September 2004

Data Synchronization Unifies the Customer View

By Steve Miranda, Oracle Corp.

What do John Doe, Johnathan B. Doe and J.B. Doe all have in common? Aside from a first initial and last name, the answer may be absolutely nothing. They may be the same person.

Confused? You're not alone. This scenario illustrates the challenge that many companies face in trying to better understand their customers. In fact, The Data Warehousing Institute estimated that poor data cost U.S. businesses $600 billion annually in wasted postage and marketing costs as well as lost customer credibility.
While many companies have come to re-alize the value of a unified, enterprisewide customer view, the reality for many is that customer knowledge is fragmented. Most businesses do not have the knowledge to be customer-centric and approach projects independently, without leveraging ' or building upon ' their existing customer information. As a recent Gartner report put it, most companies are in a 'state of denial regarding their data quality.'

Adding to the challenge is the fact that business environments and processes are constantly changing and evolving. What is needed is a method that enables departmental innovation while providing enterprisewide customer knowledge as a means to delivering a single customer view.

Traditional technology approaches to solving the customer data problem, such as consolidation of transaction systems, integration projects, business intelligence systems and analytical applications are only partial (and temporary) solutions. None address the problem of providing a single customer view on an enterprisewide level. Point-to-point integration is costly and unwieldy. As businesses expand through mergers and acquisitions and as departments upgrade and add systems, the integration challenge becomes a logistical nightmare, riddled with resource and staff redundancies and high maintenance costs.

Importing customer data into a data store or warehouse can also be problematic. Without standardized processes, data from multiple systems are difficult to reconcile since systems often intersect at multiple entry points. As the 'John Doe' example illustrates, this can lead to an 'identity crisis.' Even if this problem gets resolved, the root problems are never addressed. Data warehouses are unidirectional and can analyze 'historical' data only, meaning they do not synchronize with the original source to allow for continued updates.

To be competitive in today's business climate, companies need transactional systems that operate in real-time. Because so many companies have invested heavily in 'best-of-breed' solutions to run both front- and back-office operations, it's unrealistic to expect them to scrap the lot and move to a single, unified system. The most effective and efficient path for these companies to follow is to manage customer data centrally, or in a hub. With the hub serving as a shared resource for all applications and data collection sites, there is less movement of data, less need for data preparation and data quality services, and faster data availability.

The hub model provides an optimal solution. Instead of jury-rigging disparate systems to talk to each other, all customer data are centralized into a single repository to which the systems connect through a 'two-way street.' The clean data are then distributed to all systems. This continuous loop ensures that everyone in the organization is accessing and using the same accurate customer information. When companies begin relying on consistent, concise customer data, employees and executives speak the same language, make better business decisions and provide improved customer service.

Following is a description of the data hub model and the four steps toward implementing a hub system: centralize the data, improve the quality, synchronize with other systems and leverage the 360-degree customer view to improve all aspects of business.

Centralize
The first step is to consolidate all customer data into a single repository. This is typically the most difficult phase of the implementation, especially in larger organizations using multiple home-grown systems. Some companies have had to reconcile thousands of records for the same customer. Therefore, it is important for project managers to map out all the systems and fully understand what information each system needs for daily operation.

The view of a given customer will often vary by department and across business units. Thus, in building a master customer file, the company must model each customer as a complex set of relationships and roles (i.e., a family of companies or multi-person households). For instance, a customer organization may also be a trading partner, so each role must be noted in the master file. Similarly, subsidiaries of a holding company should be rolled into one master customer file to document the umbrella organization's true purchasing clout and revenue potential (i.e., upsell and cross-sell opportunities, volume discounts). The hub should model key individuals within the customer organization. For instance, it's important to know that a new CEO for a major customer account previously headed sales for another customer and already has contacts within the company.

