The healthcare landscape is undergoing radical change with the introduction of big data analytics. As it helps prevent diseases, predict health outcomes, and reduce medical errors, saving countless lives. As well as, it improves the quality and cost of healthcare. Even so, not every healthcare system has integrated big data analytics into its operations.

A recent study found that 93% of healthcare organizations have started looking for ways to use big data. However, only 41% have made any steps towards getting started with big data analytics. So, what are the main challenges to the large-scale adoption of big data analytics in healthcare?

Here are some of the key challenges:

Integration and storage of data

A unified architecture known as the big data ecosystem has to be built in order to ingest and store vast amounts of data coming from diverse data sources. Thus, facilitating improved communication among systems, simplifying the exchange of health information, and making big data more accessible.


Most healthcare organizations possess traditional systems which lack scalability or are an extremely expensive endeavor. If the infrastructure can't keep up with the data volumes, the analytics workloads will become bottlenecked. The healthcare continuum needs to switch to the cloud in order to gain extensive computing capability.

Quality of data

Authentic insights can only be created by using reliable data input that is free of duplicates and errors. Poor quality can lead to doctors misidentifying patients or prescribing the wrong treatment. To improve the quality of data, healthcare settings need to implement data governance and master data management solutions.

Standardization of data

Data in healthcare comes from a variety of sources such as EHRs, medical devices, sensors, wearable devices, provider reimbursement, and more. This data is stored in different formats across different database systems and data models. It needs to be brought into a common format for analysis and interpretation.

Exchange of data

Due to the lack of standardized data, sharing health information across the care continuum and systems becomes increasingly challenging. Furthermore, such sensitive information needs strong privacy protection. Especially in health emergencies, when the data must be shared in a timely and accurate manner.

Security and privacy of data

In the healthcare industry, data security is a primary concern. As healthcare is a strictly governed sector, there are strict laws regarding storing and sharing sensitive health information. As a result, setting up appropriate policies, running regular evaluations, performing risk assessments, and training employees to follow best practices is essential.

Lack of big data analysis skills

The scope and diversity of big data in healthcare is enormous. But such large amounts of data can be hard to collect, clean, process, manage and analyze. Thus, healthcare organizations require reliable experts to help them overcome these issues.

Data visualization

Visualizing big data helps decision-makers see the big picture and enables them to respond quickly. Therefore, real-time monitoring, dynamic dashboards, and performance reports are necessary. However, visualizing health data requires specific tools and expertise in healthcare.


The shift from volume-based patient care to value-based patient care has catalyzed the need for the adoption of big data analytics in healthcare. The global healthcare big data analytics market size was valued at USD 23.51 Billion in 2020 and is projected to reach USD 96.90 Billion by 2030, growing at a CAGR of 15.3% from 2021 to 2030. The above events clearly show that the adoption of big data analytics in healthcare will rapidly rise in the coming years.

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