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Optimizing Data Quality in the Enterprise

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De kwaliteit van data is de focus geworden van veel bedrijven. Fouten in informatie zijn volgens onderzoek de reden van 75 procent van de problemen waar bedrijven mee te kampen hebben. In deze whitepaper worden data quality management oplossingen besproken waarmee de juistheid, compleetheid en consistentie van alle vormen van informatie binnen een bedrijf verbeterd kan worden. Dit is een van de belangrijkste voorwaarden om bedrijfsprocessen te kunnen optimaliseren.

mei 2011
  • Verbeteren van datakwaliteit
  • Managen van kwaliteit
  • Software
  • Strategie
Optimizing Data Quality in the Enterprise How to Tackle Your Bad Information A White Paper by Vincent Lam Vincent Lam Vincent Lam is the product marketing director responsible for marketing iWay Software's entire product line. Mr. Lam has a diverse background in the technology sector and has helped position iWay Software's products as best-of-breed in the competitive marketplace. Earlier in his career at the company, Mr. Lam was a strategic product manager. He introduced new strategic and innovative products to the marketplace such as WebFOCUS Magnify, the world's first real-time transactional enterprise search solution. Mr. Lam's background includes innovation at Wall St. firms, technology firms, and entrepreneurship. Mr. Lam holds a Bachelor of Science degree from Cornell University in Ithaca, New York. Table of Contents 1 Introduction How to Improve Data Quality Profiling Cleansing, Standardizing, Enriching, Matching, and Merging Scoring Extensibility and Flexibility for Custom Data 2 2 3 6 6 7 7 7 8 Managing the Quality of Data Throughout Its Lifecycle Upstream Instream Downstream 9 9 9 Leveraging Data Quality Effectively Ensuring Quality Data From External Sources Why Real-Time Data Quality Is Critical 10 10 11 iWay Software: Unparalleled, Enterprise-Wide Data Quality Management iWay Data Quality Center iWay Data Profiler 13 A Critical Part of Any Information Management Strategy 14 Conclusion Introduction Data quality ­ the measure of data accuracy, completeness, and consistency across a business ­ has become the core focus of information management efforts among many of today's organizations. Problems with data quality continue to plague corporations of all types and sizes. According to a PricewaterhouseCoopers survey, 75 percent of large enterprises currently face major challenges due to bad information.1 A separate study from SiriusDecisions claims that ­ even at process-optimized companies ­ approximately 10 percent of customer and prospect records contain critical data errors, such as incorrect demographics or outdated dispositions. At companies without formal data management strategies in place, that number can skyrocket to as high as 25 percent.2 These data quality issues can be caused by a variety of reasons. In the not-so-distant past, a vast majority of information found its way into the corporate environment through error-prone manual data entry. However, new inbound information channels ­ such as Web portals and businessto-business (B2B) interactions with suppliers and partners ­ are increasing the complexity of the corporate data environment. These varied electronic real-time sources deliver information that is more sophisticated and multifaceted, providing greater value to the business. But, they also make enterprise-wide data quality harder to achieve, requiring a real-time data-quality firewall to preserve information integrity. Additionally, similar information ­ such as customer details ­ may be stored in multiple disparate sources, including customer relationship management (CRM) applications or accounting systems. Information may get updated in one, but remain unchanged in the other, creating the kinds of inconsistencies that often lead to multiple versions of the truth. Further, data can simply be difficult to retrieve. An inability for users to locate and access the information they need to most effectively perform day-to-day activities, or make on-the-fly decisions, can significantly minimize the value of corporate data. Just one small error can pollute data throughout an entire business, and the impact of corrupt data can be devastating. The Data Warehouse Institute estimates that quality issues in customer data alone costs U.S. businesses a whopping $611 billion each year.3 In his book, "Data Driven: Profiting From Your Most Important Business Asset," Thomas C. Redman contends that, although companies may be quite pleased if their data is 99 percent accurate, that single percent of error could be disastrous. He cites an example of a customer order requiring a dozen pieces of data. If 100 orders containing a total of 12 information elements each are entered perfectly, it costs the company $100, or $1 each. If you factor in the 1 percent error rate, 12 orders will be processed incorrectly, which can more than double related costs.4 In this paper, we will discuss some techniques companies can implement to enhance data quality across the entire enterprise. We will also highlight iWay Software's suite of data quality management solutions, which provide businesses with the ability to effectively and economically enhance the correctness, completeness, and consistency of information in each and every system within their technology infrastructure. 