The fast growth of the digital health industry in India due to COVID-19 has led to the reshaping of customer health experiences. Innovations like mobile healthcare apps, telehealth services, e-pharma services are witnessing higher adoption rates and transforming the digital health customer experience.

Lack of awareness regarding the use of mhealth apps, uncertainty about apps’ working efficiency, security issues, etc. were the root cause of its wavering development, prior to the pandemic. During lockdowns, nearly 67% of Indians felt comfortable receiving medical advice over calls and video sessions, according to a Royal Philips survey.

The healthcare industry has shifted towards a patient-centric model to deliver convenient and meaningful experiences from the patient’s home. Below are the top five trends that are shaping digital health customer experiences:

Customers are relying on mHealth apps

Mobile health apps in India have witnessed an increase in downloads due to changes in lifestyle, increased interest in fitness & wellness programs and to track & monitor a variety of health data — sleep patterns, calorie intake, physical activity, etc. Followed by the telehealth segment, the mHealth segment is expected to dominate the Indian market by reaching approximately USD 1.87 Bn by 2024.

Mobile health apps in India such as Practo, PharmEasy, 1mg, Medlife, cure.fit etc. allow customers to order healthy food, buy medicines with discounts, receive health tips and attend virtual doctor consultations by staying at home. Even though mHealth apps are general wellness related, the number of condition management apps are likely to increase with customer engagement. Moreover, the growth of the mHealth segment will ensure cost effective healthcare services that will prompt the consumers to use health apps. With the rise of mobile health apps, more benefits are likely to be incorporated such as in the case of health emergencies where an app can send the location of the needy to the hospital, thus saving ambulance drivers’ time in following directions.

Increase in Demand for Personalized Care

Customers have begun to feel empowered and valued through wearable devices and other digital health tools as it is enabling them to take control of their health. With electronic health records in hand, healthcare organizations are leveraging patients’ health records that are helping in optimizing the digital health customer experience. Predicting problems and providing solutions before they bother the patients has become the new model. This has paved the way for hyper personalization. By analyzing an individual’s DNA, it allows HCPs to monitor patients’ medication, provide health tips and helps them to diagnose diseases early. For instance, DNAfit offers genome-personalized health advice, workout plans, etc. that help customers in framing a daily routine. Apple Healthkit also functions in a similar fashion to personalize healthcare services as patient data is collected, compared and mined to result in a customized health experience.

Younger generation has more trust in tech companies

Around 32% of gen X and 43% of millennial are open to receive virtual healthcare, according to an Accenture survey. As the younger generation provides active feedback to the healthcare organizations, examining their behaviour can provide significant insights that might help mending the existing gaps between HCOs and customers. According to a recent Deloitte survey, empathy and reliability are the two factors that customers expect from healthcare providers. This shows that when customers are given the option to own their personal data related to health, healthcare organizations are more likely to attract customers. Considering how consumers are sensitive about their data, data interoperability is likely to help organizations in meeting consumer needs. In addition to this, increase in digital touchpoints are likely to multiply to meet diverse consumer needs.

Increased Demand for Value-added services

According to an Accenture survey, around 57% of customers are open to remote virtual care. This shows the increasing appreciation of real-time assistance and contactless healthcare. Healthcare providers are likely to produce more value-added services by enhancing patient engagement, data collection, digital health channels. Traditional ways of treatment will change when HCOs leverage patient data from technologies and smart devices. Expert advice of HCPs in developing value-added services will further assist in producing accurate solutions for patients. Consumer demand for value-added services shows the increasing expectations from the digital health industry that will transform the customer experiences, as the leading health organizations are likely to produce more digitally enabled health solutions. Post COVID-19 when people begin to socialize, the contactless health services will be useful in cases of health emergencies, or for old people who find it hard to travel.

Consumers are open to omnichannel virtual care

Be it buying of medicines, or keeping a regular check on health, the digital health tools such as mHealth apps, fitness trackers, etc have been adopted by consumers to satiate their healthcare needs. Openness to various digital health channels shows the strengthening of consumer trust. Recently Apple launched Apple Watch series 6 that allows users to take on-demand readings of blood oxygen level anytime. Its potential to give readings anytime and anywhere reflects customers’ increase in usage of digital health tools. Apart from tracking steps, fitness trackers also have advanced health features like, heart-rate monitors, SpO2 monitors, sleep tracking, etc. Web apps and chatbots are being used by healthcare organizations to assist people with health-related problems. Digital healthtech company Your.MD uses chatbot and web app to help customers get personalized health information. The future of the digital health industry is likely to witness an enhancement and increase in the number of access points. Increased acceptance of omnichannel will lead to optimization of customer engagement as HCOs will have more resources from where they can leverage patient data.

To know about how healthcare industry is bringing hospitals to a customer’s doorstep, watch our webinar on Digital Health Beyond COVID-19.

Know about our work in Digital Health and how we have helped clients such as Suraksha Diagnostics, Abbvie, Religare Health Insurance, and SBI Health Insurance build mobile and web applications improving their operational efficiency and customer experience.

Why was Data Warehouse the Best Move on the Board?

