Short answer

Prioritize defining clear business objectives and decision-making needs before embarking on the design and deployment of data-driven predictive maintenance solutions.

Field: Innovation & Markets
Source: Information Systems and e-Business Management (2017)
Method: Expert Interview
Sample: 13 participants
Evidence: Strong effect

Successful implementation of data-driven predictive maintenance hinges on a clear strategic objective rather than simply leveraging available data. This innovation & markets research insight is drawn from a 2017 study published in Information Systems and e-Business Management. Using Expert interview with 13 participants, researchers explored how this design variable affects real-world outcomes. The key design takeaway: Prioritize defining clear business objectives and decision-making needs before embarking on the design and deployment of data-driven predictive maintenance solutions.

Study

Innovation & MarketsHigh ImpactStrong effect

Target-Driven Design Outperforms Data-Led Approaches in Predictive Maintenance Deployment

Successful implementation of data-driven predictive maintenance hinges on a clear strategic objective rather than simply leveraging available data.

Information Systems and e-Business Management · 2017

Key Findings

01A target- or decision-led approach is more effective than a data-led approach for designing predictive maintenance systems.
02Long-term resourcing is crucial for the sustained success of predictive maintenance deployments.
03The complexity of supply chains and the need for knowledge maintenance are significant challenges.
04Fostering technical competency within end-user organizations is essential.
05A maintenance-driven strategy is vital for effective deployment.

Application

Design takeaway

Prioritize defining clear business objectives and decision-making needs before embarking on the design and deployment of data-driven predictive maintenance solutions.

How to apply

When initiating a project for predictive maintenance or similar data-driven systems, begin by clearly articulating the specific problems the system is intended to solve and the decisions it will support, rather than focusing solely on data collection and analysis capabilities.

Project actions

01Clearly define the problem your design is solving and who will make decisions based on its output.
02Consider how users will be trained and supported long-term.

Method & Evidence

AimWhat are the key human and organizational factors influencing the successful deployment of data-driven predictive maintenance across different industrial sectors?

MethodExpert Interview

ProcedureConducted semi-structured interviews with 13 experts involved in remote condition monitoring, asset management, and predictive analytics across various industries to gather insights on their experiences with data-driven applications.

Sample13 participants

ContextIndustrial asset management and maintenance

Variables

IVApproach to design (target-led vs. data-led), resourcing strategy, user competency development, maintenance strategy.

DVSuccess of predictive maintenance deployment (implied by expert experience).

CVSector (utilities, power generation, manufacturing, transport).

Strengths & Limitations

Strengths

+Cross-sector analysis provides a broader perspective.
+Expert interviews offer practical, real-world insights.

Limitations

The study relies on expert interviews, which might be biased towards their specific experiences.

Reliability & validity

The validity of the findings relies on the collective experience and insights of the interviewed experts. Reliability could be enhanced by triangulating these findings with quantitative data on deployment success rates.

Think critically

How might a 'data-led' approach, which seems intuitively beneficial, actually hinder the practical application of predictive maintenance?

Design Principles

"Strategic alignment: Ensure technology solutions are directly aligned with overarching business goals and decision-making processes."

This insight highlights a critical pitfall in the adoption of advanced technologies. Focusing on business goals and decision-making needs from the outset ensures that technology serves a purpose, leading to more effective and valuable deployments.

What This Means for Your Design

When designing systems that use data to predict problems (like in factories), it's more important to know what decisions you want to make with the data than just collecting lots of data.

How to use in your project

1.Use this insight to justify why your design focuses on specific user needs and decision-making processes, rather than just technical features.

Add to My Project

Quick Cite

Paragraph starter

This research indicates that the successful deployment of data-driven systems, such as predictive maintenance, is significantly influenced by human and organizational factors. A key finding is that a target- or decision-led approach, focusing on specific business objectives and decision-making needs, yields better results than a purely data-led strategy. Furthermore, long-term resourcing, user competency development, and a clear maintenance strategy are critical for sustained value realization.

Source

Information Systems and e-Business Management

A cross-sector analysis of human and organisational factors in the deployment of data-driven predictive maintenance

journal · 2017

View source

Questions About This Research

What does the research say about target-driven design outperforms data-led approaches in predictive maintenance deployment?: Prioritize defining clear business objectives and decision-making needs before embarking on the design and deployment of data-driven predictive maintenance solutions. Evidence: Information Systems and e-Business Management (2017).
Why does "Target-Driven Design Outperforms Data-Led Approaches in Predictive Maintenance Deployment" matter for design?: This insight highlights a critical pitfall in the adoption of advanced technologies. Focusing on business goals and decision-making needs from the outset ensures that technology serves a purpose, leading to more effective and valuable deployments.
How can designers apply this research?: Prioritize defining clear business objectives and decision-making needs before embarking on the design and deployment of data-driven predictive maintenance solutions.
What were the main findings?: A target- or decision-led approach is more effective than a data-led approach for designing predictive maintenance systems.. Long-term resourcing is crucial for the sustained success of predictive maintenance deployments.. The complexity of supply chains and the need for knowledge maintenance are significant challenges.. Fostering technical competency within end-user organizations is essential.
What research method was used?: Expert Interview with 13 participants.
How strong is the evidence?: Evidence strength is rated Strong effect, based on a 2017 journal from Information Systems and e-Business Management.
What should I do differently in my next project?: When initiating a project for predictive maintenance or similar data-driven systems, begin by clearly articulating the specific problems the system is intended to solve and the decisions it will support, rather than focusing solely on data collection and analysis capabilities.
What are the limitations?: Findings are based on expert opinion and may not represent the full spectrum of user experiences or organizational contexts.