Data, Analytics + AI Consulting Services 

AI empowers research by rapidly processing and analyzing large volumes of data, uncovering patterns and trends that humans might miss. It helps surface insights from complex datasets, automate routine tasks and enable more timely decision-making. At Material, we see AI as a force multiplier — accelerating and scaling insight generation, while human expertise remains essential for interpretation, context, strategic judgment and actionability. 

Synthetic data is artificially generated data that replicates the patterns and statistical characteristics of real-world datasets. It helps teams simulate larger samples, fill gaps in under-represented audiences, and run rapid scenario testing without the time, cost or privacy constraints of new data collection.

However, synthetic data isn’t a replacement for real data. It must be grounded in high-quality source data to avoid amplifying biases or fabricating unrealistic behaviors. It should also be validated carefully to ensure it reflects true consumer dynamics rather than overly “clean” or idealized patterns. When used responsibly — and with transparency about its limitations — synthetic data can significantly accelerate research while preserving rigor and trust.

AI-powered models can identify patterns within large datasets more quickly than previous automation tools, as well as find patterns all but invisible to people and non-AI technology. This allows for more detailed and more accurate insights.

What’s more, AI “learns” over time, so allowing systems to continually improve. The result: faster, more accurate analysis.

The immediate processing and analysis of data, real-time data analytics enables almost-instantaneous decision-making. Product recommendations, dynamic pricing and credit-card approvals are just a few functions that rely on real-time analytics.

These and other applications contribute to increased revenue and improved resource allocations — for instance, marketers can use real-time data analytics to monitor response to live campaigns and quickly make tweaks to improve performance and ROI.

Many businesses are incorporating AI simply because they fear being left behind. In their haste, they often lack a solid foundation to support the technology.

Here at Material we work with organizations to identify the optimal tools for each strategic goal and use case, pinpointing exactly where AI will add value. We also analyze existing tech, processes and organizational willingness to change, as well as ensure AI governance is in place and set up necessary training. We even developed our AI Accelerator to help teams and companies prepare to implement and optimize scalable AI solutions.

A customer data platform (CDP) is a centralized system that collects, unifies and stores customer data from multiple online and offline sources to create a single, comprehensive customer profile. Unlike traditional customer relationship management systems, a CDP integrates data from a wide range of touchpoints – such as point-of-sale systems, e-commerce platforms, social media interactions and customer support channels – into one consistent and accessible record.

By eliminating data silos, a CDP enables organizations to gain a complete, 360-degree view of each customer.

Traditionally the IT or data department controlled data, and teams that wanted access needed to request it. This slowed down the decision-making process and often led to a great deal of back-and-forth until the data was in a form meaningful to the teams using it. Data democratization makes data readily accessible to the end users via user-friendly tools and training.

In addition to eliminating barriers to data access and speeding up decision-making, data democratization enables teams to uncover insights and identify opportunities they might not have otherwise seen.