Data Management Services

The vendor is required to provide that data management services for data and data management activities, initiatives and projects as determined on an agency-by agency, project-by-project basis.
- Provide qualified personnel that possess up-to-date department or other certifications appropriate to the task order;
• That the personnel are experienced with state government and other public sector consulting engagements in the data management space 3
• The contractor personnel are available during regular business hours.
• Additional work hour constraints may be specified in individual work orders.
- Work closely with other staff or customer-designated individuals, companies or contractors responsible for the successful completion of a project.
- Maintain the necessary physical and technology security controls to ensure that customer data is secure from disclosure to or access by unauthorized parties.
- The quality of work products through quality control reviews; the quality control approach, including checklists, must be approved by the customer.
- Store customer data on its servers, storage systems, laptops or any portable storage devices.
- Remove copies of customer data in any form from customer premises without written authorization
- The property of customer and shall be treated as confidential.
- A non-disclosure agreement prior to commencement of work, when requested.
- Disclose any actual or possible conflict of interest with respect to the services that customer renders to its customers.
- It is the obligation of the contractor to inform customer of financial interests with service offers and contractors at any time during the contract.
- Maintain a document library in a location to be approved by the customer, including relevant emails, memos, and other documents that provide a comprehensive audit history of the project.
• Provide customer with access to this document library during the project and upon completion of the project.
- Data management and governance requirements:
• Review, assess and measure data management maturity using the council data management capability assessment model (DCAM) as a framework or other framework specified by the customer;
• Make recommendations on the roadmap agencies should take to establish data management best practices, and provide methodology and measurement metrics to continuously measure progress
• Train and educate staff on data management best practices using the DCAM and the data management body of knowledge (DMBOK) published by the data administration and management association (DAMA) as approved frameworks.
• Assist customers in the implementation of data management tools and establish sustainable business processes to support the data management function;
• Consult with customer on the organizational elements of a data management program and collaborate with the human resources division to assist with the development of roles, job descriptions and performance metrics;
• Recommend and draft policies, standards and operational procedures;
• Train customer staff to gain a working knowledge of perform assessments and appraisals.
- Data solution consulting, architecture, support and development requirements
• Maintain high quality standards and deliver:
1. Data quality initiatives, including measurement, data
2. Cleansing, data profiling and stewardship;
3. Data security, including data classification methodologies, tools and metadata repositories;
4. Data privacy, including working knowledge and applicability regulations;
6. Design, deliver and support reliable reference and master data management
• Migrate data into a hyper scale cloud services provider’s SAAS (software as a service) or PAAS (platform as a service) solutions.
• Migrate data into a solution that is installable in a state owned hyperscale IAAS (infrastructure as a service) account.
• Possess and apply knowledge of:
1. Data architecture and data modeling management, including data architecture and modeling best practices in an agile development environment and within the system development life cycle;
2. Data operations management including disaster recovery planning, data acquisition, data integration, data retention best practices in a state government environment, and data life cycle documentation;
3. Data warehouses, data lakes, data mesh environments, “big data” initiatives, including data virtualization, implementation and data migration;
4. Data gathering and storage initiatives, including architecture, selection of relational and alternative architectures;
5. Data integration, including data migration, deduplication, entity resolution, master data management and reference data management;
6. Metadata management including business glossary and data catalog initiatives;
7. Inter-agency or intra-agency data exchange initiatives including, without limitation, data standards, design, architecture, development, data mapping, protocols, data conversion and related documentation
8. Implementation of data exchanges using the national information exchange mode
- Data science, business intelligence, and analytics consulting, architecture, support and development requirements:
• Provide business intelligence, reporting, analytics, dashboards, data visualization, data mining;
• DEVELOP, implement and provide third-party review of analysis
• Algorithms, models, results and data sources
• Provide expertise, development and implementation of data science services utilizing open-source libraries and technologies.
• Provide expertise, development and implementation of data science services utilizing python and/or r-based technologies.
- Artificial intelligence consulting, architecture, support and development:
• Assess and enhance data readiness for artificial intelligence (ai) and generative ai (genai) use, including data quality, biases, vulnerabilities, and governance.
• Metadata management, data lineage tracking, and version control strategies for datasets used in ai projects.
• Provide data anonymization and encryption solutions to protect sensitive information during ai model training and deployment.
•  Establish robust data governance controls and practices to support ai and genai initiatives.
• Develop, implement and provide third-party review of artificial intelligence and genai algorithms, models, results and data sources;
• Provide expertise, development and implementation of machine learning technologies for the purposes of data classification, matching, and anonymization.
• Integrate advanced data management platforms optimized for ai use cases.
• Provide training on best practices for agency staff to evaluate and maintain ai-ready datasets.
- Contract Period/Term: 3 years
- Optional Pre-Offer Conference Date: February 12, 2025
- Questions/Inquires Deadline: March 02, 2025