I love analyzing data. For some clients that means taking data from one source. For others, it means sorting through multiple data sources with different formats from different data platforms. When that happens, a once easy data analysis project becomes a bit more difficult. More difficult, but not impossible. Luckily, one thing I love as much as analyzing data is solving problems.
Enter the Data Rosetta Stone. Just as its namesake helped archaeologists understand hieroglyphics, a Data Rosetta Stone helps bring multi-format data from different platforms into clear focus. In simple terms, it is a collection of data connections, usually called a lookup table. It makes it possible to understand each data source and how it connects to other data sources in a client’s data pool.
While it may be time consuming to build a Data Rosetta Stone, your data analysis will become more powerful once you’ve assembled it. Creating one gives you the ability to tie insights from multiple data sources together. You may even uncover insights in the newly compiled data that were not easily seen using separate data sources. A Data Rosetta Stone also helps you with documenting your data sources, which is an important part of data governance.
Here are some simple steps for building and using a Data Rosetta Stone.
- Start with client data. Are there natural categories the client wants segmented? Client data is often defined by divisions in geography or by sales representative. This is the level to which all data sets will connect.
- Look at the other data sources in the client’s data pool. Web analytics data will often have a domain attached. Direct marketing will have call tracking numbers. Programmatic data tends to be targeted by a geographic identifier. Add columns for each data source (e.g. Website Domain, Call Tracking Number, Programmatic) and fill in the columns with matching data for each location in the table. This creates the ability to make connections between data files.
- Now you can link your data by using the table as a connector between different data sources. Unlike the original Rosetta Stone, your table can be easily updated and changed.
Connecting data sources requires more than your newly created Data Rosetta Stone. You may need to match on other fields, such as date, to create trends over time or look at date-based KPIs such as Year-to-Date. In the end, you’ll have a richer data analysis experience and your clients will be able to see business and marketing insights more clearly.
Just like those famous archaeologists faced with a seemingly impossible challenge, finding a new approach to a complicated problem can lead to the creation of an effective tool. With the help of a Data Rosetta Stone, you can become the master translator of data sources.