Data quality monitoring is the ongoing process of checking and ensuring that data used within a system is accurate, complete, consistent, and up to date. It involves regularly reviewing data for errors, missing values, duplicates, or inconsistencies. By monitoring data quality, organisations can trust the information they use for decision-making and operations.
Category: Data Science
Data Harmonization
Data harmonisation is the process of bringing together data from different sources and making it consistent so that it can be compared, analysed, or used together. This often involves standardising formats, naming conventions, and units of measurement to remove differences and errors. By harmonising data, organisations can combine information from various places and get a…
Data Standardization
Data standardisation is the process of converting data into a common format so that it can be easily understood, compared, and used together. It involves making sure that data collected from different sources follows the same rules and structure. This helps prevent confusion and mistakes when analysing or sharing information.
Analytics Center of Excellence
An Analytics Center of Excellence (CoE) is a dedicated team or group within an organisation that focuses on promoting best practices, standards, and strategies for data analysis. Its goal is to help different departments use data more effectively by providing expertise, tools, and support. The CoE helps ensure analytics projects are aligned with the companynulls…
Data Literacy Training
Data literacy training teaches people how to read, understand, and use data effectively. It covers skills such as interpreting graphs, spotting trends, and making decisions based on data. This training helps individuals become more confident in working with numbers, charts, and reports in their daily tasks.
Data-Driven Culture
A data-driven culture is an environment where decisions and strategies are based on data and evidence rather than opinions or intuition. Everyone in the organisation is encouraged to use facts and analysis to guide their actions. This approach helps teams make better choices and measure the impact of their work more accurately.
Data Visualization Strategy
A data visualization strategy is a planned approach to presenting data in visual formats such as charts, graphs, or maps. It involves choosing the right visual tools and methods to help people understand information quickly and accurately. A good strategy considers the audience, the message, and the type of data to ensure the visuals are…
Prescriptive Analytics
Prescriptive analytics is a type of data analysis that goes beyond simply describing or predicting what might happen. It suggests specific actions or strategies to achieve the best possible outcome based on available data. By using mathematical models, simulations, and algorithms, prescriptive analytics helps decision-makers choose the most effective path forward.
Predictive Analytics Strategy
A predictive analytics strategy is a plan for using data, statistics and software tools to forecast future outcomes or trends. It involves collecting relevant data, choosing the right predictive models, and setting goals for what the predictions should achieve. The strategy also includes how the predictions will be used to support decisions and how ongoing…
Real-Time Analytics Framework
A real-time analytics framework is a system that processes and analyses data as soon as it becomes available. Instead of waiting for all data to be collected before running reports, these frameworks allow organisations to gain immediate insights and respond quickly to new information. This is especially useful when fast decisions are needed, such as…