corporation, Formalux Divson
1900 Corporate Way N
Cincinatti, OH, 58999
For the record to be useful, it needs its various components identified and standardized– as in changing corporation to corp and correcting divson to division. It then must match and consolidate with the other records pulled from the source systems. Measuring and analysis kick off the process by providing metadata as to the level and types of defects found in the source data, so subsequent cleansing operations can be tailored for the greatest effect.
The first six functions–including enhancement where additional data is appended like demographic or geo codes–improve data to the point where it can be matched and consolidated. Matching and consolidation is where a tremendous amount of value is delivered to marketing in that duplicate records are eliminated, best of records are built, and the manager now has a single view of each prospect or customer within the context of the applied source data. Now able to build a corporate or retail household for target marketing, the marketing manager can identify the top 20% of the customer base or form demographic groups for segmentation in the next campaign.
Last, monitoring uses the business rules and definitions created in the measure and analyze phases to create an automated profiling project that provides managers with defect information (metadata) at any time, so they can make decisions as to whether the data is good enough to use or needs to be improved for the next operation.
DELIVERING DATA QUALITY FUNCTIONALITY
Once the marketing manager has determined the nature and scope of data problems and has determined the data quality functionality needed, there are a number of options available for connecting the functionality to the data. In broad terms, those options are:
- On-premise software
- Internal hosting via Web services
- On-demand (for example, Software as a Service (SaaS))
- Service bureaus
These options range from having the greatest control and largest footprint (on-premise software) to least control and no footprint (service bureau). From a cost basis one might suspect that on-premise software would be the most expensive. However, the cost of maintaining a level of data quality is not limited to the initial expenditure. Consider that the data and its usage will extend as far into the future as the organization remains in existence. Maintenance fees, per record charges (otherwise known as click charges), or subscription fees over time will exceed initial software license fees. What becomes important when calculating the cost of data quality processing is the breadth and depth of functionality needed and the volume of records processed each month.
Next to service bureaus, on-premise software is the oldest form of data quality delivery mechanism. Early data quality software vendors such as Postalsoft began selling and distributing on-premise data quality software in the mid 1980s. On-premise software is simply that: a software application—either commercial or hand-coded—that resides in your facility and is run by IT or the marketing staff. Sometimes the software is run by third-party consulting or contracting agencies and can be operated locally or remotely via a virtual private network (VPN) or Internet connection. The advantages of on-premise software are you control the application, the parameter settings, the computing environment, processing schedule, and so on. The disadvantage is that your organization is responsible for all of the above. You need to have the system resources, personnel, and training to run the software. For most firms, however, the advantages far outweigh the disadvantages. Basically, if a firm has grown to the size where it has any sort of customer data management system and an IT staff to match, it usually has the capabilities to host and run data quality software internally.
INTERNAL HOSTING VIA WEB SERVICES
Internal hosting of data quality functionality was made possible by the advent of Web services. Today, most commercial data quality software packages support Web services to some level. With Web services, a corporate IT group can install a centralized data quality server, such as Business Objects Data Services, and publish the data quality functionality to departments and business units within the enterprise. IT does not need to install its own software. The elegance of this approach is that the marketing department, for example, can see and leverage the customer processing rules and jobs established by the sales operations department. This business rules reuse dramatically cuts project development time and allows corporate data governance to create data standards and common definitions to be applied across the enterprise. The advantages and disadvantages of internal hosting are the same as on-premise software with the exception that the advantages are magnified by each department or operation that connects to the service. Each new connection and project leverages the single installation, thus keeping IT complexity and maintenance burdens to a minimum. Moreover, when demand begins to exceed IT capacity, rather than having to add another server or deployment, IT can increase the size of the existing server, thus keeping the installation footprint to one deployment.
