What’s Wrong with Legacy AdTech Architecture
The core issue with legacy AdTech is that the systems aren’t built for streaming. Several challenges keep cropping up:
• Data Silos Everywhere: Mergers and acquisitions often leave companies with disparate data systems that don’t talk to each other. Integrating these systems is tough. It’s also necessary.
• Short Data Retention: High storage costs force many to delete data after just a few days. This lack of historical data hampers long-term analysis and strategy.
• Poor Query Performance: As data volumes grow, queries slow down. Waiting hours or days for results isn’t feasible anymore.
• Difficulty Adding New Metrics: Rigid data architectures make it hard to incorporate new data fields or sources without significant engineering effort.
• Limited Data Access: It’s not uncommon for engineering teams to control data exposure, leaving analysts in the dark.
Rethinking Legacy Systems: It’s Time for an Upgrade
Given these hurdles, it’s clear we need to rethink how we’re handling data. Rising data volumes and the demand for faster insights compel us to modernize infrastructure. Doing so delivers new capabilities that we can’t afford to leave in the “nice to have” bucket any longer. Why? Because our competitors are already doing it. Let’s look at the reasons to make the leap:
First, a shift to modern architecture enables the move from batch to streaming. Batch processing is becoming obsolete for the reasons stated above, and real-time operations require data platforms designed for streaming ingestion and processing.
Another benefit of a more modern architecture is that it supports machine learning. AI and machine learning are becoming integral in AdTech, from customer segmentation to fraud detection. But these technologies require vast amounts of recent, relevant data and robust pipelines to be effective. This means our systems need to handle larger volumes of data more efficiently.
Upgraded architecture also allows us to embrace privacy-first models. With increasing privacy concerns and the decline of third-party cookies, we need to shift from user fingerprinting to privacy-first, probabilistic attribution models. These models respect user privacy while still providing valuable insights for targeting and personalization.
In addition, a modern architecture allows us to leverage the scalable, affordable object storage solutions that are now available. They make it feasible to store vast amounts of data without breaking the bank. The key is to ensure that this storage is also performant and accessible for real-time querying.
What to Look for in a Modern Data Platform
The above benefits of a modern data platform can sound like a wish, but we’re closer than ever to having ready-made platforms that do it for you. And you can build a platform much more easily today than even six months ago. It’s the right time for AdTech teams to upgrade and embrace real-time analytics. As you’re exploring your options to up your competitive game with streaming, here’s a checklist of critical features:
• Real-Time Data Ingestion and Transformation: The platform should handle streaming data and allow for real-time transformations, adding context and standardization right at ingestion.
• Optimized for Analytical Queries: Columnar databases optimized for analytics can drastically improve query performance, even with massive datasets.
• Time-Series Optimization: Since much of our data is time-based, efficient handling of time-series data is crucial.
• Handling Late or Out-of-Order Data: The system should gracefully manage data that doesn’t arrive sequentially.
• Flexible Schema and Multi-Source Integration: A dynamic schema allows for easy addition of new data fields, and the ability to ingest multiple data sources into one table simplifies data correlation.
• Long-Term, Cost-Effective “Hot” Storage: The platform should make it affordable to keep data readily accessible.
• Independent Scalability: Components should scale independently to handle peak loads without over provisioning resources.
• Resource Isolation: Separate query pools prevent one team’s heavy workload from bogging down the entire system.
Making the Shift: From Theory to Practice
I have one other piece of advice in making the shift to a modern data platform: Transitioning isn’t just about technology — it’s also about culture and mindset. You’ll want to pay close attention to the best practices of navigating change. Start small, and do a pilot on the new platform with a specific use case to demonstrate value before a full-scale rollout. Engage your main stakeholders early and get their buy-in—data engineers, analysts and business stakeholders all need a seat at the table.
Now’s the Time to Embrace Real-time
The data deluge is overwhelming our current infrastructure in the AdTech industry, and the need for real-time data is only accelerating. That’s the bad news (“the challenge”). The good news (“the opportunity”) is that with the right tools that are now available and a willingness to adapt, we can turn our data deluge into a strategic asset. By embracing real-time analytics and modernizing our data infrastructure, we position ourselves to make faster, smarter decisions.
Conclusion
The article highlights the importance of modernizing AdTech infrastructure to handle the increasing volume of data and the need for real-time analytics. It emphasizes the benefits of upgrading to a modern data platform, including improved query performance, cost-effective storage, and the ability to handle large volumes of data. The article also provides a checklist of critical features to look for in a modern data platform and offers advice on making the shift to a new platform.
FAQs
Q: What are the challenges with legacy AdTech architecture?
A: The challenges include data silos, short data retention, poor query performance, difficulty adding new metrics, and limited data access.
Q: What are the benefits of upgrading to a modern data platform?
A: The benefits include improved query performance, cost-effective storage, and the ability to handle large volumes of data.
Q: What features should I look for in a modern data platform?
A: Look for real-time data ingestion and transformation, optimized for analytical queries, time-series optimization, handling late or out-of-order data, flexible schema and multi-source integration, long-term, cost-effective “hot” storage, independent scalability, and resource isolation.
Q: How do I make the shift to a modern data platform?
A: Start small, do a pilot on the new platform, engage your main stakeholders early, and get their buy-in.
