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.
1. Real-Time Analytics Definition
2. What is Real-Time Analytics?
3. How Does Real-Time Analytics Work?
4. Who Uses Real-Time Analytics?
5. What are the Latency Measures for Real-Time Analytics?
6. What is Batch vs Real-Time Analytics?
7. What are the Costs of Outdated Data Analytics and Reporting?
8. Challenges of Real-Time Analytics
10. Benefits of Real-Time Data Analytics
Real-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.
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:
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.
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:
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.
Businesses across industries benefit from real-time analytics. Some of the best examples are those within finance to:
To determine latency for real-time analytics, it can be broken down into two categories, namely:
Data latency measures the time between when data is generated to when it can be queried. Typically, there’s a lag time involved here. However, systems that operate in real-time to perform real-time analytics minimise the time between data generation and data usage.
Query latency refers to the time it takes to run a query and provide a result. To provide for the best possible user experience, applications aim to also minimise the time it takes to do so. Query latency is really important for user experience because it can make the difference between a conversion versus a dropped customer.
The biggest difference between batch and real-time analytics is the latency measures.
Batch analytics is high latency and returns queries on data that has been generated many minutes in the past. For so many years, the standard of data analysis was based on batch analytics and processing.
Of course, there are still many use cases for batch analytics today. Like the name implies, systems pull data in batches and load it over time to then perform analytics all at once. Data processing in batches can be used to reduce costs and be run at specific times rather than having to always be on.
On the other hand, real-time analytics is optimised for low latency, and therefore processes data as soon as it’s been generated. For businesses, this can offer a massive advantage when it comes to business intelligence, reporting, logistics, compliance, and more.
Real-time analytics are a great resource because it allows teams to be agile and respond to events in real-time before they become too large to manage. It also empowers timely, informed, and strategic business decisions.
The benefits of real-time data analytics include quick decision-making, responsive applications, timeliness, and efficiency, to name a few.
If you think investing in real-time analytics is going to be too large of a cost, then you need to be aware of the cost of having outdated analytics and reporting.
Let’s break down what we mean:
There’s an associated quantifiable cost of having to collect, store, and analyse data. This is likely the cost you’re most likely to compare or consider when thinking about your data analysis processes because it’s straightforward.
Now come the costs of collecting, storing and using outdated data. Data expires and changes by the second. If you’re using old data or redundant data, then your analysis is going to be stuck in the past or skewed. That’s why maintaining data accuracy and keeping it up-to-date is of utmost importance to inform your business decisions.
Agile businesses that can be proactive and/or react immediately tend to do better than those that are delayed. Real-time analytics makes it possible to spot issues as they arise to quell them before they grow or affect more aspects within the business. Real-time analytics also enable your team to deal with customer needs quickly, which results in more satisfied customers.
Data automation solutions like SolveXia can resolve all of these challenges. They help to reduce errors, alleviate tedious manual tasks, streamline workflows, and provide organisations with transparency and security.
Like 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:
The 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.
Using 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:
Here’s a look at some use cases of real-time data analytics in action:
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.
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