The increasing emphasis on first-party data and privacy has generated significant challenges for the advertising industry, causing signal loss and making measurement more difficult. Fragmentation, harnessing, and utilizing 1P behavioral data in a privacy-first strategy are the modern challenges facing brands and marketers.
Democratizing actionable real-time data insights is the core ingredient to address challenges and drive strong business outcomes. As these challenges continue to compound, many organizations are still using legacy platforms that do not address current and future challenges to tackle:
First, it starts with different data types and platforms that store data. Most organizations apply a one-size-fits-all approach to data, which creates complexity and will impact AI-driven insights.
Each data platform is designed for different types of data:
Over the past 15+ years, companies have centralized data during the “big data era” because it was the best technological approach. This has created a “black box” challenge in extracting valuable time-based actions. However, based on technological advancements that do not require (log) centralization and changes in the industry, this is no longer the only approach to processing time-series event data.
Data Warehouses, for example, have been around for more than 30 years. Its ability to operate in the cloud made it easier to centralize structured relational data for interoperability. However, due to increasing data demands, there has been a push to include semi- or unstructured time-series data sets in data warehouses. However, relational databases or warehouses were not natively built to handle time-series data. Teams have to retrofit the data with ETL or customizations, and the value diminishes because the data is not continuous, resulting in static or delayed insights.
Data lakes centralize and store many data types, including structured and unstructured. This approach has tremendous value, especially for unstructured data such as text files, images, etc. However, data lakes require extensive management, data modeling, and customization to make data usable. Even though data lakes are more flexible, they were also not designed natively to ingest and process time-series data across multiple sources without complex customization.
As data volumes continue to increase, extracting and processing raw time-series event data directly at the source will be paramount. This data type is simply too critical for First Party insights, as it is machine-generated from applications, devices, sensors, and other telemetry sources. This data should not be stored or copied in a data warehouse or lake because it delivers true real-time business insights, especially in the AI economy.
The most effective way to leverage First Party signals is by leveraging a time-series analytics data warehouse. Event data is the digital oil that fuels 1P insights. This data can be captured by taking the raw events in log files and translating them into actionable insights with a purpose-built platform. Time-series analytics unlocks critical signals that lead to actionable outcomes and need to be democratized for decision intelligence across teams.
This modern approach to faster insights is foundational for AI adoption because time is a critical component of all actions. This is especially important for AI, agents, and LLMS. Capturing time-based events in real-time delivers predictive forecasting, monitoring agent performance, and training ML Models.
For example, analyzing time-series events from LLM and agent adoption will be crucial for intelligence. Currently, AI drives AI agent-driven customer experiences, but translating signals from LLM conversations that trigger buying or dissatisfaction insights will help drive outcomes. Adopting time-series analytics to streamline and understand behaviors will improve experiences and will be vital to powering and measuring investments in AI.
First-party data has become a key differentiator for businesses seeking a competitive edge in today's AI and privacy-driven economy. By harnessing its power, advertisers can unlock new revenue streams, enhance customer experiences, and drive business outcomes. As the advertising industry continues to evolve, the importance and value of first-party data will only continue to grow, making it an indispensable asset for any organization seeking to thrive in the digital age.
AI Analytics Platform for First-Party Insights, Decision Intelligence, and Real-Time Data Collaboration.
SCUBA is the only unified time-series analytics data warehouse for real-time insights with Privacy Enhancing Technology (PETs). Our platform enables teams to gain behavioral insights within seconds, empowering marketing, product, ad ops, and data science teams to drive informed decision-making deployed in any private cloud.
SCUBA's innovative Privacy Enhancing Technology provides an on-demand approach to data privacy, ensuring actionable insights without compromising sensitive information. Our platform allows companies to analyze data across diverse partners and geographic locations while anonymizing data at ingestion to maintain integrity. Additionally, SCUBA provides comprehensive audit capabilities to satisfy compliance teams. By harnessing SCUBA's capabilities, organizations can unlock the full potential of their 1P data, driving business outcomes and propelling growth in today's fast-paced digital landscape.
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