KCS combines the Data Science Lifecycle (DSLC) with the Software Development Lifecycle (SDLC) to conduct research and turn validated analytical results into usable systems.
Combined Data Science and Software Development Lifecycle
The KCS approach combines the Data Science Lifecycle (DSLC) and Software Development Lifecycle (SDLC). DSLC structures the research and analytical work; SDLC turns validated requirements and analytical results into a reliable, testable system. The two lifecycles advance together and return to earlier stages whenever evaluation reveals a data, model, requirement or implementation problem.
DSLC · RESEARCH AND ANALYTICS
1
Research problem
Question, domain and success criteria
→
2
Data understanding
Sources, quality, rights and context
→
3
Preparation and modelling
Features, baselines, models and experiments
→
4
Evaluation and interpretation
Performance, limitations and research findings
Research questions guide system requirements · Data and models shape implementation · System use generates new evidence
SDLC · SOFTWARE AND OPERATION
1
Requirements
Users, workflows and acceptance criteria
→
2
System design
Architecture, interfaces and data flows
→
3
Implementation
Software, integration and reproducibility
→
4
Testing and operation
Verification, deployment, monitoring and improvement
Figure: Combined DSLC–SDLC framework for applied data-science research and software development
The upper track begins with the research problem and proceeds through data understanding, preparation, modelling, evaluation and interpretation. The lower track begins with the users and operational requirements, then proceeds through system design, implementation, testing and operation. The tracks are connected: a model may require new software components; system testing may expose a data-quality problem; and operational feedback may lead to a revised research question. The final result is therefore both scientifically justified and technically usable.
Application in different research domains
The framework remains stable while the domain questions, data, methods, risks and users change. The examples show how the same reasoning backbone can support very different research settings.
Hermeneutics
Computational analysis remains connected to historical, linguistic and cultural interpretation.
MICE
Event data support preparation, live operations, participant engagement and post-event learning.
Healthcare
Clinical requirements, governed data, AI models and decision support are evaluated together.
Green port
Operational, energy and environmental data support efficiency and sustainability decisions.
Who this is for
Researchers, universities and organisations working with data-intensive questions in hermeneutics, MICE, healthcare, green ports and other applied domains.
The problem we help solve
A data-science result is not automatically a usable research contribution or software solution. The research question, domain evidence, data work, modelling, system design, implementation and evaluation must remain connected.
How KCS approaches the work
Work progresses through four connected stages, from clarifying the need to producing a result that can be reviewed and used.
Define the domain problem, research question, users and intended decision.
Apply the DSLC to understand, acquire, prepare, analyse and model the data.
Apply the SDLC to specify, design, implement and integrate the required software system.
Evaluate the research result and the implemented system, then document limitations and future work.
Concrete examples
In hermeneutic research, documents are interpreted computationally while the analytical results remain connected to their historical and cultural context.
In MICE research, event data support programme planning, participant engagement and post-event learning through an integrated service platform.
In healthcare, clinical requirements, governed data, predictive models and user-facing decision support are evaluated as one system.
In a green port, operational, energy and environmental data support measurable decisions on efficiency, emissions and sustainable infrastructure.
What you can receive
Research and data-science plan
DSLC and SDLC workflow
Evaluated model or analytical result
Tested prototype or software architecture
Documented findings, limitations and next steps
ENGAGE WITH KCS
Could this expertise help your organisation?
Tell KCS about the situation, the people affected and the result you need. We will determine whether this expertise fits and recommend a sensible first step.
Your enquiry will reference: Data Science and Artificial Intelligence.
