How to Approach Building a Healthcare Data Lake Roadmap

In recent times, it has been proven that data drive the client-focused healthcare which global healthcare leaders strive for. Healthcare organizations collect a lot of data which can be appropriately stored, managed and analyzed towards the provision of high-quality care.

In this post, you will learn how to approach building a healthcare data lake. You should trust us because We have been featured as Top Custom Software Development Company on SoftwareDevelopmentCompany.co

What are data lakes?

Data lakes are some of the tools for maximizing healthcare data as the valuable resources they are, uniquely poised towards making the most of healthcare data; they can also be defined as tools for storing a large variety, volume, and velocity of data in the original form for Big Data as well as real-time analytics.

Data lakes are central storage points for data, whether in a structured or unstructured form for further analysis through existing technology. Data that can be stored in a data lake can be sourced from an unlimited number of sources. These sources include videos, IoT sensors, and social media feeds.

The limitation of typical data warehouses has been overcome with data lakes. Data warehouses do not allow the storage and analysis of unstructured data where the bulk of insight for proper healthcare management lies. Data lakes are a step further in technology, allowing storage and analysis of data not just from a variety of sources but also in a variety of forms.

The suitability of data lakes in the healthcare sector is especially linked to the ability of these tools to accept data in different forms from a wide variety of sources and on a large scale; because of the ability to data lakes to accept data in their raw form for processing, data lakes have been described as the future of healthcare.

How data lakes differ from a data warehouse?

Data warehouses are other storage tools for large-scale data. However, the core differences between data lakes and data warehouses make data lakes, an upgrade over data warehouses.

The form of data that can be stored and analyzed from a data warehouse is structured data. Data lakes, on the other hand, allow the storage and analysis of both structured and unstructured data, a major reason for the acceptance of data lakes over data warehouses.

Before data is loaded into a data warehouse, there is the initial process of modeling to ensure that the warehouse and data are properly aligned. Data warehouses allow the loading of raw data from a wide variety of sources.

The cost-effective of data lakes is another reason why it is gradually replacing data warehouses. Data lakes run on an open source platform. Hadoop platform, where data lakes are run, is open source, thus requiring no licensing and is cost-effective.

Data warehouses are also highly structured tools with an underlying structure that can be subjected to little to no change. Data lakes, on the other hand, have an underlying structure that is easily modifiable as the configuration is available on the go.

The basic differences between data lakes and data warehouses make these tools more suitable for different settings. Data warehouses are suitable for the analysis of structured data within a short period and with maximum efficiency for purposes that could be regulatory as well as managerial. Data lakes, on the other hand, are suited for extracting information from data in different forms which can be loaded from different sources. In organizational settings, data lakes are thus more suited for ad-hoc analysis of data from a variety of sources.

The applicability of data lakes in the healthcare sector

The volume of data collected within the healthcare sector is especially notable. These data can be categorized into clinical data and claims data. Both data are required for proper accountability between all the players of the healthcare sector. As regards reimbursements, for example, health insurance companies analyze available data in ensuring accountability. Healthcare practitioners, such as doctors also rely on available data in determining the most suitable course of action.

Claims data are usually available in more structured forms than clinical data because of the reliance of healthcare providers on traditional means of collecting data which are largely unsuitable for further analysis. With a data lake, data which include clinical and claims data can be collected within a single repository and subjected to further insights to gather required insights.

Because of the applicability of data lakes in healthcare organizations, scalable data lakes could be developed by healthcare organizations to serve as a repository for gathering information in the long term. The application of data lakes in healthcare organizations also puts healthcare organizations in positions where they are ready to tackle the demands of the future as regards data management and analysis.

The cost-effective nature of data lakes makes these tools particularly applicable in the healthcare industry because of the global burden of the cost of health and call for the delivery of healthcare services at the most sustainable and affordable rates.

The ways in which the application of data lakes can revolutionize the healthcare sector include the following.

Proper management of population health: With the insights available when data collected in data lakes are processed, doctors will be equipped to cater to healthcare needs in the most suitable way, combining their education, skills, and experience with insights available from the analysis of a large chunk of data.

Efficient healthcare management: When healthcare professionals have access to both comprehensive clinical and claims data in the real-time, the delivery of healthcare will be especially more efficient as it will be prompt. Timely delivery of healthcare services is strategic towards attaining the most gains. Data lakes power the timely delivery of healthcare services.

The processes that could be maximally enhanced by using data lakes include query processing. Query processing is enhanced with data lakes because the available insights from data that had been stored and analyzed would fast track the process of attending to queries.

