How can you ensure data accuracy when analyzing KPIs?

When setting your KPIs ensure they are measuring the right thing and that you have the ability to and are in agreement on how to measure when it comes to achieving the strategic objectives/MVV. Leader statement: "Let's measure turnover this year." Deeper Dive - All turnover, Top Talent Turnover, Turnover within the last 90 days, Annualized Turnover? All of these metrics tell a different story. KPIs must ensure that the data ties back best to our strategy and will move us forward unified. Now, do we have the ability to measure it accurately? Let's agree on the source of the data, the metric to measure, calculation to get there and the date even that it's pulled and distributed.

KPIs are driven from the main strategic objectives for the organisation to be tailored for each division aligned with the strategy used to achieve the mission and vision. KPIs has to be used for elevating the results and encouraging the team, especially when it comes to a team who is result oriented

Measuring KPIs can be challenging. However, ensuring data accuracy through a few simple techniques and methods helps the decision-making process. Try establishing data validation processes to identify any errors, inconsistencies, and missing data values. Clean the data by removing duplicates, outliers, and irrelevant information to improve quality. Coordinate data from multiple sources, which should be integrated to provide a comprehensive view of performance metrics and thus - ensure accuracy. The last key step is automated monitoring systems, which should be implemented to detect real-time discrepancies, enabling prompt corrective actions.

Validating data sources is a crucial step in the data analysis journey, serving as the foundation for informed decision-making and driving business performance. Clearly defining the specific data fields, formats, and quality standards necessary to generate accurate Key Performance Indicator (KPI) measurements. Ensuring data accuracy by cross-referencing it with multiple sources or conducting validation checks. By comparing data from different sources, you can identify discrepancies and ensure the integrity of your analysis. Seek input from subject matter experts or data specialists to validate data sources and assess their suitability for KPI analysis. Leveraging their expertise adds an extra layer of validation.

Triangulate your data sources with other reporting and KPIs. If this all stacks up but something still looks awry review your pipeline, attribution modelling and assumptions.

- Use reliable data sources - Verify data from different sources - Use a prototype modelling for the KPIs using: 1- Old data over a certain period and cross check the results 2- Modelling a pilot period over a quarter to verify the data, set benchmarking and finalize the SOP for setting you KPI, data sources and calculations. After data sourcing validation, - Ensure data security from unauthorised access - Data checkup over time - Data validating, cleaning and normalisation.

For data cleaning; 1) Remove duplicate data to avoid wrong counting 2) Cross check, verify and handle the missing data by either assuming the missing values or removing the whole missing records (I usually utilise the second way) 3) Standardise data formats 4) Standardise calculations methods and automate the calculations methodology

We can ensure data accuracy when analyzing KPIs through few steps like: *Data Collection: Use reliable data sources and ensure that data is collected consistently over time. *Data Cleaning: Regularly clean and validate data to identify and correct any inaccuracies. *Data Integration: Integrate data from multiple sources to get a comprehensive view. *Data Verification: Cross-check data against known benchmarks or historical trends. *Documentation: Maintain clear documentation of data sources to ensure transparency. Quality Assurance: Implement quality assurance processes. *Use of Technology: Utilize advanced analytics tools and algorithms for data validation, anomaly detection, and error correction.