Data problems in SPM and how to solve them

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In the current era of information-based economies, data management has become an essential component of every organization, particularly significant in the context of sales performance management (SPM). For companies operating in competitive markets, effective resource allocation, precise strategic market entry planning, and motivating the sales team are key aspects of achieving success.

This insight will explore the importance of data in the SPM system and discuss potential challenges encountered in working with them. Furthermore, we will provide examples of best practices.

The Most Common Data Issues

Data Quality

This term refers to the degree to which data is accurate, comprehensive, consistent, up-to-date, and useful for organizational purposes. High-quality data is crucial for effective management of activities within an organization. Data quality issues can have serious consequences such as incorrect decision-making, analytical challenges, loss of trust among customers and employees, and a reduction in the efficiency of business operations.

Lack of Data Standardization

When using multiple data sources without standardization, many problems can arise. These issues may stem from using different levels of precision (the number of decimal places) or applied rounding methods. Lack of standardization in integrating data from various sources increases the risk of incorrect interpretations

Large Volume of Data

As the volume of data increases, so does the difficulty of effectively managing, storing, and processing it. Working with a vast amount of data can lead to a decrease in performance and the emergence of delays, especially for data that requires real-time processing. Large volumes of data complicate process optimization and result in overloads and constraints on memory resources.

Incorrect interpretation of data

Misinterpretation of data stems from a lack of understanding of the proper context. Ambiguity in definitions or lack of a clear definition of the terms used in the data can also lead to misinterpretation. Different interpretations of terms can yield varying results in analyses. The lack of data standardization, errors in collection, and gaps in timeliness directly contribute to misinterpretations. 

Time Data

Time data is information that relates to a specific point or period in time. It includes various types of time-related information, such as dates, hours, time intervals, or time stamps. Time data is crucial for analysis, forecasting, and decision-making, especially in the context of monitoring changes over time, identifying trends, or modeling dynamic processes. Common challenges when working with time data include the occurrence of gaps, time loops, and duplicates.

How to deal with the above problems?

To avoid issues with data quality, it’s important to implement a comprehensive set of actions and procedures aimed at minimizing errors and ensuring the consistency, completeness, and timeliness of data. These actions cover various aspects of working with data, from collection to processing and analysis. The key solutions to ensure the highest data quality for the SPM process are:

  • Standardization and normalization of data – helps minimize ambiguities and errors resulting from differences in data interpretation. Establishing uniform standards for data, such as formats, units of measurement, and terminology, is essential.
  • Verification of input data – To reduce errors during the collection stage, precise data entry processes, including checks for accuracy and completeness of information, should be implemented.
  • Early definition of business rules – Creating and implementing business rules that define expected standards and conditions for data. This helps maintain consistent data quality throughout the organization.
  • Comprehensive documentation of processes – Understanding the context and source of data requires thorough documentation of data collection, processing, and analysis processes.
  • Updating and maintaining data – Clear procedures for updating schedules and responsibilities for specific tasks need to be defined.
    Implementation of ETL process – Automation allows for more efficient data management, minimizing human errors in the process.

While working with the SPM system, we may encounter several problems related to the data itself. This directly affects the efficiency of operations. Key issues include ambiguity in data interpretation, time discrepancies, and lack of standardization. Solutions for these problems require effective data quality management, standardization of time and numerical formats, and the implementation of a precise ETL process. All these actions contribute to strategies for ensuring data quality. Flexibility in processing large amounts of data, monitoring the quality of time data, and focusing on information consistency are crucial for the effective implementation of the SPM system. Ensuring the quality, consistency, uniformity, and security of data forms the foundation of an effective strategy for managing sales outcomes and supporting organizational decision-making processes.


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Ola Wojdyła


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