This post is the ninth in a series of eleven posts I am writing about key trends in the Identity Management industry.

Whenever data is amassed and made available for analysis, the odds are great that someone will  figure out ways to derive new meaning from this data.  So it is with data related to personal Identity.  I believe we will see an explosion of data analytics being applied to Identity-related data for a number of applications.  Three emerging areas are briefly described in this post.

Authentication/Discovery

Considerable evidence is available to show how each of us is progressively establishing a historical, logical  “fingerprint” based on our personal patterns of accessing online resources.   In a blog post entitled, “Anonymized Data Really Isn’t,” I discussed how correlating “anonymized” data with seemingly unrelated publicly available data can pinpoint personal identities with frightening accuracy. 

In his address at Digital ID World, Jeff Jonas’ discussion about using data analytics to discover space-time-travel characteristics of individuals was both challenging and disturbing.  Mobile operators are accumulating 600 billion cellphone transaction records annually and are selling this data to third parties who use advanced analytics to identify space/time/travel characteristics of individual people, to be used for authentication and focused marketing activities.

I expect we will soon see many ways data analytics will be used for both positive and negative purposes, to very accurately identify individual people and leverage that identification for authentication and personalization purposes.

Context/Purpose

Just like data analytics can be used to identify who we really are, these methods can be leveraged to personalize the experience online users have with each other and with online applications.  As I discussed in my Identity Trend blog post about Personalization and Context, personalization increases the value of online user experience by presenting relevant content to a specific user at a particular time and tailoring the user experience  to fit what a user is doing at that time.  Data analytics can be used to evaluate both real time and historical information to answer questions such as:

• What are you doing now?
• What did you do recently in a similar circumstance?
• Will historical patterns predict your preferences?

Perhaps the best-known example of this is Amazon.com’s recommendation service illustrated in the photo above.  In this case, based on my historical purchase pattern, Amazon recommended two books to me.  Ironically, Amazon recommended I purchase Seth Godin’s book entitled “Permission Marketing, which addresses some of these very issues we are addressing in this post.  In the next few years, we will most likely see more powerful and refined recommendation engines based on complex data analytics, adapted to a wide variety of user interfaces.

Auditing

The big question surrounding IT auditing is, “Who really did what, when and where?”  While many tools exist for maintain audit trails and evaluating compliance with audit policy, I believe we will see and emerging class of tools to evaluate audit trails and logs in ways not anticipated by current tools.  A few examples:

Sophisticated ad hoc analytics may make it easier to discover patterns of fraudulent access that may be missed by more structured audit tools. 

Enhanced analytics may help improve the business role discovery process by detecting obscure usage trends in log data.

Recommendations:

Some questions you may consider to explore how Identity Analytics may affect your enterprise include:

1. What Identity data do you currently store?
2. What related data do you store that could be correlated with Identity data?
3. Can data analytics be used to correlate data you store with publicly-available data to provide value to your enterprise and your customers?
4. What additional business value could accrue to your organization base on such analytics?
5. That privacy and security threats may exist to your employees and your organization if advanced analytics are used to correlate publicly-available data with data you make available?
6. How could data analytics related to Context and Preference be used to enhance the way users interact with your organization?
7. How can advanced analytics help you combat fraud or other cybercrime?
8. How can you use advanced analytics to improve corporate processes?