Data Governance Discover Processes Assess and Scope Your Environment

As mentioned in my post describing the major business processes that comprise a data governance function, the Discover processes capture the current state of the organization’s data lifecycle, dependent business processes, supporting organizational and technical capabilities, as well as the state of the data itself. Insights derived from these steps are leveraged to define the data governance strategy, priorities, business case, policies, standards, and the ultimate future state vision.  This process runs parallel and is iterative to the Define process stage as Discovery drives Definition, and Definition drives more targeted focus for Discovery.


Data Governance
Data Governance Discover Processes Assess and Scope Your Environment

The most relevant processes that comprise the Discover stage include:

Data Discovery.  Involves automated and manual efforts – often led or enabled by supporting technology – to discover, document and assess relevant data, metadata, domains, business rules and other objects across all relevant sources, targets and technology infrastructure that touch the data.

  • Provides visibility to pervasiveness of data entities, identifies areas of risk exposure or opportunities to improve or mitigate root causes of data quality issues. 
  • Identifies where any data anomaly or business scenario occurs across all data sources.  Many perform data discovery for the purpose of figuring out “what data do I have that is relevant to this analysis or business decision?” 

Data Profiling. Process of evaluating targeted data sets to determine current state and act as a baseline for rule and policy definitions

  • Profiling helps answer the questions “what does our data look like today”, “how does data in one system relate to data in another system” and “what rules and policies should we consider defining to improve”. 
  • Critical data quality, security, archiving, masking or other types of policies or business rules cannot be effectively defined and implemented without first understanding the current state of the data. 

Building Data Inventories. Process of identifying and documenting an inventory of relevant master data, transactional data, reference data domains and attributes in context of usage, applications, ownership and relationships.   While maintained independently as well, data inventories are a common by product of a data modeling exercise.

  • Provides consolidated view of all relevant data and attributes to consider when defining data models, rules, policies, relationships, classifications and other data-dependent efforts.

Building Process Inventories.  Process of identifying and documenting an inventory of the business processes that run an organization.  Process inventories are usually maintained at multiple levels, with major processes like “Order to Cash” or “Procure to Pay” at the top, with more granular process steps (“e.g., Account Creation”, “Order Fulfillment”) defined beneath.

  • Data governance and enabling technology investments (e.g., DI, DQ, MDM, ILM, BI, DW – pick an acronym!) exist to improve and optimize critical business processes, decisions and interactions.  DG prioritization without the context of the processes that run a business – and the data these processes rely upon – will fail to deliver value.

CRUD Analysis.  Current state “Create, Read, Update, Delete (CRUD)” analysis maps the business processes, supporting applications and systems to the data to provide easy to understand visibility to the life cycle of critical data.

  • Understanding the current state processes, applications and stakeholders that have the ability to create, read, update or delete/purge/archive critical data is necessary to identify behavioral, system and policy changes required to improve the trustworthiness and security of that data.

Capabilities Assessment.  Assessment of the current state of organizational competencies data governance depends upon across multiple dimensions including technology/architecture, functional, people skills and process/policies.

  • This process will allow data governance drivers to understand what organizational strengths can be leveraged, as well as provide insight to organizational weaknesses where investments can be made to mitigate risk and improve ROI of the DG efforts.

Stay tuned next week for the “Define” process stage deep dive…