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By Ro King, Executive Vice President, Quaero, LLC www.quaero.com
Data warehouses are the foundation of CRM but their implementation can be risky and expensive. Ten ways to effectively implement a customer warehouse are listed below.
Information about the customer is the foundation of Customer Relationship Management. Every step in the CRM process, every principle of the CRM approach, requires tangible data about customers. To apply CRM to the entire enterprise, information must be consistent, reliable, and in a usable format.
Piecing together information about customers from multiple systems for every analysis or marketing campaign creates inefficiencies and bottlenecks. Centrally storing a single, comprehensive view of customers can significantly reduce demands on IT and improve the efficiency of marketing and customer communication processes.
In a previous article, I described the importance of crafting a customer strategy prior to focusing on CRM technology. Once the strategy is in place, CRM cannot take place effectively without an investment in technology to access customer information. The foundation for CRM efforts is the customer data warehouse.
Yet studies on data warehousing that have been released over the past two months raise questions as to the success of data warehouse projects. A Data Warehousing Institute study of 1,600 companies released in March found that 41 percent of respondents said they are experiencing difficulties with their implementation and 42 percent found that the data warehouse implementation met their expectations.
Another finding in this study caused greater concern – only 13% of respondents track data warehousing return on investment and less than 40% said they plan to begin tracking ROI. As more companies seek to measure the return on their CRM spending, the pressure to implement data warehouses on time and within budget will increase.
If data warehouse implementation projects are expensive and have a high risk of going wrong, why would I recommend that any firm undertake such a project now? A data warehouse is essential for a business to have consistently clean, reliable customer level data that is continuously accessible enterprise-wide. The key is to plan and manage the warehouse implementation with risk and return in mind. Below, I outline ten steps to implement a data warehouse effectively.
Implementing a Data Warehouse
The first three steps are combined into a “define the project” phase. This phase should take six to twelve weeks to complete, depending upon the complexity of a firm’s customer data and the number of technical and business users interviewed to develop the business requirements. The time to complete depends upon the amount of time the project team and business users can devote to interviews. The deliverables for this phase of building a data warehouse are a working project team, a clear list of business requirements, and a conceptual data model.
Assemble the Team
The success of a data warehouse depends on a technical and business partnership that endures long after the construction of the warehouse is completed. The project implementation will require a team well versed in technology, analysis and marketing and may include current and future users of customer data. The team must be large enough to include the appropriate knowledge, and small enough to meet frequently and make decisions quickly.
Gather Business Requirements
The first order of business for the project team is to collect system requirements specific to each business unit that will use the warehouse. Business requirements are simple statements of what the system is expected to do. In fact, each statement implicitly begins with “the system shall . . .” and describes a discrete detail of a business need.
For example, after interviewing the sales department, the sales requirements may include the statement, “provide summary reporting of the top 10 products sold each day/week/ month,” along with twenty or even fifty additional requirements for the sales function. Including every customer point of contact in the requirements gathering process ensures long-term support for the data warehouse.
Define Technical Requirements
How each business unit plans to use the data warehouse will shape decisions about hardware and software, architecture and interfaces. Questions like who will use the system, what level of technical or analytical expertise does the user group possess, and what types of remote connections will be needed require consideration during this step.
At the end of this first phase, the project team can complete a conceptual data model which illustrates what tables will exist in the warehouse and how the tables will typically be related to each other. For example, in a banking environment, the conceptual data model may show a table for each product type (checking account, credit card account.) These account tables will be related to the customer table as each customer may have one or more accounts.
The next three steps in building a data warehouse make up the “understand the data” phase. This phase may take another six to ten weeks to complete, depending upon the volume and complexity of the customer data as well as what information is currently available about the data. The deliverables for this phase are collectively referred to as metadata, literally “data about the data.”
Identify Data Requirements
In this step of the process, the project team converts the business and functional requirements gathered in the first phase into a logical data model. A logical data model is a picture showing all the tables in the data warehouse, all the data elements in each table and the relationships among the tables. There are several software packages available that simplify producing logical data models. The time consuming process in this phase is the review of the data model with the business users interviewed in the first phase. Although this may be a tedious step, it is important to gain approval and understanding from the business and technical users of the warehouse.
Create Data Maps
The data mapping step of the implementation charts the movement of data from the source system, through data processing and conditioning, to its location in the data warehouse. This step also describes how data is extracted, transformed, and loaded (the process known as ETL) into the data warehouse. The result of this step will be a physical data model, showing how each element will be stored in the data warehouse.
Develop the Data Dictionary
Finally, the project team will develop a data dictionary – the reference guide for designers, builders and users of the data warehouse. Data definitions are presented for each element detailing exactly what the field represents using definitions that are common across systems, channels and time.
The final four steps in the process cover the “execute the plan” phase. In most projects, the two planning phases take about two-thirds of the project time, while executing the plan takes about a third. The time to complete this phase will depend upon how rapidly the firm is able to make decisions regarding technology and how willing the firm is to put the appropriate resources toward the data warehouse build. The deliverable for this phase is a populated, accessible data warehouse.
Determine Whether to Use Outside Support
Companies have a variety of support options to choose from for the development and maintenance of their data warehouse. Consultants can handle design, development, software/hardware selection and implementation and can coordinate vendor relationships. Service bureaus can house your warehouse on their hardware on a contract basis, although access to your data may be limited. Your firm needs to assess the support they need from third parties based on the size and scalability of your warehouse needs, your available resources in the short and long term and your budget.
Decide on Software and Hardware
Each firm will need to choose hardware and software from a myriad of combinations to meet the needs uncovered in the planning phases. Software packages to review include database servers, ETL tools, and processors. Database servers support data transport, querying and table management; many are provided by large, well-known companies. Tools for extracting, transforming, and loading (ETL) data transfer data from source systems to the data warehouse and may clean or reformat data on the way. A firm may also require a processor to condition data for analysis or to household data for marketing.
Perform Individual Warehouse Build
During the first several builds of the warehouse, expect to fine-tune the production process. The production process is the automated building of the warehouse on a regular (nightly/weekly/monthly) basis. In this step, the project team should celebrate successes and communicate setbacks to a wider audience in preparation for regular warehouse communications.
Develop a Production Calendar
After the first several builds of the warehouse, the project team can create a production calendar to let users know when and with what frequency data will be available for querying and reporting. The production calendar should also be widely shared.
Completing the customer warehouse merely places customer data into a box. Next, your firm must select and implement the tools that will allow you to extract and use customer information for analysis and marketing. The completed warehouse is the foundation for CRM, so celebrate its successful implementation and begin designing and building the rest of the CRM infrastructure to realize the return on your investment.
Ro works with Quaero clients to execute their CRM strategies by optimizing their technology choices, improving their use of customer data and refining their marketing approaches. Her clients include leaders in the financial services, telecommunications and pharmaceutical industries. Prior to Quaero, Ro worked at Tillinghast-Towers Perrin, where she was Senior Consultant, and Furash & Company, where she was a Principal. Previously, as Senior Vice President, Retail Marketing and Analysis at Signet Bank, she developed and implemented profitable customer acquisition and retention programs as well as led marketing teams, managed deposit portfolio growth, and oversaw branch sales.
Ro earned her BA at Harvard and her MBA at the Darden School of Business at the University of Virginia. She teaches CRM Fundamentals at New York University.
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