1 "Global Data Management Survey 2001," PriceWaterhouseCoopers, 2001. 2 "The Impact of Bad Data on Demand Creation," SiriusDecisions, 2008. 3 Eckerson, Wayne W., "Data Quality and the Bottom Line," The Data Warehouse Institute, 2002. 4 Redman, Thomas C., "Data Driven: Profiting From Your Most Important Business Asset," 2008. 1 Information Builders How to Improve Data Quality The key to maintaining optimum levels of data quality is to ensure consistent data-quality procedures are applied to every single information source across your business. Some of the more popular data-quality management methods include: Profiling Also referred to as data discovery, profiling is the process of gathering statistics about enterprise data. What are its primary characteristics and attributes? How was it created and by whom? Which users access it most frequently? For what purposes is it primarily used? And, most importantly, what kind of shape is it in? Profiling is one of the most effective means of obtaining an in-depth understanding of corporate data. It is this kind of insight that will make it easier to precisely determine overall data health; identify, prioritize, and correct any issues or errors (some of which may be expected, others may be surprises); and rectify the underlying causes of quality problems. Once an initial profile has 2 Optimizing Data Quality in the Enterprise been created, the ongoing monitoring of profile-related metrics will allow companies to be more proactive in detecting and fixing future quality problems. Cleansing, Standardizing, Enriching, Matching, and Merging Seemingly unrelated, these steps are all integral to achieving and sustaining optimum levels of data quality. Cleansing eliminates mistakes within databases and other information sources through the alteration of existing data based on pre-defined business rules and criteria. In the example below, records with erroneous names have been identified. During the cleansing process, missing entries are amended and completed fields are automatically standardized to a specific format based on pre-defined rules. Source Data Birth Date 12/16/1978 16.12.1978 781612 11/16/78 16.11.1978 16.11.1978 095252433 16.11.1978 420347213 1982 420-347-213 5.1.1982 SIN420347213 1982-01-0 420-347-213 SIN 000000000 095-242-434 095242434 095242433 095252433 Name Dr. John Smith Smith W. John John William Smith Dr. J.W. Smith John Smith Smith John John Smiht Jane Watson Watson Jane Jane Smith J. Smith Data Before Cleansing G M M M Address 14618 110 Ave Surrey V3R 2A9 Surrey 14618 110 Ave 25 Linden Str Toronto M4X 1V5 8500 Leslie L3T 7M8 Toronto 8500 Leslie street Marham 600-8500 Leslie str. Toronto L3T 7M8 8500 Leslei street Toronto L3T 7M8 F F First John John John John John John Jane Jane Jane J. Last Smith Smith Smith Smith Smith Smith Smith Watson Watson Smith Smith G M M M M M M M F F F Cleansed Data SIN Birth Date Address 1987-12-16 V3R 2A9; BC; Surrey; 14618 110 Avenue 095242434 1978-12-16 V3R 2A9; BC; Surrey; 14618 110 Avenue 095242434 M4X 1V5; ON;Toronto; 25 Linden Street 1987-11-16 095252433 1978-11-16 L3T 7M8; ON; Markham; 8500 Leslie Str. 1978-11-16 L3T 7M8; ON; Markham; 8500 Leslie Str. 095252433 1978-11-16 420347213 L3T 7M8; ON; Markham; 8500 Leslie Str. 420347213 1982-01-01 L3T 7M8; ON; Markham; 8500 Leslie Str. 420347213 1982-01-05 420347213 Data After Cleansing 3 Information Builders Enrichment improves comprehensiveness, dynamically extending and enhancing information by comparing it to third-party content ­ such as consumer demographics or geographic distributors ­ and appending its attributes when appropriate. In this scenario, previously missing zip codes have been determined based on existing addresses, and are added as a separate field in each record. Cleansed Data Birth Date Address 1978-12-16 BC;Surrey;14618 110 Avenue 095242434 1978-12-16 BC;Surrey;14618 110 Avenue 095242434 ON;Toronto;25 Linden Street 1978-11-16 095252433 1978-11-16 ON;Markham;8500 Leslie Str. 1978-11-16 ON;Markham;8500 Leslie Str. 095252433 1978-11-16 420347213 ON;Markham;8500 Leslie Str. 420347213 1982-01-01 ON;Markham;8500 Leslie Str. 420347213 1982-01-05 420347213 SIN First John John John John John John Jane Jane Jane J. Last Smith Smith Smith Smith Smith Smith Smith Watson Watson Smith Smith G M M M M M M M F F F Data Before Enrichment First John John John John John John Jane Jane Jane J. Last Smith Smith Smith Smith Smith Smith Smith Watson Watson Smith Smith G M M M M M M M F F F SIN 095242434 095242434 095252433 095252433 420347213 420347213 420347213 420347213 Enriched Data Birth Date Address 1978-12-16 BC; Surrey; 14618 110 Avenue 1978-12-16 BC; Surrey; 14618 110 Avenue ON; Toronto; 25 Linden Street 1978-11-16 1978-11-16 ON; Markham; 8500 Leslie Str. 1978-11-16 ON; Markham; 8500 Leslie Str. 1978-11-16 ON; Markham; 8500 Leslie Str. 1982-01-01 ON; Markham; 8500 Leslie Str. 1982-01-05 Zip V3R 2A9 V3R 2A9 M4X 1V5 L3T 7M8 L3T 7M8 L3T 7M8 L3T 7M8 Data After Enrichment Merging and matching promote consistency by automatically uncovering related entries within the same system ­ or across multiple systems ­ then linking, matching, or merging as needed. The example below demonstrates how the matching and merging process works. 