Difference between revisions of "Data Engineering"
WendyDobson (talk | contribs) m (→Project Members) |
WendyDobson (talk | contribs) m (→Objectives and Timelines) |
||
| Line 57: | Line 57: | ||
= Objectives and Timelines = | = Objectives and Timelines = | ||
| − | + | ||
{| class="wikitable" | {| class="wikitable" | ||
|- | |- | ||
| Line 66: | Line 66: | ||
|Gather & curate information || 01 June 2018 | |Gather & curate information || 01 June 2018 | ||
|- | |- | ||
| − | Produce papers/Posters for EU Connect|| 01 November 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 | ||
| + | |- | ||
| + | |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 | ||
|} | |} | ||
Revision as of 04:27, 15 May 2018
Project Overview
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.
Project Leads
| Guy Garrett | Project Co-Lead | Achieve Intelligence | guy.garrett@achieveintelligence.com |
| Bev Hayes | Project Co-Lead | JNJ | bhayes2@its.jnj.com |
| Wendy Dobson | Project Manager | PhUSE | wendy@phuse.eu |
Project Members
| Amy Gillespie | Participant | Merck | Amy_Gillespie@merck.com |
| Beate Hientzsch | Participant | HMS | Beate.Hientzsch@analytical-software.de |
| Jagdev Bhogal | Participant | BCU | Jagdev.Bhogal@bcu.ac.uk |
| Mike Carniello | Participant | Astellas | michael.carniello@astellas.com |
| Mark Bynens | Participant | JNJ | Mbynens@its.jnj.com |
| Paul Slagle | Participant | Inventiv Health | Paul.Slagle@inventivhealth.com |
| Sascha Ahrweiler | Participant | Bayer | Sascha.Ahrweiler@phuse.eu |
| Shaaz Ansari | Participant | Gene | ansari.shaaz@gene.com |
| Vince Marinelli | Participant | MDSOL | Vmarinelli@mdsol.com |
| Vijay Pasapula | Participant | Gilead | vijay.pasapula@gilead.com |
| Jatin Patel | Participant | Parexel | Jatin.Patel@parexel.com |
Project Updates
Provide project updates in this section.
Date: Description of Update
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 | |
| 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 |
Project Activities
This section can document project activities or serve as a jumping off point to other pages in the project.
Meeting Minutes
