The first step in gathering data is to ask what information might be helpful to answer the questions being asked. Identify pertinent datasets from various sources, a wide array of structures or file types can be used. Each data source that is included will need to share a common dimension in order to be combined.
The ability to transform these different types into a common structure that allows for a meaningful blend, without manipulating the original data source, is something that modern analytics technology can do in an automated and repeatable way.
Combine the data from various sources and customize each join based on the common dimension to ensure the data blending is seamless.
Think about the desired blended view and only include data that is essential to answer the questions being asked as well as any fields that may give additional context to those answers when an analysis is stressed. The resulting dataset should be easy to comprehend and explain to stakeholders.
Circle back to this step to include or remove data from a workflow and further build out the analysis.
It’s no secret that combining data from different sources can usher in a whole host of compatibility or accuracy issues. Examine the data to validate the results, explore unmatched records, and ensure accuracy and consistency throughout the dataset.
First, cleanse and structure the data for its desired end. Then, review the new dataset to ensure that the data type and size are in their desired format for analysis.
Finally, review the outcome of the blend with a critical eye. This is a great opportunity to explore the results for any unmatched records and perhaps circle back to additional data preparation tasks upstream of the blend.
Once the heavy lifting of data blending is done, it’s time to implement the data into the right business intelligence system so that the blended dataset can assist in fulfilling the objective.
This means that resulting outputs can then be pushed back into a database, incorporated into an operational process, analyzed further using statistical, spatial, or predictive methods — or pumped into data visualization software such as QlikView or Tableau.