Teavaro lead consultant Ben McDermott outliines a solution that enables "Data Dreamers" to gain the true visibility into their enterprise data assests that they crave.
In last month’s article, we spoke of the “Data Dreamers”, marketers with their sights cleanly set on the value that their data assets can provide, but without the ability to achieve “big data” in reality. The mountains of data that their ad tech partners profess to be available at the flick of a switch have proven not that easy to come by, at least in most large enterprises.
Upon inspection, we found that fragmented data siloes and legacy architecture turn a monolithic task into a fractured one. Nevertheless, we concluded there were approaches that can facilitate data integration, but with weaknesses.
The first, replacing the entire marketing stack, marked cost, time and effort as negatives. The second, implementing point solutions for every issue, merely led to a multiplication of the pains of a diverse technological architecture. As a solution, we proposed Teavaro’s FunnelConnect to overcome the issues of disparate data. This month, we look at a specific GDPR-related example of this.
GDPR and the exacerbation of enterprise fragmentation
Teavaro’s suggested approach is one of gradual change, but for the GDPR scenario, a limited timeframe meant enterprise clients in particular were caught in a schedule that aimed at fulfilling GDPR requirements, rather than efficiency, design or cohesion. This approach has undoubtedly led to problems.
Due to the sheer scale of larger enterprises, no single solution to GDPR has been truly implementable. Complex IT and business process structures have been analysed, evaluated and shoehorned into various solutions alongside legacy architecture on the basis of department and division, as opposed to a holistic approach. These hasty responses have led to further division as solutions were implemented, with fragmented repositories being set up for existing and new data sets. From a data perspective, customer touchpoints, data systems and even different IT stacks aligned to different business entities might scatter data on a single customer across the organisation’s data architecture.
To focus on a specific requirement for GDPR, customer consents and permissions were often localised in this manner. For example, a retail group might treat GDPR requirements on a brand-by-brand basis, thus each brand will have a specific customer profile with permissions and consents linked.
Even within the brand operation, the permissions might be stored in a different location to, say, contract data, traffic data and so on. However, several customer profiles across several brands might be a single person. In addition, there is no template as to how data is arranged; prospect permissions might be kept separate from a central customer database, or marketing consents might be handled by channel, and thus held by each channel system.
The impasse: segmentation or integration?
It becomes quickly apparent how such fragmentation makes the 360° view of the customer required by marketers virtually impossible. It is even hard for the company to know which information on the customer/user exists across its multiple siloes, or how to use these resources in the best way. Specifically, on a permissions basis, it becomes impossible to give a consistent view of gained consents, and thus almost impossible to remain compliant with GDPR.
Perhaps enterprise-level is not the way to manage the customer relationship, you might say. Yet the view of enterprise-level permissions management is easily transposed to department-level, or product-level, or contract-level. Nowhere does segmentation, the separation of customer data to a specific entity or even consent type, make the task any easier. Thus data integration is the only effective solution to effectively manage customer permissions.
Teavaro has put forward a design that stores permissions information centrally and subsequently parametrises the required information to different stacks, rather than storing the permissions information in different stacks directly. We do this through a generic API to different touchpoints, channels, core and front end systems that provides the simple means to store the permissions centrally. This centralisation can be realised immediately or over time – as a copy of the master data.
There is a great advantage in the fact that migration of data and mastership does not have to come alongside a complete redesign of the existing interfaces. It instead incorporates them. This reduces the effort and time for bringing in the central permission management system. With further extension of functionality, a redesign and versioning of APIs can be combined. Separating the permission management function from the core CRM system(s) results in a sufficient proximity to all relevant partners like customer care, channels/touchpoints, campaign management, analytics, third party interfaces, as well as new and incoming products (e.g. IOT).
This design gives the freedom to implement new functionality outside the usual IT prioritisation, as no core impact exists. These extensions might be the connection of new interfaces/services, or adaptations to existing interfaces due to new requirements via interface versioning or changing functionality with agile partners (such as that seen during the lead up to the GDPR deadline).
With all customer and visitor profiles for different digital and human, online and batch processes in one place, holistic digital identification and subsequent capabilities, such as profile merging, among different internal and external channels becomes possible.
A migration like the proposed one is not a short-term project – but starting the transformation step-by-step with minimal intervention to the existing solutions, the new central platform can evolve over time, with minimal disruption to existing architecture and service, and the capability to provide an agile response to new business opportunities. Through this design, “Data Dreamers” can have a workable template for engaging with their valuable data assets across their enterprise and ensuring that their aspirations for big data become a reality.