The app economy is big business. Apple’s App Store ecosystem alone generated a staggering $1.1 trillion in total billings and revenue for developers in 2022. But as users demand more relevant and immediate experiences, often driven by AI, developers increasingly need a competitive advantage to stand out.
Real-time analytics powered by generative AI provides a key advantage by allowing developers to extract critical insights and quickly adapt apps to reflect changing user expectations. . However, adoption remains slow, with only 17% of companies currently having the ability to perform real-time analytics on large amounts of data. On the other hand, even when businesses can perform real-time analytics, there are some common mistakes that can prevent them from reaping its full benefits.
Too much emphasis on speed over accuracy and data quality
Timeliness is clearly important for apps that use real-time analytics. But don’t sacrifice speed. The old adage “garbage in, garbage out” applies here as well. If a service utilizes low-quality data, it will not produce the intended results. Outdated or incomplete datasets will only lead to inaccurate insights and undermine customer trust in your application. Organizations should instead prioritize regular audits as well as data validation checks and cleaning to maintain data integrity and accurate results.
ignore the importance of context
Real-time data requires broader context and correlation for accurate insights. Therefore, organizations need to dig deeper to uncover the true relationships between variables. For example, a sudden spike in sales of a product may be due to increased consumer demand, macroeconomic conditions such as scarcity of complementary goods, climate-related indicators, or promotional campaigns. Correlation does not imply causation.
The purpose is not clearly defined
Without specific, measurable goals, an analytics project is unlikely to achieve the desired outcome. Therefore, organizations should define clear goals, such as increasing customer retention by a certain amount within a set period of time. This will help guide your data collection and analysis efforts. Without clear goals, it’s difficult to identify actionable insights and measure success.
Choosing the wrong tool
It’s important to remember that not all analytics tools are created equal, and existing analytics tools may not have the ability to handle real-time data. It is important for organizations to choose technology that is customized for real-time data processing and visualization. Failure to do so may result in bottlenecks, delays, and accuracy issues.
get it wrong and get it right
If organizations can harness real-time data and combine it with AI, they can open the door to hyper-personalized and instant “adaptive applications.” These provide the customized, dynamic, and responsive experiences that customers are increasingly demanding. This is achieved by adjusting behavior and functionality in real time based on factors such as user preferences, environmental conditions, data input, and changing conditions.
For example, adaptive retail apps allow businesses and advertisers to offer the right product or service to the right target audience at the right time. Similarly, adaptive booking apps may update regularly and suggest personalized trips and deals based on real-time travel information, events, and user history.
Integrating generative AI with real-time analytics offers many additional benefits, including enhanced predictive capabilities, personalized user experiences, and increased operational efficiency. This greatly enhances use cases ranging from fraud and anomaly detection to customer service and retail checkout experiences. By leveraging these technologies, companies can gain deeper insights, respond faster to change, and deliver better products and services to their customers.
Similarly, getting real-time analytics wrong can have a significant impact on your business. Approximately 41% of businesses claim that they could go out of business within three years if their app no longer meets user expectations. An even larger share (46%) believe they will lose out to the competition if they do so. But while these capabilities are already in use in mature, technology-centric enterprises, most organizations are still struggling to get the right tools and know-how to overcome barriers such as siled data systems. I’m having a hard time.
A lot of it depends on your organization’s database architecture. A problem that database departments have faced for decades is ensuring that real-time analytical results are instantly written back to operational databases and the applications they serve.
However, things are improving, and more modern databases can weave together both operational and analytical workloads while ingesting and processing real-time data. Ideally this would be done in a single environment. This avoids moving data from the database to the data warehouse and eliminates costly extract, transform, and load (ETL) processes in OLTP and OLAP systems, which can also cause delays. It will be.
If app developers can avoid common mistakes, leverage AI, and modernize their database architectures, we could soon start to see apps becoming more sophisticated and user-centric than ever before.
Photo credit: doomu/Shutterstock
Rahul Pradhan is Vice President of Product and Strategy at Couchbase.
