ETL (Extract, Transform, and Load) tools provide a mission critical function for marketing organizations and their agencies. Many companies have customer lists or potential clients stored in a spreadsheet or database. They may also have information about target markets, or a list of potential contacts that have been provided by sales, or a set of leads that have been purchased. This data may be scattered into multiple different formats. Information exists within them but finding it or making sense of it can be a difficult process, and in some cases, if the data sources are large enough, truly formidable.
ETL tools, at the basic level, enable loading data from multiple data sources, combine and transform them into a format which can then be loaded into a database for further querying. Beyond these rudimentary functions, many of these tools contain a wide set of extra features. These can include everything from data analytics tools including many predictive modeling features, to useful output functions, including the ability to create graphics, charts and full-fledged dashboards.
To assist these companies to profit the most from their data, data scientists have developed some reliable procedures for helping to navigate these vast seas of information.
They have evolved beyond simple ETL functions to being complete, or nearly complete Business Intelligence tools.
In this article we are going to look at two tools side by side: KNIME, which is a powerful free open source ETL and BI tool, and Alteryx, which is a commercially licensed product. Both tools boast a wide range of capabilities. We will be examining them from the perspective of a Direct Marketing organization, seeking to gain knowledge from either simple or broader data sources.
Our approach will be to address these from the perspective of those who have a strong business need but may or may not have advanced technological know-how or capabilities. Information used in this article is based on both personal and general public consensus about the use of these two products.
The pieces we are comparing will include:
- Input/Data Preparation
- Data Blending
- Data Analysis