As disruptive technologies like AI continue to leave a mark on the world economy, data-driven companies are faced with navigating new challenges around automation, data silos, and ethics. Leveraging AI requires a key understanding of business intelligence and the ability to shift gears and business goals as new data comes in.
The ability to process and analyze data in real time is a mission-critical skill that allows organizations to make decisions swiftly and efficiently. It has far-reaching benefits across various fields where delay can mean missing out on key opportunities or failing to respond to emerging challenges.
In a day and age where technology has disrupted every known business frontier, the ability to gather, read, and react off of real-time data analytics will provide any business with a much-needed advantage to compete in today’s data-driven landscape.
What is Real-Time Data Analytics?
Real-time data analytics is the process of gathering data in real time to provide insights for making better decisions quickly. When a data warehouse receives raw data, the information is immediately processed to empower near-instant decision-making.
The need for real-time data analytics is exemplified when organizations, governments, and people need quick access to data regarding events as they happen.
With this data, questions surrounding the possible implications and outcomes are resolved. For example, the 2020 global chip shortage affected computer silicon, leading to significant price increases for computer parts, gaming consoles, and other consumer electronics requiring integrated circuits. With that context, one could imagine how businesses would need real-time data analytics to answer questions on supply chain, shipment updates, and customer order trends.
Apart from the retail space, here’s how real-time data analytics positively impacts other verticals:
Healthcare:
In healthcare, real-time data analytics plays a role in patient monitoring and care, especially when dealing with patient vital signs, tracking bed availability, and optimizing staff allocation.
Finance:
Financial institutions rely heavily on real-time data analytics for fraud detection, risk management, and algorithmic trading. Loading...Artificial intelligence (AI) and Loading...machine learning (ML) are often leveraged to find unusual patterns in the dataset. Loading...Real-time analytics allow investors to make rational split-second decisions based on market movements.
Manufacturing:
The production process can be improved with the help of real-time data. Sensors and automation on the floor can provide information on production performance and quality. More importantly, it could be used to identify inefficiencies and predict potential equipment failure.
Transportation and Logistics:
Real-time analytics in the logistics industry aids in route optimization for delivery vehicles, tracking shipments, and predicting delays. Analytics can be used to avoid congestion and save time and fuel costs.
How Real-Time Data Analytics Works
Real-time data analytics is based on a centralized data catalog system that acts as a repository to store and organize historical data from various sources. When creating a repository or a warehouse, every department lead needs to agree on the protocol and best practices to keep their data sets hygienic.
Breaking down analytics and data in real time is a rather straightforward process, though the intricacies and technical nuances often add complexity to the process.
1. Data Retrieval
The first step in the process is data retrieval. The data collection process may look different for each organization. Companies may gather structured and unstructured data from various sources: cloud storage, mobile applications, a website, or sensors on the factory floor.
When collecting data, organizations must pay close attention to their data architecture and integrity. Building a data management system capable of quickly storing copious amounts of data while using AI to clean up data sets and maintain good data hygiene in real time is essential before you move on to any processing.
2. Data Processing
The second step is real time data processing and data query. This involves querying, where data values are pulled from a data set via a series of specific questions, filters, and parameters. To do this, an organization must constantly clean, validate, and update data to ensure it is accurate and complete. Robust data architecture is essential to retrieve and query data in a cost-efficient manner.
3. Reporting
Once it’s ready, we can turn Loading...big data into deep insights through reporting. The queried data can be studied to identify patterns and relationships. Often, visualizations are built to find relationships and create comparisons in real time. The process of converting real-time raw data into insightful dashboards begins an end product that is digestible for any stakeholder. A quick glance at the dashboard provides information that leads to better decision-making.
Real-Time Analytics Examples
A published report from the Center for Economics and Business Research and KX reveals that using real-time data analytics has already positively impacted revenue – with instant data adding $4.2 trillion in revenue across industries.
IoT (Internet of Things) and internet-based companies are poised to experience the most benefit of these industries.
Social media companies like Facebook and X (formerly known as Twitter) can collect and utilize massive amounts of data from user activity. For example, X and Facebook use streaming analytics to label and remove fake news posts 24/7. Social media platforms also use real-time analytics to update their user’s algorithms. In doing so, users are kept engaged and interacting with their platform. IoT companies can create physical objects with embedded sensors and software to analyze data in real-time. In most cases, artificial intelligence and machine learning are often employed, especially for error correction. If, for example, an anomaly is detected in a series of temperatures sent from your watch to your phone, then AI will recognize that this is not aligned with the historical average and ignore it in real-time. That said, it’s important to remember that none of this is possible without being able to collect accurate information over an extended period via well-manufactured sensors and inputs.
