Why do I need to purchase MDM or CDI software? Won’t existing technologies I already own solve this problem? Why does a “black box” work better than my staff continually adjusting rules and parameters?

Master Data Management (MDM) and customer data integration (CDI) encompass the strategies, processes, technologies and people that create and maintain a single, complete view of all your customer data. CDI-MDM is more than just technology and is the focal point that enables the “customer hub,” allowing continuous customer data management.

Due to the customer-centric nature of many of the technologies that companies already have in place, people often ask us, why not use one of the exiting tools instead of CDI-MDM? Or, they ask how CDI-MDM complements those tools:

• CRM
• Data Warehousing
• Operational Data Store
• Customer Data Model
• Extract, transform and load
• Thirrd-party data providers

CRM: Customer relationship management (CRM) covers methods and technologies used by companies to manage their relationships with clients. While CRM technology has been used to this end, CRM solutions and data typically exist in business silos, and CRM has never been good at integrating across those data silos, nor was it designed to do so. Some CRM vendors do offer MDM hub technology, but in most cases these solutions are built with the vendor's own application technology in mind and thus only suited for their specific application architecture. The value of CDI-MDM is in reconciling customer data across heterogeneous source systems both at implementation and continuously as new data sources are added over time.

Data Warehousing: Data warehouses archive and provide a mechanism for analyzing an organization’s historical data, such as sales, salaries or other information from day-to-day operations. But w ith a data warehouse there is no guarantee that the data is good or clean, and the data may not be trustworthy. CDI-MDM does not remove the need for a data warehouse but acts as a trusted source of information for the data warehouse, streamlining reporting and query functionalities. CDI-MDM provides an authoritative view of the customer data that is accurate and real time.

Operational Data Store: An operational data store (ODS) is designed to store data from multiple sources to facilitate operations, analysis and reporting. But the data is not typically integrated, nor is it meant to provide an overall picture of a customer in real time. As mentioned in the book Customer Data Integration: Reaching a Single Version of Truth by Jill Dyche and Evan Levy, an “ODS isn’t intended to perform the rigorous data matching, cleansing, and reconciliation that is at the core of CDI.”

Customer Data Model: Data models are used by companies to describe how the information or data is represented in a business and typically define the data’s structure. While a data model is important and some industries have standard data model templates, data models do not say anything about the quality of the data or processes to resolve and manage data stewardship tasks. A core function of CDI is supporting data quality, so interactions around detection of data quality concerns must also be exposed – link, unlink, merge, unmerge, compare, etc. To facilitate data quality, CDI solutions facilitate a proactive workflow model for data stewardship and remediation, and because of the sensitivity of information typically managed in a CDI hub, they have robust audit and versioning capabilities.

Extract, Transform and Load: ETL technologies provide a process for acquiring data from source systems, formatting the data and loading it, typically into a data warehouse. Though customer experiences are real-time, ETL tools by definition operate in batch mode. CDI-MDM on the other hand cleanses, matches and merges data in real time as the data is introduced to the systems ,making the data accurate and available for real-time operations.

Third-Party Data Providers: Data providers like Dun & Bradstreet and others primarily provide trusted business data to companies. This data typically augments existing customer lists; companies will use the data as a baseline to compare their own data. Some data providers are also offering on-site customer data integration solutions. However, these solutions are likely to be optimized toward deeper integration with the vendor’s own data and services and typically more appropriate for comparing the customer’s data to the service provider data than actually creating a trusted single view of the customer. CDI-MDM solutions can complement third-party data providers in many scenarios. For example, CDI-MDM can be used to consume multiple third-party sources of organizational hierarchy information and create a logical master hierarchy that is far more accurate, efficient and comprehensive.

The only other approach is "do-it-yourself," where companies build a home-grown MDM solution and then rely on their own staff to continuously adjust matching rules and parameters manually. This approach does not address data quality and also introduces a strong element of risk through cumulative human error. Mistakes will be made as different people apply inconsistent quality and decision making. Duplicates will proliferate. In addition, home-grown solutions most often rely on manipulating rules (deterministic matching), and deterministic matching has a relatively lower degree of accuracy compared to probabilistic matching. Such systems are best suited for applications where the number of records is relatively small (less than two million), there are few data attributes, and there is no great consequence of error. Deterministic systems do allow organizations to better leverage their in-house IT staff for system implementation and to develop matching rules. When the number of data attributes and rules required are small, this can make implementation times shorter and less expensive. However, the more attributes involved and the larger the data sets, the more complex the rules-based matching routines become. This means implementation can involve many man hours of development and testing time and longer deployment times than probabilistic systems.

Really, the bottom line here is do you want to build something that just isn’t part of your core competency?

Some companies do approach building their MDM or CDI solution using a mixture of the technology components described. Trying to weld all these together into a single solution creates multiple challenges. While these technologies address important issues and can be complementary to CDI-MDM, these technologies were not designed to address CDI-MDM as a core capability. All organizations have most of these tools yet they lack accurate, complete customer views. Companies such as Microsoft, Intuit and Countrywide Financial all weighed total cost of ownership (TCO), time to value and return on investment (ROI) before purchasing Initiate software. Some invested years of effort, with teams of 20 or more, and millions of dollars trying to build and maintain a customer data management solution.

Initiate software tackles the hardest problems for master data management - accuracy, scalability, performance and flexibility. With typical implementation times of three to four months, Initiate Systems can provide very compelling ROI and risk mitigation to meet your customer-centric MDM and CDI goals.

 Return to the questions you must consider when evaluating a master data management or CDI solution.