Managing the production process of raw materials is a heavy task when it comes to monitoring data and interpreting results so you can make informed decisions. In any production cycle, you are dependent on data, either internal or external, to help guide you through the process. For that, a solution has to be adopted to gather and convert data into actionable insights
Encountering bad data
Consolidation of multiple data sources with different types of data
Deciding on which sources provide the most relevant data and which don’t
How to combine internal and external sources of data to provide unified metrics
A data analytics part of the solution is created to convert data into meaningful and actionable insights and metrics. The process consists of data collection, transformation, and analysis. Not all data comes in the same formats and volume. There have to be systems built to draw data from their sources into one place and in formats that can be used to present information relevant for making decisions on daily business operations.
Our approach is to find valuable data that will be analyzed and presented to users. The goal of data analytics is to convert data into numbers that users can easily understand and interpret. In line with that, they can easily make assumptions and analyze information. Through data engineering and data science methods, data is extracted and put through scientific methods, algorithms, and processes so the software solution can communicate findings on business operations, especially production.
As a part of a web software solution that Deegloo worked on, it was necessary to also develop methods to collect data from multiple sources in milk production and farm management. Great amounts of data are generated daily and there was a need for data aggregation and visualization so users can receive insights that are a basis for making informed strategic decisions.
Farms collect data on milk, herd, and feed, and that information needed to be compared to weather information, milk quality, and price data. Data like that comes from internal and external sources in different formats, so we used multiple methods to be able to aggregate that data and turn it into easy-to-interpret results.
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