Real Time Data | 2 mins read

What is Real-Time Data and What Are the Benefits?

what is real time data and what are the benefits
Chloe Henderson

By Chloe Henderson

Companies are continuously trying to find the best ways to gather and interpret data sets to improve their operations and responsiveness.

With real-time data, organizations gain the ability to monitor activity, such as sales, transactions, and orders, as they occur to mitigate risks and capitalize on timely opportunities. This has influenced many businesses to switch from traditional data processors to real-time analytics.

What is Real-Time Data?

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There are several ways to perform data processing, and the appropriate method for a business will depend on their objectives. Companies wanting to analyze historical data may not be on a strict time limit, but those looking for predictive insights would need results as quickly as possible.

Therefore, organizations should consider the different types of data processing and how they operate-

1. Real-Time Data

Real-time data is the most up to date information made available immediately by a continual recording of inputs and outputs as they happen. This is commonly used in mobile banking, integrated management software, and radar systems, where users can view different activities as they occur.

2. Near Real-Time Data

Near real-time data still presents updated information, but processing takes a few minutes rather than a few seconds. This is seen in IT systems and financial tracking, where the software needs time to retrieve requested information from the data warehouse.

For example, complete event processing (CEP) records, aggregates, and analyzes data streams from several sources to detect trends and potential threats. CEP uses near-time data processing to allow time to perform these functions and present the most accurate information.

3. Batch Data

Batch data refers to information that is not time-sensitive, usually taking hours or even days to retrieve. Batch processing uses three different procedures. First, the information is gathered from a data source, then processed through separate software, and finally sent out.

Examples of batch data include operational, historical, and service information. These datasets do not need access to real-time metrics, as they typically focus on a previous time frame.
Businesses often use batch data for processing payroll, billing, and customer orders. It is also analyzed to recognize patterns in sales, demand, and revenue.

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Benefits of Using Real-Time Data

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Most businesses desire real-time data, as it provides impactful insights into customers, performance, and profitability. Not to mention, real-time data processing is the fastest analytics method.

Real-time data analytics is the only data processing method that enables businesses to-

  • Save Money and Time
Companies that still utilize manual data consolidation must finance a person or team of employees to record, aggregate, and analyze data. This process is time-consuming and can drain a significant amount of funds in labor wages alone.

With a real-time data solution, businesses can access metrics immediately without human intervention, enabling employees to dedicate their time elsewhere.

  • Boost Productivity
Slow-moving information can lag internal processes, which can restrict profitability and productivity. The speed of real-time data enables businesses to discover each operation's productivity level and determine how to enhance performance.

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  • Increase Accuracy
Inaccurate information can sabotage sequential processes, resulting in costly repercussions that require extensive time, labor, and capital to reconcile. However, real-time data automatically inputs information without human intervention, avoiding the risk of human error.

It also considers metrics from all integrated solutions, providing the most accurate values. This enhances procedures that are data-based, including budgeting, demand forecasting, and income reports.

  • Improve Decision-Making
Real-time data solutions use the most relevant information to generate detailed reports, key performance indicators (KPIs), and detect trends to enhance decision-making. Management can then use these datasets to produce actionable insights that pinpoint where improvements should be made to optimize performance.

  • Quickly Resolve Problems
With access to real-time information, software can anticipate and alert users of emerging risks, allowing management to take immediate action.

Solutions can also catch mistakes that would otherwise go unnoticed, such as inventory discrepancies and manual data inputs. This can help businesses to quickly detect internal and external fraud, technology malfunctions, and other technical issues, enabling prompt resolutions.

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  • Better Data Management and Communication
Modern solutions can consolidate real-time data from several sources into one centralized database, streamlining communication throughout the company and improving business intelligence.

This eliminates the need to manually share documents via email or by hand, which can introduce human error. Instead, verified employees can access the information digitally and communicate through the software's dashboard.

  • Better Marketing Schemes
Depending on the industry of a business, real-time data provides useful information on buyer personas, demographics, and customer behavior. These insights enable marketing teams to design effective campaigns and targeted promotions to drive traffic, sales, and revenue.

  • Customize Analytics
Advanced solutions allow users to customize analytics to focus on the real-time datasets that are relevant to their operations.

For example, warehouse managers can access information from the point-of-sale (POS), inventory, and ordering systems while disregarding irrelevant data. This would help them to account for all items in-store, in storage, and in transit to determine accurate stock levels. With this information, management can avoid over and understocking products.

Users can also set reporting parameters that determine what the system analyzes. For example, management can pull data from POS systems to view insights on average sales, generated revenue, and profit margins of individual product lines.

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