In the AI Revolution, Real-Time Data Platforms Are the Hidden Drivers of Innovation
The next generation of real-time data platforms will be defined by three key characteristics: speed, scale, and simplicity.
Organizations today are awash with data — and data streams are only growing larger and more complex. By 2025, global data creation is projected to grow to more than 180 zettabytes, a nearly three-fold increase since the start of the decade.
This surge of information is good news for a trend like AI, which hinges on a data-rich environment. However, the legacy database architectures many organizations still rely on are proving a significant bottleneck to the AI revolution.
To navigate the growing deluge of data, organizations require advanced data tools that enable them to sift through the valuable information at their disposal and derive meaningful insights. However, traditional data platforms were not designed with the flexibility and speed to process and automate real-time data analysis. As a result, too many organizations are unable to leverage their data effectively, missing out on critical insights capable of driving the right choices within their organizations.
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Real-time data platforms are critical enablers of AI adoption and a must-have for organizations aiming to maximize AI usage. With a simple and scalable real-time data platform, organizations can move beyond the constraints of their legacy systems, unlocking the full potential of their data and paving the way for more informed decisions with greater longevity and success.
Why legacy database architectures fall short of current AI ambitions
Despite the pivotal role real-time data platforms play in the AI landscape, their critical nature is often overlooked in favor of legacy databases.
Unlike real-time data platforms, which more effectively facilitate the exploration of vast arrays of unstructured data and enhance the ability of Large Language Models (LLMs) to retain and recall these relationships over time, legacy systems were designed primarily for batch processing and handling pre-aggregated datasets. Legacy database architectures’ lag time cannot meet the demands for real-time analytics and high concurrency that modern AI applications require.
In a business world where rapid decision-making is an everyday reality, leaders must ensure there are no delays in the delivery of actionable insights that can impact the organization’s ability to improve customer experiences and boost sales. Instead, companies must adopt data architectures that support instant analytics so employees can act on fresh insights in milliseconds.
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To fulfill its potential, AI requires an appropriate foundation — a platform that embodies simplicity and enables rapid processing and analysis of vast datasets without compromising speed or scalability. If your organization hasn’t made this transition yet — whether it’s due to budget constraints, lack of technical expertise, or resistance to change — now is the time to overcome these hurdles and ensure your ability to achieve long-term AI success.
Three key attributes of a real-time data platform
The next generation of data platforms will be defined by three key characteristics: speed, scale, and simplicity. The absence of any one of these attributes compromises the accuracy of outcomes derived from LLMs and other AI-powered experiences.
- Speed
Speed is a necessity, especially when it comes to processing live data for LLMs. Fresh, accurate data ensures LLMs function at their very best, providing insights and decisions based on the most current information available.
A real-time data platform that can swiftly process and analyze live data — accommodating both structured and unstructured formats (including vectors, JSON, time-series, text, SQL, geospatial data and more) — harnesses the power of AI with both speed and precision. This agility in processing diverse data types sharpens your competitive edge and strengthens your decision-making with fresh insights, especially in scenarios that require swift choices.
For example, in the fast-paced and high-stakes world of financial exchanges, a real-time data platform can power live credit card fraud detection. This type of real-time assessment enables financial institutions to quickly identify and block suspicious transactions before they result in significant financial loss or damage to customer trust, something legacy database architectures struggle to achieve.
- Scale
Unlike legacy architectures prone to hitting capacity issues, a real-time data platform enables seamless processing and management of massive data sets — ranging from terabytes to petabytes — no matter how the volume shifts. Whether it’s a sudden increase in traffic during a timely event or an unforeseen outage, a real-time data platform can handle high concurrency and scale to accommodate peaks and valleys, all while maintaining optimal performance and agility.
For example, in the telecommunications industry, a real-time data platform can enable a telecom operator to swiftly detect call quality issues through cell tower data analysis and conduct IoT or video stream anomaly detection. This capability to scale performance alongside needs significantly enhances service reliability and security across the network.
Across industries, the ability of real-time data platforms to scale is essential for accommodating the growing and ever-changing characteristics of data, enabling proactive responses.
- Simplicity
While speed and scale have always been critical components in a data platform, the advent of generative AI has increased the importance of simplicity in databases. Simplifying data management and making data-driven work and insights available to everyone involves reducing the complexity that has historically been associated with handling various data types, as well as streamlining core data processes like Extract, Transform, and Load (ETL).
With a simple-to-use data platform, teams beyond IT can more easily navigate the complexities of real-time analytics and unlock the full potential of data. As more employees embrace a data-centric approach, simplicity becomes crucial to prevent the dangers of incorrect or deceptive data insights.
For example, a real-time data platform enables publishers and managers to quickly analyze performance across various marketing channels for enhanced revenue generation. By leveraging application telemetry, geospatial, and clickstream data, a real-time data platform simplifies market segmentation and ad targeting.
This streamlined approach to data analysis allows for faster, better-informed decision-making, ensuring marketing efforts are precisely aligned with real-time audience behaviors and trends. By eliminating the manual task from this data analysis exercise, AI automation also ensures the marketing team (a team known for moving quickly) doesn’t launch campaigns based on poorly formed insights.
Don’t let your current data architecture undermine your future data success
Legacy database architectures hinder organizations from tapping into their data’s full potential, especially in the age of AI. To maximize the capabilities of your AI investments, your organization needs a real-time data platform capable of processing and analyzing data quickly and efficiently.
The AI revolution is waiting for you. And with a real-time data platform designed around speed, scale, and simplicity, your organization can officially leap.
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