The phases of the Data / Information / Knowledge / Wisdom pyramid stack, albeit loosely, atop one another. As data and their derivatives move from the bottom of the pyramid to the top, they become more complex and valuable.

The higher-order aggregates formed in the information layer are more informative and useful than the disjointed facts of the data layer. But, by and large, enterprise value doesn’t occur until the knowledge layer of the pyramid.

It is within the knowledge layer of the pyramid that brands leverage the contextualized information. Brands use knowledge for many reasons, but none more important than in informing programs to communicate more efficiently with customers. This tends to take the shape of customer messaging and marketing campaigns.

Knowledge drives offers, directs channels of engagement, shapes creative, and is the basis of customer engagement strategy. Knowledge in information and strategy drives customer interaction.

Actions Speak Louder than Analytics

Adding context to raw data through analytics is valuable because data alone are not typically usable. In fact, data qua data are a cost on the enterprise.

Operating a data warehouse, a data lake or other similar storage technologies is expensive in terms of capital outlays and operating costs. Analytics alone, however, are not sufficient. Indeed, analytics can be even more expensive than data management in terms of people costs, IT expenses and time.

As discussed in the prior article, data do not become useful until they begin to be monetized; the same is true for analytics – they do not become useful until they are used as a basis for making business decisions. In a marketing context, using data and analytics require interacting with users, customers, and prospects to generate profitable revenue.

These interactions must occur as part of a marketing strategy. Successful marketers that collect data are also analysts who in turn leverage analytic outcomes to define broader strategy.

Customer engagement is created through this synthesis of data, information, and strategy. Value is created through successful customer engagement. Successful customer engagement, then, requires an execution framework that brings data, information/analytics, and strategy to action. Action is the differentiator of the knowledge layer of the DIKW pyramid.

Knowledge-driven actions can take the form of e-commerce optimization, real-time interaction management, traditional or omnichannel marketing channel campaigns, affinity marketing, new product marketing, life-cycle (cross-sell/upsell) marketing or any other form of customer engagement. It is only through customer engagement, enabled by a data-driven execution framework, that businesses can begin to monetize customer data and recover the cost of storing, processing, and mining said data.

Customer data collection and performing analytics are the starting points—necessary, but not sufficient. Making data actionable also requires businesses to have the right tool set and an open ecosystem that provides secure access. With this accessibility, stakeholders throughout the organization can fully leverage insights from contextualized customer data for deeper engagement over the long term.

No Access or Limited Functionality = Little-to-No Value for Data-Driven Marketing

As I’ve mentioned previously in this series, having access to data and generating insights from these data is where we begin to generate business value. Having access to data is just as important as the functionality of the applications using the data. For modern data-driven marketing, this functionality must include the ability to manage communications with customers and prospects over time and across many personae and channels.

A contemporary data marketer must be able to identify customers where they “live” (online, on a mobile device, in store, at home, in transit) and synchronize messages across the various channels associated with these locations. This can include email and direct mail messages synchronizing with online messages or long-term customer engagement strategies harmonizing with real-time product and message decisioning—all driven by a mix of analytics, real-time decisioning and strategic goals.

It is not only within marketing that customer data can be leveraged. Other business functions should also be able to act based on the same data and information marketing uses. Sales, operations and customer service should be aware of marketing history, customer segmentation and lifecycle, current and planned offers, and next product recommendations.

In a world where empowered customers reign supreme, business results can only be achieved if data enhanced by analytics and other advanced information are the basis for customer engagement. This is true in data-driven marketing and across the enterprise; there is little point to performing customer analytics or even to gathering data otherwise.

Blending Across the “Lines” of the DIKW Pyramid

As I wrote in the first part of this series, the first three components of the DIKW pyramid flow from one to the other in a continuous process. Data is collected in its raw form and then added into the operational data store for further action. Information arises from conducting analytics on the stored data, presenting the ability to make decisions.

Lastly, knowledge involves taking action on the contextualized data within an execution framework informed by a marketing strategy.

Each phase builds on, and is coupled to, the ones on either side. Right now, the only thing missing from this process is making it repeatable … and that’s where the wisdom layer (the subject of my next article) will come into play. Stay tuned.

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