The role of real-time data in Indian business development and its implications for future business success

Modern technologies allow digital consumers to quickly access information, products and services. Therefore, companies need real-time data processing technologies to stay competitive and keep up with their customers.

“With the introduction of technology, more and more companies are moving into data analytics, which leads to a sharp increase in data-driven models and the normalization of automation of operational processes.”

Deb Dutta is General Manager, Datastax.

How can Indian companies use real-time data to drive effective time-to-market to achieve sustained success despite all the challenges that might bring?

As the use of real-time data is revolutionizing, companies are faced with not just an option, but the need to use data to generate insightful insights, becoming a prerequisite for future success.

Real-time data can open up a range of new opportunities to improve efficiency.

According to the data report Mckinsey, by 2025, smart workflows and human-machine interaction will be the standard for business, and employees will use data to optimize every aspect of their work. Automation, artificial intelligence and machine learning can work closely with real-time data to enable seamless collaboration between people and technology and will become a standard that will require employees to use data to optimize every area of ​​activity.

Despite accelerated digitization in recent years, Indian organizations are not making full use of the data available to them. Report MMA Global shows that in 2022, only 54% of Indian companies used consumer data in their marketing activities. If Indian companies want to remain competitive and provide personalized customer service, they need to move from collecting and storing data from applications to advanced analytics – turning data into insights.

Companies can try a new approach to using real-time data to get real-time insights and set up automation in any operating or business environment. This helps the company remain agile and able to quickly discover and focus on operational and market challenges, and respond to them with data-driven solutions. Ultimately, this leads to gaining insights to customize and personalize offers along the customer journey.

What are the main challenges when using real-time data?

The complexity of IT systems, driven by disparate architectures and isolated systems, makes it difficult for businesses to leverage real-time data. Without effective digital transformation, moving to a real-time data model can incur unnecessary costs and unintentionally create even more isolated datasets. A haphazardly implemented model can also lead to undesirable business outcomes.

The problem with real-time data is fluctuating data flows and unpredictable volume. And since the data is constantly coming in, it will be impractical to restart tasks when errors occur in the pipeline, which can lead to a domino effect in the results.

An effective solution starts with maintaining data quality, and every stakeholder in any function must understand the importance of accurate data and handle it carefully. Automated procedures with data must also be carefully quality checked to ensure that only reliable data is used, which reduces the need for data processing during the analysis phase.

Why is real-time data an essential element of business in 2023 and beyond?

As technology enables digital consumers to access information, products and services at an accelerated rate, businesses need real-time data technologies to stay competitive and keep up with customers. In addition to improving internal processes, this data also allows companies to track inventory, detect fraud, and even monitor their fleet deliveries in real time.

According to IDC (International Data Corporation): provider of information, consulting services and organizer of events in the markets of information technology, telecommunications and consumer technology), India’s public cloud computing sector will reach $13 billion by 2026, so more companies are realizing the power of real-time data to streamline and improve their offerings – a clear signal that Indian businesses are paying attention to the technologies that offer them data in real time.

Real-time analytics enable businesses to extract more value from data and automate its use. While the cloud has changed the speed of how companies interact with customers, real-time data adds a layer of intelligence to customer interactions, turning a casual site visit into a compelling sale.

How important is NoSQL for machine learning and AI in data analytics?

Many companies today are turning to the NoSQL database standard as the basis for more advanced technologies such as artificial intelligence and machine learning. This is due to the need to have access to a highly reliable and high-performance database to support the huge amounts of data needed for high-speed AI and ML algorithms.

NoSQL databases, including Apache Cassandra, do all of the above, and some of them come with in-memory data processing, enabling businesses to analyze data faster than ever. Some of them have even eliminated the need for an additional analytical layer by transferring data from AI databases in real time.

The capabilities of ML will also allow a company to train, deploy, and use abstractions to reduce development time. This is because machine learning reduces the amount of rework required by developers to add new features to existing applications. At the same time, internal stakeholders can easily and quickly use the database for any task imaginable.

An example of a successful use of real-time data analytics by an Indian company

VerSe uses DataStax powered technology to enable its more than 300 million users to consume content in their local languages, inspiring their creativity through digital empowerment. This has been the driving force behind India’s fastest growing short video app Josh. The use of DataStax technologies has allowed VerSe to grow from a startup into one of India’s tech unicorns in the field of local languages ​​and dialects.

The most important data analytics trends around the world

The three main trends that dominate today’s emerging market are Data Science, Big Data Analytics and AI. With the introduction of technology, more and more companies are rapidly developing in the field of data analytics, which leads to a sharp increase in data-driven models and the normalization of operational process automation.

At the same time, we are seeing many companies moving to data analytics to implement data-driven decision making, new data-driven models, or simply expand their business into previously untapped segments. We are also seeing a surge in interest in using AI to improve customer support, process automation, content creation and promotion, and useful non-business features such as AI fact checking.

In conclusion, I would like to invite everyone to free lesson NoSQL course, where we will consider the basics of working with InterSystems IRIS, the unique data architecture and supported models: SQL, object-oriented, document, key-value.