4 Optimizing Data Quality in the Enterprise First John John John John John John Jane Jane Jane J. Match Last Smith Smith Smith Smith Smith Smith Smith Watson Watson Smith Smith G M M M M M M M F F F Cleansed Data Birth Date Address 1978-12-16 V3R 2A9;BC;Surrey;14618 110 Avenue 095242434 1978-12-16 V3R 2A9;BC;Surrey;14618 110 Avenue 095242434 M4X 1V5;ON;Toronto;25 Linden Street 1978-11-16 095252433 1978-11-16 L3T 7M8;ON;Markham;8500 Leslie Str. 1978-11-16 L3T 7M8;ON;Markham;8500 Leslie Str. 095252433 1978-11-16 420347213 L3T 7M8;ON;Markham;8500 Leslie Str. 420347213 1982-01-01 L3T 7M8;ON;Markham;8500 Leslie Str. 420347213 1982-01-05 420347213 SIN Related entries for John Smith and Jane Watson are identified. However, in spite of some similarities between the records, not all the information is redundant: there are actually two different John Smiths. Advanced matching capabilities closely assess the data contained in each record to determine which ones are redundant and which are separate and distinct. Cleansed Data SIN Birth Date Address 1978-12-16 V3R 2A9;BC;Surrey;14618 110 Avenue 095242434 1978-12-16 V3R 2A9;BC;Surrey;14618 110 Avenue 095242434 M4X 1V5;ON;Toronto;25 Linden Street Golden Record Birth Date Address 1978-12-16 V3R;BC;Surrey;14618 110 Avenue First John John John Last Smith Smith Smith G M M M First John Merge Last Smith G M SIN 095242434 Merging then consolidates the matched data into a single, comprehensive record. Here, the duplicate entries for John Smith are unified into one complete record containing the information from all duplicated records. There was conflicting data in the address field, so the most frequent occurrence was automatically used, based on pre-defined rules. Householding, a technique similar to merging where related information from disparate systems is collected and stored in a data warehouse or other central location for easy access, also falls into this category. With householding, companies can consolidate similar information about a family, company, etc. to provide the most complete picture possible to end users. 5 Information Builders Scoring Many organizations have begun to rely on scoring to more effectively evaluate data quality and to better prioritize problems if and when they occur. With scoring, a number is assigned to every data record, providing insight into its quality. For example, a pristine company record may be rated with a score of "5," while a completely invalid record would receive a score of "1." Any number in between would demonstrate the level of confidence the organization has in the record's thoroughness and accuracy, and indicate if any action is needed (i.e., any record with a score of "3" or less would require manual review). Firms must be flexible when it comes to scoring procedures, applying different rules to different types of data to convey a sense of urgency ­ or not ­ when problems arise. For example, critical data such as customer information should be scored more strictly than data about officesupply inventory. Extensibility and Flexibility for Custom Data To determine which data is bad, a company must establish how records are supposed to look. Information like addresses and zip codes can be matched against a database to determine accuracy, but that kind of validation simply isn't available for most types of records. A large percentage of data is proprietary ­ product details, for example ­ and requires some level of subject-matter expertise to assess its quality. Companies must have a programmatic way to apply rules to this type of information to ensure its quality in a more proactive way. The rules must be easy to define and implement, and should be used in such a way that they do more than just uncover and correct bad data ­ they must stop it from entering the environment in the first place. 6 Optimizing Data Quality in the Enterprise Managing the Quality of Data Throughout Its Lifecycle Enterprise data has a lifecycle, moving in various directions within and beyond a business. During the course of day-to-day business activities, vital information flows: Upstream Data enters a corporation via various methods, and in countless formats. For example, it can be received as an e-mail, fax, or letter. It can be captured during the course of phone interactions, face-to-face meetings, or dynamic and automated B2B exchanges. It can even be introduced through next­generation channels, such as Web portals and self-service environments, as well as hosted or cloud-based sources, such as Salesforce.com. The multiple touch points through which data is generated and collected have become more sophisticated in recent years, leaving much room for error. This makes it difficult to ensure adherence to business rules and data-quality standards, and creates challenges when it comes to guaranteeing and maintaining information integrity. Instream Existing data is constantly transported across a company, and is often modified or changed, or aggregated with other records, during the course of complex business transactions, or in support of reporting and analysis activities. 