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TDWI Checklist Report | Seven Keys to Data Blending

August 18, 2014

Companies are using disparate data types more frequently to gain value via analytics. TDWI research indicates that companies are becoming more interested in enriching the traditional structured data found in their data warehouses or marts with other kinds of data. This might include demographic, geospatial, or even text data. These companies realize that utilizing disparate data can improve analysis by providing more attributes for discovery or improving model performance. For example, a marketing department at a retail chain trying to understand customer behavior to develop a promotion plan might want to utilize standard transaction data from its data warehouse (such as purchase type, amount spent) and combine that with non-traditional data such as distance from store or census data bought from a third party for developing a data set for analysis.

At the same time, analytic tools are becoming easier to use and the business analyst is becoming a primary user of analytics. Data access and integration are often stumbling blocks for business analysts who may have a hard time accessing disparate data and getting it ready for analysis. Doing this manually can take time or require specific skills for data integration. Companies are looking at ways to bring disparate and often dispersed data together in an analytic data set to be explored and modeled without upfront integration. In other words, they do not want to combine it into a warehouse or data cube before starting to analyze it. This is often referred to as data blending; i.e., combining data from multiple sources without integrating it into a data warehouse or other system of record. This kind of analysis is useful for discovery and analytics that doesn’t necessarily lend itself to traditional reporting from an enterprise data store.

Data blending isn’t simply a matter of throwing data together; issues like data quality still need to be addressed. This Checklist Report focuses on helping organizations understand the steps and features that are part of data blending.

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