How to manage data assets for value creation (Part I): The role of data governance

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Business processes through their inter-organizational and intra-organizational cross-functional interactions create large-scale information assets. These can be processes that describe workflows, transformations and other maneuvers, and in numerous ways manipulate the data. Each of them leave an imprint and its of vital strategic importance to delineate or govern the trace and access control of those imprints as a cohesive whole in the journey of the asset in its end-to-end lifecycle.So the raw imprint of asset becomes its refined blueprint with added value. In a way, information systems in modern enterprise solutions has to facilitate a data-value view of asset management.

In an organizational setting, data governance refers to" the framework and underlying policies of collecting data and governing the ownership and purpose of its usage". 

According to state-of-art research in data management , data-asset value-chain consists of primarily- Acquisition, curation, exploitation and generation. An organization can analyse its data value chain broadly on these levels to identify its principal challenges and further formulate a framework to exercise them as sustainable value levers.

Some of the prominent challenges to be resolved for moulding data assets as value additions for competitive advantage are-  defining and measuring, modelling value-driven governance, optimizing data-value . Broadly they come under data value quantification problems.(Judie Attard and Rob Brennan, 2018)

  1. Defining & Measuring data value: Leading research classifies data-value in terms of varied dimensions- timeliness,intrinsic business utility, costs etc.There needs a unified semantics for better understanding.The metrics are broadly based on- usage, costs, and quality.However they are either very abstract or lacks generality, and confines to a very specific scope.End-end integration of data layers  is only conducive with structural and semantic consistency especially managing the metadata.
  2. Modelling data governance: Currently most data governance models are either proprietary, or otherwise human-process oriented and thus do not support interoperable systems specification.
  3. Optimizing data governance: An overarching data governance optimization framework can be enabled with right approach to data value quantification.A very good case is that classifying and streamlining data storage.

In line with active research and industry demands, I would propose a integrated enterprise data governance protocol, based on ontology and business semantics.More on that in a different post. In the next installment of series, we see how a sound governance mechanism is closely related to enterprise capability.

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