Discovering & Analysing data
Discovering and analyzing data are key steps in the process of making data-driven decisions. There are several techniques and tools that can be used to discover and analyze data effectively.
One common technique is data exploration, which involves looking at the data in various ways to gain insights and identify patterns or trends. This can be done using visualization tools, such as charts and graphs, to help make the data more understandable and easier to interpret.
Another technique is data mining, which involves using algorithms and statistical techniques to identify patterns and relationships in large datasets. This can help to uncover hidden insights and inform decision-making.
Other tools that can be used for data analysis include spreadsheets, statistical software, and business intelligence platforms like QlikView. These tools can help to organize, filter, and manipulate data, as well as perform more advanced analysis techniques such as regression analysis and forecasting.
Overall, the goal of data analysis is to extract meaningful insights from data and use those insights to inform decision-making. By carefully discovering and analyzing data, organizations can make more informed and effective decisions that can help drive business success.
Discovering and analyzing data in QlikView involves several steps:
- Using technologies like OLEDB and ODBC, QlikView can connect to a variety of data sources, including databases, spreadsheets, and text files.
- Loading data: Once the data sources are connected, the data can be loaded into QlikView. This can be done manually or through automation using scripting or macros.
- Transforming data: After the data is loaded, it may need to be transformed to make it usable for analysis. This can include tasks such as renaming fields, removing duplicate rows, and calculating new fields.
- Creating visualizations: Once the data is prepared, it can be used to create visualizations such as charts and tables. These visualizations allow users to explore and understand the data, and can help to identify patterns and trends.
- Analysing data: The visualizations created can be used to analyse the data, by drilling down into the data, filtering the data and creating selections. The data can be further analysed by performing calculations and writing expressions.
- Creating Reports: The final step is to create reports which can be shared with others, this can be done by creating dashboards and stories.
Throughout the process, users can take advantage of QlikView’s associative model, which allows them to easily explore and understand the relationships between different data elements.