Top 5 Data & AI Trends in 2025
Top 5 Data & AI Trends in 2025 include a domain-centric focus, multi-agent customer-centric AI, and a different tact for data modernization.
Customer data has always been fragmented across functional operational applications and analytic repositories. Over time, the problem has only got worse.
Consider the development of AI models.
Data lakehouses contain a lot of data sets. Are they properly connected into a customer view? No. Data sets are aggregated into collections that ‘contain customer data’. But it isn’t joined together. It isn’t in context.
What’s the impact? When data scientists develop customer-centric AI models, they have to piece together a customer view from various data sources. Generally speaking, they often connect less than 10% of the data required. The result is poorly developed AI models. Biases. Blind spots. Hallucinations. They all contribute to bad customer experiences.
Now consider the operation of those AI models.
AI models that were built from data pulled from multiple sources will need data from multiple sources to operate, right? Yes. Where does that come from? CRM? No. ERP? No. Master data management? No. None of those systems have all customer data connected and available for AI apps. This constricts AI models to single app/single agent scope. They can’t operate if they don’t have the right data.
There have been so many solutions to this customer data problem over the years. Customer Information Files. Data warehouses. Customer Relationship Management. Customer Data Integration. Customer Master Data Management. Enterprise Resource Planning. Customer Data Platforms. Data Lakes. Data lakehouses.
What do all of them have in common? They all have a data store. They all operate on the same assumption – if you put all data into their data store, the customer data challenge will be solved for once and for all. History has proven that isn’t the case, and each one became just another fragment of customer data.
So let’s learn from the past. The answer is not a customer data repository to replace all that came before. The right solution is a customer data architecture that leverages what exists and joins it together. It’s time to stop repeating the mistakes of the past and start moving forward.
A modern customer data management solution joins together existing repositories. MDM. CRM. ERP. Lakehouses. Using principles of data fabric and mesh, a modern customer data management solution integrates and links customer data across repositories. It also blends operational and analytic environments to ensure the data used to build AI can also be used to operate AI.
Our approach is quite simple. We believe that the right way to finally solve the customer data challenge is to take a solution approach, with a scope that includes your entire data management architecture. We bring new components, pre-built assets, and our extensive experience in the customer data market. We use those components and assets to leverage your existing customer data management technologies.
Our solution delivers the next-gen Customer 360 – a Customer 3D View. Whereas a customer 360 View includes a subset of current and past data, a customer 3D View covers three dimensions: (1) all current and past data linked together, (2) future data and insights and (3) puts that data in context for each unique use case.
Q Spark’s approach is unique in that it leverages your existing technology vs. only implementing new technology in the hopes of sunsetting existing ones. We’re also unique by bring compute to the data, vs. bring data to compute. We think the market is done with implementing one more huge central database of customer data. It’s time to bring customer data compute to where the data resides, link it, and make it usable. That’s why Q Spark’s solutions are quick and cost effective to implement.
Customer data mesh services to access customer data across existing customer systems and repositories.
Customer entity resolution matches customer records and identifies & links data sets into a Customer 3D View.
Model context required for each usage scenario and create unique 3D customer views for each use case.
Modernize legacy MDM, integrate it with Data Mesh Services, and improve customer matching with entity resolution.
Improve customer prospect data with first part entity resolution and integrate with enterprise customer data.
Identify and link customer records across data sets to and create a customer 3D view for analytics and AI development.
Automatically classify & profile customer data across sources, and govern customer data for AI to reduce bias.
You have to go beyond data and beyond a 360 to truly understand your customers. You have to know their DNA – Data, aNalytics and AI.
Customer data is everywhere. MDM. CDP. Data Warehouses. Data Lakes. Applications. CRM. Despite significant investment in many technologies, customer data is still fragmented.
Customer DNA is delivered by multiple technologies. It requires a solution.
Q Spark Group are experts in customer data solutions. We can modernize your Customer 360 strategy and understand customers down to their DNA.
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