Study
Commercial ProductionHigh ImpactStrong effect

AHP Model Optimizes Big Data Platform Selection

The Analytic Hierarchy Process (AHP) provides a structured framework for evaluating and selecting complex technological platforms, such as big data analytics solutions, by breaking down decisions into hierarchical criteria.

Acta Informatica Pragensia · 2015

01

Key Findings

  • 01The AHP model effectively structures complex decision-making for platform selection.
  • 02Pairwise comparisons allow for nuanced evaluation of various criteria.
  • 03The model can be adapted to different organizational needs and priorities.
02

Application

Design takeaway

When selecting a complex system or platform, employ a structured decision-making framework like AHP to ensure all critical factors are considered and weighted appropriately.

How to apply

When faced with choosing between multiple software solutions, hardware configurations, or even material suppliers, use AHP to systematically weigh factors like cost, performance, compatibility, and vendor support.

Project actions

  • 01When choosing tools or materials for your design project, think about all the factors that are important.
  • 02Use a method like AHP to compare your options fairly.
03

Method & Evidence

AimTo develop and demonstrate an AHP model for selecting big data analytics platforms that addresses multiple decision-making criteria.
MethodMulti-Criteria Decision Analysis (MCDA) using the Analytic Hierarchy Process (AHP).
ProcedureThe study involved identifying key criteria for big data platform selection, structuring these criteria into a hierarchy, and then using pairwise comparisons to determine the relative importance of each criterion and sub-criterion. This process was applied to evaluate and rank different big data analytics platforms.
ContextSelection of Big Data Analytics Platforms for businesses and public sector institutions.

Variables

IVCriteria for platform selection (e.g., cost, performance, scalability, ease of use).
DVRanked order of big data analytics platforms.
CVSpecific set of platforms being evaluated, the decision-making team.
04

Strengths & Limitations

Strengths

  • +Provides a structured and transparent decision-making process.
  • +Accommodates both qualitative and quantitative criteria.

Limitations

The quality of the decision depends heavily on how well the criteria are defined and how accurately the comparisons are made.

Reliability & validity

Reliability could be assessed by having multiple individuals perform the AHP analysis and comparing the consistency of the results. Validity would depend on whether the chosen criteria truly reflect the most important factors for successful platform implementation.

Think critically

How might the subjectivity inherent in pairwise comparisons be mitigated to ensure a more objective platform selection?

05

Design Principles

"Complex system selection should be guided by a hierarchical breakdown of criteria and weighted pairwise comparisons."

In today's data-driven landscape, choosing the right big data analytics platform is critical for business success. A structured decision-making process, like AHP, ensures that selections align with strategic goals, technical requirements, and resource constraints, leading to more effective data utilization and actionable insights.

06

What This Means for Your Design

This study shows how to make a smart choice when picking a big data system by using a step-by-step method that compares all the important features.

How to use in your project

  • 1.Reference this study when justifying your choice of software, hardware, or materials, especially if you used a structured decision-making process.
07

Add to My Project

08

Quick Cite

(2015). AHP Model for the Big Data Analytics Platform Selection. Acta Informatica Pragensia. https://doi.org/10.18267/j.aip.64 Retrieved from https://designdex.org/study/afb11389-416b-4eb0-893d-57eb528bc286/ahp-model-optimizes-big-data-platform-selection

Paragraph starter

The selection of critical platforms, such as big data analytics solutions, can be systematically approached using decision-making frameworks like the Analytic Hierarchy Process (AHP). This method, as demonstrated by Lněnička (2015), involves breaking down the selection into hierarchical criteria and performing pairwise comparisons to establish relative importance, thereby ensuring a well-justified and optimized choice.

09

Source

Acta Informatica Pragensia

AHP Model for the Big Data Analytics Platform Selection

journal · 2015

View source

Questions about this research

What does the research say about ahp model optimizes big data platform selection?
When selecting a complex system or platform, employ a structured decision-making framework like AHP to ensure all critical factors are considered and weighted appropriately. Evidence: Acta Informatica Pragensia (2015).
Why does "AHP Model Optimizes Big Data Platform Selection" matter for design?
In today's data-driven landscape, choosing the right big data analytics platform is critical for business success. A structured decision-making process, like AHP, ensures that selections align with strategic goals, technical requirements, and resource constraints, leading to more effective data utilization and actionable insights.
How can designers apply this research?
When selecting a complex system or platform, employ a structured decision-making framework like AHP to ensure all critical factors are considered and weighted appropriately.
What were the main findings?
The AHP model effectively structures complex decision-making for platform selection.. Pairwise comparisons allow for nuanced evaluation of various criteria.. The model can be adapted to different organizational needs and priorities.
What research method was used?
Multi-Criteria Decision Analysis (MCDA) using the Analytic Hierarchy Process (AHP)..
How strong is the evidence?
Evidence strength is rated Strong effect, based on a 2015 journal from Acta Informatica Pragensia.
What should I do differently in my next project?
When faced with choosing between multiple software solutions, hardware configurations, or even material suppliers, use AHP to systematically weigh factors like cost, performance, compatibility, and vendor support.
What are the limitations?
The effectiveness of the AHP model is dependent on the accurate identification and weighting of criteria by the decision-makers. Subjectivity can be introduced during the pairwise comparison phase.
Is there evidence that big data affects design outcomes?
The research demonstrates that the Analytic Hierarchy Process (AHP) is a robust method for systematically evaluating and choosing between different big data analytics platforms by considering multiple factors in a structured way. In today's data-driven landscape, choosing the right big data analytics platform is critic Source: Acta Informatica Pragensia (2015).
Where does this data analytics research apply?
Selection of Big Data Analytics Platforms for businesses and public sector institutions. It sits within commercial production research on designdex.org.

Related research topics

big data design research · evidence on big data · does big data improve design outcomes · data analytics studies for designers · big data and data analytics findings · commercial production research evidence