A Future-Oriented Approach

Enterprises take on a wide range of projects in the name of “Digital Transformation”. Or seek to set up programs for Data Governance, Master Data Management, Data Catalog, Business Glossary, Data Protection, Data Strategy, etc.

However, legacy systems and technical debt continue to impede many organizations' efforts. And any attempt to use such a burdened environment as a launchpad for future-proof “data” projects and programs is most likely doomed to fail.

Recommendation

The future of informational infrastructure needs to be based on a business-data-driven reference system with well-defined, distinct responsibilities. Prerequisites are

• a Chief Data Office/r as introduced earlier in my post "Pondering on Data and CDO (Chief Data Officer)"
• business division managers who are ready for additional duties, but are also flexible to give up on existing influence...

Here is the industry-agnostic approach that I propose:

1. For each business unit, record the major organizational processing activities
2. For each processing activity, record the major created & updated (business) data structures
3. With recorded data structures, derive (business data) entities & relationships
4. With entities & relationships, build the Enterprise Information Management Map (= high-level concept model)





Example of High-Level Concept Model in Liability Insurance [Click to Enlarge]



5. In the Enterprise Information Management Map, identify master entities and the relationships among them
6. In the Enterprise Information Management Map, assign responsibility for modeling master entities & relationships to Chief Data Office(r) as their Data Domain Owner.
7. Divide the Enterprise Information Management Map by grouping all other entities & relationships into distinct data domains based on similar processing activities and assign modeling responsibility for each data domain (= Data Domain Ownership) to exactly one business division [Note: If necessary, restructure processing activities in a way that each data domain and its creating / updating processing activities belong to only one responsible business division.]

Example of Data Domains in Liability Insurance [Click to Enlarge]

Result

The above approach introduces a reference structure where each data domain within the enterprise concept model is represented by exactly one business division that is responsible to develop & maintain the related business data names, descriptions and constraints for business entities, attributes and relationships.

Outlook

In subsequent posts I will elaborate on this frame to show how to advance enterprise-beneficial data programs and projects.

The Importance of Data Literacy

Since EVERY person performing tasks at a (virtual or real) desk for professional reasons works with data, Data Literacy is a basic qualification for EVERYONE with an “office job” and not only required for Data Analysts, Data Engineers, Data Scientists etc. and their managers. (Note: ... as reading and writing are basic qualifications for everyone, not only for those who want to pursue an academical career.)

This been said, I suggest the following definition:

 

Data Literacy is the ability of an individual or a group within their rights and responsibilities in an organization to

• understand the definition and meaning of data
• interpret data in their respective context
• apply data terms correctly and communicate clearly and concisely about data to other individuals or groups inside and outside of the organization
• select, extract, compose, transform, create, and delete data under corporate rules & standards
• judge the quality of data for the potential impact on the purpose of subsequent processes in the organization.

In brief, Data Literacy is “knowing what (data) you are talking about, what you are doing with those data and why”.

 

A Data Literacy program must emphasize educating and sensibilizing particularly rank and file employees in business units who lay the "data groundwork" by collecting data and entering them into the respective systems, often from (unstructured) sources outside the organization such as online forms, letters, emails, messages, phone calls etc.

 

Organizations need to train employees based on 

• a Business Data Glossary related to the subject area Data Model that corresponds to the employee’s respective responsibility
• a subject-area / job-task-related Process Model with its technical and organizational measures showing
  • the potentially available sources of data
  • what to do with those data
  • the potential recipients of data inside and outside the organization

and make those artifacts available for reference.