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Intelligent Platforms and Democratization of Data Engineering
Intelligent Platforms and Democratization of Data Engineering

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Post pandemic, businesses have undergone a transformation digitally and have become more data driven. Therefore, building autonomous, and intelligent platforms is expected to help them move ahead with optimal efficiency. With the increasing need to do more with data to meet customer demands and beat competition, companies need to move to a platform centric approach that will enable them to create more value for the business without the heavy lifting of traditional data pipelines.

 

Are Intelligent platforms yielding better efficiency and enabling companies to better address customer needs?

Intelligent platforms do lead to better efficiencies, especially in sectors like “Healthcare”. The sector has challenges with respect to connectivity between the devices, resulting in siloed use of the data, and siloed applications, leading to fragmented workflows. Therefore, presence of intelligent platforms is a necessity to facilitate connectivity between all the devices, aggregate data, and provide tools to process the said information and construct and integrate seamlessly into existing clinical workflows.

In other sectors like Automotive, there have been tremendous investment in platforms to generate business and revenue, and although data aggregation is already in place, however, the industry is focusing on data monetization using these platforms, given the complexity of the automotive industry.

 

What do companies need to get right with respect to intelligent platforms?

Platform

 

  1. Understand the intrinsic value of the data and how can it be curated to facilitate maximum opportunity for the future. This requires collaborations between data and business teams to build forward facing strategies and monetizing the platform.  

 

  1. Understand the problem at hand. Not every platform is designed to solve a problem that a particular sector is facing. Each sector faces a multitude of problems, and the platform must choose a particular problem to solve. E.g. In Healthcare, one can create platforms to create digital twins of devices, platform for asset performance management, platform for serviceability of devices, platforms for optimizing clinical workflows and decision support systems. The nature of the problem is critical to build the platform.

 

  1. Data is critical while building any platform and the said platform must be able to not only absorb the data, but also has to be able to curate it in a way which makes the data usable and solve the problem at hand.

 

  1. Once the problem to be solved is decided and the data relevant to solve the problem is captured, the platform needs to provide building blocks /services that allow organizations to process the data and get insights to put them in workflows and hence open up the platform to the larger community to build an ecosystem.

 

What is Data Democratization?

Data democratization has been around for many years. It means making it easy for the enterprise to access the data and reduce the cost of accessing data that has been aggregated. Companies like ZF are planning to build externally facing data and digital businesses. Though the term data democratization is applied for the internal use of the data, but companies should also be looking at data productization i.e. how to create products that can ultimately sell business solutions.

In Healthcare, there are issues in data interoperability. If data is produced at one location, can companies use the information effectively and democratically and extract knowledge base and insights which can be applied to specific solutions? It involves making the knowledge base as captured in the data available from an insights perspective that can be then further used.

With data democratization being applied across industries, there is expected to be some data, privacy, and regulatory concerns. In Healthcare sector, today when a patient is getting treated, there is an interaction between the patient and the physician, and the patient is treated depending on the condition of the patient and the experience of the physician. What is missing is the lack of use of institutional information available on the subject and/or the disease. So, through data democratization, hospitals should be able access this vast information, process the same, and make it applicable at the time of interaction between the patient and the physician in real time. The biggest issue with data democratization, however, remains interoperability of data sets and the lack of standards due to variation between different systems.


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Vandhna Babu
Chief of Staff - President's Office

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