A featured contribution from Leadership Perspectives, a curated forum for enterprise technology leaders, nominated by our subscribers and vetted by the CIOApplications Editorial Board.

Chief Data & Analytics Officer

Building a Proactive Approach to Manage Data-Driven Changes

While interacting with your clients out there, what are some of the major challenges and trends you see that have been impacting the data analytics space?

The challenges in the data analytics space vary from analyzing the extradition to buy or build data to finding the best talent in this competitive industry. Businesses today must recognize that searching for data leaders requires them to be transparent about their data-driven activities. In light of this, the prevalent trend is towards generating a new experience for stakeholders by creating a data code and building a platform where any employee can access relevant information on dataset performance and quality.

With all the potential transformations and disruptions prevailing in this space, how do you envision the future of the industry?

It is difficult to predict the future of the industry as technology is evolving rapidly. The use and modification of different technologies are crucial factors in the space, but it's important to only use new technologies that have been proven as assets to overall business operations. The availability of technology is also a risk because it can be used without adding any value.

Take ChatGPT for example, it can generate anything you want to know, but even then, the information it provides is subject to bias and inaccuracies. Therefore, the industry's future will likely revolve around discussions on how to use AI- and other leading-edge technology-powered solutions reliably.

Please delve into a recent project you have been working on and what technological process elements you have leveraged to make it successful ?

One of the most important projects at Ualá is scoring alternatives, which involves generating scores with alternative data to provide loans and installments to people who have never had a financial product before. We use growth data, transaction history, navigation data, and modern algorithms to generate scores that allow us to determine whether to give a loan or installment. We also do scrapping of the product listing ad (PLA) and Twitter PLA get an understanding of the different reviews around the functionality of the app. We then apply sentiment analysis to these reviews to minimize the time of service of our chat for customers.

Data is crucial for building strategies and choosing the right different products and services, but it's also important to understand the trade-offs of building an infrastructure with different offerings.

Our customer service support team performs this task and resolves customer queries through a user analytical vision model that comprises dimensions of a user. This enables seamless cross-selling and the generation of personalized UXs based on preferences. Above all, the data portal combined with the analytical vision model enables Ualá to answer client questions faster and in a friendly way.

Would you like to share a piece of advice with the other leaders and fellow peers working in this space?

The data industry is constantly evolving, so it's important to adapt to changes quickly. There are no magical recipes for success in the analytics space as every instance requires a particular study before implementation. Data is crucial for building strategies and choosing the right different products and services, but it's also important to understand the trade-offs of building an infrastructure with different offerings. One last thing is to be patient and continue striving to bring about holistic change in the way companies capitalize on their data and build long-term data management habits.

The articles from these contributors are based on their personal expertise and viewpoints, and do not necessarily reflect the opinions of their employers or affiliated organizations.