Access to Real-Time Data
Making informed decisions promptly is essential to successful operations. Real-time data processing allows the delivery and presentation of data as it is captured from data sources. It enables companies to assess and respond to changes as they occur, providing a degree of responsiveness and agility.
Other data processing techniques, like batch processing, may result in obsolete data and delayed responses. Consequently, decisions are often based on out-of-date information, resulting in lost opportunities, poor decision-making, and dissatisfied customers.
This post will explain real-time data, what data streaming is, how it differs from batch processing, and how real-time data streams enhance company processes and achieve positive outcomes.
What is Real-time Data Processing?
Real-time data refers to data that frequently changes, sent normally in the form of transaction-related messages to support given triggered events. Real-time data processing pertains to a data platform that handles data as it is gathered, yielding nearly instant results. Real-time data streaming is a technology that empowers the immediate collection and processing of data from diverse sources to extract insights in real time. This capability allows for prompt analysis and responsive decision-making as data is generated, resulting in swifter and more well-informed choices. Modern data processing has evolved from batch data processing to working with real-time data streams.
Table of Contents
Section 1: What is Real-time Data Processing?
Section 2: How Data Streaming Works?
Section 3: Batch Processing and Real-Time Streaming - What's the Difference?
Section 4: How Real-Time Data is Used
Section 5: Technical Spotlight – Lambda Architecture
Section 6: Benefits of Real-Time Data
Section 7: The Bottom Line: Finworks Real-time Data Will Unlock Your Infrastructure
How Data Streaming Works?
Finworks Real-Time Data Streaming Tool & Technology You Can Use
Modern data is produced by an endless number of sources, including hardware sensors, servers, mobile devices, apps, and web browsers, both internal and external, and it is difficult to govern or enforce data format or manage the volume and frequency of data generated. Applications that analyse and handle data streams must process data packets sequentially, one at a time. Each packet will contain the source and date to allow programs to operate with data streams.
Data stream applications will always need two fundamental functions: storage and processing. Storage must be capable of recording massive volumes of data sequentially and consistently. Processing must be able to communicate with storage, consume, analyse, and compute data.
Batch Processing and Real-Time Streaming - What's the Difference?
Here’s a breakdown of the major differences between batch processing and streaming data.
| Aspect | Batch Data Processing | Streaming Data |
| Hardware | Requires most storage and processing resources to handle large data batches. | Requires less storage but more processing resources to maintain real-time processing capabilities. |
| Performance | Latency can range from minutes to hours or days. | It guarantees latency in milliseconds. |
| Data Set | Involves processing large batches of data at a time. | Involves processing continuous streams of data. |
| Analysis | Involves complex computation and analysis over a larger time frame. | Involves simple reporting or computation. |
How Real-Time Data is Used
Access to real-time data provides a competitive edge, enables faster and more accurate decision-making and drives improvements across various sectors, ultimately leading to better outcomes and increased efficiency. Real-time data is mostly used to drive real-time analytics, which transforms raw data into insights as soon as it is collected. These analytics, also known as business or operational intelligence, can be applied across industries.
Financial Industry
Organisations operating within this sector heavily rely on real-time data to stay competitive and make informed choices to rapidly correlate, analyse, and act upon various data sources such as trading data, market prices, company updates, and other relevant information. In the financial industry, access to real-time market data is essential for traders and investors to make split-second decisions based on current market conditions. High-frequency trading and algorithmic trading strategies heavily rely on real-time data. Real-time data is essential for managing investment portfolios effectively. Portfolio managers rely on real-time market data to adjust asset allocations and make investment decisions aligned with client goals.
Data helps financial institutions assess and manage risk in real-time. Whether it's credit risk, market risk, or operational risk, having up-to-the-minute information is crucial for making informed risk management decisions. It is used to detect fraudulent activities and unauthorised transactions as they occur. Sophisticated fraud detection algorithms analyse real-time transaction data to identify patterns indicative of fraud. Real-time data helps financial institutions stay compliant with regulatory requirements. Real-time monitoring of transactions and activities helps identify and prevent potential compliance violations.
