Ensuring data reliability along the KPI lifecycle

Written by andra.rotar on July 21, 2023. Posted in KPI

Image Source: Gerd Altmann | Pixabay

The lifecycle of a Key Performance Indicator (KPI) is a dynamic process involving definition, recalibration, and—sometimes—abandonment. From establishment to practical application and ongoing evolution, KPIs undergo several steps to effectively measure performance, and prioritizing data reliability at every stage is crucial to achieving their intended purpose.

1. The foundation of reliable data

The first stage of the cycle, KPI selection, may seem simple, but it is a complex process intertwined with various interdependencies and calibrations with the organization’s objectives.

Establishing data reliability should start from this initial step, and involving employees as primary sources for KPI selection is an effective approach. Their valuable knowledge about the data generated from their activities enhances the reliability of the selected KPIs. Additionally, considering data availability and reliability as criteria for the selection further enhances overall data trustworthiness.

KPI documentation plays a pivotal role in ensuring reliability. Adopting a standardized documentation form establishes a solid foundation for rigorous and dependable data collection and reporting. This approach provides clear guidelines for defining KPIs, including unambiguous calculation formulas, ensuring that the collected data accurately reflects the intended purpose of each KPI.

2. Establishing dependable data collection

During the activation of KPIs, data reliability depends on the meticulous consideration of data sources, robust data-gathering methods, and the establishment of a strong governance structure. It is imperative to utilize trusted and verified data sources that are up-to-date, accurate, and aligned with the KPIs being measured. Accountability for KPI data should be established by clearly designating KPI owners and data custodians. Furthermore, adopting a standardized data collection process that incorporates technology-driven solutions significantly enhances accuracy.

3. Communicating meaningful insights

The analysis and reporting of KPIs are significant in ensuring the correct organization and communication of data to key stakeholders. Errors in data analysis have the potential to result in misleading insights, which can have negative effects on decision-making. Therefore, correctly identifying relevant KPI content and conveying meaningful insights derived from KPI data to various stakeholder groups within the organization is essential.

4. Continuous improvement

Finally, data reliability can be enhanced through the process of refreshing KPI documentation. This ongoing effort involves recalibrating KPIs after their initial establishment and customizing them for optimal use.

Attention is given to both the content of the KPIs and the standardization of their format. Standardizing KPI content establishes uniform guidelines and criteria for measurement and reporting, ensuring data reliability and consistency. This step refines the measurement and reporting processes, facilitating accurate and dependable data for decision-making purposes.

Monitoring KPI data reliability: The role of the Data Custodian

The Data Custodian is critical in upholding the reliability of data. They actively participate in the design of performance data collection, receipt and storage, processing, analysis, reporting, dissemination, and even archival or deletion of data. They implement measures to validate and verify the accuracy, consistency, and completeness of the data. This involves conducting regular data audits, resolving discrepancies or anomalies, and implementing data cleansing processes to ensure data integrity.

To evaluate the reliability of KPI data, the Data Custodian can monitor % KPIs with reliable data. This metric measures the number of reported KPIs that contain reliable and trustworthy content out of the total number of KPIs reported, according to smartKPIs.com.

In conclusion, to succeed in a data-driven world, organizations must prioritize data reliability along the KPI lifecycle. By implementing the strategies and practices discussed above, organizations can unlock the true potential of their performance measurement systems and empower stakeholders with reliable insights for better decision-making.

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Is data visualization a science or a language?

Written by Sirisha Kodukulla on October 4, 2021. Posted in Articles, Operational performance

Image Source: StockSnap | Pixabay

Is data visualization a science or a language?/

That is a question posed by Colin Ware in his book, “Information Visualization.”

We deal with data every day, especially at work. It can fuel our decisions and change the way we work. At the same time, if we’re surrounded by a huge amount of data, we may not find it easy to arrive at an optimal decision. This is where data visualization comes in.

Data visualization refers to the graphical representation of the data. It makes large amounts of information easier to understand and helps identify patterns and trends. People can easily comprehend information and make conclusions through data visualization.

“Graphical excellence is that which gives to the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space,” wrote American statistician Edward R. Tuffe, author of the book “The Visual Display of Quantitative Information.”

Understanding how to approach data visualization allows people to equip themselves with the right tools, approach, and strategies as they gather data and present them visually. This is important to businesses who want to understand consumer behavior patterns or governments seeking data-backed insights on a crisis.

Data visualization may be considered a science because it is a process and represents data methodically and accurately. Data visualization begins with volumes of information, undergoes an intensive cleaning, classification, statistical and mathematical modeling, analysis, and design process, and ends with a visualization. 

On the other hand, many argue that data visualization is a language because it uses diagrams to convey meaning. Data is encoded into symbology and semiology. The syntax and conventions of these diagrams are not inherent and must be learned. 

Data visualization helps to communicate analytics results in pictures. In simple words, data visualization is the language of images. That is on par with the language of words both written and spoken and with the language of numbers and statistics.

Merging science and language

Science and language do not have to invalidate each other. Their elements can go hand in hand in data visualization. 

In data visualization, the challenge is how to make more people take interest in scientifically processed data. Presenting appropriate and relevant information in an engaging format through design is what makes data visualization successful. Science processes and provides information based on certain objectives while design is a form of communication shaped by visual elements.

Combined, scientific data and design can generate meaning out of raw data. The end result of data visualization is almost always a story. In storytelling, the plot (design) won’t be able to progress without the characters (scientific data) and vice versa. 

Ensuring that graphs and charts present meaningful results is important now more than ever. In MicroStrategy’s “2018 Global State of Enterprise Analytics,” 63% of data-driven organizations said that implementing analytics initiatives led to high efficiency and productivity while 57% said they became more effective in decision making.

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