Data Stewardship is an essential part of any organization and should be included in the future state architecture activities. This is of significant importance since we are at an age where it is all about the data. We are in a continuous raging momentum of digital transformation of all businesses across all industries. This is giving data teams a greater leverage and visibility within the enterprise hence the importance of mapping architectural principles to data stewardship and engaging EA team accordingly.
Having an effective data stewardship is a guarantee for the quality and integrity of one of the most vital assets of the enterprise: the data. Data cycles such as generating, acquiring, managing, enriching, maintaining,migrating, and disposing of if any, are both business as well as IT shared responsibility with collaborated governance. EA can facilitate the discussions,lead and guide both parties on producing a reference mapping matrix to the architecture principles of the enterprise. To facilitate this exercise, the EA team should start with standard templates such as the Gartner toolkit which categorizes the data stewardship requirements into three. First is definition and scope covering topics such as the authoritative information source, the audit-able system, single storage. The second is the processes, responsibilities and usage of data listing responsibilities for enforcement, system of records, standards, data repository for analytics, etc…. The third category covers data integrity and system security including the data as an asset, disaster recovery, internal and external security, encryption, and authorization.
Starting with these broad data stewardship requirements, an EA team can come up with a template to use in discussion with stakeholders and IT to map to the architecture principles thus completing another important artifact as part of the future-state architecture implementation activities.
Having an effective data stewardship is a guarantee for the quality and integrity of one of the most vital assets of the enterprise: the data. Data cycles such as generating, acquiring, managing, enriching, maintaining,migrating, and disposing of if any, are both business as well as IT shared responsibility with collaborated governance. EA can facilitate the discussions,lead and guide both parties on producing a reference mapping matrix to the architecture principles of the enterprise. To facilitate this exercise, the EA team should start with standard templates such as the Gartner toolkit which categorizes the data stewardship requirements into three. First is definition and scope covering topics such as the authoritative information source, the audit-able system, single storage. The second is the processes, responsibilities and usage of data listing responsibilities for enforcement, system of records, standards, data repository for analytics, etc…. The third category covers data integrity and system security including the data as an asset, disaster recovery, internal and external security, encryption, and authorization.
Starting with these broad data stewardship requirements, an EA team can come up with a template to use in discussion with stakeholders and IT to map to the architecture principles thus completing another important artifact as part of the future-state architecture implementation activities.
References:
"Data Stewardship Is Everybody’s Business: Best Practices for Data Quality Management". Rado Kotorov (insights.wired.com, July 21, 2014)
"L04: Future-State Architecture: Implementation Level". Retrieved from Penn State EA-872 class material.
Ziad,
ReplyDeleteThank you for bringing up the importance of data stewardship. I like the way you just used the term "steward."
Sometimes, discussions could split hairs on distinctions between "owner", "custodian", and "steward". And in large organizations which have to deal with a large complex landscape for data, this set of distinctions can be quite useful. And yet, in many cases, if the typical organization is asked “Who owns data?” there is usually many interesting answers across the firm. Thus, it might be best to establish the core spirit of the notion of stewardship. And to my mind, whatever the structure of relationships the organization has defined, all these notions simply point to the need for ownership and care of data.
The question of data ownership is critical to governing data, since it deals directly with data accountability. Without this sense of ownership and responsibility, it would be very hard to define structures, processes, and security as you have pointed out.
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