In a previous post, we discussed unifying and imbedding marketing data and analytics into business functions for greater business value. And we used the example of Unified Communications as a technology with similar evolution. If we take another page from its foundation, it might provide insight on how to currently address broader more dynamic marketing technology solutions.
If you view IP Convergence as a foundation to Unified Communications (or Unified Marketing), you would see that IP Convergence was driven from carriers with the purpose of combining voice, data communications, and video. However, IP Convergence didn’t address the application of new data items, or how to unify new with current data. Instead, before Unified Communications, there were various applications still performing “solo data” functions because they weren’t able to integrate (example IP phones only doing the application of voice).
If there was integration, it typically required some IT expertise. As a result, integration of a “solo data” application into core business functional applications didn’t occur (example there weren’t many IP phone applications imbedded into business operational applications).
The foundation, therefore, only served particular target audience needs and never looked to a broader base of holistic user activities or whether it contained new data feeds. The Data Warehouse might not be dissimilar to IP Convergence as a foundation item, if we focus on how it is leveraged for engagements. The Data Warehouse is offering “solo” convergence applications, but it isn’t unifying that data with other applications that integrate with other sources or systems. The user audience of the data isn’t seen as the broader business functions and, therefore, applications are built more “solo” and less integrated into core business activities. But to solve that next application evolution, we first need to address whether the Data Warehouse is capable of taking in all the necessary new data feeds.
Everything digital has created an explosion of new data and sources. This digital environment has also produced an always-on relationship situation that is also spawning new applications and data sources. The impact to the Data Warehouse occurs at the data model level. What the data model was a few years ago likely requires a reassessment based on channels, touch points, behaviors, preferences and more. The new data model needs the ability to quickly adapt to collecting new data from new places. And that adaptation model needs to occur with less skill available in assuring integration of data sources/systems gets put in place. So, if your Data Warehouse requires a team of IT experts to load data from new sources or cobble multiple different warehouses together with complex integration every time a business user has a new project, then it might be a good place to start assessing a better solution.
Look to establish a model that allows the primary business user to work with logical models that enable the technical capabilities of building the integrated platform and remove the burden of technical complexity. Removing the technical complexity allows business users to broaden their views on what application potentials might be possible with all the new and existing data.
The right foundation, advanced with understanding that the next wave of applications will likely stem from a broader set of business users who will increasingly have an insatiable desire to better understand the data around customers, will serve your roadmap well as the next generation of marketing technology emerges.
About the Author:
Mike Fuqua is the Chief Technology Officer at SIGMA Marketing Group, a marketing technology and strategy agency. Connect with Mike on or follow him on .