Data Quality: What Are the Key Strategies for Making Your Data Useful?
Data quality is a major issue that affects many different areas. Since large amounts of data are gathered in modern agriculture and used for decision-making and planning activities, this problem is persistent in this domain as well.
This issue is often overlooked because it is not considered while modelling different systems and databases. As a result, agricultural data is underutilised, preventing farmers from making more accurate and informed decisions.
In the following text, we will explore the types of data used in agriculture, the challenges of maintaining high data quality, and the key aspects and techniques for effective data management.
Types of Data in Agriculture
Data in agriculture applications comes from a variety of sources and can be categorised into different types. Understanding these types can help us better appreciate how information is used in farming.
At the core, data can be raw or derived. Raw data is the initial, unprocessed information collected directly from sources like sensors or satellites. For example, it might include real-time temperature readings or initial crop yield counts. Derived data, on the other hand, is created by processing or analysing raw data to reveal more actionable insights, such as calculating average yields from initial measurements or generating detailed vegetation maps from satellite images.
Data can also be classified as primary or secondary. Primary data is collected first hand for a specific purpose, like measurements taken directly from a field survey. In contrast, secondary data involves using information that was collected by others for different purposes, such as historical crop records or previous research findings.
Furthermore, regular data and geospatial data are the most common kinds of data.
- Regular data is information that can be written as text or numbers and saved in common formats like spreadsheets or text documents. For example, descriptions of crops from official sources.
- Geospatial data is location-based information that shows where things are on a map. This can include images taken from satellites or files showing road networks. Geospatial data can also come as continuous streams of information, similar to a live weather update. This type of data can be collected from satellites, sensors on the ground, or weather stations, providing constant updates like temperature readings.
Challenges in Agricultural Data Quality
The variety and complexity of data sources and formats present significant challenges in maintaining data quality in precision agriculture. Integrating data from a range of inputs—such as sensors, drones, satellites, GPS, RFID, cameras, and other devices—alongside external sources like weather stations, market prices, and agronomic research—requires careful management. Each source may use different standards, formats, and units, leading to potential inconsistencies and errors when data is combined.
Additionally, the reliability, timeliness, and completeness of data sources can vary, affecting the overall validity and usefulness of the data. This variability is a form of data quality bias, a topic we explored in detail in our blog, Data Bias Explained: Insights into 9 Types Affecting Agriculture.
Key Aspects of Data Management
Adopting effective strategies from data collection to presentation is essential for ensuring that agricultural data remains accurate and useful, addressing common errors like missing or outdated data, false precision, and inconsistencies.
- Modelling and Management: Integrating diverse data sources requires effective modelling and management to ensure compatibility and utility.
- Quality Control and Assurance: Rigorous quality control and assurance processes are essential for detecting and correcting inaccuracies and inconsistencies, thereby upholding the data’s reliability and validity.
- Analysis: Analysing data with an awareness of its quality is crucial for deriving meaningful insights. Ensuring that analyses are based on accurate data helps in making informed and effective decisions.
- Storage: Efficient storage solutions are needed to handle large volumes of data while maintaining its integrity. Proper storage systems facilitate easy access and management of data, preserving its quality over time.
- Presentation: Data must be presented in a clear and accessible manner. Effective presentation enables users to interpret and act on data accurately, making it essential for decision-making processes.
Techniques for Maintaining Data Quality
To maintain data quality, it’s important to regularly assess data sources and processes through several key techniques:
- Data Profiling: Examining and analysing the structure, content, and quality of data helps identify characteristics and potential issues, such as missing values or inconsistencies.
- Data Validation: Ensuring data meets predefined rules and standards checks for errors or anomalies, verifying that the data is accurate and reliable.
- Data Cleansing: Correcting or removing errors and inconsistencies, such as duplicate entries or incorrect values, improves the overall quality and usability of the dataset.
- Data Auditing: Tracking and reviewing data quality over time helps identify and document issues and trends, enabling ongoing improvements and ensuring compliance with standards.
STELAR’s Solution for Enhancing Data Quality in Agriculture
To effectively tackle the challenges of agricultural data quality, a sophisticated platform and comprehensive tools are essential for streamlined data discovery, AI readiness, and semantic interoperability.
STELAR addresses these issues through the development of a Knowledge Lake Management System (KLMS) that promotes intelligent data management and advances food safety. By adhering to the FAIR (Findable, Accessible, Interoperable, Reusable) principles, STELAR ensures that data is managed in a way that supports these fundamental goals, enhancing the accuracy and usability of agricultural data.
Conclusion
Ensuring high data quality in agriculture is crucial for making informed decisions, optimising practices, and ultimately enhancing the effectiveness and sustainability of farming operations.
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