Today, we are interviewing Lourdes Pradeep Arulselvan, one of the Partners at WiseAnalytics. We would like to better understand what you do at WiseAnalytics, what path led you to become a partner, and what you normally focus on.

First of all, let me explain a bit about WiseAnalytics. WiseAnalytics is a data consultancy that helps companies harness the power of their data. Our main goal is to drive growth by leveraging data and marketing technology (MarTech) to build and implement effective data strategies. As a partner, my role involves working closely with clients to understand their needs, guiding them through their data transformation journey, and ensuring that the solutions we deliver drive real business impact. I focus on creating strategies that connect data with business outcomes, making sure our clients are not just collecting data but using it to fuel sustainable growth and create an impact.

For me, it all started at PegaSystems, where I was a product manager focused on decisioning capabilities. I was responsible for building their Decision Hub product, which was a critical piece of their enterprise-grade CRM and automation systems. While I enjoyed building products, I felt somewhat disconnected from the real-world impact these products could have. I wanted to be in the thick of things, delivering tangible value to businesses rather than staying in the "ivory tower" of product development.

After Pega, I worked across various industries and companies like VEON, LeasePlan, and Luxottica. I built data solutions such as Data Management Platforms, implemented marketing automation, and developed Customer Data Platforms. These experiences gave me a broad perspective on how different industries use data, which ultimately led me to WiseAnalytics. I joined WiseAnalytics because it aligned with my passion for being hands-on in creating impactful data-driven strategies, and it gave me the opportunity to be a key player in shaping the company's direction.

Before Pega, I was an entrepreneur, building lean products for SMEs. Moving to Pega was a big shift; I went from building for small businesses to developing solutions for large S&P 500 companies. At Pega, we were building what I like to call a "mammoth brain" for enterprises—this huge, centralized system that could manage complex processes and decisions at scale. I worked with some of the brightest minds in the industry, and we pioneered AI-driven process management and decisioning with products like the Data Strategy Canvas, Next Best Actions, and Customer Data Hub.

One of my key contributions was evolving the concept of the Interaction Hub into something we called the "Customer Movie." Essentially, this was a way to capture individual customer interactions and create a timeline that could be used to drive decision-making processes. It was a precursor to what we now call Customer Data Platforms (CDPs). I developed this concept alongside Maarten Keijzer, who was Pega’s VP of Product Management at the time, and we turned it into a product that really pushed the boundaries of what was possible in CRM and data management.

The Customer Movie captures individual touchpoints of the customer journey, creating a comprehensive timeline of their interactions. This data is then used to drive decision-making processes. For example, it helps determine which product to offer a customer, when to communicate with them, and through which channel, making sure that the interaction is relevant, timely and more importantly in the customer’s timeline rather than the company’s campaign timeline.

Decisioning can start with something as simple as making the right choice at a single touchpoint—like deciding when to send an email, what content to include, or the optimal price for a product. The key is understanding the end-to-end value chain and identifying where decisioning is critical. We work closely with our clients to map out these key decision points, assess the data they currently have, and determine what additional data they need to make more informed decisions.

In an ideal world, you'd have all possible data points to make the perfect decision, but the reality is often different. The question is more about how you can make the best possible decision with the data you have. Our approach is to start with what the company already has, improve the quality of those data points, and then identify additional data to acquire for better decision-making. From a system perspective, all you need is a rule engine to begin with, which can be an out-of-the-box solution. The real challenge—and our focus—is ensuring that the systems within the company can feed the right information to this rule engine at the right time, and enhancing the data using AI/ML models to make decisions more effective.

The use cases vary significantly between industries. In retail, for example, it’s about being relevant at each customer touchpoint—offering the best product with the right margin, at the right time, through the right channel, and with the right content. In the supply chain industry, it’s more about making a series of decisions during the fulfillment process—like deciding whether to consolidate items into a common shipment or which shipment method to use to minimize cost while maintaining an adequate level of service.

The challenges are twofold:

• Helping the organization understand that they don’t need to rely on a single expert but on a central brain that constantly learns and adapts.
• Ensuring people realize we’re not replacing them but giving them a co-pilot so they can focus on more value-added tasks.

Tackling these aspects of change management isn’t easy unless top management understands the nuances and is committed to driving this change. When these conditions are met, the pace of change can accelerate.

Decisioning improves speed of delivery, quality of decisions, and reduces costs. It also enhances employee engagement by removing repetitive tasks and allowing people to focus on more impactful work. Companies that don’t embrace decisioning will find it increasingly difficult to compete unless they operate in a non-competitive environment or have exceptionally high margins.

The future of decisioning is incredibly dynamic and full of potential. We’re moving into an era where the sheer volume of data points, signals, and digital interactions is exploding. Each of these interactions presents an opportunity to make a decision that can enhance customer experience, optimize operations, or drive revenue. The challenge for companies will be how they integrate this vast amount of data in a way that’s not only manageable but also meaningful.

We’re already seeing Machine Learning and AI starting to play a larger role in this space. These technologies, which have mostly been used in pilot projects or as proofs of concept, will soon become central to decision-making processes. They’ll help businesses sift through data noise to extract actionable insights, and they'll be crucial in automating decisions at a scale and speed that humans simply can’t match.

As the landscape evolves, companies that are ready to adapt and innovate will have a significant advantage. Those that hesitate or resist change may find it increasingly difficult to compete. The key takeaway here is that decisioning isn’t just a trend; it’s becoming an essential component of business strategy. The companies that prepare now, that invest in the right technologies and foster the right culture, will be the ones leading their industries in the years to come.

Thank you, Lourdes, for sharing these insights and offering such a comprehensive view of the decisioning landscape. It’s clear that there’s a lot of potential here, and companies would be wise to start preparing for this future now.