Real-time data can have a transformative impact on businesses, and it’s easier than ever before to leverage them. Yet, why do so few of the enterprises get it off the ground correctly? It has to do with simplifying the architecture and preventing database silos from forming. . .
While the idea of real-time apps has been around for decades, the technology needed to support this kind of instant intelligence and action has only been widely available for the past five to seven years. The applications fueled by real-time data have become engines of innovation—automating operational decisions, creating powerful digital experiences, and, as recent research shows, generating revenue growth for enterprises.
Here I’ll define real-time data, the advantages of leveraging it, and how to start building applications that produce the quality of experience that users have come to expect.
What Exactly is Real-Time Data?
The concept of “reactive” apps has been around since the 1990s, but the underlying technology only brought it to life during the past decade. A lot has changed. Technological advances, including the establishment of cloud and contributions from the open-source community, have made real-time data available to most enterprises worldwide.
Unlike data that is stored for batch analysis long after it is produced, real-time data is available the moment it is produced. It can be “data in motion,” streaming between IoT devices, for example; or “data at rest,” captured in a database.
Companies such as Snowflake and Databricks have driven interest in storing large amounts of data in warehouses or lakes and mining it for intelligence via sophisticated analytics. However, while this type of data informs the business, real-time data runs the business. It powers the modern-day applications that are part of our daily lives and enables organisations to automate instant decision making.
Firms understand that enormous value can be dervied from the data created while interacting with their customers. However, in their attempts to harness it, many have developed complex architectures that, paradoxically, inhibit the activation and leveraging of real-time data. At the same time, these architectures have also created siloed and distributed databases, which increases cost, complicates maintenance, and reduces scalability. But architecture is precisely the key.
Enterprises that succeed tend to be those that simplify their data architectures by decreasing the number of operational databases and standardize on a single, unified stack.
Siggy.ai, a real-time ecommerce recommendation app, is a good example. The company’s attempt to build its own data architecture resulted in a complex and difficult-to-use solution. To solve this, the company then standardized their architecture on DataStax Astra DB, simplified development, eliminated server issues and the complexities of operating multiple databases. The results were transformative.
Today, leading-edge, best-of-breed data stacks (including DataStax, my employer) are designed to push the boundaries of compelling experiences. They lean heavily into open source technologies including Apache Cassandra, Kafka, Spark, and, lately, Pulsar and Flink.
That’s the technical aspect of real-time data. What about the benefits?
Firms understand that enormous value can be dervied from the data created while interacting with their customers. However, in their attempts to harness it, many have developed complex architectures that, paradoxically, inhibit the activation and leveraging of real-time data.
General Manager, EMEA, DataStax
How Real-Time Generates Revenue?
Be it from a customer’s, employee’s, or partner’s perspective, benefiting from useful intelligence in real-time is a key differentiator. In many industries, however, it has become a table stake. For banks, the faster suspicious activity can be detected, the faster they can act to prevent harm to their customers as well as their reputation.
“The State of the Data Race 2022,” a recent survey of 500 technology executives and practitioners, tied revenue growth directly to their use of real-time data. 71% of respondents say they could attribute revenue growth directly to leveraging real-time data and 42% that are making use of real-time data organisation-wide say it has had a “transformative impact” on revenue.
How to Get Started with Using Real-Time Data?
How do companies that are at the start of their real-time journey and thinking about building real-time applications get started on the path to success?
Firstly, with today’s best-of-breed technologies available as a service, the timeline is months or quarters, not years. There are no capital costs and nothing to stop anyone launching a proof-of-concept exercise in minutes. It’s really very simple.
1. Choose a customer experience or business process.
2. Create the conditions for the relevant team(s) to use real-time data to improve it.
3. Communicate the results, far and wide.
4. Repeat steps 1-3 until leveraging real-time data (and celebrating the value it creates) becomes common practice.
ABOUT OUR GUEST WRITER
General Manager, EMEA, DataStax
Jude Sheeran is General Manager EMEA at DataStax, which provides an open stack for modern data applications. Jude leads the team locally, helping customers build and deploy the data infrastructure for high-growth apps. Prior to joining DataStax, he held roles as Principal, International Education and Research at AWS, CEO at Eduserv and Chief Operating Officer at Shaw Trust. Learn more about DataStax here.