Benchmarking and Analysis
Discover our data analysis and benchmarking services, which cover everything from high-level management system audits to detailed reviews of key systems such as the CMMS (Computerised Maintenance Management System). We offer broad overviews and in-depth examinations of specific details.
Work with experts to build a framework that consistently applies these strategies, allowing you to enhance decision-making, operational efficiency, and overall success.
The importance of data analysis
Managing assets can be complex and challenging, particularly in industries with large and diverse portfolios. One of the most significant challenges is understanding and monitoring asset data quality and ensuring that your data is up-to-date, accurate, and accessible.
Poor data quality impacts all aspects of an organisation, from safety compliance to operational uptime. It is essential to have accurate, reliable and high-quality data, which requires collaboration, commitment, and a proactive approach.
What is benchmarking and analysis?
Data analysis is a fundamental part of the operational excellence journey and involves identifying not only gaps and deficiencies that need to be addressed, but also the development of phased action plans detailing the actions required to achieve data excellence.
Data benchmarking compares the business to external industry standards and internal shared best practices (from site to site or cross-departmental) to provide insights into where improvement can be made. This method of sharing intercompany best practices leverages the psychology of individuals and teams, allowing their work to be recognised and benefit the business through increased efficiencies.
Benchmarking and analysis challenges
Data efficacy is an ongoing challenge faced by companies of all sizes. As the world has been increasingly driven by data over the last two decades, the size of data sets and the speed of change have accelerated to unprecedented levels. It is crucial that businesses have the means to understand the data they hold, and that it is both accurate and timely.
Some challenges of analysis and benchmarking include:
- Faulty data leads to inaccurate decision-making, strategic errors, and overall business misdirection.
- Safety risks increase when incorrect hardware or settings are used, endangering people and property.
- Poor data quality reduces operational efficiency, leading to wasted time spent locating information, higher costs, and holding incorrect materials.
- Data errors cause financial losses, discrepancies, incorrect stock levels, and lost revenue opportunities.
- Inaccurate or incomplete data causes mistakes which negatively affect customer service, leading to dissatisfaction and a loss of trust
- Poor data quality can lead to non-compliance with regulations, risking legal penalties and reputational damage.
Our benchmarking and analysis solutions
Effective data analysis encompasses a spectrum of approaches, ranging from high-level overviews down to meticulous examinations of data systems.
Our software, Effio™, provides the means to deliver the assessment of data quality, by subdividing quality checks into tailored groups that take into account whether the data is mandatory or preferred. Effio™ delivers the platform to hold the data checks and address the highlighted data anomalies.
Data Analysis and Benchmarking:
- Conduct thorough data analysis to understand the current state of the data. This involves examining data distributions, identifying anomalies, and assessing completeness.
- Use benchmarking tools and data to compare areas where best practices can be utilised to improve missing values, outliers and inconsistencies.
Data Cleansing and Standardisation:
- Implement data cleansing to fix inconsistencies, such as incorrect spelling, inconsistent date formats, and remove duplicate records.
- Establish or improve your data quality rules and apply them consistently across the data sets.
Data Validation and Verification:
- Conduct validation checks specific to your data requirements, ensuring mandatory data requirements adhere to defined standards, and identifying preferred (“best practice”) data is validated against your business rules.
- Verify data accuracy by cross-referencing with trusted sources or conducting periodic audits.
Data Governance and Stewardship:
- Definition and storage of data quality checks and metrics along with tracking of data improvement progress over time.
- Provide expert data governance advice and data standard documentation.
Automated Quality Checks:
- Implement automated data quality checks within data pipelines. Covering completeness, consistency, uniqueness, and referential integrity.
- Set up alerts for anomalies or breaches of predefined thresholds.
User Training and Awareness:
- Educate data users about the importance of data quality. Encourages best practices for data entry and maintenance, along with building a culture of data quality awareness across departments.
The value our data analysis solutions bring
Informed decision-making
Accurate and relevant data allows for decisions to be made on a solid foundation of reliable information, enabling better strategic planning and operational effectiveness.
Operational efficiency
Reliable data reduces errors and redundancies in business processes, saving time and resources. For example, precise inventory data facilitates effective stock management.
Compliance and risk management
Quality data supports adherence to regulatory compliance as well as quick risk identification and mitigation.
Feedback loops and continuous improvement
Implement feedback loops that allow users to understand and address data quality issues and update processes as business needs evolve.
Benchmarking and analysis case study
Get in touch
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The 2023 Global Maintenance Manager Report
Produced by our Asset & Integrity Management specialists, this report features survey responses from hundreds of maintenance professionals from around the world as well as analysis and tips from industry experts.