A diverse, varied and voluminous amount of data procured from extensive clinical studies, laboratory trials, and diagnostic information from various patients holds a potential facet in the field of Biotechnology. The application of data management processes and tools have become a pivotal stride in creating a structural computational approach to store, manage and analyze this massive data.

The advancements in technology are growing at breakneck speed and staying with that pace is a way to build futuristic and value-based healthcare models. Integrating data management tools enabled biotechnology and related fields to be more effective and smart. Just to give an example by touching the surface of the horizon, strategic data management through predictive analytics and contact tracing helped the authorities to surf the data of the spread of COVID-19 virus and its impact.

Data management acts as a bridge between the information available in the database and the knowledge of the biotechnologist to derive better and more successful outcomes. At the present time, where we are being faced with new diseases and viruses that are mutating to be more resilient, logistic tools and techniques facilitate molecular studies and gene sequencing of microbes at a much faster rate to develop drugs and vaccines. The cross path of data management with biotechnology significantly reduced the cost and time of clinical R and D which otherwise would usually take billions of money and years of time.

The data involved in the biotechnology field is humongous. Right from the sphere of genome sequencing, environment monitoring, and pharmaceutical compound combination to predictive diagnosis, data will be collected from various sources including electronic mediums, hospital records, the internet and social media. The AI/ML-enabled data management tools sift through this complex and large set of data to understand the interconnectivity of human disease and genetic codes and derive an accurate and safe result.

The implementation of data management tools empowered the biotechnology industry to widen its horizon for more probable experiments and better outcomes. To substantiate it with some instances

  • Electronic Health Records (EHR) : It has been assimilated in every healthcare unit to attain a comprehensive patient health record, which helps to bring out customized treatment approaches.

  • The systematic use of algorithms and computerized statistical data analysis helped in expediting the genetic studies.

  • Not just on living organisms, data management technology has continued to make its impact on finding the systematic genetic alteration of crops to obtain a better yield.

Data management has evolved to be an inevitable aspect of the Biotechnology field. It has helped to shape various sectors of the industry for better efficiency and performance.