Helena Cho, Director of B2B Marketing at Expedia Group, speaks with Diena Lee Mann, Founder and CEO of Spectio, in a video interview about the skills required to build data and analytics products, the challenge of the translation gap in teams, prioritizing between sufficient solutions and solutions to scale, and the critical points to consider while scaling solutions.
Expedia Group, Inc. is an American travel technology company that owns and operates travel fare aggregators and travel metasearch engines.
According to Cho, building and delivering data and analytics products and services calls for individuals with three specific skills:
- Front-end UX design
- Back-end Extract, transform, and load (ETL)
- Understanding the business model and prioritization
Typically, she says, some people would be superstars in one or two areas, but not all three. Therefore, it is crucial to build a team with the diverse strengths and skills needed to create a data and analytic product.
However, in a diverse team, people end up doing different things, which leads to translation gaps, says Cho. To address this issue, people suggest documenting everything, thinking it would facilitate onboarding, she adds.
Still, the approach can be problematic, as even after investing significant time in documentation, it may turn out to be obsolete when published, says Cho. This leads to a feeling of making a bad business decision, especially for those working in the technology industry, and documentation loses its value, she adds.
The second problem arises when people try to bridge the translation gap by relying on “super analysts” or individuals with more than two key skills, notes Cho. The super analysts are then expected to even take on the responsibility of a program or project manager, which is not their strongest suit.
Consequently, this creates a divide between what the super analysts want their careers to be and how their talents are utilized inefficiently.
Next, Cho discusses balancing priorities between sufficient solutions and solutions that scale for data and analytic teams. More often than not, companies and teams resort to putting one person who can translate business or technical terms or knows all the different domain knowledge in a role.
Elaborating, Cho says that while having a dedicated program manager is crucial for building a successful project, it will not be scalable with one person. Therefore, she recommends exploring external solutions that may answer certain problems. While no solution will completely solve the problems, it gets better if one knows what the problem is.
Moving forward, Cho lists out the crucial points to consider while finding a scalable solution. First, there must be a demo of the solution, and one must know the pain points to assess if the tool fixes the pain points. Then, it is necessary to understand how quickly a solution can be delivered.
Continuing, she states that while some data solutions are out of the box and can be used right away, others need to be integrated into the current structure. Therefore, it is critical to set the right timeline.
Most importantly, since there is a need to solve the translation gap, the learning curve for implementation should be extremely low, states Cho. The solution should be easy for all stakeholders, from engineers and analysts to field marketers.
If the system is hard to use and demands extensive training, it can be a major impediment to scaling collaboration and translation.
Furthermore, with the rise of AI, some solutions leverage AI to lower the implementation learning curve, says Cho. The key is to find solutions that work for the organizational processes and teams, whether they are internal or external.
A recent demo for Spectio impressed Cho with how seamlessly it integrated documentation and processes into a single user interface. She encourages others to explore Spectio and highlights that Spectio has the potential to bridge the translation gap.
In conclusion, Cho emphasizes that any tool or solution that lowers the learning curve will help solve the translation gap.