Benefits of Real-Time Data Analytics
In an era where data is often referred to as the new currency, the ability to analyze and act on information in real time has become a cornerstone for success across various industries. Aside from improving your bottom line, here are other benefits to real-time data analytics.
Time-critical analysis of data
Real-time analytics primarily allows organizations and their users to react to take immediate action. For example, in cybersecurity, the ability to detect and respond to threats instantly is the difference between a minor security incident and a data leak.
Similarly, the importance of real-time analytics becomes apparent in time-sensitive industries. The world of journalism is one such example. If a news company were to beat its competitors to the newsstand, it could capture the audience first and gain a competitive advantage.
Data democratization
Data democratization is the ongoing process of making data accessible to everybody within an organization, irrespective of their technical knowledge. As a result, data-informed decisions become a more prevalent occurrence inside the organization. Real-time data analytics allow for democratization by consistently providing up-to-date information to decision-makers.
Real-time data analytics enhance collaboration amongst different departments and teams by providing the most current data to all stakeholders. It’s a virtuous circle; the easier and faster it is to access the data, the more it will be used.
Learn how to develop an expert data democratization strategy.
A Strong Competitive Advantage
While we have proved how consumer data has led to over $4.2 trillion in economic inflow, this number was never meant to be equally distributed amongst the constituents of an industry. With business being such a cutthroat environment, having data is precisely how a company gains and maintains its elite position against competitors.
The more customers a firm has, the more data it can gather and mine.
With real-time analytics, a firm could quickly draw deeper insight and create a better product that attracts more customers. Having current information allows a company to beat its competitors in the market and provide a better overall customer experience.
Identify data pipeline issues, fast
Customer information and financial reports are often the flashier side of real-time analytics. However, understanding the health of a firm’s internal systems is just as important as finding ways to get more customers.
Identifying issues quickly allows for these errors to be rectified faster. With this ability to quickly address inaccuracies, real-time analytics contribute to overall data reliability and transparency improvements.
More often than not, a company’s internal health directly impacts its ability to deliver quality products and services. If your company’s offer isn’t delivered well, how could any rational business person expect their customers to remain loyal?
This holds especially true in product-based businesses, where inefficiencies in your manufacturing line directly impact your production capacity and targets. By continuously analyzing your data pipelines with real-time analytics, one can ensure that their manufacturing and business KPIs are met. In doing so, they maintain and improve their relationships with their customers.
Which Businesses Benefit Most from Real-Time Data?
Though we’ve briefly touched on the benefits of real-time analytics in previous sections, we’d like to provide concrete examples of precise use cases within industries.
Retail
Item availability and customer experience are essential in retail.
Retailers use real-time analytics to adjust prices based on demand, competition, and inventory levels. Their monitoring tools allow them to a.) adjust their prices to match their competition and b.) ensure the availability of products across stores based on their analytics. This works exceptionally well when retailers can adjust prices instantaneously, such as on a website or over the phone.
Speaking of websites, retailers could also offer a more personalized shopping experience in real time based on user analytics. By analyzing customer behavior as they are generated, retailers could provide targeted product recommendations through marketing campaigns that create a complete solution for the consumer. Through artificial intelligence and real-time analytics, retailers can enhance engagement and sales.
Finance
The finance industry prioritizes security and capitalizing on trends for profit.
Credit card companies and financial institutions detect and prevent fraud using real-time analytics. Analyzing transactions as they occur, organizations can flag unusual patterns that may indicate fraudulent activity in real-time.
They can also plug their analytics into algorithms capable of simultaneously reading thousands of stock charts. Bloomberg famously use real-time data to capitalize on small price fluctuations and profit off high-frequency trading.
Hospitality
Like finance and retail, the hospitality industry capitalizes on room demand trends and improves the customer experience. Hotel chains often use real-time analytics to dynamically change room pricing. Prices are adjusted based on currency demand, competitor pricing, and news events around the area.
Restaurant chains might also benefit from real-time analytics to manage their food stocks for example. Having a real-time view of orders combined with the ingredients they have in stock can help to quickly replenish any popular item that’s running low, ensuring happy customers and maximizing revenue.
Security
As theft and crime become more complex, organizations will continue to face challenges in protecting their data from cyber threats. Through data collection and analytics, security companies can look for correlations and anomalies within the data to detect threats to the ecosystem. Organizations can monitor network traffic, video feeds, and user behavior in real-time to reduce the risk of significant data loss or damage.
As you can see, real-time analytics can play a pivotal role in virtually any industry. Whether it’s for increasing security, improving the bottom line, or ensuring safety and compliance, businesses would benefit heavily from having real-time data on hand.
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