I recently joined the company as a DW data modeler in the production support group. The DW is large and was partially delivered by a large consulting firm which has their own knowledgeable data modeler, but who fails to communicate or transfer any knowledge. I have been told that I need to know what that guy knows by the end of the year. I guess my question is, what is my role and responsibilities as a data modeler in production support?


Sid Adelman's Answer: I'm just going to address the issue of the data modeler from the large consulting firm who fails to communicate or transfer any knowledge. Since your assignment is to know what that consultant knows by the end of the year you need to change his or her behavior, to start communicating and transferring that knowledge. I suggest you invite the consultant to lunch and let him or her know what you are expected to know and by when and ask for his or her help and participation in your training. If the request is declined (or accepted and nothing happens), take it to your boss and ask for a four-way meeting with you, your boss, the consultant, and the consultant's boss. Ask for specific, measurable deliverables and the consultant's activities such as briefing you daily on what he or she is working on, copies of the data models, copies of the consultant's notes and working papers. Ask the consultant if you can participate in the modeling activities and as you get more confident and skilled, you should be allowed to perform much of the modeling yourself.

Evan Levy's Answer: Here's a general description of the roles/responsibilities associated with a logical data modeler. Because some environments include data management as part of the data modeler role, I've included those aspects, too. (Obviously, the size of the data warehouse development team will affect the breadth of these responsibilities.)

Data Modeling - providing support to both end users and developers on data modeling information. This includes ensuring the consistent use of data element names, data types, business definitions as well as domain/range values.

Source System Analysis - awareness of source data content associated with each discrete operational system. While the DBAs and ETL staff members should be knowledgeable to the specific detailed elements, the logical data modeler should be aware of the source contents and maintain the data model when source system changes affect the data warehouse.

DBA Support - working with the DBAs to ensure that any new business issues, rules or concepts are reflected in the physical database design. Developing ongoing data quality assessment profiling activities should also be managed with the DBA team.

ETL Support - working with the ETL team to ensure that business rules are accurately reflected in both the database and ETL job processing.

Meta Data Support - maintaining any meta data documentation to support end-user access. This would also include gathering meta data content from the ETL team to integrate it appropriately with the end-user meta data.

Chuck Kelley's Answer: If you are only in production support, then I think you role is to make sure that the data warehouse is built in tip-top shape from a physical data model standpoint. I know that this is a large jump in belief, but to be honest, I don't really see a role for a data modeler for production areas. I would think that development and test would have physical aspects taken care of.

Clay Rehm's Answer: Is there anything in the contract with the consulting firm that knowledge transfer is required? If so, raise your concerns to your management and let them take care of this issue.

The roles and responsibilities of the data warehouse data modeler include:

Qualifications and Skill Sets Required

  • Complete understanding of data warehouse architecture, normalized/denormalized, star schemas, snowflakes
  • Data warehouse experience strongly preferred
  • Strong technical background, but not pure technologist
  • Responsible for manifestation of business
  • Presents self well
  • Grasps business; able to translate business challenges into a model (i.e., thinks in star schema)
  • Articulate
  • Complete understanding of data warehouse modeling, architectures, OLAP data sets, etc.
  • Flexible and okay with change
  • Comprehensive knowledge of problem analysis, structured analysis, design and programming techniques
  • Working knowledge of one or more multiuser, multitasking operating systems
  • Expert in tuning VLDBs to perform optimally with DSS front-end packages
  • Ability to source data from legacy systems and load it into the warehouse
  • Ability to effectively adapt to and apply rapidly changing technology to business needs
  • Ability to establish and maintain a high level of customer trust and confidence in the DW team's knowledge of and concern for customer requirements

Data Analysis

  • Consults with project teams who model data warehouse and legacy data
  • Understand source data, validate that source data maps to business rules
  • Determine which source data will be used
  • Understand status of legacy data quality, recommend cleansing
  • Gather, document and coordinate user requirements
  • Prioritize user requirements
  • Uncover and resolve discrepancies, conflicting user views and conflicting user definitions
  • Responsible for data integration (merging data from multiple source files)
  • Write transformation specifications
  • Helps determine the selection of which legacy data to use when there are multiple sources

Logical Data Design and Data Modeling

  • Aligns DW information requirements with DW models
  • Ensures the quality and integrity of the DW model
  • Create logical models
  • Coordinate physical design with DBAs
  • Map the logical to physical of the entities into tables, attributes into columns, legacy files into entities, and legacy data elements into attributes
  • Load meta data from the CASE tools into the repository
  • Manages the models
  • Responsible for internal support and vendor management of the data modeling tool

Meta Data

  • Generate, load and use meta data
  • Develop standards for the entry and modification of meta data
  • Provide easy access to meta data
  • Manage repository vendor
  • Coordinate and control entry and modification of meta data
  • Coordinate user meta data with the
  • Coordinate technical meta data with the DBA
  • Collects and controls meta data

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