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Data Consultant
Overview
Baseline’s Data Consultants lead and guide our clients in developing and implementing enterprise information strategies, data architectures, and data management policies and procedures.
Data Consultants typically have expertise in more than one industry. They provide in-depth knowledge about subject areas, industry regulatory and statutory requirements related to data. Data Consultants are current with industry trends and best practices around the strategic and operational use and management of data. They combine this strategic and industry knowledge with hands on skills in data analysis, data modeling, data governance, and data quality practices.
Objectives
Job objectives include:
- Educate customer stakeholders on information-related issues, processes and practices and how those issues relate to the customer’s environment.
- Assist in the successful completion of assessment projects that lead to additional
- Baseline planning and implementation services.
- Build credibility by helping key stakeholders understand the root cause and business impact of data issues in their organization.
- Recommend actions practical for that customer. Facilitate consensus and decision-making that address the root cause rather than the symptoms.
- Maintain and enhance client enterprise information architecture that: maximizes information utilization by providing optimized, integrated information and data; improves productivity through strict management of data; and can be the basis for future knowledge management processes.
Responsibilities
- Work with Business Analyst to identify and capture client’s business, data, and functional requirements.
- Categorize data elements into domain types; maintain code mappings and source-to-target mappings; maintain a living document that provides information where data comes from, its location in the warehouse, how data is processed, how clean it is, and how data is used.
- Gather, categorize and prioritize data and information requirements to feed data models and process models.
- Analyze business requirements and translate them into detailed conceptual data models, process models, logical models, and physical models.
- Assist business end users with identifying data elements related to their applications. Collaborate with business end users to identify business rules governing the data relationship.
- Inventory and evaluate the customer’s existing data and metadata resources; present prioritized recommendations for addressing critical problems and positioning data resources for the incremental development of a shared data resource.
- Analyze the customer’s current environment and recommend actions practical for that customer. Facilitate consensus and decision-making that will address the root cause rather than the symptoms.
- Develop and Implement metadata repositories.
- Develop logical data models and work closely with DBA team to produce physical designs inclusive of entities, attributes, relationships and definitions and integrate them with the enterprise data model for Data warehouse and Data marts.
- Plan, execute and document audits of data projects and programs; present recommendations to the customer based on audit findings.
- Develop and Implement data modeling standards, policies and procedures.
- Develop and Implement metadata management, data dictionary, data quality, data profiling, data security, shared data usage and data cleansing standards.
- Spearhead the resolution of complex data problems related to data mapping, cleansing, standardization and implementations.
- Work with ETL specialists to assist in the definition of extract, transformation and load processes used to populate tables.
- Develop and present project deliverables in MS PowerPoint.
- Classify and assess the skills and knowledge of the customer’s knowledge workers, data management resources, and IT team. Develop short- and long-term development and implementation plans to help the customer build the right kind of competence in the right place across the enterprise.
Requirements
- 10+ years of work experience with enterprise data solutions, including: data warehousing, data modeling, data analysis, data governance, data management and/or data quality.
- Experience in clarifying requirements and participating in analysis sessions with the user community.
- Hands-on experience with Erwin or other data modeling tools.
- Experienced with Conceptual, Logical and
- Physical Modeling including 3NF and dimensional models.
- 3NF modeling experience.
- Strong working knowledge of Data Profiling and
- Quality Analysis tools (Trillium, Data Stage IA, Data Flux, SAS, SPSS, etc.)
- Strong working knowledge of data structure, data architecture concept and analysis techniques.
- Proven track record in implementing enterprise metadata management processes and tools and managing metadata for decision-support and operational systems.
- Experience developing, implementing and enforcing ETL best practices standards.
- Experience working with Six Sigma, TQM, or other similar quality best practices standards.
- Strong analytical and problem solving skills.
- Must be self-motivated with the ability to prioritize multiple tasks.
- Must be able to work independently and participate as a member of a cross-functional team.
- Strong working knowledge of MS Office tools, such as Word, Excel, PowerPoint, and Visio.
- A College degree from an accredited university.
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To request
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us via e-mail or call us at 1-818-906-7638.
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November 2, 2008. TDWI Conference, New Orleans. BI from Both Sides: Aligning Business & IT with Jill Dyche
November 3, 2008. TDWI Conference, New Orleans. Keynote: Five Levels of MDM (and Data Governance!) Maturity with Evan Levy
November 3, 2008. TDWI Conference, New Orleans. Introduction to MDM for BI Professionals with Jill Dyche
November 3, 2008. TDWI Conference, New Orleans. Implementing MDM for BI and Data Integration with Evan Levy
November 4, 2008. TDWI Conference, New Orleans. Ten Mistakes to Avoid when Launching a Data Governance Program with Jill Dyché and Kim Nevala.
November 4, 2008. TDWI Conference, New Orleans. Change Management for MDM with Frank Dravis and Evan Levy
November 5, 2008. TDWI Conference, New Orleans. Understanding MDM Technical Deployment: Architecture and the Vendor Landscape with James Masuoka
» See
our full schedule
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Ten
Mistakes to Avoid The Baseline on MDM: Five Levels of Maturity for Master Data Management.
Jill Dyché and Evan Levy offer an MDM taxonomy that separates and describes discrete capabilities, helping you understand your company’s “as is” environment to help you accelerate toward your “to be” objectives for master data.
» Read the White Paper
A Data Governance Manifesto: Designing and Deploying Sustainable Data Governance.
Jill Dyché details the importance of establishing and maintaining a corporate-wide agenda for data and shares practical steps for getting started.
» Read the White Paper
Eight Steps to Align Business and IT.
Learn the Baseline approach and understand how alignment can be formalized into planning business intelligence initiatives, application development projects and data integration programs by allowing companies to align responsibility and accountability along the hemispheres of Business and IT.
» Read the White Paper
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