7 Information Builders This constant momentum creates the potential for major quality issues. A lack of active checks and balances as data is consumed can cause data to become mismatched, redundant, poorly categorized, and even lost. These issues can be difficult to detect until it is too late. Downstream End users frequently access data for reporting and analysis operations. Data can be retrieved directly from back-end sources or from data marts and data warehouses, and is then leveraged for operational, financial, or compliance reporting; presented to executives and managers via dashboards and scorecards; or loaded into multidimensional cubes for more in-depth manipulation and analysis. Accessibility issues such as duplications or inconsistent semantics can negatively impact the ability of end users to leverage data to support critical business activities. This can negatively impact operational efficiency, business performance, and ultimately, profitability. It's a proven fact that bad data multiplies. Much like a polluted river contaminates lake water as it flows in, just a single corrupt record can infect multiple systems as it moves upstream, instream, or downstream. The longer an organization waits to correct bad data, the more damage it can do. Companies must take a proactive approach to managing data quality or run the risk of having even the smallest quality issue become a major one. 8 Optimizing Data Quality in the Enterprise Leveraging Data Quality Effectively Ensuring Quality Data From External Sources Most people discuss data quality from the perspective of information contained within back-end databases, data warehouses, and other internal sources. But a lot of data comes from outside corporate walls. It is collected from applications maintained by suppliers, distributors, and other partners; gathered and aggregated from various Web sites; or provided by customers in numerous unstructured formats. Yet, few data-quality initiatives take this information into consideration, minimizing the success of data-quality enhancement efforts by leaving huge gaps and creating an environment fraught with risk. Since just one invalid record can pollute numerous other systems, these external sources pose a significant threat. That's why it is so important to apply scoring, cleansing, matching, merging, and other proven data-quality management techniques to any and all sources a corporation leverages. Implementing data-quality policies and procedures that cover only internal systems will protect only a subset of critical information, rendering data-quality programs somewhat ineffective. Why Real-Time Data Quality Is Critical While identifying and correcting bad data after it enters the environment is important, the ability to manage data quality in real time will deliver benefits that are far more substantial. In the SiriusDesigns report, the firm presents the "1-10-100 rule," which demonstrates the benefits of being proactive when it comes to data quality. The rule states that it costs only $1 to verify a record, $10 to cleanse and de-dupe it after it has been entered, but $100 in potential lost productivity or revenue if nothing at all is done.5 Consider, once again, the example of the river. Stopping contaminated water at its source would be less costly and require less effort than cleaning a large body of water ­ the lake that the dirty river runs into ­ after it has been polluted. The same principle holds true with enterprise data. Cleansing information that is scattered across several sources will consume far more human and financial resources than simply catching a bad record as soon as ­ or before ­ it enters a database. 5 "The Impact of Bad Data on Demand Creation," Sirius Decision, 2008. 9 Information Builders iWay Software: Unparalleled, Enterprise-Wide Data Quality Management iWay Software delivers powerful solutions, packed with capabilities that optimize the completeness, accuracy, consistency, and integrity of enterprise data. Our next-generation tools help organizations of all types and sizes achieve and maintain peak data quality across each and every system they interact with ­ inside and outside the business. iWay Data Quality Center iWay Data Quality Center (DQC) is an essential tool for complex data quality management. With comprehensive business rules and localized dictionaries, iWay DQC is designed not only to evaluate, monitor, and manage data quality in different information systems, but also to prevent incorrect data from entering these systems in the first place. As a result, companies can: Control data quality in transactional and analytical applications Cleanse and unify data during system migrations Ensure quality throughout software integration projects Enhance the integrity of address and contact information Improve customer data for client identification purposes Validate and correct incomplete records within customer profiles Validate data input via online self-service applications Perform in-depth data profiling as a part of data-integration project analysis 10 Optimizing Data Quality in the Enterprise iWay DQC delivers a broad array of cutting-edge features in a single, affordable, intuitive solution. Key capabilities include: Centralized management of all data-quality activities, including business