Improve key customer metrics like NPS, customer effort, and CSAT
Data Generation Is Not The Problem
The opportunity for scalable advanced analytics in predictive maintenance.
The ability to manage a variety of asset types and avoid unplanned downtime is no trivial task. An entire operation can come to a halt if just one critical process, asset, or machine fails. But too often organizations are forced to rely on outdated, incomplete, or inaccurate data to make decisions resulting in inefficient operations.
The #1 driver for investments in predictive maintenance among service leaders is the need to have a faster response to product quality and service issues.
As more products and equipment are connected providing a wealth of data points, data availability is no longer the primary problem impacting organizations’ ability to improve maintenance, service, and support.
Siloed processes and data, lack of a scalable, high-performance data analysis platform, and analytic methods focused only on past performance hinder the ability to shift from reactive maintenance to more proactive, predictive, and prescriptive models.
Source: IDC Manufacturing Insights Product and Service Innovation Survey, 2021
A Modern, Scalable, Data Analytics Platform
Support for data exploration and decision automation
Decision Making, Data, and Analytics Usage Patterns
Key driver identification
Guided root-cause analysis
Data exploration and investigation is about helping users understand and explain what happened and why it happened.
Conditional decision automation
Algorithmic decision automation
Decision automation is about automating tactical decision making in the flow of operations.
Consider the following predictive maintenance decision variables:
Requirements of a modern data analytics platform for predictive maintenance
Source: IDC, 2022
What steps should organizations take to solve the problem or seize the opportunity?
Insights at Scale
Complexity of Maintenance Operations Doesn’t Require a Complex Response
Develop a long-term data and analytics strategy that considers various decision-making patterns and related data and analytics IT requirements – both for streaming and batch data processing and analysis at scale.
Assess data quality and availability guaranteeing data-driven decisions can be made.
Consider IT partners that provide a modern data, analytics and AI platform that is extensible and leverages a broad partner ecosystem as no single vendor can do it all.
Don’t expect a single technology to address all requirements. SQL-based, columnar, MPP analytic databases have a role, so do data lakes and streaming data processing software, and a range of upstream and downstream data integration and data analysis and visualization tools.
Select appropriate data and analytics technology that is not just about finding solutions with the most compute power or storage capacity (and flexibility); also consider security, support from solution provider, and overall total cost of ownership.
Source: IDC, 2022
View PDF of infographic created by IDC Insights.