The shift to a centralized hub is not an easy task. It often requires large-scale process changes since all users and systems must align to the same information model. But once these processes are in the place, the master customer identities built and the data cleansed and centralized, a well-designed hub essentially becomes self-sustaining.

Improve
Consolidating data does not automatically create greater visibility. On the contrary, the information from multiple sources will remain fragmented and inaccurate unless there's a standardized framework for vetting, cleansing and organizing the data. This process is known as data quality management (DQM).

Using data quality workbench tools built into a data hub provides companies with the ability to automate and integrate DQM functions at the point of collection. Among the features to look for are a configurable duplicate matching engine (a tool that automatically determines if two different customer entries are actually for the same person); an extensible merge routine logic (a set of business rules that govern when and how customer records are merged when it is determined that there are duplicates) and out-of-the-box address validation and data certification capabilities (integration to third-party data sources, such as Dun and Bradstreet, that help fill in incomplete customer records).

A complete tool set with all of the above characteristics is key to processing large volumes of data in real-time. For instance, a data librarian can set up match rules using sophisticated algorithms to flag spelling errors, formatting irregularities, nicknames and abbreviations. Data resolution tools offer scoring functions and thresholds to more granularly define what constitutes a duplicate. Some of today's pre-built hubs can identify and resolve duplicate data from across more than 100 transactions.
 

Quality assurance is at the root of file creation, revision and maintenance. As data enter the hub, duplicates are identified, information is cross-referenced and verified, and the blended records are either assigned to the correct master customer file or flagged for human review. This approach frees individual source systems from all data management, a traditional source of redundancies and costs.

Synchronize
The third step is to close the information loop. The hub model is designed as a two-directional information system, so that after data are centrally processed, the information is pushed back to the systems. This continual synchronization keeps systems and users across the enterprise notified of real-time customer events.
To achieve this 'real-time synchronization,' a hub model requires built-in mapping and control capabilities. While all users access the same customer data, the various applications may run on different types of information. Therefore, the hub system should cross-reference and selectively update systems based on a 'need-to-know' basis. Many hubs are built with an 'editor' to regulate the synchronization process. The hub should support the programming of both real-time and delayed system updates.

Leverage The 360-Degree View
Once the data have been centralized, improved and synchronized, the hub begins to deliver on its real promise: a single source of truth across locations. To achieve this 360-degree visibility, the hub must provide users with the means to review, search and use the master files.

A universal 'viewer' provides a window across the enterprise, offering an overall summary of transactional and profile information. Advanced hub models use analytical tools to model customers in all their complexity. For instance, customer information can be consolidated from across all global units, rolled up into hierarchies (i.e., parent households or corporate umbrellas) or revealed as individual business relationships.

The strategic value of a single customer view is immense. With rising demand for customized products and services, businesses require more complete customer knowledge. An organization can implement a 'just-in-time' inventory approach or lean manufacturing operation with the certainty that it knows who its customers are, what they are buying and what volume deals are in the pipeline. Good customer knowledge also drives decisions for product development, channel strategy and acquisitions. Moreover, accurate customer knowledge has essentially been mandated by corporate reform laws like Sarbanes-Oxley, which requires disclosures of material risk to revenue and profit.

A centralized hub model also represents a smart investment in the future. As a closed information loop, the hub keeps getting 'smarter.' As data accrue on new and existing customers, reporting accuracy improves and analytics become more valuable. Faster response times allow companies to operate more efficiently while improving the customer experience.

The need for better customer data is no longer a secret. Data-quality software is now a $600 million a year industry, according to Forrester Research. As more companies recognize the need for good data, customers are starting to notice those that don't. 'John Doe' can only tolerate so many flyers in his mailbox from the same company before he says, 'Enough.'

A centralized data hub is an ideal way to solve the customer data problem by enabling companies to gain a single source of real-time 'customer truth,' no matter how diverse their IT environments are.

Steve Miranda is vice president of Applications Development at Oracle Corporation (www.oracle.com).

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