We have entered the era of Data Analytics a few years ago and since periodicals like the Economist published their first articles about Big Data, this term became commonplace among business people. Big Data is promoting novel approaches to Data Analytics e.g. using as much data as possible and making predictions based on correlation. These approaches seem very promising and many businesses are already working or at least experimenting with them.

When we at Acrea started to think about the role of identities in Data Analytics, two important subject areas became apparent. The first deals with the realm of security and Big Data. This subject area is concerned with securing access to huge collections of data, and also elaborates on how security can benefit from Big Data e.g. by adopting access control to the risk profile of the user and the properties of his current session.

The second subject area in which identities play a key role is marketing & sales. Here, the ultimate goal consists of increasing sales and improve profitability per customer. In order to do so, it is crucial to relate to individual customers (and their identities) and to actually know who they are and in which contexts they interact with the enterprise. The remainder of our blog post will talk about this very relevant role of identities for marketing & sales.

We have experienced that companies have a lot of potential in the way identities are modelled because what is called ‘identity’ is often spread across multiple systems for Identity Management (IDM) as well as Customer Relationship Management (CRM) and lacks a properly established relation model.

Traditionally, Identity Management has dealt mainly with what we call accounts rather than dealing with real identities. This means that an individual person can (and will) end up with multiple accounts and the linkage between them (i.e. the same person using multiple accounts) is not known to the enterprise. To make things worse, in CRM, the same person also is given an identity which often is not linked to the identities in the Identity Management system.

However, in order to leverage Data Analytics for marketing & sales, it would be interesting to know about the actual identity of a person and about all the different contexts (e.g. on the online channel, by phone, visiting a branch office) in which it interacts with the enterprise. Relating such different interactions to an individual person can help to come up with better insights and predictions about future behavior and about various kinds of opportunities e.g. for upselling or cross-selling. As a precondition to achieve these goals, the enterprise needs to take its Identity Management and the corresponding processes to a next level – far away from purely implementing an IDM or CRM system. At its core, this includes the act of collecting the necessary data about persons, their identities and the various contexts in which they operate. To enable such a step, a proper concept for dealing with identities is key. Such a concept also needs to ensure that systems like the IDM and the CRM work closely together in order to enable an integrated view of identities (persons) and their interaction with the enterprise.

In a specific case we helped one of our international clients to develop a new business object model for Identity Management. Providing a better foundation for Data Analytics was not an explicit part of the assignment but interestingly enough, the outcome was. The resulting identity object model specifically contained persons (we denoted it ‘DNA’) with relations to each other and with different roles such a person could have. Since it was an Identity Management project, the model also provided for assigning accounts to a person’s roles. Over time, the identity data collection can now grow, improve its quality, be better linked with data in the CRM, and will thereby provide better opportunities to be leveraged in Data Analytics.

A well designed identity model can provide a good foundation for your customer Data Analytics. If employed in an effective manner throughout the enterprise and in all the systems that deal with identities and customers, this can lead to better customer retention, increased share of wallet, and more successful cross-selling. However, and because of its foundational characteristics, building up such an identity model and collecting the necessary data, requires a clever approach and some patience. To this end, we encourage you to start leveraging the potential of Data Analytics and Identity Management today.