Data warehouses act as the strategic command centers for enterprises. By employing a schema-on-write approach, they ensure data is cleaned, validated, and formatted before storage. This guarantees high accuracy and consistency, making them indispensable for industries like finance and healthcare. For instance, global banks rely on data warehouses to calculate real-time risk assessments or detect fraud—a necessity when billions of transactions are processed daily, tools like Amazon Redshift, Snowflake Data Warehouse, and Azure Data Warehouse are vital. Similarly, hospitals use them to streamline patient care by integrating records, billing, and treatment plans into unified dashboards.

The impact is evident: according to a report by Global Market Insights, the global data warehouse market is projected to reach $30.4 billion by 2025, driven by the growing demand for business intelligence and real-time analytics. Yet, much like Deep Blue’s limitations in analyzing Kasparov’s emotional state, data warehouses face challenges when encountering data that doesn’t fit neatly into predefined schemas.

The question remains—what happens when businesses need to explore data outside these structured confines? The next evolution takes us to the flexible and expansive realm of data lakes, designed to embrace unstructured chaos.

The True Depth of Data Lakes

While structured data lays the foundation for traditional analytics, the modern business environment is far more complex, organizations today recognize the untapped potential in unstructured and semi-structured data. Social media conversations, customer reviews, IoT sensor feeds, audio recordings, and video content—these are the modern equivalents of Kasparov’s instinctive reactions and emotional expressions. They hold valuable insights but exist in forms that defy the rigid schemas of data warehouses.

Data lake is the system designed to embrace this chaos. Unlike warehouses, which demand structure upfront, data lakes operate on a schema-on-read approach, storing raw data in its native format until it’s needed for analysis. This flexibility makes data lakes ideal for capturing unstructured and semi-structured information. For example, Netflix uses data lakes to ingest billions of daily streaming logs, combining semi-structured metadata with unstructured viewing behaviors to deliver hyper-personalized recommendations. Similarly, Tesla stores vast amounts of raw sensor data from its autonomous vehicles in data lakes to train machine learning models.

However, this openness comes with challenges. Without proper governance, data lakes risk devolving into “data swamps,” where valuable insights are buried under poorly cataloged, duplicated, or irrelevant information. Forrester analysts estimate that 60%-73% of enterprise data goes unused for analytics, highlighting the governance gap in traditional lake implementations.

Is the Data Lakehouse the Best of Both Worlds?

This gap gave rise to the data lakehouse, a hybrid approach that marries the flexibility of data lakes with the structure and governance of warehouses. The lakehouse supports both structured and unstructured data, enabling real-time querying for business intelligence (BI) while also accommodating AI/ML workloads. Tools like Databricks Lakehouse and Snowflake Lakehouse integrate features like ACID transactions and unified metadata layers, ensuring data remains clean, compliant, and accessible.

Retailers, for instance, use lakehouses to analyze customer behavior in real time while simultaneously training AI models for predictive recommendations. Streaming services like Disney+ integrate structured subscriber data with unstructured viewing habits, enhancing personalization and engagement. In manufacturing, lakehouses process vast IoT sensor data alongside operational records, predicting maintenance needs and reducing downtime. According to a report by Databricks, organizations implementing lakehouse architectures have achieved up to 40% cost reductions and accelerated insights, proving their value as a future-ready data solution.

As businesses navigate this evolving data ecosystem, the choice between these architectures depends on their unique needs. Below is a comparison table highlighting the key attributes of data warehouses, data lakes, and data lakehouses:

Feature	Data Warehouse	Data Lake	Data Lakehouse
Data Type	Structured	Structured, Semi-Structured, Unstructured	Both
Schema Approach	Schema-on-Write	Schema-on-Read	Both
Query Performance	Optimized for BI	Slower; requires specialized tools	High performance for both BI and AI
Accessibility	Easy for analysts with SQL tools	Requires technical expertise	Accessible to both analysts and data scientists
Cost Efficiency	High	Low	Moderate
Scalability	Limited	High	High
Governance	Strong	Weak	Strong
Use Cases	BI, Compliance	AI/ML, Data Exploration	Real-Time Analytics, Unified Workloads
Best Fit For	Finance, Healthcare	Media, IoT, Research	Retail, E-commerce, Multi-Industry

Conclusion

The interplay between data warehouses, data lakes, and data lakehouses is a tale of adaptation and convergence. Just as IBM’s Deep Blue showcased the power of structured data but left questions about unstructured insights, businesses today must decide how to harness the vast potential of their data. From tools like Azure Data Lake, Amazon Redshift, and Snowflake Data Warehouse to advanced platforms like Databricks Lakehouse, the possibilities are limitless.

Ultimately, the path forward depends on an organization’s specific goals—whether optimizing BI, exploring AI/ML, or achieving unified analytics. The synergy of data engineering, data analytics, and database activity monitoring ensures that insights are not just generated but are actionable. To accelerate AI transformation journeys for evolving organizations, leveraging cutting-edge platforms like Snowflake combined with deep expertise is crucial.

At Mantra Labs, we specialize in crafting tailored data science and engineering solutions that empower businesses to achieve their analytics goals. Our experience with platforms like Snowflake and our deep domain expertise makes us the ideal partner for driving data-driven innovation and unlocking the next wave of growth for your enterprise.

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