Real-time Data Collection Tool Software | Groopit
Groopit Actions focus everyone on collecting the most important data
You’ll create Groopit Actions to collect the important data in real-time. Every Action tells people what to look for and why it’s important. Start an Action from scratch or with a template. It’s so fast, you can begin collecting real-time data before you finish a cup of coffee.
9 data types designed for real-time data collection
You’ll define the specific data your frontline will collect within every Action. Choose from 9 data types designed for real-time collection – images, location, tags, text, numbers, data & time, lists, responses and hidden fields. It’s so simple, your frontline will capture precisely the right data every time.
How Groopit Works
Watch this 1-minute tour to see how it works. Groopit can be used on any device with a web browser, an iOS or Android app.
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Use Groopit on Any Device
Use the iOS app, Android app, or use Groopit on any device with a web browser. Learn more >>
Share Real-Time Data
Groopit makes it easy to empower people with real-time data.
Access & Integrate Data
Groopit’s Enterprise Edition provides advanced data access and control. Learn more >>
Real-Time Data Collection Features
Define the data needed from your frontline.
Make reporting data in real-time easy by setting up Groopit Actions.
Collect structured data using location, images, numbers, tags, dates and times and response fields.
Collect unstructured data using a text field.
Hide sensitive data fields so only administrators see the information reported.
Allow members to define data options on the fly with user-generated tags and lists.
Create Actions on the fly to collect new data from the frontline.
Prepare Actions in advance, then make them active when you’re ready to mobilize people.
Download data into a spreadsheet to slice and dice it anyway you want.
Integrate data into existing systems like HubSpot, Salesforce, Tableau, Power BI, Slack, and more, with an API.
Real-time data – Wikipedia
Real-time data (RTD) is information that is delivered immediately after collection. There is no delay in the timeliness of the information provided. Real-time data is often used for navigation or tracking. [1] Such data is usually processed using real-time computing although it can also be stored for later or off-line data analysis.
Real-time data is not the same as dynamic data. Real-time data can be dynamic (e. g. a variable indicating current location) or static (e. a fresh log entry indicating location at a specific time).
In economics[edit]
Real-time economic data, and other official statistics, are often based on preliminary estimates, and therefore are frequently adjusted as better estimates become available. These later adjusted data are called “revised data”.
The terms real-time economic data and real-time economic analysis were coined[2] by Francis
X. Diebold and Glenn D. Rudebusch. [3] Macroeconomist Glenn D. Rudebusch defined real-time analysis as ‘the use of sequential information sets that were actually available as history unfolded. ‘[4] Macroeconomist Athanasios Orphanides has argued that economic policy rules may have very different effects when based on error-prone real-time data (as they inevitably are in reality) than they would if policy makers followed the same rules but had more accurate data available. [5]
In order to better understand the accuracy of economic data and its effects on economic decisions, some economic organizations, such as the Federal Reserve Bank of St. Louis, Federal Reserve Bank of Philadelphia and the Euro-Area Business Cycle Network (EABCN), have made databases available that contain both real-time data and subsequent revised estimates of the same data.
In auctions[edit]
Real-time bidding is programmatic real-time auctions that sell digital-ad impressions. Entities on both the buying and selling sides require almost instantaneous access to data in order to make decisions, forcing real-time data to the forefront of their needs. [6] To support these needs, new strategies and technologies, such Druid have arisen and are quickly evolving. [7]
See also[edit]
Geographic information system
Real-time business intelligence
References[edit]
^ Wade, T. and Sommer, S. eds. A to Z GIS
^ Dean Croushore (2011), ‘Frontiers of Real-Time Data Analysis’. Journal of Economic Literature 49 (1), pp. 72-100.
^ Francis X. Rudebusch (1991),
‘Forecasting Output with the Composite Leading Index: A Real-Time Analysis’. Journal of the American Statistical Association 86 (415), pp. 603–10.
^ Glenn D. Rudebusch (2002), ‘Assessing Nominal Income Rules for Monetary Policy with Model and Data Uncertainty’. Economic Journal 112 (479), pp. 402–32
^ A. Orphanides and J. C. Williams (2006), ‘Monetary Policy under Imperfect Knowledge’. Journal of the European Economic Association 4 (2-3), pp. 366-375.
^ Rodgers, A. Publishers embrace real-time bidding as data takes centre stage: new report, 8 January 2014
^ Lorica, B. Big Data and Advertising: In the trenches, 4 August 2013
External links[edit]
ALFRED: Archival Federal Reserve Economic Data, real-time data series at the Federal Reserve Bank of St. Louis
Real-time data set for macroeconomists at the Federal Reserve Bank of Philadelphia
Real-time database of the EABCN
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.
Frequently Asked Questions about realtime data collection
What is real-time data collection?
Real-time data (RTD) is information that is delivered immediately after collection. … Real-time data is often used for navigation or tracking. Such data is usually processed using real-time computing although it can also be stored for later or off-line data analysis.
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 are the advantages of real-time data collection?
6 top business benefits of real-time data analyticsMake decisions at the speed of your business. … Increase business agility and optimization. … Quickly detect and address operational issues. … Identify and act on short-term market changes. … Personalize the customer experience for online marketing.More items…•Jan 11, 2021