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Analytics Magazine

Human Resource Management: The magic of managed autonomy

September/October 2015

How online lender Enova International built a successful analytics team by balancing talent engagement with business priorities

Vinod CheriyanBy Vinod Cheriyan

Enova International, a technology and analytics-driven online lender based in Chicago, was in the swing of rapid growth in 2013. The analytics team more than doubled to 51 in a matter of two years in order to support five new products in four countries. Since the business was entirely online, it meant we had to run as many as 10 models and provide loan applicants with a decision within seconds after they clicked “submit.” We hired a team with diverse backgrounds and specializations, from biostatistics to mechanical engineering, in order to keep improving our analytics capabilities and maintain our competitive edge.

With all of this highly intelligent, intellectually curious talent, one of the key questions that was staring Enova in the face was this: How do we keep these highly talented folks engaged while at the same time drive maximum business value?

This challenge is nearly universal in today’s business environment. In order to survive, companies need to leverage analytics – not just linear and logistic regressions, but advanced algorithms such as deep machine learning. This requires attracting and retaining people with advanced degrees and specializations that were once purely in the purview of academia. It’s no secret that these talented team members are hard to come by and even more difficult to keep. Therefore, it is all the more important to keep them engaged.

Looking back on how Enova was able to successfully balance talent engagement with business priorities, we have identified several approaches that worked for us and which we think could work for other growing tech companies in a variety of industries.

1. Create an environment that values engagement

Quite ironically, the groundwork for addressing the question of team engagement vs. business priorities must take place much before an answer is needed. It is important to have an environment that values team members’ engagement and builds loyalty.

At this point, the rationalist in you (or your CEO) might ask, why care about team engagement? After all, business success is the top priority, right? Well, yes. At the end of the day, work that drives business value is essential. However, when thinking about the projects to assign to analytics team members, a quick reflection shows that completely ignoring their wishes is not a good idea.

The cost of acquiring talent is very high. And turnover can be difficult to manage, especially for a hot area like analytics, where there is always a competitor trying to recruit out of your team.

It should not be difficult to show leadership how a short-term gain in revenue may hurt in the long run if employee engagement is ignored. Once you’ve achieved buy-in, consistently communicate across your organization that while driving the business forward is the number one priority, people have a say in the types of projects they would like to work on and have the freedom to achieve business targets using their own unique strengths and methods.

2. Implement a “managed autonomy” organizational structure

Once you have achieved a culture that values engagement, it is essential to put in place an organizational structure that is conducive to effectively allocating projects and resources in order to meet business goals and drive engagement. The solution of managed autonomy has two components: strategic alignment and tactical/resource allocation.

Strategic alignment. Strategic alignment sometimes requires big changes, even across departments. For instance, one of the first things that Joe DeCosmo, our CAO, did in his role was bring out the business intelligence and data services teams from under the technology department and put them under the analytics team so that all of our analytics operations could be streamlined. By its very nature, strategic alignment requires someone at the C-suite level with necessary leverage to make these high-impact changes.

The second part of strategic alignment is investing in good hiring practices. As was mentioned earlier, it is important to hire the right people with specific skill sets that, when combined, form a balanced team. To facilitate this, Enova has a hiring manager dedicated to the analytics team who works closely with the CAO to understand the current and upcoming resource needs and helps with recruiting specific analytics team members.

Proper resource allocation. Once the high-level strategic alignment is in place, the tactical resource allocation and project assignment can take place under the purview of the division managers. (In smaller analytics teams, the CAO and the division manager can be the same person).

The first step in resource allocation should always be, “ask your team members.” This might sound like an obvious piece of advice, but it is often overlooked amidst day-to-day work. It is critical to have a good understanding of not only what employees’ specialty skill sets are but also what else they would like to work on. This information can be obtained through a direct one-on-one conversation or baked into annual goal-setting processes.

For example, at Enova we support employees to drive their own career development. We provide clear expectations for each level on our internal wiki. Each team member can design their own goals and skill-development plan; they are even encouraged to recommend one or more projects that bring them closer to their next career step.

The next step is to identify projects based on business goals and to give autonomy to the team member on how to implement them. (Hence the term “managed autonomy.”)


Real-time analytics is critical for a fast and easy online customer experience. For a company like Enova with its business operating entirely online and spread over six countries, this is of paramount importance.

