Huge efficiencies have been made in BioPharma companies over the last few decades, notably in the area of data capture (with moving to eCRFs) business process improvements, and data standardisation efforts by CDISC.
However, the sector is increasingly competing on the basis of their analytical capabilities, which requires a centralised, combined, and, as much as possible, automated data environment to support these deeper insights. It is clear that data is R&D needs a major transformation; it is too siloed, fragmented and manually intensive, to be utilised effectively.
This project will explore how established Data Engineering techniques, successfully deployed in other industries, could be utilised in our industry. From traditional data warehousing; to the arrival of the big data lake; with data marketplaces; ePRO and IoT; the challenge is on – to identify analytical value from all of these disparate data sources.
The aims of this project are two-fold. Firstly, to gather the myriad resources available on traditional methods of data engineering, to provide a breadth of knowledge that could immediately bring benefit to our existing clinical data estate. This will be achieved by curating and organising content into an easy-to-use structure (such as Wiki).
Secondly, to prepare us for the “big data tsunami” which is to arrive shortly in our sector, so that we can learn about the more thought-leading subjects in this area and help disseminate and share this information with the Data Science & AI/Machine Learning co-projects – a natural fit for these new types of data analysis – alongside the more tradition methods on the PhUSE Wiki.
Terms of Reference:
- Team meetings frequency will be no more than fortnightly and no less than monthly
- Meeting agendas and minutes are stored in the projects Teamwork area (access to project members only)
- The Data Engineering section of the Educating for the Future Working Group Squarespace website will be maintained by admin volunteers and the co-leads. Any content for the website can be prepared by any member of the project and uploaded to our project space on Teamwork for review before uploading to Squarespace by the admins and co-leads
|Guy Garrett||Project Co-Lead||Achieve Intelligenceemail@example.com|
|Bev Hayes||Project Co-Lead||JNJfirstname.lastname@example.org|
|Wendy Dobson||PHUSE Project Manager||PHUSEemail@example.com|
|Rohit Banga||Participant||Lyfe Sciencefirstname.lastname@example.org|
|Susan Olson||Participant||EDJ Analytics||Solson@edjanalytics.com|
|Martin Houben||Participant||Antrim Consultingemail@example.com|
|PHUSE EU Connect Frankfurt - 2018||Presentation on Data Engineering||Guy Garrett & Bev Hayes|
Objectives and Timelines
|Initialize project & build team||01 Febuary 2018|
|Pick initial topic areas||01 March 2018|
|Gather & curate information||01 June 2018|
|Produce papers/Posters for EU Connect||01 November 2018|
|To facilitate education of the pharmaceutical industry on data engineering solutions successfully implemented in other industries||ongoing|
|Topics should relate to current and foreseen challenges in pharma, e.g. vast data streams, big data, real time learning and decision making||ongoing|
|Audiences may include but are not limited to, data managers, statistical programmers/analysts, IT experts, executives||ongoing|
|Material shared is not to endorse or prescribe, but is to present data engineering techniques in a manner that allows the audience to draw their own conclusion regarding the potential application in pharma||ongoing|