Definition Of Real Time Data

What is Real-Time Data? – Definition from Techopedia

What Does Real-Time Data Mean?
Real-time data refers to data that is presented as it is acquired. The idea of real-time data handling is now popular in new technologies such as those that deliver up-to-the-minute information in convenience apps to mobile devices such as phones, laptops and tablets.
Techopedia Explains Real-Time Data
The basic definition of real-time data is that it is data that is not kept or stored, but is passed along to the end user as quickly as it is gathered. It is important to note that real-time data does not mean that the data gets to the end user instantly. There may be any number of bottlenecks related to the data collection infrastructure, the bandwidth between various parties, or even just the slowness of the end user’s computer. Real-time data does not promise data within a certain number of microseconds. It just means that the data is not designed to be kept back from its eventual use after it is data is enormously valuable in things like traffic GPS systems that show drivers what is going on around them. It is helpful for all sorts of analytics projects and for keeping people informed about their natural environment through the power of instant data delivery. During the early days of computing, the model was to capture any data for storage. Now, with the proliferation of mobile devices and other advancements in technology, it is becoming more common for software to simply port collected data directly to an end user.
What are Real-Time Analytics: Examples & Benefits - SolveXia

What are Real-Time Analytics: Examples & Benefits – SolveXia

Delays in decision-making and operations cost businesses money. Real-time analytics resolves this challenge by allowing business leaders to make decisions with immediate and informative insights drawn from data. This means that businesses can prevent costly delays, take hold of opportunities and preclude problems in advance. Let’s take a look at what real-time analytics means and how software solutions can empower your business with this necessary tools to uncover data-driven insights. Coming Up1. Real-Time Analytics Definition2. What is Real-Time Analytics? 3. How Does Real-Time Analytics Work? 4. Who Uses Real-Time Analytics? 5. Challenges of Real-Time Analytics6. Empowering End Users7. Benefits of Real-Time Data Analytics8. Examples and Use Cases9. The Bottom LineReal-Time Analytics DefinitionReal-time analytics is defined as the ability for users to see, analyse and assess data as soon as it appears in a system. In order to provide users with insights (rather than raw data), logic, mathematics and algorithms are applied. The output is a visually cohesive and understandable dashboard and/or report. What is Real-Time Analytics? Real-time analytics encompasses the technology and processes that quickly enables users to leverage data the second it enters the database. It includes data measurement, management, and analytics. For businesses, analytics that is real time can be used to meet a variety of needs including enhancing workflows, boosting the relationship between marketing and sales, understanding customer behavior, finalising financial close procedures and more. Understanding live analytics is best done by breaking down the terms:Real-time: operations are performed milliseconds before it becomes available to the user Analytics: a software capability to pull data from various sources and interpret, analyse and transform it into a format that is comprehensible by humans Without real-time analytics, a business may absorb a ton of data that gets lost in the shuffle. Leading a finance team means leveraging data for both financial statement procurement, as well as to understand insights about the business and its customers’ needs. The ability to work in real-time and respond to a customers’ needs or prevent issues before they arise ends up benefitting the bottom line by reducing risk and enhancing accuracy. ‍How Does Real-Time Analytics Work? Real-time data analytics works by pushing or pulling data into the system. In order to push big data through into a system, there needs to be streaming inplace. However, streaming can require a lot of resources and may be impractical for certain uses. Instead, you may set data to be pulled in intervals, from seconds to hours. Given the choices, outputs from real-time analytics can take place in just seconds to minutes. In order for real-time data analytics to work, the software generally includes the following components:Aggregator: Pulls real-time data analytics from various sources Analytics Engine: Compares the values of data and streams it together while performing analysis Broker: Create the availability of dataStream Processor: Executes logic and performs analytics in real-time by receiving and sending data Real-time analytics is also made possible with the aid of technologies like in-database analytics, processing in memory (PIM), in-memory analytics and massively parallel programming (MPP). With all the data flowing into an organisation, it’s only of use when the information can be transformed into insights. Without automation tools, you’ll need to hire experts (coders, data analysts, etc. ) and wait for the manual production of data into reports. The required time, effort and opportunity cost can be detrimental to a business’ bottom line and decision-making abilities. However, with the aid of the automation solutions, a cloud software tool like SolveXia performs real data analytics and specifically offers financial teams with deep insights from data in just seconds. Who Uses Real-Time Analytics? Businesses across industries benefit from real-time analytics. Some of the best examples are those within finance to:Assess whether to extend credit to a borrower based on real-time credit scoring Maximise customer satisfaction by assessing customer behaviour Detect fraud at points of sale Target customers based on their actions to upsell and cross-sell products Challenges of Real-Time AnalyticsLike all aspects of business, if there’s an upside, there’s also likely to be a downside. The challenges of real-time analytics aren’t nearly as extensive as its benefits. In most instances, a well-made automation software solution like SolveXia can help your team overcome broader business hurdles. More specifically, the challenges it can help solve is the ability to provide error-free reporting and share reports with necessary stakeholders, for example. When implementing real-time analytics into your organisation, you may face the following: Definition: Real-time data analytics require that everyone within your team and stakeholders of the business agree on what “real-time” means so you can integrate a solution that meets expectations (in terms of timing)System