Healthcare Industry
Real-time data is crucial in healthcare for patient monitoring and diagnostics. Wearable devices and medical sensors can transmit real-time health data to medical professionals, enabling early detection of health issues. Real-time data from medical devices and patient monitoring systems enables healthcare providers to deliver timely interventions and personalised care to patients. In addition, real-time data empowers patients to take an active role in their healthcare by providing them with access to their medical records, test results, and treatment plans in real-time.
Data supports telemedicine by enabling remote consultations and monitoring of patients at home. Healthcare providers can assess patients' conditions and adjust treatments without the need for in-person visits. It helps healthcare organisations monitor the health status of populations and identify trends and risk factors, guiding preventive measures and health promotion initiatives. Additionally, data on disease outbreaks and epidemiological trends enables public health officials to respond quickly, implement control measures, and prevent the spread of infectious diseases.
Manufacturing Industry
Real-time data plays a crucial role in augmenting supply chain management. By harnessing live data streams, manufacturers can gain insights into their production processes, inventory levels, and demand patterns, enabling them to make timely adjustments and optimise their operations. Real-time data aids in dynamic production planning and scheduling, allowing manufacturers to adjust production volumes and schedules in response to changes in demand, supply chain disruptions, or equipment breakdowns.
Access to data in real-time helps manufacturers identify and mitigate risks in a timely manner, whether they are related to safety, compliance, supply chain disruptions, or other potential challenges. It also helps monitor workforce productivity and track work-in-progress. Manufacturers can allocate labour resources effectively and identify opportunities for skill development. Real-time data allows manufacturers to monitor product quality at every stage of production. Any deviations from quality standards can be detected in real-time, minimising the production of defective goods and reducing waste.
Government Industry
The benefits of real-time data within the government sphere are multifaceted. It fosters seamless communication between different government entities. Timely sharing of information ensures that all relevant parties are on the same page, promoting coordination and collaboration. This facilitates more coherent policymaking, effective resource allocation, and improved public service delivery.
Real-time data allows governments to assess the impact of policies and programs in real-time. This information enables evidence-based decision-making and adjustments to policies as needed. Governments can provide online services and engage with citizens through digital platforms, leveraging real-time data to enhance the efficiency and accessibility of government services. Access to real-time data allows governments to respond quickly and effectively to emergencies, natural disasters, and public safety threats. Timely information about incidents, resource availability, and population movements helps coordinate emergency services and protect citizens. _
Technical Spotlight – Lambda Architecture
Lambda architecture is a way of processing large quantities of data, often referred to as Big Data, and provides a deployment model to combine batch processing and stream processing in the same hybrid approach. With lambda architecture, new data goes through a batch layer or a speed layer. The speed layer gives users real-time access to data. The use of lambda architecture provides the benefits of flexible scaling and high availability, so all data inputs are processed by the platform. The lambda architecture mode is crucial to organisations that strive to become more data-driven and event-driven to respond to huge volumes of rapidly generated data.
The main components of the Lambda Architecture are data sources, a batch layer, a serving layer and a speed layer that provide users and other applications insights and query outputs.
The batch layer is associated with the traditional data warehouse and usually has a set schedule for data input. The batch layer has important functions:
To manage the master dataset
To ensure a trusted and historical record of data
The serving layer incrementally indexes the batch views to make it queryable by end users. This layer can also reindex all data. Overall, the processing is done in parallel to minimise the time to index the data set.
The speed layer handles data that are not already delivered in the batch view due to the latency of the batch layer. The speed layer gives access to streaming data to provide a complete view of the data to the user by creating real-time views. The speed layer complements the batch/serving layers by indexing all the new, unindexed data. The result is a consistent view of data in the batch/serving layers that can be recreated at any time, along with a smaller index from the speed layer that contains the most recent data.
Faster Decision-Making with Data-Driven Insights
Real-time data allows organisations to make informed decisions quickly. This speed is essential in fast-paced industries where delays in decision-making can lead to missed opportunities or increased risks. Real-time data facilitates data-driven decision-making, enabling organisations to base their strategies on accurate and up-to-date information rather than relying on assumptions or outdated data.
