DataRPM
DataRPM, founded in 2012 and headquartered in Bedford, Massachusetts, emerged as a provider of cognitive predictive maintenance solutions for the industrial IoT (IIoT) market.
Profile
Provides predictive maintenance solutions using machine learning for industrial IoT.
DataRPM, founded in 2012 and headquartered in Bedford, Massachusetts, emerged as a provider of cognitive predictive maintenance solutions for the industrial IoT (IIoT) market. The company was acquired by Progress Software in 2017 for $30 million, marking a strategic move to integrate machine learning capabilities into Progress’s digital experience suite. DataRPM’s core technology focuses on predictive analytics, leveraging machine learning models to anticipate equipment failures and optimize maintenance schedules in industrial settings.
This acquisition positioned Progress to enhance its offerings in predictive maintenance, a critical area for manufacturing and industrial operations. Post-acquisition, DataRPM’s technology has been integrated into Progress’s broader portfolio, aiming to deliver more robust predictive analytics solutions. The company’s trajectory has been shaped by its ability to address the growing demand for predictive maintenance in industries where downtime can be costly.
However, the integration process has not been without challenges, including aligning DataRPM’s technology with Progress’s existing product lines and customer base. As of 2026, DataRPM’s solutions are deployed across various industries, contributing to Progress’s revenue growth and market position in predictive analytics. The company’s focus remains on leveraging machine learning to drive operational efficiency and reduce maintenance costs for its clients.
Who buys this
- Manufacturing companies
- Industrial equipment operators
- Energy and utilities firms
- Transportation and logistics companies
- Healthcare equipment providers
Strengths and what to watch
Strengths
- Integration with Progress Software’s broader digital experience suite
- Proven machine learning models for predictive maintenance
- Strong focus on reducing operational downtime in industrial settings
Watch for
- Challenges in aligning technology with Progress’s existing product lines
- Potential customer churn during integration phases
- Dependence on industrial IoT market growth
Key Information
- Founded
- 2012
- Headquarters
- Bedford, Massachusetts
Frequently Asked Questions
What is DataRPM and what does it do?
DataRPM is a predictive maintenance provider specializing in industrial IoT. Founded in 2012, it uses machine learning to anticipate equipment failures and optimize maintenance schedules. Acquired by Progress Software in 2017, its technology helps reduce downtime in manufacturing, energy, and transportation industries.
How does DataRPM's predictive maintenance work?
DataRPM applies machine learning models to industrial equipment data, analyzing patterns to predict potential failures before they occur. This enables companies to schedule maintenance proactively, minimizing unplanned downtime. The system continuously learns from new data to improve its predictive accuracy over time.
Which industries use DataRPM's solutions?
DataRPM serves manufacturing companies, industrial equipment operators, energy firms, transportation providers, and healthcare equipment manufacturers. Its predictive maintenance technology is particularly valuable in industries where equipment failure leads to significant operational disruptions and financial losses.
Why did Progress Software acquire DataRPM?
Progress Software acquired DataRPM in 2017 for $30 million to enhance its digital experience suite with machine learning capabilities. The acquisition allowed Progress to offer advanced predictive maintenance solutions, strengthening its position in industrial IoT and predictive analytics markets.
What are the challenges with DataRPM's technology?
Key challenges include integrating DataRPM's machine learning models with Progress Software's existing products and maintaining customer satisfaction during transition periods. The solution's effectiveness also depends on industrial IoT adoption rates and the quality of equipment data available for analysis.
How effective is DataRPM for reducing maintenance costs?
DataRPM's predictive maintenance can significantly reduce costs by preventing unexpected equipment failures. By analyzing machine data to schedule maintenance only when needed, companies avoid both unnecessary routine maintenance and costly emergency repairs, optimizing their operational budgets.
Sources
- www.cmswire.com — DataRPM’s acquisition by Progress Software in 2017
- www.technology.org — Predictive analytics case studies involving DataRPM
- www.pwc.com — AI and predictive analytics trends relevant to DataRPM
- investor.wdc.com — Financial context for predictive analytics market