architecture: After defining the meaning of “real-time, ” you must be sure to select a tool that can process data at high speeds. The tool should also be able to grow and scale, as data does. One example of this is how automation solutions like SolveXia can process thousands of records within minutes when completing an account reconciliation (increasing efficiency over manual reconciliation by 98%) Implementation: Implementing a real-time analytics tool may require technical know-how or an IT team to make sure that the system works well with existing tools. It can also be used as an out-of-the-box, no code required automation solution. Empowering End UsersThe ability for a technology to process mass data is paramount. However, if it can’t be made easily readable for the business leaders who consume it, then it’s a moot point. Software solutions that provide real-time analytics must be designed in a way that anyone who needs access to the information can readily and easily interpret what’s available. Automation solutions ensure this with customisable executive dashboards (like those designed for CFOs) and automated reports. Benefits of Real-Time Data AnalyticsUsing real-time data analytics allows your business to thrive and reach optimal productivity. You can minimise risks, reduce costs, and understand more about your employees, customers, and overall financial health of the business with the aid of real-time data. Here are some of the key benefits: Data visualisation: With historical data, you can get a snapshot of information displayed in a chart. However, with real-time data, you can use data visualisations that reflect changes within the business as they occur in real-time. This means that dashboards are interactive and accurate at any given moment. With custom dashboards, you can also share data easily with relevant stakeholders so that decision-making never gets put on hold. Competitive advantages: Compared to a company that is focused on historical, stale data, your business can gain a competitive advantage by accessing real-time data analytics. You can easily understand benchmarks and view trends to make the wisest choices to boost your business. Precise information: Since real-time data analytics is focused on creating outcomes, there is no wasted effort. Rather than spending resources, time and money collecting data that’s unnecessary, the software is set up to capture only the data you need. Testing: With the ability to test how changes will affect your business’ processes in real-time, you can take calculated risks. As you make changes, you can gauge if there’s any issues or negative effects and be able to revert and try again without undergoing too much damage. Monitor customer behaviour: With knowledge and insights about customer behavior, you can dive deep into customer behaviors and be able to monitor what is and isn’t working to your business’ advantage. Lower costs: Big data used to require extensive mathematical understanding and IT support. With SolveXia, you can leverage all the benefits of real-time data analytics. This means that you can lower the costs of hiring coding experts to take advantage of business data, reduce bottlenecks within processes and ensure team members have what to pull insights from the data. Apply machine learning: Machine learning improves as more data enters the system. Rather than requiring a human to update algorithms and spend time on tedious tasks, the machine manages to become more efficient as time goes on. Drive better decision making: Ultimately, one of the biggest benefits of real-time data analytics is the ability to move forward on both small and big decisions in a timely and productive manner. Through accurate insights, you can strip, update and introduce new business ideas and processes to your organisation with little risk as the analytics provides you with all the necessary information to make sound business decisions. ‍Examples and Use CasesHere’s a look at some use cases of real-time data analytics in action:Marketing campaigns: When running a marketing campaign, most people rely on A/B tests. With the ability to access data instantly, you can adjust campaign parameters to boost success. For example, if you run an ad campaign and retrieve data in real-time of people clicking and converting, then you can adjust your message and parameters to target that audience directly. Financial trading: Financial institutions need to make buy and sell decisions in milliseconds. With analytics provided in real-time, traders can take advantage of information from financial databases, news sources, social media, weather reports and more to have a wide angle perspective on the market in real-time. This broad picture helps to make smart trading decisions. Financial operations: Financial teams are experiencing a transformation by which they not only are responsible for back-office procedures, but they also add value to the organisation by providing strategic insights. The production of financial statements must be accurate to help inform the best decisions for the business. Analytics in real-time helps to spot errors and can aid in reducing operational risks. The software’s ability to match records (i. e. account reconciliation), store data securely (in a centralised system) and transform raw data into insights (real-time analytics) makes all the difference in a team’s ability to remain accurate, agile and ahead of the curve. Credit scoring: Any financial provider understands the value of credit scores. With real-time analysis, institutions can approve or deny loans immediately. Healthcare: Wearable devices are an example of real-time analytics which can track a human’s health statistics. For example, real-time data provides information like a person’s heartbeat, and these immediate updates can be used to save lives and even predict ailments in advance. The Bottom Line Real-time data analytics serve a wide range of purposes in virtually every type of business (and even on an individual basis). When it comes to running a business and keeping a finance team operating at full capacity, it basically becomes a requirement to utilise real-time data analytics. Finance teams can utilise real-time data analytics for a multitude of benefits, like assessing how daily operations are performing (spot bottlenecks), implementing process improvement (analyse KPIs) and overseeing a business’ financial status (reporting), just to name a few. ‍Automation solutions like SolveXia can provide real-time data analytics by pulling data from any source and transforming it into high level insights based on data analysis. With this accurate knowledge, business leaders are able to make swift decisions, lower costs, and boost overall efficiency.
Real-Time Data: The Key to Digital Transformation | DataCore Software