On-demand software is the newer form of what previously was known as an application service provider, or ASP. With on-demand software, the marketing manager contracts with a third–party service provider and accesses the contracted software via the Internet. The advantage of on-demand is the service provider bears the complete burden of installing, running, and maintaining the software at its own facilities. The disadvantage is the user must trust the provider to safeguard any data that is stored at the provider’s facility, and functionality offered by the provider may be limited when compared to on-premise software. With Internet reliability constantly improving, access to contract software is rarely a problem, and almost always the user interface is Web-enabled and therefore accessible via an Internet browser. A downside of on-demand for data quality processing is the customer’s data must be uploaded to the service and then returned after cleansed. This round-tripping of data adds latency to processing times. However, if the marketing manager is not interested in real-time processing, the added latency may be of little concern.
On-demand actually allows the marketing manager the option of creating a hybrid solution. Look at address cleansing as an example. A firm may have 5 million customer and prospect records, 80% of which have U.S. addresses, 15% have European addresses, and the remaining 5% have Japanese addresses. At the volumes and frequency of processing the firm averages per month, in addition to the complexity of cleansing, the marketing manager, with guidance from IT, may determine it is more practical to process the records for both the United States and Europe in house. But the additional cost of adding Japanese processing given the fewer number of records and the fewer number of campaigns run against those records dissuades the marketing manager from purchasing that capability for that region. On-demand allows managers to contract out their Japanese records and process the data when they need to. These types of hybrid solutions are expected to increase in frequency as globalization continues to expand and more and more customers come from outside of the firm’s nation of origin.
Service bureau processing is yet one step further removed from when compared to on-demand. With a service bureau a complete project including data file, processing rules and delivery instructions is sent to a third party agency that processes the job in batch with relatively little interaction with the customer. The advantage of a service bureau is the marketing manager or their IT counterpart need not run any software, either on-premise or on-demand. Once the initial effort is taken to establish the contract and job requirements, the hard work is done. The files are delivered to the service bureau and the customer awaits either their return or, in the case of a direct mail/email campaign, the marketing pieces are sent to the prospects. The disadvantage of using a service bureau is the client must trust the bureau to follow all the requirements and perform the proper cleansing, and the work performed is largely on the service bureau’s schedule. There are ways, of course, to validate that the bureau has complied with all the requirements, and when negotiating the contract the customer can set the desired delivery date of the finished product.
TYING IT ALL TOGETHER
In the grand scheme of things, marketing managers have numerous options for ensuring and uplifting the quality of their data. EIM provides a framework for deploying those options together in one streamlined process flow. The EIM framework contains everything from data integration–extract, transform, and load (ETL) or enterprise information integration (EII)–through metadata management, data quality, and building specialized reporting marts. Data integration applications are a primary deployment mechanism for data quality functionality, which makes it convenient to cleanse data when it is being moved into or out of your CRM/CDI system.
Today’s data quality vendors have built rich and deep functionality that can remediate almost any customer data problem, and they’ve structured their deployment mechanisms to give you the greatest flexibility in deciding when, where, and how to cleanse data. On-premise, internal hosting, on-demand, service bureaus, or any combination thereof are the options that can be tailored to infrastructure and marketing needs. With the latitude of options available, there is really no reason why suboptimal data should be used to deliver suboptimal results in your marketing efforts, whether you’re identifying cross-sell opportunities or distributing leads to the appropriate sales person. The question for marketing managers becomes: Why marginalize your marketing efforts when better results lie in improving your data?
ABOUT BUSINESS OBJECTS
As an independent business unit within SAP, Business Objects transforms the way the world works by connecting people, information, and businesses. Together with one of the industry’s strongest and most diverse partner networks, the company delivers business performance optimization to customers worldwide across all major industries, including financial services, retail, consumer-packaged goods, healthcare, and public sector. With open, heterogeneous applications in the areas of governance, risk, and compliance; enterprise performance management; and business intelligence; and through global consulting and education services, Business Objects enables organizations of all sizes around the globe to close the loop between business strategy and execution.