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CRM Data Integration Challenge: Data Quality Web Services, Part 2

  Article published in DM Direct Special Report
December 6, 2005 Issue
 
  By Shadab Hussain and Shashidhar Kalwa and Venkatesh Ramakrishnan and Mahesh Velapakam

Part 1 of this two-part series appeared in the November 29 issue of DM Direct Special Report. Click here to read it.

ASP Features - Business Model

The following facts provide a hint about the growth and business value of the application service provider (ASP) market and also data quality strategy acceptance by enterprises.

Figure 1

  • IDC and Ovum predicts a $25 billion global ASP market by 2003.
  • Dataquest suggest ASP market growth to $25 Billion by 2005.
  • The western European ASP market is also predicted to grow to $5.8 billion by 2005.
  •  Data quality (DQ) adoption rates will expand 20 to 30 percent annually during the next three to five years, with the services component (not addressed in this evaluation) growing at a commensurate pace.

Conclusions that can be made from the above facts are:

Outsourcing: In face of data quality professional skill shortage, organizations would be increasingly looking forward for outsourcing, leading to a growth in data quality services market

Application service provider: As shown above, the ASP market is ever growing, as there are more cost pressures ever with in enterprises. The data quality ASP will significantly bring down the cost of data quality investment.

Web services factor: Web services technology come to meet the demand for online integration, especially in case of bridging of the externally hosted data quality solution with CRM infrastructure with in an enterprise

Following points elaborate the cost components associated with the business initiation and sustenance of an ASP providing data quality services:

  • Independent software vendors (ISVs): The data quality vendors who provide tools required for data quality infrastructure.
  • Data quality infrastructure: Custom Solutions - Generic business rules can be maintained with common flow for related data across many client organizations. Generic Solutions - Custom business rules tuning will require expert data quality consulting resources.
  • Staff/operations: Team to manage daily operations and specific functions of improving the client specific requirements like processing business rule changes etc. and general requirements across the clients like postal directory updates and maintenance.
  •  Management: Team to manage at corporate level with focus on procurement, customer acquisition and alliances.
  • Web services infrastructure: A host of hardware and software required to maintain Web services.
  • Network services provider: Network services provider required to relay Web services through out the service timing typically 24 hours.

Challenges contemplated needing careful examination are:

  • Data security: Data being confidential and mission-critical, the highest degree of data security will be expected amid skepticism arising from competitors subscribing to the same ASP.
  • Privacy: Privacy of client information will be of paramount importance.
  • Availability and bandwidth: ASP needs to make sure that infrastructure provides desired bandwidth to clients as well as proper service time for data processing requests.
  • Scalability: A fast scalability in terms of new geographies' data, data storage and processing capability, new source systems and integration with other ASPs will be of significant importance.
  • Service level monitoring: An uninterrupted service with performance metrics report will be expected for performance monitoring by clients.
  • Pricing models: Different price models, which suit clients, should be available as options to project an advantageous value proposition to clients.

Advantages of the ASP-hosted data quality services compared to an in house implementation are:

  • Fast and real-time data quality solutions through Web services or similar technologies.
  • No cost of implementation and maintenance for data quality infrastructure.
  • Custom and generic data quality business rules available on-demand without maintaining full time staff.
  • The charge for services will be pay-per-usage and less for users as generic rules and data quality infrastructure is shared by many clients.
  • Such an ASP has a considerable scope to consolidate and be a one-stop shop for most of the data quality name and address directories.
  • Quick scalability is possible for new data, geographies and new source systems without in-house long-term projects.

There are a few critical risks identified with the proposed ASP business model as listed below.

  • Market risk: Data quality/ASP adoption fails to grow
  • Development risk: In the face of skills shortage and big infrastructure requirements development poses a risk.
  • Exit risk: A high-end big data quality infrastructure is difficult to be liquidated.
  • Legal issues: Growing skepticism over data security, privacy and IP violations pose danger to ASPs, as companies are wary of legal breaches.
  • Maintenance risk: Requires different tools for same functionalities, owing to different preferences from clients.

ASP Features -Technology Model

The ASP business model comes with certain technology challenges as the model is targeted to sell software application services, relying on networks and connectivity.

The two principle features related to ASP's technical function are that it:

1. Hosts, operates and maintains the application software and hardware. It must also include the staffing arrangements for support. This will introduce a possible operation cost sharing across multiple clients.

2. Can provide services to all customers, irrespective of their geographical location.

Potential customer expectations and challenges from a customer relationship management (CRM) data integration point of view, which has to be considered during this technology modeling, would include:

  • Integration: Ability to provide a consistent view to customers across different channels; lack of back- to front-office integration and legacy systems.
  • Customization: Noncustomized implementations provide reduced competitive differentiation. There is a definite tradeoff between what to customize and what not to customize.
  • Trust and security: Authentication and access control; data integrity and disaster recovery plans.
  • Maintainability: Providing customer support; addressing customer's concerns quickly.
  • Performance: The bandwidth; scalability with increasing of customer volumes.
  • Stability and reliability: Application uptime in case of crashes;procedures for reliability and redundancy; technical and application support.