Real-Time Data: The Key to Digital Transformation | DataCore Software

Companies today are overwhelmingly embracing digital transformation as one of their key strategic priorities. Every single CIO, regardless of industry, company size, or geography, is undergoing some version of a digital transformation journey.
And what is the essential enabler of digital transformation? It’s real-time data. Real-time data is the key to driving innovation and business strategy, and to automating critical business processes, which forms the core of digital transformation.
Real-time data is about the performance, availability and resiliency of data. Achieving real-time data means that access to data is always fast and uninterrupted, and that IT services are always on and connected.
Software-defined storage and workload optimization are the technologies that deliver real-time data. Software-defined storage technology enables a single data integration platform that lets an enterprise query and access all types of data across the network on premises within various sources and systems, as well as the data residing in the cloud, as if it were stored in a single place. It creates a “universal access” environment without ever having to physically move the data.
This frictionless access means required data is delivered to business users and apps quickly, regardless of where it is physically located. It also simplifies application management and supports the unification of endpoint management.
A software-defined storage infrastructure also provides a flexible data management approach that delivers significant savings over traditional data replication and consolidation. This lays a foundation for increased productivity, an empowered digital workspace, and rich desktop and mobile experiences for users, delivered where and when they need them.
Workload optimization is equally important for digital transformation because it decreases latency and delivers faster response times for applications. Workload optimization technology can make workloads more responsive and productive by removing a number of software chokepoints responsible for sluggish behavior and underutilized resources. It’s especially effective during peak periods when many users or tasks compete for data access. Under these conditions, systems tend to bog down as simultaneous requests are serialized waiting on previous requests to complete.
Our history, experience and technology make DataCore uniquely positioned to help business drive their digital transformation initiatives. We pioneered software-defined storage. Our groundbreaking parallelization technology overcomes I/O bottlenecks, delivering dramatic performance improvements, and delivers high availability in a very simple and cost-effective way. With these two technologies, we can help companies take advantage of real-time data while radically improving the economics of their systems.
What does this ultimately mean to the business? More insight from data, and faster responses for analytics and business intelligence applications. Ultimately, the business becomes empowered with more agility to respond to changing conditions.
Don’t just take our word, try it for yourself! Try a 30-day trial of our software-defined storage and workload optimization technologies today.

Frequently Asked Questions about definition of real time data

What is real time data example?

Healthcare: Wearable devices are an example of real-time analytics which can track a human’s health statistics. For example, real-time data provides information like a person’s heartbeat, and these immediate updates can be used to save lives and even predict ailments in advance.Dec 2, 2020

What is real time data availability?

Real-time data is about the performance, availability and resiliency of data. … This frictionless access means required data is delivered to business users and apps quickly, regardless of where it is physically located. It also simplifies application management and supports the unification of endpoint management.Jun 7, 2018

What is realtime data processing?

Real-time processing is defined as the processing of unbounded stream of input data, with very short latency requirements for processing — measured in milliseconds or seconds. … Processed data is often written to an analytical data store, which is optimized for analytics and visualization.Jun 18, 2021

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