Transforming Raw Data into Actionable Insights
Driven by an intense desire to understand data and fueled by the opportunities presented during the COVID-19 pandemic, I enthusiastically ventured into the vast world of Python, Machine Learning, and Deep Learning. Through online courses and extensive self-learning, I immersed myself in these areas. This led me to pursue a Master's degree in Data Science. To enhance my skills, I actively engaged in data annotation while working at Biz-Tech Analytics during my college years. This experience deepened my understanding and solidified my commitment to this field.
Four actions to transform raw data into readily accessible data:
Collect and transform the data: When using multiple data sources, BI tools allow you to extract, transform, and load (ETL) data from structured and unstructured sources. When that process is complete, we can then store the data in a central repository so that an application can analyse and query the data.
Analyse data to discover trends: The term 'Data Analysis' can mean many things, from data discovery to data mining. The business objective, however, is all the same. BI often provides users with a variety of modeling and analytics tools. Some come equipped with visualization options; others have data modeling and analytics solutions for exploratory, descriptive, predictive, statistical, and even cognitive evaluation analysis. This helps users to explore the data - past, present, and future.
Use visualization options in order to provide data clarity: Visualization options often include reporting, dashboards, charts, graphics, mapping, and key performance indicators.
Taking action and making decisions: The process culminates with all the data at your fingertips to make actionable decisions. With the help of BI, we can quickly reduce inefficiency, correct problems, and adapt the business to support the market conditions.