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Are you late to Data driven Business model transformation?

Is your organization looking to transform its business model emphasizing Data Driven environment or Data Monetization as its primary driver? Does this call for a transformation of the current business, Information technology landscape while defining Data Governance strategy?

Often we keep referring to hundreds of processes that are supported by thousands of systems that create, store data in a fragmented way. Thus, complexity of landscape still remains the largest challenge that most organizations are tackling to overcome -

  • Increased Reputation and Regulatory risks driven by fragmented data landscape & Inflated enhancements
  • Increasing Operational costs associated with maintaining redundant functions, redundant data, cleansing and quality
  • Increased Upfront costs to assess the current landscapes, meet integration needs
  • Future costs, such as maintenance of legacy systems, transformation, and decommissioning of information systems
  • Increased Information security & Privacy issues
  • Inability to leverage rapidly evolving advanced technologies, approaches and business models

The focus should be on getting the “Town plan” right before improving a “Route plan”. The organizational priorities for the much required org transformation should drive the definition of DATA strategy. Data is the plasma that reaches every corner of an organization and keeps its functioning. There is a need for capabilities governed by Data Governance policies that manage and control this data. This enables the organization to look beyond regular business models, cost and risk reduction while aiding discovery of new revenue streams.

As the application portfolio grows to several thousand overlapping systems; intertwined processes, point to point solutions make enhancing any system become an exercise of tracing highly complex dependencies, confusing definitions, relations and reduced data quality. There is a need for discrete non-overlapping, loosely coupled business capabilities (service domains) that identify discrete functions while service operations define the characteristics of information exchange. Should your Governance policy encompass Sourcing, Integration, Information security, Data and Technology Architecture as well?

Rationalizing data while harmonizing meanings of data would help in the creation of controls that would assist in governing data in service operations. Metadata can serve as your infrastructure support to Ope-rationalizing Data Governance practices. The definition of Governance strategy, structure, funding model, Policy, standards, Operating model, Control framework, and monitoring and assessment plan can leverage various architectural views already existing in EA practice.

Start by integrating the already available models that have been used to carve and motivate the current business. If you are looking top down, getting your entity model to identify the entities in your business domain might be your first step. Tackle the ones that are hard to get or would yield greater benefits in the order they are managed. Getting to a common understanding of what a "customer" means to the organization along with related attributes will uncover further diamonds along the way for you to pick for your Data Governance road map. 

In the attached exhibit you would be looking at Togaf methodology, Archimate standards mapped with Zachman framework, BIAN meta model and project life cycle that would assist in creating a data focused discipline in your organization. Ope-rationalizing Data Governance practices defines the success of the reaching the governance milestones and Strategic goal of transforming your business model.

Architecture views to address stakeholder data needs

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