However, implementing models in real time can be challenging. The legacy system on which we used to run models was a homegrown system written in C. Though it was fast, it was tightly integrated into our production system, making changes very difficult. Once built, models had to be translated for software engineering to implement them – many times resulting in multiple cycles of debugging and testing.

We wanted a system that was fast and that supported easy deployment of models without restricting modelers in the use of their choice of technology. So we set out to build a new real-time platform from the ground up. We evaluated multiple software solutions, and we finally partnered with a leading scientific computing vendor to design our custom real-time platform, Colossus.

Through a phased approach, we gathered the initial requirements, had two paid prototypes made by external vendors, completed the design, built and implemented the system, and trained team members. The system was up and running within one year of the idea’s inception.

Not only has Colossus provided sub-second performance for running models (0.07 seconds on average), the average model deployment time has been cut in half. It also helps with the decoupling of development and running the models. Now, modelers can use their specialized knowledge and develop models using their favorite tools; once finalized, the models are translated and deployed on the Colossus platform. More importantly, the translation is done by the analytics team itself, so there is no longer dependency on the software engineering team for day-to-day model deployments.

3. Give employees autonomy to leverage their strengths

Properly assigning projects to meet business goals while keeping team members engaged can be challenging, but here are a few tips to make the process easier:

Focus on the larger goal – not how it is achieved. Keep project direction limited to achieving business requirements; to the extent that it makes sense, do not specify how they are achieved. Managers need to keep in mind the strengths and aspirations of the team members as soon as business users come up with projects. It is important to identify and capture the business needs and separate them from the implementation details. For some projects, the manager can design a high-level solution and match the best-suited team members to the project. For other projects, the manager can relay the business requirements to the team member and let the team member decide on the implementation. As one of Enova’s managers says, “You hire smart people. You shouldn’t have to tell them what to do. They should tell you the right solution.”

Be flexible. It is also important to provide sufficient flexibility for the team members – both in terms of hours and in terms of deadlines. Though some projects are very deadline driven, many model-building projects do not have strict external deadlines. In these cases, the manager can negotiate an additional week or more, so that a team member can investigate and implement a new methodology. Not only does this give an opportunity for the team member to improve her skills, but also, if successful, it adds another tool to the toolbox.

Be tool-agnostic. Another aspect of flexibility is being able to use the tool you know best and that which is suitable for the job. This is why Enova does not mandate one language for conducting analysis. Team members can use their technology of choice to create models. The worth of a model is judged by the ROI it produces.

Though there are some advantages in having a uniform set of technologies, more often than not, different tools and technologies can in fact help with creating an innovative solution. For example, one of the business problems we faced at Enova was how to parse and store XML data into relational databases. The problem was made harder because proper schema definition was not always available. One of our experienced team members came up with an effective solution using a combination of Mathematica, XSLT and UNIX scripts.

Always have an upside. On the other hand, managers must also be very vigilant that the team as a whole is making progress. They should use sound judgment when deciding when a research project has gone too far, and when the team should switch gears to produce something more tangible. A corollary of this is that when identifying research projects, it is important to design them such that even in the worst case, there are some useful by-products (e.g., reusable code to run a specific algorithm) that can perhaps be used in a future project.

Have the right system architecture. But then, you would ask, how do you actually support all these different models? This is where another aspect of the organization comes in – having a flexible, centralized analytics platform. At Enova, successful models that make the cut are translated and deployed into Colossus Engine (see sidebar story). “Isn’t that duplication of work?” you might ask. Yes, it is. But the duplication is far outweighed by the benefits we obtain by letting team members work in the technology with which they are most comfortable.

In Conclusion

So there you have it. Successfully balancing employee engagement and production of business value can be achieved by instituting a “managed autonomy” architecture that consists of strategic alignment and proper resource allocation. The organization must be designed so that the analytics operations – developing models, providing business value and keeping employees engaged – are streamlined. Managers need to make sure that employees’ strengths and aspirations are taken into account while designing projects. Providing sufficient flexibility, decoupling project goals from implementation details and nurturing a tool-agnostic environment are some of the ways to improve employee engagement. With engaged, talented employees, there is no doubt your analytics team will be ready to achieve incredible results.

Vinod Cheriyan is a senior data scientist at Enova International. He is a member of INFORMS.


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