Consider a World Cup soccer match in which everything is at stake. The players on each team are working hard to break the tie. The score remains 0-0 as the referee blows the half-time whistle. Consider the following scenario: Throughout this time, Team A's data analysts are tracking their players' every move, second by second. While the players are devising a strategy, the analysts notify the coach about which players are more likely to become fatigued in the final minutes, who may be substituted, and which positions require further development. Based on the forecasts, the coach then makes the appropriate replacements, and Team A scores a late goal to win the match 1-0 in extra time.

This is just one of the many applications of streaming analytics that data-driven enterprises may benefit from. Let's look at what it is, where it is applicable, and whether it is appropriate for your organization.

What is Streaming Analytics?

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In today's world, there is a never-ending stream of data. Our phone, smartwatches, cars, routers - practically everything emits data. Naturally, this data stream is processed in the same way "simultaneously." Streaming analytics intakes vast amounts of real-time, in-motion data, using continuous queries to create real-time actionable insights. It connects to external data sources and integrates that data into the application flow. The data is usually gathered from IoT devices, log files generated by smartphones, live streams, real-time transactions, financial trading floors, e-commerce purchases, movements and statistics of sports players, and in-game activities. Data can be anything that you can measure within a system at a specific time, such as a click, a post, or a tweet. Once the data is gathered, it is processed sequentially and aggregated, correlated, filtered, and sampled. Businesses can then utilize the data to get visibility.

How is Streaming Analytics different from Batch Processing?

The historical approach to data processing is downloading the data and moving it in batches. Large amounts of data are processed simultaneously, with long latency periods, and the processes take hours, days, or even weeks. Additionally, the batch processing approach is inherently reactive. Businesses can only react to past events. Although the traditional method works best for most organizations, it may not be the best solution for time-sensitive data, which may lose its value. On the other hand, streaming data flows in continuously and is processed the second it is generated.

Where is it applicable?

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Cybersecurity

Streaming analytics allows real-time logging of event data and continuous monitoring of internal IT systems to find threats, detect fraud, identify abnormal behavior and nip the problem in the bud instead of taking a reactive approach.

E-Commerce

Streaming data obtained from clicks improves the shopping experience with more targeted promotions, real-time pricing and offers, users' spending patterns, and inventory management.

Energy

Power throughput maintenance of solar and power companies is maintained through streaming analytics by initiating workflows, monitoring panels in real-time, and generating alerts. This minimizes the periods of low throughput and reduces the chances of associated penalty payouts.

Financial Services

Streaming analytics aids financial institutions in preventing credit card fraud by monitoring real-time customer transactions, detecting anomalies, and warning.

Healthcare

Streaming analytics is used in patient care by monitoring clinical researchers' data to detect diseases, and smart pills send real-time data from inside the patient to understand the illness.

Investment Services

Stock market institutions track changes in real-time, adjust settings to customer portfolios, compute value-at-risk, and rebalance portfolios based on stock price movements and real-time risk analysis.

Manufacturing

Sensors embedded in the production line and supply chain enable manufacturers to detect problems and correct them while the product is still in the production line, thus saving money, time and improving efficiency.

News Media

Clickstreams from millions of users and demographic information optimize content for a more customized, relevant article suggestion experience for the audience.

Social Media

Geographic preferences, paired with real-time recommendations, enhance the browsing experience, feed recommendations, and even prevent cases of trolling, fraud, bullying, and other nefarious activities.

Sports and Gaming

Online gaming companies collect information on in-game interactions to provide more dynamic experiences for the players. On the other hand, Sports analysts stream real-time data during matches to give players and coaches better insights about their movements, stamina and match statistics.

Transport

Vehicles equipped with sensors monitor real-time traffic data, location tracking, pricing, and time estimates based on real-time and historical data to help cab drivers provide a more optimum experience to the rider.

Why is it important?

Streaming data provides visibility to companies regarding users' buying habits and preferences, which gives them the ability to provide tailored customer experience, reduce costs, increase customer retention, identify errors, and react to mitigate risks more quickly. It also accelerates decision-making and provides a granular view of their KPIs. Additionally, streaming analytics aided by cloud services enables organizations to manage workloads efficiently by auto-scaling clusters to optimize costs.

Things to consider

Streaming analytics can bring businesses on the path to better long-term profitability. However, there are still some lingering issues that need to be addressed:

Consistency

There is a need to ensure that the data entering at a given time is not modified and stays durable and consistent throughout the stream.

Scalability

Data streams can fluctuate from kbps to Mbps. Ensure that the servers and applications have enough capacity to scale in such scenarios.

Ordering

While debugging the data, developers need to maintain each line in order, as sometimes streaming data has discrepancies in timestamps and metadata.

Conclusion

Today's world moves at a hyperspeed, making it difficult for outdated data processing approaches to remain viable. As the new meta world generates more data, organizations must act, react, respond, and adapt to the constant incoming data stream using real-time streaming analytics to realize the full potential of their data.