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Strategic Objectives

  • Business Strategy corporate objectives, use cases and organizational mechanisms necessary for sustainable business value from the Enterprise Knowledge Graph
  • Corporate Goals Alignment (shared vision) on why the organization is building a knowledge graph
  • Business Unit Goals Support of the EKG value proposition by key \gls{lob} stakeholders
  • Organizational Considerations Operational and resource plans for EKG strategy implementation and governance
  • Data Strategy enterprise data management framework (policies, target data architecture, quality assurance, governance) necessary to support the knowledge graph environment
    • Data Goals & Objectives Importance of unique identification and the value of unambiguous shared meaning
    • Knowledge Graph Positioning Role of the EKG as the underlying data fabric for the organization
    • Business Case ROI of linked data as an essential component of operational infrastructure
  • Technology Strategy environment for successful knowledge graph implementation including direction for physical infrastructure, applications and process automation
    • Infrastructure Strategy Physical infrastructure (i.e. cloud}, containerization, software layer) for the EKG
    • Application Strategy Applications approach including rationalization, build vs. buy and standards adoption
    • Automation Strategy Use of Artificial Intelligences and robotic process automation

High-Level Business Goals

Create integrated views, enhance product innovation, profile behavior, determine preferences, understand relationships, implement target selling, determine customer and product ROI, perform predictive modeling, define social connections, segment customers, enhance product satisfaction, understand the dynamics of the market, operate with more agility, maximize time-to-market.

  • Do LOB stakeholders clearly understand the relationship between data management and business objectives
  • Is all the data that is important to meet business priorities been defined and classified
  • Is the data management strategy aligned with business priorities, implementation plans, technical capabilities and operational processes
  • Has the data management strategy been mapped to business and organizational objectives
  • Have business use cases and user stories been defined and aligned to data concepts
  • Have LOB business outcomes (and dependencies) been defined and sequenced across the organizations
  • Is there alignment between organizational/business objectives and data (concepts and repositories)
  • Has the organization defined and aligned data metrics and Key Performance Indicators (KPIs) with business objectives
  • Has the organization defined the business architecture for both strategic and tactical objectives
  • Is the data strategy aligned with lines of business objectives
  • Have service level agreements been defined and verified for critical systems and processes
  • Are the lines of business engaged in (and understand the rationale of the data management program
  • Are lines of business and functional organizations committed and accountable to the data management objectives

High-Level Technology Goals

Optimize infrastructure investments, automate business processes, perform security surveillance, protect privacy, support continuous deployment.

  • Does an integrated technology architecture strategy exist (and has it been implemented)
  • Have IT and platform governance processes been defined and aligned with the data management strategy
  • Do executive stakeholders have confidence in the ability of IT to manage realignment of fragmented architecture to meet strategic objectives (i.e. customer 360 and automated regulatory reporting)
  • Has the data storage strategy been defined and aligned with the goals of data reconstruction, security and archive

High-Level Data Goals

Adopt data management principles of identity and meaning, monitor and ensure F-F-P quality, ensure data accessibility, eliminate data duplication, reduce reconciliation, govern the data lifecycle, control data at source, control the data manufacturing process.

  • Is there a clearly defined and sanctioned data strategy for the organization (aligned to organizational and business objectives)
  • Does the organization have “data management delusions” and are they aware of their fallacy
  • Does the organization have a plan on how to execute their data-centric strategy
  • Has the data management business case been linked to organizational strategy and business pain points
  • Are data requirements defined and aligned with funding processes
  • Is there a mechanism for obtaining and verifying data management feedback from stakeholders
  • Have the full suite of policies for data management been defined, approved and implemented
  • Does the organization document and track the data production and consumption process (where data lives, the applications that are used, how it flows and where transformation occurs)
  • Have logical and conceptual data models been defined and verified
  • Is meaning of data in \glspl{sor} verified and locked down
  • Are the criteria for designating criticality (and other classifications) consistent and scalable
  • Does the organization have a data management and governance strategy to deliver against business objectives
  • How does the organization evaluate the costs and effectiveness of the data strategy
  • Has the data management strategy been translated into a operational roadmap
  • Is funding and resource allocation plan in place to deliver against the data strategy
  • Are communications, positioning and training programs about data management designed and operational

High-level Organizational Goals

Perform flexible analysis, trust, operational resiliency, achieve efficiency / save money, comply with regulatory obligations, comply with contractual obligations, avoid fines, mitigate risk, preventing fraud, manage organizational change, enhance worker satisfaction, aggregate reporting, negotiate smart contracts with vendors, leverage capital, enhance market position, manage TCO and profitability.

  • Does the organization view data as an instrument to transform the business
  • Do the executive stakeholders within the organization understand the reasons why data is not harmonized across repositories and business processes (causes of incongruence)
  • Do executive stakeholders understand the business rationale for establishing a “data control environment”
  • Have high-level organizational goals been translated into data concepts
  • Are knowledge workers focused on value-added activities
  • Are key stakeholders committed to the principles and priorities of the data management program
  • Does the organization have the skill sets and people talent to implement the data management strategy

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