Value-added data-asset lifecycle management (Part II) : Enterprise capability development

                        

Welcome back to the expository series on data assets and data governance!

In my previous article, we learned about value-added data assets and how and why you have to manage them strategically.In this episode, I discuss how these assets can be transformed for competitive enterprise capability development.

Before delving into capability development from data assets management, an important distinction claim attention here and needs to be delineated. That is between info quality and data quality , even though the two are used interchangeably , the former has user quality (known as information consumer) as its focus .The first major strategy shift required here is viewing information as a product to be managed as against limiting to data quality in databases and data-warehouses.

Two models are considered industry standard for structuring the quality assurance which is the forerunner to information product management.They aid to assess info quality based on needs of info product user.

i. Information quality assessment

ii. Product and Service Performance Model for Information Quality

It defines info quality on the parameters product quality and service quality

After we have dialled down the quality dimensions its imperative define the processes and roles which manages these requirements.Two roles that of info quality management are info suppliers and info custodians.In line with TQM, research has proposed a Total data quality management (TDQM) framework, consisting of continually defining, analysing and improving info quality.

           

    "To improve the quality of information products,the processes that produce those information products must be defined, measured, analyzed,and improved."

(Diane M. Strong, Encyclopedia of database systems, Springer, 2018)

One aspect of achieving the above goal is modelling information flow through an information manufacturing system (IMS) definition ,that could capture, assimilate info metrics and derive transformations based on the quality assessment. So reviewing the first two phases of asset based governance are-

1.Info Quality definition for the user

2.Info production process management

The next two pivotal stages are-

3. Lifecycle management of info product

4. Deciding suitable governance structures for managing info as a product.

Roles need to be assigned for control authority of data access points along the info manufacturing process.One such governance role is that of a information product manager (IPM). Note that IPM is different from CIO, latter being mostly responsible for info manufacturing, and less involvement on info suppliers and info consumers, for both intra and inter-organizational interfaces.

There is a certain maturity and growth to be levelled as the enterprise transcends from a broad data governance structure towards a comprehensive end -to-end governance framework that ascertains data asset management. 

"The framework ( for governance of data asset management) divides asset life cycle into seven perspectives, i.e. competitiveness, design, operations, support, stakeholders, lifecycle efficiency, and  learning perspective."

[Haider A, Information systems for engineering and infrastructure asset management, 2013 ,Springer]

In this work the author explains that maturity of data governance in the context of asset management depends on - asset lifecycle processes, functional requirements and critical factors that lead to enterprise competence .Our focus here is on the factors that lead to capability and competence.Organizational competence is mostly prioritized based on the long term strategy of business organization.Here,I propose technology leadership. information driven culture, human resource capability and sustainability as the four pillars of competence.

A comprehensive end to end governance framework could facilitate an in-depth gap analysis of how much connected or disconnected is your governance with asset capability.