Figure 2 illustrates a high-level ASP setup to provide data quality services:

Figure 2: High-Level ASP Setup

Elaborating on the preceding diagram data quality services essentially should include whatever customers may look for and beyond in their CRM data integration endeavor. A sample enterprise modeled in Figure 2 would be extended to several enterprises, with dedicated processing setups within the ASP to cater to. In effect, the customer name and address data from the client's enterprise CRM applications will be posted through Web services requests to the ASP with special identifiers for the action to be performed on the same. ASP data quality functions will immediately process the same and provide a common de-duplicated reference to the client, which the client application will use for cross-linking.

Data Quality Server - Parsing: A best-of-breed data-cleansing tool can be procured from a number of options available in market, which essentially should be good in parsing, verifying and standardizing the name/address or any other unformatted information.

Data Quality Server - Postal Directories: This setup will be backed up with postal directory databases from various countries and vendor directory integration with selected online vendor directory providers and so forth.

Data Quality Server - Matching Engine: A matching engine generally comes with a data cleansing tool. However, as the main focus remains on linking and deduplication of data, a specialized matching engine can identified and integrated into this function

Data Quality Server - Business Rules Engine: Generic as well as custom business rules will be required for data standardization as well as enrichment for each of the geographies supported. Though this is supported to some extent in available data cleansing tools but a custom-grown database of business rules would be a value add for the clients.

Data Quality Server - Transliteration: Transliteration and/or translation becomes a critical function for processing data in languages worldwide, if the client's data comes in different languages. This is an area slowly gaining attention and would drive technology trends to come up with universal transliteration possibilities soon. Modern day technology has yet to offer considerable capabilities of automated transliteration, which would definitely be a value add for clients requiring that feature.

Data Profiling Tool: A standalone data profiling tool or the one that arrives with the data cleansing tool can be chosen for profiling data, a very important exercise.

Data Consolidation: A database of significant capacity will support each enterprise client for identifying their application data, saving the pre- and postcleaning states of the customer name and address profile records and storing a common identifier for common customer address data for reference. Clients requiring a constant monitor of their data would opt for building the consolidation database with in their enterprise and use only the data quality services of the ASP.

Metrics/Performance Monitoring Server: A server or a utility which would produce reports on data anomalies, data flow and frequency distribution would be a handy tool, which clients can use for review and analysis.

Data Quality Consultation Services: These are normally comprised of online or offline professional interaction for day-to-day data quality issues of different client enterprises that consolidate the solutions and breakthroughs for cross application.

Future

The future of Web services-based data quality ASP will be driven by these two growth factors:

  • Growth in ASP market: Declining profit margins and increasing cost pressure will force corporate to outsource the services which are important but need a big investment and expensive upkeep when deployed in house.
  • Data quality adoption rate will increase, further fueled by growing dependence on CRM and other strategic and sophisticated systems on data quality.

Apart from basic services that ASP needs to begin, a few more can be added later as part of moving up the value chain. The future growth-plan for ASP may consist of the following ventures:

  • The ASP can develop homegrown solutions for new, unavailable geographies as well as for data types other than customer name and address.
  • Develop an authoritative reference database for customers worldwide, with additional information like corporate hierarchy, taxonomy information and so on.
  • The ASP can provide further data-oriented analysis and business intelligence tools to be accessed through Web services using the data that a client might opt to keep with ASP. This would be a step up in the value chain for ASP.
...............................................................................

For more information on related topics visit the following related portals...
Business Intelligence (BI), CRM, Data Integration and Outsourcing.

Shadab Hussain is a data warehousing consultant at Wipro Technologies. He holds an MBA from IIITM-Gwalior and a graduate degree in electrical engineering. He has worked in data quality initiatives and data warehouse implementation. His interest areas are data quality, data warehouse design and architecture and data modeling.

Shashidhar Kalwa is a Web technology consultant with Wipro Technologies. He holds bachelor degree in Information sciences and engineering. His interest areas are data quality and web technologies.

Venkatesh Ramakrishnan is a data warehousing consultant at Wipro Technologies. He holds a master degree in science. He has worked in data quality initiatives and data warehouse implementation. His interest areas are data warehouse implementation, databases and data quality.

Mahesh Velapakam is a business intelligence solutions consultant at Wipro Technologies, currently located at Bangalore, India, supporting clients on data warehousing and data quality projects. He a chartered mechanical engineer and has served in different functional and IT areas of high-tech and light engineering manufacturing organizations. His interest areas are databases and business analytics solution design and implementation.



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