Industry Leading Approach is to create an Enterprise Data Economy built on Data Collaboration, Data Democratization, and improved User Experiences for Data Research and Artificial Intelligence. It has ingrained Data Privacy, Security, and Resiliency capabilities. The solution design uses the following guiding principles those are from the article, Data Platform Innovation: Industry Leading Practices.

• Cloud First while extracting the life of current IT investments
• Utilize Data Ops to automation for Data Services and Backup & Restore Data
• Utilize Distributed Data Architecture and Data Mesh for Data Democratization
• Design for real-time use cases and ease of data research tools integration
• Flexible Data Stores with anytime availability of the Raw Data
• Identify and Use AI Tools to Handle Data Quality Issues
• Utilize Data Hub, Data Fabric, and Data Navigation for ease of Data Discovery and Collaboration

Use Reference Architecture from the article, Data Platform Buzzwords: Introduction and So What?, to assess and design the Data Platform Target State Architecture.

Availability and Applications of Data have emerged as a business innovation engine of the present time and for the foreseeable future.

Data Platforms are a conglomerate of Business Requirements, Architectures, Tools & Technologies, Frameworks, and Processes to provide Data Services. Hence, the Data Platform Innovation foundation is from people, processes, and technology managing and utilizing an enterprise Data Platform. In the article, I have organized the emerging Industry Leading Practices into seven Pillars to maximize data value at speed in an enterprise environment.

• Autonomy
  • Implement Data as a Product with an operating model that establishes data product owner and team.
  • Support Data Democratization utilizing Distributed Data Architecture and Data Mesh.
  • Enable end-to-end service delivery ownership to the Data product owner.


• Artificial Intelligence (AI)
  • Create a raw Data copy availability to enable AI Data Models yet to be discovered.
  • Utilize AI Tools to manage Data identification, correction, and remediation of Data quality issues.


• User Experience
  • Create and manage data literacy and data-driven cultural activities for employees to learn and embrace the value of data.
  • Enable data navigation and data research tools for employees.


• Automation
  • Utilize DataOps at the heart of provisioning, processing, and information management to deliver real-time use cases.
  • Implement automatic backup and restoration of Data and digital twins of the Data estate.


• Center of Excellence
  • Shift from stakeholders' buy-in approach to delivery partners' approach that finds and enables innovation.
  • Create Data Eco-System utilizing Data Alliances, Data Sharing Agreements, and Data Marketplace to develop an Enterprise Data Economy.
  • Publish Common Data Models, Policies, and Processes to promote ease of collaboration within and across organizations.


• Data Security
  • Contribute actively to individual data-protection awareness and rights.
  • Communicate the importance of data security throughout the organization.
  • Develop Data privacy, Data ethics, and Data security as areas of competency, not just to comply with mandates.


• Cloud Services
  • Cloud First mindset for quickly exploring and adopting innovation at speed with minimal sunk cost once that becomes mainstream. Let the business model drive the Cloud equilibrium.
  • Enable cloud for flexible data model tools supporting querying for unstructured data.
  • Enable edge devices and high-performance computing available at Data sources to deliver real-time use cases.

Disclaimer

The views expressed in the blog are those of the author and do not represent necessarily the official policy or position of any other agency, organization, employer, or company. Assumptions made in the study are not reflective of the stand of any entity other than the author. Since we are critically-thinking human beings, these views are always subject to change, revision, and rethinking without notice. While reasonable efforts have been made to obtain accurate information, the author makes no warranty, expressed or implied, as to its accuracy.