rules and data flows, from a single, unified platform Bundled administration tools that allow for easy configuration, without the need for external applications A platform-independent architecture based on open standards Parallel processing methods that ensure scalability, support batch and on-demand modes, and accelerate data-quality procedures ­ performing the entire data-quality process in less than 0.1 s and processing more than five million records per hour Advanced semantic profiling for fast and accurate information analysis Seamless integration into any B2B, A2A, or portal application, as well as popular ESB, SOA, and ETL tools The ability to easily tap into external data sources, such as national address or name registries, as well as third-party dictionaries and custom lists for the purposes of parsing, cleansing, and validation A set of powerful algorithms that efficiently perform approximate matching in record unification, regardless of internal data structures iWay Data Profiler The iWay Data Profiler integrates output from iWay DQC with business intelligence (BI) technology in a simple yet powerful way. Administrators can view, monitor, compare, and report on any mission-critical data ­ with no additional client software, plug-ins, or report viewers required. iWay Data Profiler provides sophisticated integration capabilities bolstered by mature tools for dataquality monitoring, reporting, and analytics, giving users the ability to query, analyze, deliver, and display electronic profiling data in an almost unlimited number of ways. 11 Information Builders Advanced data profiling information ­ generated via iWay Data Quality Center's semantic analytics and complex business rules ­ provides basic data statistics such as uniqueness and frequency, and uncovers relationships between data using primary and foreign keys. This profiling data can then be further analyzed using intuitive and graphical reporting tools, helping users to uncover variances in data profiles over different periods of time. Users can also drill down on profiled categories to reveal the details of the exact records within that group. The iWay Data Profiler provides a wide array of powerful capabilities, including: Customizable data quality indicators (DQIs) that allow companies to define various levels of validity. These DQIs can then be applied to data to provide immediate insight into the integrity of specific records Dynamic collection of profiling data from iWay DQC Tagging and archiving of profiling data as sets within an associated RDBMS for easy retrieval Advanced data manipulation and graphics Comparison of multiple data profiling sets for more rapid variance discovery Printing and exporting of any data profiling view into HTML, PDF, Excel, and other industrystandard formats Portable analytical capabilities ­ embedded directly within the profiling report ­ that allow users to view and analyze profiling data in an almost unlimited number of ways Additionally, iWay Data Profiler is available as a software-as-a-service (SaaS) application, offering many significant benefits, including: Accelerated deployment and set up Increased budget-friendliness through a convenient pay-per-use model that eliminates the high upfront expenditures associated with on-premise tools The ability for detailed profiling information to be more easily shared with those who own the data being profiled ­ non-technical users working across various divisions and lines of business Immediate, cost-efficient scalability whenever it is needed to satisfy changing requirements and emerging needs 12 Optimizing Data Quality in the Enterprise A Critical Part of Any Information Management Strategy Data profiling and quality management are key components of any broad-reaching enterprise information management (EIM) strategy. EIM combines the principles and technologies of enterprise integration, business intelligence, and content management to streamline and formalize the activities associated with data generation, storage, access, and handling. As a result, companies can boost the value of their corporate information, tapping into it to gain a substantial competitive edge through increased operational productivity, reduced overhead costs, and better business performance. iWay Software offers a comprehensive portfolio of tools and solutions to support all aspects of EIM. 13 Information Builders Conclusion Data-quality issues continue to permeate businesses of all sizes, across all industries. Regardless of their cause, these problems are costing companies billions of dollars each and every year. The longer the issues go undetected or uncorrected, the more damage they can do. Advanced techniques and technologies are emerging, helping companies overcome their greatest data-quality challenges. With these methods and solutions, companies can efficiently and effectively implement and enforce formal data-quality policies throughout the enterprise. iWay Software offers a full suite of data quality management solutions, including a robust data profiling tool and a comprehensive data quality platform, that make it faster, easier, and more affordable for organizations to manage data quality from end to end. 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