The Power of Data Series
Here at KaarbonTech, we’re driven by data. This month we’re looking at the importance of good quality data.
Why is it important to have good quality data? Quality data drives accurate insights and better decision-making. The decisions driven from these insights will then be reliable and lead to better outcomes. On the flip side, decisions made using bad quality data are often costly and ineffective. Accurate and current information makes analysis more efficient and streamlined, allowing more time for extracting insightful interpretations from the data, rather than spending time cleaning it. More time spent on analysis leads to innovation and creativity, which means more interesting results.
Good quality data is fundamental for insightful and efficient analysis and informed decision-making.
How do we ensure we have good quality data? All incoming data is assessed for duplicates, anomalies and inconsistencies, and these are fixed before importing into one of our systems. All data is cleaned and matched by our expert data analysts, ensuring it adheres to our system’s requirements. Each field entered onto the system has a specific data type and structure, data cleaning ensures that each field is populated with its correct data type. Data can be re-ordered, joined and split to create new fields that will allow further adherence to the system and provide the opportunity for deeper insights.
How do we establish baselines? Baseline data for KaarbonTech comes from our systems. Due to the work we undertake to ensure good data goes into each system, it allows for data continuity between them, and means we can benchmark different systems of the same type – e.g. drainage – against one another. By benchmarking the data across different Gully SMART systems, we can glean valuable insights on metrics such as crew productivity, silt levels and average inventory change before and after a KaarbonTech survey.
What is the goal? If a customer approaches us with new data or we find some interesting new data during research tasks, we can often find a home for it within the system, meaning the data can be made available and insights drawn from it. The goal is to gather as much ‘good’ data as we possibly can, so that it can be used in the systems, adding value to our customers, and helping provide more valuable insights through our analysis.