Data Analytics / Predictive Analytics , Technology

Breaking Down Data Silos With Real-Time Streaming

Modern enterprises face an uphill task with data fragmentation, where information remains locked within departments, creating isolated silos. This lack of unified data access not only limits insights but also causes inefficiencies and unnecessary cost escalation. Real-time data streaming addresses these challenges by breaking down silos, enabling immediate, organizationwide access to data.

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"For decades, data was stored and then queried well after the fact. Our approach enables data to be written once and accessed by any downstream system, without costly integrations," said William LaForest, global field CTO at Confluent - a data streaming platform. "By creating a 'write once, use anywhere' model, real-time streaming can help businesses make fast, data-driven decisions without the lag of traditional data processing."

According to Confluent's 2024 Data Streaming Report, 51% of IT leaders now consider data streaming platforms, DSPs, a top strategic priority, up from 44% in 2023. The report surveyed 4,110 IT leaders on the growing role of DSPs in driving business agility.

Today, data-streaming technology powers more than 80% of Fortune 100 companies. Streaming data includes log files generated by customer interactions on mobile or web applications, e-commerce purchases, in-game player activity, social media, financial trading floors, geospatial services, and telemetry from connected devices and instrumentation in data centers.

The Importance of Real-Time Data Streaming for Enterprises

Across industries, real-time data streaming enhances decision-making and operational efficiency. The core challenge for most enterprises today is reducing the gap between data and decisions. Whether it is food delivery giants such as Zomato and Swiggy or financial services firms handling sensitive transactions, processing data in real time is crucial. Delays of even a few seconds can impact customer satisfaction and profitability.

Real-time data streaming allows enterprises to ingest, process and analyze data as it is generated, ensuring that decisions are based on the most current data possible. "This capability is no longer just a technical advantage - it's a business imperative," said Rohit Vyas, India head of solutions consulting and customer success at Confluent.

Transforming Big Data With Real-Time Streaming

Big data has traditionally relied on batch processing, which creates a major temporal disconnect from the immediate reality of business. This is no longer acceptable for modern business demands. As enterprises today demand instantaneous insights, real-time data streaming is becoming the go-to solution.

Traditional "extract, transform, load" and "extract, load, transform" data pipelines have historically been the primary method for moving data into analytics. But analytics consumers have often had limited control or influence over the source data model, which is typically defined by application developers in the operational domain. Data is also often stale and outdated by the time it arrives for processing.

"By shifting data processing and governance, organizations can eliminate redundant pipelines, reduce the risk and impact of bad data at its source, and leverage high-quality, continuously up-to-date data assets for both operational and analytical purposes," LaForest said.

Real-time data streaming is especially crucial in sectors such as finance, e-commerce and logistics, where even a few seconds of delay can negatively impact customer satisfaction and profitability. As more businesses demand actionable insights on the go, the industry is seeing a shift toward real-time data models.

Data Streaming in Distributed Environments

Businesses are increasingly operating across distributed environments, including multi-cloud environments, data centers and edge networks. This, coupled with the dramatically increasing data volumes and the need to act on data in real time, has been another driver in the adoption of data streaming.

Processing vast quantities of data in a distributed environment can be prohibitively expensive and can add significant latency to the end-to-end process. Data streaming can provide a solution to these challenges by enabling businesses to process data as it's created, as opposed to batch or micro-batch processing, thereby reducing latency. Data streaming is also more pragmatic to deploy at the edge.

"Data streaming is enabling companies to maximize their return on investment in their analytics infrastructure. Data warehouses and data lakes demand high-quality business data more than ever, making the prevention and mitigation of bad data across the entire organization a crucial priority," LaForest said.

Real-World Use Cases: Tackling the Data Challenges

To address specific business needs, enterprises across various industries are already transforming their data strategies through real-time data streaming:

• Retail and e-commerce: Food delivery companies heavily rely on real-time data streaming to optimize food delivery services, track orders, manage inventories and adjust to dynamic market conditions, all while ensuring smooth operations during peak times. These business models would be ineffective without real-time data.
• Financial services: In the financial sector, real-time data streaming aids in fraud detection, high-frequency trading and transaction monitoring, as well as in handling online transaction traffic in banks, ensuring secure and seamless financial transactions.
• Media and entertainment: Real-time data streaming helps companies, such as Viacom18 and Netflix, manage millions of concurrent users during high-traffic events, such as the Indian Premier League (IPL), while optimizing ad bidding and ensuring uninterrupted viewing experiences. For gaming and eSports companies such as Mobile Premier League, data streaming enables real-time fraud detection and personalized gaming experiences.
• Manufacturing and logistics: Real-time data streaming helps enterprises track equipment performance and monitor supply chains, preventing costly downtime and ensuring smooth operations. This continuous access to data allows companies to predict maintenance needs before they affect the bottom line.

Streamlining AI Adoption With Data Streaming Platforms

Real-time data streaming is emerging as the foundation for the next wave of AI innovation. For predictive AI and pattern recognition, data needs to be available in real time to drive accurate, immediate insights. Real-time data pipelines are essential for enabling AI systems to deliver smarter, faster insights and drive more accurate decision-making across the enterprise.

"Without real-time data, AI models struggle to deliver accurate results, especially in fast-paced sectors such as e-commerce, food delivery and financial services," LaForest said. "Enterprises, such as Mercedes, are already using real-time data to deliver highly personalized customer experiences, integrating vast streams of data to optimize interactions with customers."

The 'Data-as-a-Product' Approach

As businesses continue to mature their use of data streaming, many are moving toward a "data-as-a-product" approach. The survey indicates that 91% of IT leaders plan to use DSPs to achieve their data goals, with 72% seeing benefits from embracing the data-as-a-product approach.

"Enterprises must not only collect and store data but curate it in a way that ensures it is discoverable, contextualized, trustworthy, reusable and actionable in real time," Vyas said.

For example, Vimeo transformed its data architecture from traditional daily batch processing to real-time data streaming using Confluent Cloud. This shift enabled Vimeo to deliver personalized user experiences and make product decisions in real time, rather than days later. The results were significant, helping Vimeo reduce its time-to-market from days to hours, save costs and build a scalable foundation for future AI/ML initiatives.

Data Governance and Security

The accessibility to data in real time raises concerns about security and compliance, especially in heavily regulated industries, such as finance and government. "Data streaming seems counterintuitive to security. The easier it is to access, the less secure it might seem. But the reality is, you have to govern and secure data at the source so that only those with the right permissions can access it," LaForest said. Robust governance ensures data remains secure while being accessible for analysis and decision-making.