Improved Efficiency and Operational Transparency
Real-time data helps identify inefficiencies and bottlenecks in processes, leading to streamlined operations and resource optimisation. This can result in cost savings and better resource allocation. Real-time data provides visibility into various aspects of operations, allowing stakeholders to monitor progress, track milestones, and assess performance in real- time.
Optimised Resource and Supply Chain Management
Real-time data enables organisations to monitor and manage their resources more effectively. This includes inventory management, workforce allocation, and equipment maintenance, among others. Real-time data allows organisations to track shipments, leading to a more efficient and responsive supply chain.
Better Risk Management
Real-time data allows for the early detection of potential risks and issues. This proactive approach to risk management helps organisations mitigate or respond to challenges before they escalate.
Predictive Analytics and Forecasting
Real-time data feeds into predictive analytics models, improving the accuracy of forecasts and projections. This is valuable for demand forecasting, financial modelling, and strategic planning.
How does a Workflow Platform Help Government
The Finworks Workflow platform uses a low-code workflow architecture and revolutionises organisational collaboration through optimal automated procedures. The platform facilitates safe collaboration, task prioritisation, document management, reporting and advanced data handling. This allows faster workflow development and business process automation without replacing expensive and time-consuming processes. The Finworks helps government departments through the following benefits:
1. Automation:
Workflow management platforms automate repetitive and manual tasks, reducing the need for manual intervention and increasing efficiency. By automating processes, organisations can eliminate errors, reduce processing time, and ensure consistent execution of tasks.
2. Standardisation:
These platforms allow organisations to define and standardise their workflows, ensuring processes are executed consistently across teams and departments. Standardisation limits risks, promotes efficiency, reduces variability, and enables easier progress tracking and monitoring.
3. Collaboration:
Teams can work on a shared platform, collaborate on tasks, exchange information, and provide real-time feedback. It fosters transparency, improves communication, and enables better decision-making.
4. Scalability:
Collaborative workflow management platforms are designed to handle processes of varying complexities and scales. As organisations grow and evolve, these cloud-based platforms can accommodate changing needs and accommodate additional workflows, tasks, and users, all of which require more data storage without sacrificing performance.
5. Accountability:
Organisations can assign ownership and responsibility for specific tasks or stages of a process. This promotes accountability and ensures that each team member knows their role and expectations. By tracking progress and monitoring performance, organisations can identify areas for improvement and address any issues that arise.
6. Continuous Improvement:
Through data analytics and reporting, organisations can identify inefficiencies, bottlenecks, and opportunities for optimisation. This enables them to refine and enhance their workflows over time, increasing efficiency and effectiveness.
How does a Workflow Platform Help Government
The Finworks Workflow platform uses a low-code workflow architecture and revolutionises organisational collaboration through optimal automated procedures. The platform facilitates safe collaboration, task prioritisation, document management, reporting and advanced data handling. This allows faster workflow development and business process automation without replacing expensive and time-consuming processes. The Finworks helps government departments through the following benefits:
1. Automation:
Workflow management platforms automate repetitive and manual tasks, reducing the need for manual intervention and increasing efficiency. By automating processes, organisations can eliminate errors, reduce processing time, and ensure consistent execution of tasks.
2. Standardisation:
These platforms allow organisations to define and standardise their workflows, ensuring processes are executed consistently across teams and departments. Standardisation limits risks, promotes efficiency, reduces variability, and enables easier progress tracking and monitoring.
3. Collaboration:
Teams can work on a shared platform, collaborate on tasks, exchange information, and provide real-time feedback. It fosters transparency, improves communication, and enables better decision-making.
The Bottom Line: Finworks Real-time Data Will Unlock Your Infrastructure
Access to real-time data enables the rapid capture and analysis of critical information, resulting in more informed decision-making. The Finworks platform is simple to obtain real-time data by using a proven robust data architecture. Data is easily discoverable by utilising industry-standard identifiers in conjunction with our Data Platform and Data Discovery APIs. These capabilities allow simple onboarding, and data is made available through integration with your downstream apps. This adoption in different sectors has resulted in increased process efficiency and cost savings, demonstrating the technology's considerable impact.
If you want to set up data streaming pipelines for your organisation, Finworks offers a scalable and high-performance real-time data pipeline solution that allows you to easily gather, store, and analyse real-time data.