At KaarbonTech, we’re driven by data. Our next blog in this series looks at why data analytics are so important.
What is the key principle in data analytics? The underlying data has to be accurate and reliable. We can then produce complex and accurate reports in an easily readable and visually exciting format, offering insightful information from which to make decisions.
What is the process of data analysis?
First, we define the objectives and the questions we are trying to answer. For example – which locations are most at risk of flooding.
Then we gather the data needed for this – system outputs including drainage inspections showing silt levels, flood zones etc – this is often a combination of primary system data and data from third parties
We ‘clean’ the data and any anomalies and identify patterns and trends.
We carry out numerical and spatial analysis of the gullies most at risk – for instance, those with high silt levels and in a flood zone.
Finally, we visualise the data in a graph, table or map and gather the insights and interpretations from this, so that informed, data-led decisions can be made.
How does it help us visualise data and why is this so important? Data analysis, in essence, is visualisation – it identifies patterns and correlations within the data that brings out valuable insights. Visualising the analysis means that data can be transformed into actionable insights that are easy to understand and interpret, facilitating data-driven decision-making.
What are the benefits to our customers? We provide clarity, create insights and display data clearly so our customers can use it to improve their operational management. Data-led decisions can be made as the result of our analytical review, to steer the organisation forward in an efficient and effective way. For example, raw exported files may look like a jumble of numbers, however our data analysts transform this into meaningful graphs and tables that show the relationship between the data sets. Insights and proposals can be drawn from these clear visuals, helping customers recognise areas of improvement within their drainage management strategies. For example, identifying gullies with over 50% silt level in a given vicinity can demonstrate which areas need cleaning more frequently and where the high-risk gullies are. Then we can identify and highlight patterns or trends relating to that location.
Our analytical approach is personalised for each customer, based on their operational challenges, and resource demands. Interpretations and predictions for future drainage programmes can then be built around the analytical review, as well as benchmarking the outcomes against other similar customer profiles using anonymised averages from our databases. This coupled with our industry expertise will assist the customer to focus on areas for improvement and setting achievable and deliverable efficiency targets.