Data Architecture Principles

Do you know how well Data is treated in your organization?

Yes or No?

Data Concepts

The Current State Data Architecture of your organization is the set of implemented data concepts in your organization.

Data Concepts are approaches on working with data that exist outside and inside your organization.

For example concepts like Data Sharing and Data Validation.

Once a concept is made part of your data architecture, we call it a Data Architecture Concept.

Do you have a list of implemented data concepts?

Data Principles

Data principles are the way that data concepts work.

The data principles tell you what the effect is of treating data, using data, storing and retrieving data in a certain way.

Once you have made a data concept part of your data architecture, the principle of that concept is a data architecture principle for your organization.

Most importantly, your implemented data architecture principles tell you how well you integrate data in your business and let it flow uninterrupted as fuel through your processes and your systems.

Do you have a list of implemented data principles?

Having Insight and Overview Helps

So knowing which data architecture principles are implemented (and how well) in your organization, reveals a lot of information you can use to innovate and compete better.

The question is also which data concepts and principles you need to have implemented at which maturity level because of your strategy and business model. This is all about the future state data architecture.

The better your data architecture (ie. concepts and their principles) is aligned with your strategy and business model, the better you can execute your strategy and run your business model.

The benefit of working with Concepts and Architecture Principles

A concept (an abstraction of an implementation or approach) always has one or more principles (the way the concept works, producing results).

Scientists discover and develop new concepts and principles every day.

They help you to innovate and compete.

Knowing the principle of a concept, helps you decide whether you need the concept for your company or not, because it learns you which results are produced.

List of Data Architecture Principles

The following data architecture principles help you improve your data architecture and thus your organizations strength.

First the concept is named, then the first principle of the concept is stated. Where possible a literature reference is provided.

Data (Asset) Management...n/a
Data ValidationBy validating all data at the point of entry, it is ensured that the quality of the data in the system is increased.n/a
Data DiscoveryBy automating regular data discoveries, it is ensured that the organization knows how much data it is getting in, which data sets are aligned and which applications need to be updated.n/a
Data SharingBy sharing data with other departments, it is ensured that silos in the organization are removed and more people have a 360 client view.n/a
Optimal InterfacesBy providing the right interfaces to users, it is ensured that data can be easily shared and is accessible for others.n/a
Data Security and Access ControlBy developing access policies and data access controls at raw data level, data is much more secured and access is controled better.n/a
Data Privacy...n/a
Common VocabularyBy establishing a common vocabulary it is ensured that consistency is realized.n/a
Data CurationBy curating data (like modeling the correct data relationships and cleansing data), it is ensured that the perceived and actual data quality is increased.n/a
Data IntegrationBy integrating data in a logical way, it is ensured that less data is copied for completeness of data view.n/a
Data Elimination By eliminating data copies and movement of data, it is ensured that costs are lower, quality of data higher and the organization is more agile.n/a
Data Analyses/Intelligence...n/a
Data Algorithms...n/a
Data Prediction...n/a
Data Visualization...n/a
Data Lake...n/a
Data Warehouse...n/a
Data Virtualization...n/a
Data Hub...n/a
Data Complexity...n/a
Data Transactions...n/a

Are some of these data principles of interest to you? Give it a thought for a moment!

The above list is available as open data architecture principles set on Dragon1 (in JSON format).

You can download the data set here and upload to watch them in the Dragon1 Viewer.

What to do with Data Architecture Principles?

Here follows a checklist on what best to do with the principles:

  1. Make an inventory of data concepts and data architecture principles that are currently implemented in your company.
  2. Use the provided list of principles here as a reference or starting point.
  3. Collect the business process flow diagrams (BPMN), data diagrams (DMN) application components diagrams (UML and ArchiMate) that and IT-Infrastructure diagrams (Azure, Amazone, Citrix or IBM models) are or should be affected by the data architecture principles.
  4. Identify how processes, data, applications and IT infrastructure are or should have been affected by the principles.
  5. Analyse the gap.
  6. Create a roadmap to fill that gap.

Measure, Visualize and Rationalize

It is important for any organization out there to measure how well data architecture principles are implemented and at which maturity level?

Also it is important to rationalize which data architecture principles one needs and does not need.

This is all important because it make you sit in the driver seat of the strategy of the organization.

Data, in many organizations, will soon be a uncontrollable complex whole.

The better you control or manage your data or its complexity, the better you can compete.

Using Dragon1 for Data Architecture Principles

The above steps are supported by the Dragon1 platform.

Create an account and get guided to document, measure, rationalize and improve your data architecture principles.

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