Do it yourself, commonly referred to as DIY, is a popular concept most often associated with crafts and home improvement. But DIY extends far beyond weekend landscaping projects – all the way to core business infrastructure. In the business world, the motivation for DIY is triggered by factors such as the perceived economic benefits, the lack of availability of a suitable solution, poor solution quality, or the need for high levels of customization. While DIY can work for many initiatives, this is not the case for consumer-facing identity and access management.
First, let’s cover some background on identity and its importance to marketing in the 21st century. How people define their respective identities varies widely: perhaps it’s their names and hometowns; perhaps it’s their jobs. In the digital marketing world, identity can include all of these data points. However, in order to make a connection and a subsequent sale, today’s marketers must look beyond these attributes and gather data from an individual’s entire digital footprint (which, by the way, amounts to a lot of data). Locations, on-site behaviors, purchasing patterns, social profile information, and much more all make up first-party identity data.
Collecting and making good use of customer identity data is not quite as straightforward as one would like to think it is. The technological requirements far exceed the capabilities of DIY identity management solutions, which generally are built using traditional identity and access management (IAM) components designed for managing internal employee and partner identities. Forrester Research agrees. In a recent report, the firm cited the volume and variety of unstructured and cross-channel data, the need for scalability, and requirements for compliance as the top factors that trip up DIY identity management solutions. Let’s take a closer look.
Big Data Equals Big Opportunity and Big Challenges
Consider these statistics from 2014:
- Facebook users shared 2,460,000 pieces of content.
- YouTube users uploaded 72 hours of new video.
- Yelp! users posted 26,380 reviews.
If you think these statistics were per week or per day, think again: All of this happened per minute. That’s a lot of permission-based identity data that marketers could use to connect with customers on a more personal level. Add to this the data points generated across devices by every online purchase, media stream, and ad click-through, and it’s clear that marketers have a deluge of unstructured and cross-channel data that could help further define their customers’ preferences and create more personalized and relevant experiences.
However, DIY identity management solutions aren’t sophisticated or intuitive enough to manage, normalize, and make sense of all types of customer data. With so much unstructured data to sift through, it’s essential to have a dynamic-schema identity repository that is capable of normalizing and reconciling both relational (structured) and unstructured data.
Growth Is a Good Thing, Right?
If your organization is using a homegrown identity management solution, expect some challenges because customer identity management has a greater need for scale. Remember, DIY identity management solutions are usually based on traditional IAM technologies, which are built on-premises for managing employee identities. These systems cannot be scaled easily to account for tens of millions of new and evolving customer identities. Scaling on-premises technology is expensive as well, requiring high CAPEX costs that can include physical data storage infrastructure and servers. Furthermore, any addition of domains, subdomains, mobile applications, subsidiaries, and so on makes managing and unifying customer identity data a complex endeavor.
While on the topic of costs associated with managing a DIY identity management solution, let’s not forget to add in the allocation of internal resources. Companies dedicate significant resources to keeping their customer databases up to date. In fact, large enterprises have entire teams – sometimes as many as dozens of people – dedicated to customer identity projects. This obviously translates to a significant investment in time and opportunity costs. Rather than having so many architects and developers dedicated to identity management, businesses could have more employees focused on innovation and new product development.
DIY and Concerns Over Privacy Controls
Because data privacy is, rightfully, a monumental concern among consumers, solutions designed to manage customer data must have comprehensive capabilities around privacy and preference management. These include, among many other controls, opt-out options for marketing emails and data sharing with third parties and affiliates. Consumer-facing identity and access management solutions also must include policies and reporting features that are essential for internal auditing and compliance purposes.
Unfortunately, DIY identity management systems have limited scopes for consumer-controlled data privacy, which makes compliance questionable at best. This is because in traditional employee-centric IAM systems (again, on which DIY identity management solutions are usually based), data privacy is only a moderate concern. Privacy settings are usually determined at the corporate level, and employees have only limited ability to modify them.
You wouldn’t try to rewire the electricity in your entire house all by yourself, would you? As it turns out, assembling a customer identity and access management solution is not the right project on which to hone your DIY building chops either. Leave DIY projects to shelf-building in the garage. For identity management – specifically, customer identity management – look to experts who already have the infrastructure in place to consolidate, manage, and leverage first-party, permission-based identity data.
Suresh Sridharan is Senior Director of Technology and Product Strategy at Gigya, the leading customer identity management platform with more than 700 customers. He is a 20-year veteran in enterprise software and has held key product management positions at Sun Microsystems, Oracle, and Okta prior to joining Gigya. When Suresh is not helping IT teams realize the power of customer identity data, he enjoys spending time with his wife and two daughters, hiking, and practicing yoga.
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