Building a healthcare data lake roadmap

Proper collection storage and analysis of data require the implementation of established measures towards the different processes involved.• Structured and unstructured data

Since structured and unstructured data are the major forms of data that are collected within healthcare organizations, it is important to establish measures for the collection of both forms of data. In leaving no stone unturned while ensuring that data collected are actionable, the proper collection of appropriate healthcare data is necessary. The proper collection of both structured and unstructured data ensures that data lakes serve more purposes than silos. Data lakes are already known to be capable of receiving structured and unstructured data. The data collection tools and processes implemented will determine how actionable the data will be.

Structured data is a form of data that is collected in a structured manner, probably based on established methods. Thus standardized data could include data stored within a file with references to established systems such as medical coding. Other forms of structured data include patient information as well as history. Structured data is the form of data that is generally accepted by data warehouses. Data lakes trump data warehouses because of the ability to accept both structured and unstructured data for further analysis.

Unstructured data include forms of data that include images and other forms of complicated data sets that are particularly difficult to analyze. It is noteworthy that, as opposed to data warehouses, unstructured data can be loaded in data lakes. Even though unstructured data can be loaded in data lakes, such data may remain with the minimum application if appropriate analytical tools are not applied.

In building a healthcare data lake roadmap with the focus of making data more actionable, organizations are thus tasked with applying analytical tools to structured and unstructured data within a data lake. In making structured and unstructured data within a data lake actionable, tools such as Hadoop can be applied. Hadoop allows the analysis of both structured and unstructured data and is an open source tool.

This application is able to organize and analyze structured and unstructured data because data stored are distributed to different processing nodes, as part of the whole, as against the more tedious and probably unachievable task of processing the entirety of the data as a whole. Thus, structured and unstructured data are processed in nodes, and the final outcome determined by accumulating the results gotten from the different nodes.MapReduce and Hadoop Distributed File System (HDFS) are some of the tools behind the storage and analysis of structured and unstructured data in Hadoop.

Data storage as objects

In ensuring that data are maximally actionable, it is also important to utilize the most efficient storage systems. Such a storage system should be readily accessible with the ability to accommodate a large scale of data, as applicable in data lakes. The features of object storage make that form of storage suitable for a large scale of data that should be readily accessible. Object storage is also a cost-effective form of storing large scale data.

With object storage, data can be stored within a single space, in multiple petabytes and can be scaled up to exabytes. Object storage allows the storage of a vast amount of data as an object which is readily accessible. The ease of access to data stored as objects is also notable, and object storage is an inexpensive form of data storage.

Features of object storage that ensure the ease of accessing and storing a vast amount of data include the fact that object storage does not require the storage of data with hierarchy. Instead of storing data as files, with object storage, data is stored as an object with unique identifiers. The unique identifiers ensure the ease of access to stored data.

With the unique identifiers attached to each stored object within object storage, data can be stored in any location. The nature of data storage thus makes it a very suitable option for creating data storage systems that are actionable and especially suited for the healthcare industry.

Healthcare organizations collect large pools of data which they are typically required to store for a long period. Data lakes allow the storage of such data in both structured and unstructured forms. The stored data can be subsequently analyzed, to gather insights that will drive a patient-centered healthcare system. However, the proper storage of data is the rate-limiting step to the analysis of data for available insights.

Object storage is the form of storage required for building a proper data lake roadmap because it allows the storage of a large pool of data in a way that is easily accessible. Thus, whenever further analyses are to be carried out, the data can be easily extracted to the applicable analytical tools. Object storage is thus currently a solution for accessible, applicable, and inexpensive data storage.

Cloud storage

Cloud storage is another tool that is aimed towards the ease of access and analysis of large pools of data. Data lakes are usually maintained, and efforts at maintaining these large pools of data include storage in servers.

For such storage, organizations can utilize on-site servers as well as other storage options such as cloud storage. The on-site server is limited because of factors such as the cost of maintaining the servers, which would require the services of dedicated IT personnel. The physical space which physical servers will contain is another limiting factor.

Cloud storage, which could be public cloud storage or private cloud storage involves the storage of a large pool of data in a virtual space which can be easily accessed and maintained. It is especially noteworthy that cloud storage is a particularly ineffective form of data storage. It is also notable that this form of storage allows the use of analytical tools such as Hadoop for gathering necessary insights from large pools of data.

The available forms of cloud data storage include public and private cloud storage, although organizations can also apply a combination of both forms of cloud storage. Public cloud storage is an inexpensive option, with limited access to stored data, though. Private cloud storage costs more than public cloud storage, with control over stored data.

Data lakes are tools that allow the storage of a large scale of data in varying forms. For healthcare organizations to utilize data to drive patient-centered care, appropriate tools and methods are required.