In order to effectively discuss multi-channel analytics, you will need to have some working vocabulary around big data, data warehouses and data marts. Below is an introduction to these terms.
What is Big Data?
In 2012, Gartner updated its definition of big data as follows: “Big Data are high-volume, high-velocity, and/or high-variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization.”
Like most definitions of big data, there is inclusion of the 3Vs:
- Volume= Increasing amount of data
- Velocity= Speed of data in and out
- Variety= Range and data types and sources
Implementing multi-channel analytics in a big data world is increasingly challenging. Often we see marketing clients try and manage multi-channel analytics with a collection of massive spreadsheets created by individuals at different times using different data sources and rules for defining metrics in an organization, creating a fractured view of the enterprise. Marketing professionals today who are trying to master multi-channel analytics need to get out of spreadsheet jockey mode and instead look to data marts and data warehouses as the keys to success.
What is a Data Warehouse?
A data warehouse is a central repository and is a relational database that is designed for query and analysis rather than for transaction processing. There are three key characteristics: data is integrated, nonvolatile and historically robust.
When data is integrated that means it is put into the warehouse in a consistent format by resolving problems such as naming conflicts and inconsistencies among units of measure. The data in a data warehouse is considered nonvolatile, which means that once it’s entered into the warehouse, it should not change. This is important so different are operating off one version of the truth. And finally, the data warehouse should have a robust amount of historical data in order to be of use in trend analysis and predictive analytics.
What is a Data Mart?
If a data warehouse is a central repository, then the data mart serves as the access layer of the data warehouse environment, according to Bill Inmon. It’s used to get data out to the users. Without a data mart, users in the marketing department will struggle to extract quality data and turn it into any usable insight.
Usually in an enterprise situation, there are multiple data marts, marketing has their data mart, sales has their data mart, finance has theirs and so on. Each data mart is a collection of subject areas organized for decision support based on the needs of a given department.
Today data marts are typically cloud based applications. This allows each department to use, manipulate and develop their data any way they see fit; without altering information inside other data marts or the data warehouse. And this fits with the trend we see in marketers demanding more “self-serve” technology verses going to their IT department to get a report created.
Big data, data warehouses and data marts are concepts you must understand in order to begin to master multi-channel analytics. To learn more about how to improve or launch a multi-channel analytics system for your organization, call Dr. Cedric Alford at 972.496.7033 to schedule a no obligation appointment.
