Bringing IAM Insights in Healthcare Big Data

With the introduction of innovation in technology, a range of security layers rightly get integrated in IT Systems. As conventional security systems cannot address today’s dangerous threat landscape alone – introduction of network forensics and big data analytics is made to capture, process and analyze any security loophole. Furthermore, experts believe that Identity and Access Management also play a crucial role in the Big Data security. Big Data Management & Analytics is highly adopted by progressive organizations for generating and utilizing insights across industries including Healthcare, Pharmaceutical and Life Science.

According to an estimate by Mckinsey Global Institute 1, revenue of up to $100 billion can be generated annually by US pharmaceuticals if Big Data strategies are optimized to make informed decisions.

One of the most prominent use cases for Big Data Analytics linked with IAM Technology is to make full use of relevant data by integrating networking devices, applications, tools, and software with the authorized user(s). This is based on premise that the right user should gain access to authorized information. While effective utilization of data acquired from several sources such as IPRs, R&D Process, PII, PHI, retailers, hospitals, laboratories, patients’ records, etc. security considerations related to sensitive information cannot be overlooked. Various combinations of information received from different sources can be instrumental in innovating new potential drug experiments.

Now with Big Data and advancements in processing information through various assessment/analytics tools greater insights are gathered that lead to various capabilities such as:

• Setting up an effective criteria for clinical trials negating the possibilities of delays and errors.
• Assists & accelerates virtual trials for varied drills to build accurate predictive research models.
• Monitor, classify & layer unutilized accesses by users to refine the access protocol for enhanced security.

Considering the importance of governed access to information, it is crucial to synchronize Big Data Management with the Identity and Access criteria.

Organizations can harness IAM Technology principles in Big Data Management endeavors in the following ways:

1. Deal with Data Overload and Untangle Multiple Streams of Information:

   The health industry gathers, generates, and processes wide-ranging data and information. Technology can be instrumental in ensuring that redundant data is not channeled to the entire user base. It is crucial to strategically integrate IAM with Big Data Management by defining, integrating, and managing data. Big Data Analytics inter-linked with IAM technology can aid Life Science organizations by scrutinizing irrelevant and unutilized access of users and information acquired through various sources such as doctors, patient’s records, R&D, path labs, etc.

2. Helps Making Informed Decisions and Aids Clinical Trials and R&D:

   While Big Data Analytics is resource intensive and detail oriented, linking it with IAM technology can provide a unified view of identity, social, behavioral and transactional data captured across various channels. Big Data Analytics provides the essential foundation for greater insights in less time. Furthermore, bringing an edge in clinical trials and R&D advancements needs sophisticated modeling techniques, tools, systems, and data-driven technologies. Technical integration can make it possible, quicker and more accurate, which will help get better insights and analytical outcomes.

3. Regulate your IT System and Achieve Data Security Compliance:

   Life Science organizations must adhere to regulations and compliances involved in the sector such as HIPAA, HITECH to operate functions and achieve data security compliance for a smoother workflow. The penalization imposed for not adhering to these compliances is high and may result in degrading the brand reputation of the enterprise. Big Data Management enables the most strategic information assets and ensures that it is used for optimal effectiveness.

Big data security analytics help improve incident detection/response. From a security perspective, this can help enterprises reduce their overall cyberattack surface and lower IT risk. It will also ensure compliance-proof systems leading to better efficiency, productivity, and outcome in the industry. Big Data Analytics also aids in minimizing the cost by managing and interpreting the data to retrieve its best-utilized value.