Digital disruption asks for new priorities in ICT-strategy. From killer app to killer data – this is how to start:

This blog is a new view to my earlier presented Data Value Chain-model. Data is an enterprise asset which represents value. The Data Value Chain visualizes and puts all data assets in sequence of increasing value. It is intended as a communication-tool to position our services and explain the opportunities and challenges of a data-related landscape.

The model is briefly discussed below:

General:

The Data Value Chain describes an abstract data-landscape. It displays data-products from left to right in increasing relative value. 6 stages divide the model horizontally, grouping elements of similar value. Elements may be in multiple stages as they may be organized in different ways. For instance: Processes that cross functional borders may or may not be based on corporate data models. The first case would describe Shared Data, the second Managed Data.

Some data assets may be inputs to others. Main data flows are drawn in the model as blue arrows. Green arrows imply guidelines. The model does not pretend to be exhaustive though: It most certainly miss certain elements and flows.

Stages:

The stages intend to group elements in order of increasing value (from left to right). The model consists of the following stages:

1. External data sources: All data that is external from enterprise perspective and needs to be acquired before it can be used. Examples are external transactions on the supply chain or block chain, external web-content and API-s or social media and mails from external parties.
2. Local Data: Data that is gathered/acquired, produced (manually or by systems) and used inside the enterprise for “local” purposes, like in a specific functional area. It contains enterprise applications and services and local content that is created by employees or “things”.
3. Shared Data: Data that is shared between functional areas within the enterprise. It may be produced by one function and leveraged by another. Examples are shared documents and business processes that cross functional borders.
4. Managed Data: Data that complies to enterprise defined quality standards. It is organized according enterprise data models and managed though corporate governance processes and roles in order to guarantee the required level of quality and availability. Master Data is a well know example of Managed Data. Other products are API-s and data virtualization that provide controlled access to managed data, Enterprise Business Rules that are maintained centrally and follow enterprise guidelines and Data Warehouse that uses master data for its dimensions.
5. Intelligence: Data reports, visualizations and predictive models that provide insights in operations and enterprise context to support decision making. Further it contains any functionality with decision logic that is capable of supported or autonomous “learning”.
6. Data Products: Data / Intelligence products bring direct value to external parties. They may be sold or otherwise applied for direct commercial purposes.

Supporting concepts

Over the entire Data Value Chain the following concepts are used:

• Data Integration: Means to exchange data between data assets
• Data Tracing: Means to log all data use and exchange in order to be able to trace data back to its source
• Data Security: Means to protect data from any unauthorized exposure, modification or removal
• Data Architecture: Means to organize, structure and describe data assets and there interactions at high abstract levels

Abbreviations

The following abbreviations are used:

Read our previous blog about this subject Data Value Chain!