KPI definition
A KPI, or Key Performance Indicator, is a quantifiable measure used to assess the success of an organization, project or activity in relation to specific objectives. KPIs help to determine whether quality standards and results achieved are in line with expectations and strategic objectives.
Example of a key performance indicatorÂ
- Sales conversion rate
- Customer satisfaction (NPS)
- Customer acquisition cost (CAC)
- Customer loyalty rate
KPI tracking
To keep track of KPIs (Key Performance Indicators), it's essential to put in place a rigorous and effective action plan. This involves clearly defining the objectives to be achieved, selecting the right quality KPIs in line with these objectives, and regularly collecting the data needed to evaluate them.
It is also crucial to analyze this data in depth, in order to draw relevant conclusions and make informed decisions. KPI monitoring must be integrated into a continuous improvement approach, aimed at optimizing company performance and costs. To achieve this, we recommend the use of appropriate tools, and the implementation of relevant monitoring indicators and dashboards. Finally, it is essential to involve all stakeholders in this process, to ensure a shared understanding of the issues and results.
KPI analysis
Theanalysis of qualityKPIs (Key Performance Indicators) is essential for assessing a company's performance. By taking a close look at the various KPIs, it is possible to identify areas requiring improvement and make strategic decisions based on concrete data. Quality KPIs can include metrics such as sales, profitability, customer satisfaction, conversion rate, etc. By analyzing this data, companies can better understand their overall performance and identify opportunities for growth. It is also important to track the evolution of KPIs within dashboards over time, to assess the impact of decisions taken and adjust strategies accordingly. In short, KPI analysis is a valuable management tool for assessing business performance and making informed decisions.
Data quality and indicators
The achievement of a company's objectives and its ability to meet challenges are intimately linked to the quality of the data collected, stored and processed. The better the quality of this data, the more the company will optimize its time and make strategic decisions that are both reliable and, above all, profitable.
Companies that have realized the importance of data quality and implemented continuous improvement processes are seeing revenue growth of 15-20% thanks to "intelligent" decision-making. Conversely, it has been estimated that in 2016, US companies lost more than $3 billion as a result of decisions based on poor-quality data.
Improvement processes must integrate data quality monitoring, optimization solutions for incoming data (collection format, standardization, integrity, security, etc.) and specific processing in relation to internal uses.
To estimate the level of data quality KPIs, companies and stakeholders base themselves on seven characteristics:
1 - Data consistency
Data is recorded in the same way, regardless of the collection point and destination table. If two separate processes of the same data produce the same result, the data must match, or at least be aligned.
Quality control: data range, calculation of variance and standard deviationÂ
2 - Data accuracy
The precision of data is determined by its accuracy.
The accuracy rate evaluates the measured value and the exact value of the data. The data must be free of errors, obsolete or duplicates.
Quality control: error rate calculation
3 - Completion
A high-quality data set is made up of complete datasets with sufficient usable information. High-quality data has a positive impact on the decision-making process.
Quality control: calculating the percentage of data containing all the necessary information
4 - Auditable
All data is stored and freely accessible.
All processing actions and modifications to this data are recorded and logged. At the very least, in the event of an audit, information on what has been done, what action has been taken and the date can be exported.
Quality control: Rate of corrupted data, Rate of data not found
5 - Validity
Recording data in the same format, depending on the point of collection, to facilitate subsequent use of the data. Unique formatting rules must be established to ensure a high validity rate.
Quality control: Percentage of data where all values are in the required format
6 - Uniqueness
Data is recorded only once in a single location.
The creation of duplicate entries is to be avoided, as it is a source of error. Tracking this indicator helps to identify and avoid double data entry.
Quality control: Number or percentage of repeated values
7 - Obsolescence
The interval between data collection and the expected analyses meets the company's needs.
Data is retrieved at the right time for the intended use. Enables effective tracking of changes.
Key performance indicators: time variance
It's important to understand that maintaining and improving the quality of your data is an ongoing process within your company. The implementation of Data Quality Management solutions necessarily has an upward impact on this level of quality throughout the data lifecycle. It's important to choose the right DQM solutions for your needs and expected quality objectives.
In addition to these key features for assessing data quality, Data Enso implements other key performance indicators and dashboards that provide additional insight into the use of our DQM solutions and the availability of our services.Â
When we set up our partnership, all these KPIs are accessible in your My Data Enso space:
- Number of data items submitted for validation
- Response rate for our solutions
- Data validation rate ("ok" status)
- Corporate enrichment rate
- Average response time
- Service availability: server, website, solution APIs
