data, whether in a data lake, enterprise data warehouse or an operational data store, is not useful. More accurately, data are not useful in and of themselves. Data only become useful when used, and in that use, context and perspective are added to the discrete facts that data represent.
Knowing discrete facts like “Frank,” “Topeka,” and “purchase on 12/1” have little impact on the larger business. Knowing that “Frank” from “Topeka” made his most recent qualifying “purchase” on “12/1” – now we have something to work with.
This brings us to the Information layer of the "data, Information, knowledge and wisdom" (DIKW) pyramid. “Information” in the pyramid can best be described as where perspective begins to be added, allowing facts to be connected. This transformation from data to information is typically the result of users overlaying meaningful context, from straight forward data aggregation or from analytics producing higher-order measures.
Some examples would be:
In the example above, the additional context and derived content added to all three data points is what begins to make them useful.
In the DIKW model, the Information layer is the domainof customer data analytics. [2] Analytics (whether descriptive, statistical, data completion or inferential) are how the disconnected facts of the Data layer are enhanced to provide business value. Using customer data analytics, business users can determine a given customer’s identity and preferences, understand their history, and determine their position in the customer journey. This makes customer data analytics a required step to create value from all the collected data.
In my last post, I discussed the shift from traditional data warehouses to operational data stores; this evolution means business users can more directly access their customer data. With greater accessibility, marketers and others can use cleansed data to meet their goals without as much dependence on the IT department and scarce data warehouse resources. This, in turn, decreases time-to-insight and increases flexibility throughout the enterprise.
Data lakes significantly ease the infrastructure and operational costs of collecting and storing big data. [3] Capturing data, however, is only the first step. Holding onto data, in and of itself, doesn’t provide value; using data to provide information and make decisions does. But the simple reality is that data lakes are too unwieldy and useful content is buried in the detail. Most business-user-friendly BI and analytic applications are unable to access the data lake, and if they can, they are overwhelmed by the volume and variety of data sloshing around it.
While businesses have increasingly become overwhelmed by the tsunami of data in the data lake, the rise of the empowered customer has heightened the need for effective customer communications.
