Data Services Design | System integration for data-driven enterprises

Data Services Design, an introduction

Data Services Design, an introduction

Data Services Design is the process of creating a data-driven enterprise. A managed approach of continuous improvement to enable an organisation to unlock the potential of its data and information technology.

Most organisations do not use data and information technology to anywhere near its potential capacity. Despite the advancing trends, very few organisations use data and information technology to their strategic and tactical advantage.

How do you:

  • integrate technology, process and people into a business enterprise?
  • get the information you need, when and where you want?
  • enable effective change in to keep up with trends in technology and your market?
  • ceate and maintain, a data-driven enterprise?

The answer is Data Services Design.


Data Services Design is the process of automating data management and the integrating information technology, within an organisation. The purpose of Data Services Design is to enable organisations to unlock the full potential of their data sources and information technology. To enable decision makers to become more data-driven and thus, reduce costs, save time and decrease risk.

Data Services Design brings together the best practices of user centered design, continual process improvement, business analysis, solution architecture, rational unified process, AGILE software development and technology assessment.

Data Services Design is a project method, framework and architecture of process improvement. An approach specifically designed for enabling managers in organisations to use data-driven information in strategic and tactical decision making.

Data Services Design is the process of creating data-driven enterprises.



For the benefits of data-driven technology to be realised, it must be connected to a self-sustaining economic model. It doesn’t matter what this model is, if it’s a team within your organisation, or outsource to another, the point is someone must pay. In Data Services Design, the business, department, division or team organisation of people who are response for the provision of the data and technology is named the Data Services Enterprise.


To be useful, data needs to be presented as information to the decision maker in the form of nice graphs or meaningful displays and infographics. This is how people can get meaning and understanding that’s useful and relevant and timely for the decision that you want to make.

To support decision makers, it is also necessary to have available appropriate user services. People whose job it is specifically to enable the decision makers to get the best out of their system, to get the best out of the data. Please to enable the technology to present the appropriate information when and where needed, but also capable advisers who can help them to interpret what the data means.

The architecture of Data Services Design describes the generic components of an individual data stream within an organisation. It the bits and piece that need to be in place to connect a data source to a decision maker, to connect data and decisions together.

Data Services Design Architcture for creating a data driven enterprise

Data Services Design Architecture describes the component of a data service

The Data-Driven Services Architecture, contains the ten broad components of a Data Services Enterprise:

  1. Data sources (1): Source and streams of data consumed by the data-driven service (1).
  2. Data-driven solution (2-6): The technology platform that takes data from the input source and processes, analyses, transforms data to present useful outputs to the end user, in a usable and available form.
  3. Decision Makers (8): People whom the technology directly benefits (i.e. farmers, grower). This includes other data-consuming solutions or technologies downstream of the architecture.
  4. Support Team (7): People who provide direct support to end-users to enable them to access and use the system, includes sales people.
  5. Solutions Team (9): People who develop, maintain and manage the data-driven solution such as solution architects, developers and testers.
  6. Governance Team (10): People who provide executive management and services to ensure the enterprise remains viable across financial, social and environmental key performance indicators.


The Data Service Design Framework is used to design and deliver individual projects of data focused integration and automation within your organisation. The Framework applies a project constrained and iterative approach to the continuous improvement of your organisation. AGContext enable you to manage the time and costs of your data, technology and process improvement, without major disruption to your daily operations.

The Framework makes the continuous improvement of your data and information into continuous iteration.  That is the project based framework, breaks down the tasks of process improvement, data automation, and systems integration into individual projects with a scope, time and budget. The Framework ensures that the work you need to do, is prioritised, stages and controlled. To ensure that the important work of process improvement that drives your business success, is work that gets done with the least amount of time and cost.

The Data Services Design has three iterative phases: Discover, Conceptualize and Prototype. Each of these phases has four sequential stages, in which specific project work is completed. The project resources are defined by the Scope & Vision. The project definition includes the User Groups and Data Sources involved in the specific processes being improved by the work of the project.

Data Services Design Framework for process improvement in a data driven enterprise

Data Services Design Framework of how to build a data service in a data driven enterprise.


Data Services Design is based on five foundation assumptions.

  1. Business is a system of systems
  2. Process improvement is hard and boring.
  3. Time and money needed for process improvement is always limited.
  4. Data is only valuable, if supported by a service that delivers useful information.
  5. Data is an end-user and has its own requirements.

The first foundation assumption is that business and government organisations are a system of systems.  As Wikipedia states: A “system of systems is a collection of task-oriented or dedicated systems that pool their resources and capabilities together to create a new, more complex system which offers more functionality and performance than simply the sum of the constituent systems.”. A data-driven organisation aims to improve and optimize its systems to enhance and enable both tactical and strategic outcomes, primarily being to reduce costs and save time. That is, to connect data and decisions together.

The second foundation assumption is that process and systems improvement of business systems is unattractive work for employees. Process improvement is action needed to ‘connect data and decisions’. For most businesses, this is boring, uninteresting and frustrating work. Process improvement is about understanding how a technology works, who is working it, what technologies are involved and the history of why it is that way. Process change is often very difficult because it inevitably means, people change, which few people enjoy.  Especially so, if that change is uncertain, as it often can be when it comes to process improvement and systems upgrades.

The third foundation assumption is that most organisations have a limited capacity and capability for improving data and technology systems and process. They do not know what the technology is capable of, and they don’t know how to get the best out of it. They do not know the reasons why their data doesn’t tell them anything useful. They are frustrated that their technology is a cash sink hole that doesn’t appear to deliver any added value. Most organisations have data as a liability and do not have the ability to turn it into an asset.

The forth assumption is that only when data sources and information technology are properly resourced and supported, do they deliver upon their potential. Systems (i.e. techniques and frameworks), without services for delivery, are little more than books on a shelf. In other words, unless there are people paid to support the provision of data and technology within an organisation, the potential benefits of said data and technology, cannot endure.

The fifth foundation assumption of Data Service Design states that the data is of itself, an end-user of technology. In modern technology, the data is independent of the platform and may move between architecture layers from collection through to user interface and back again. Each transition from one architecture component to the next involves a transformation of data, and hence must be managed to ensure that the valuable traits of data, such as timeliness, relevance and availability are not lost during the data-transformation journey from satellite to soil management decisions. Data has its own requirements and should be considered as a special end-user so far as the design and implementation of the process or solution.


Data Services Design is the life’s work of AGContext’s founder and principled leader, Carl Sudholz. It is at the heart of everything that is AGContext. The early foundations of the approach where identified while Carl was working with the then, Victorian Department of Primary Industries in Horsham, Victoria. By his work on the agricultural decision support system AgriGater which started in 2005, Carl become aware of the inability for organisations to unlock to potential of data and information technology. Since that time, Carl’s career, research and entrepreneurism has been focused on solving this problem. Data Services Design is the result of this work, and it continues to evolve.

To learn more about Data Services Design and how AGContext can help you create a more data-driven business or organisation,


Carl Sudholz

Carl Sudholz

Managing Director at AGContext
Carl Sudholz is the Principled Leader and Founder at AGContext, editor of the AGContext Data and Decisions Blog and Host of #AGContextTV on LinkedIn. Carl is an industry leader in data-driven technology with over 10 years and 15,000 hours logged in the design and delivery of data-driven systems in Australian food and agriculture.
Carl Sudholz

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