Assignment 4D
Introduction
I am a lab technician working in the development department for pharmaceutical company
that develops and produces new drugs. The lab stores confidential information relating to
drug development. It also stores personal and confidential information relating to volunteers
who are used for drug trials. My manager has asked me to produce a report evaluating the
challenges in storing and communicating the range of information recorded and processed
in the laboratory, comparing the systems in the development laboratory to those in the
company’s manufacturing department.
AbbVie
AbbVie is the company I work for, this is a pharmaceutical company mostly known for its
ability to treat autoimmune diseases including Crohn's disease, plaque psoriasis and
rheumatoid arthritis.
Explaining how useful scientific information is obtained from large data sets
Large data sets are often collected from different sources like experiments, in this companies
case it is medical trials and experiments. AbbVie has a vast amount of scientific data related
to their drug discoveries and developments. Before the data is analysed it needs to be
processed and checked for any missing values, duplicates, anything to make it more
manageable when reading.
AbbVie uses a tool called ARCH to extract knowledge from data, it pulls different data sets
together into one place. The data is then summarized, for example patterns and
relationships between different variables in the data are identified, this helps scientists
extract meaningful information. In this company scientists may need to evaluate
experiments in where they went well and wrong so they can better the experiment or batch
of new products. This tool Arch allows AbbVie scientists to identify problems in the data,
they will research what is causing that problem. Validating the findings from large-scale data
analysis is essential. This entails applying methods like holdout validation, bootstrapping,
and cross-validation to evaluate the validity, accuracy, and generalizability of findings.
Effective interpretation and communication of the analysis's findings are required after it is
finished. Complex discoveries can be communicated in an easily understood way by using
visualisation techniques like plots, charts, and graphs. It is common practice to examine
huge data sets iteratively, using the lessons from previous analysis to guide subsequent
actions. To further improve their understanding, researchers can adjust analysis methods,
gather more data, or improve theories.
There can be some issues found in the ARCH systems like duplicates in electronic health
records , gaps in patients data records which can lead to inaccurate assessments. The ARCH
system process is quite complex which obstructs the successful use by patients and
healthcare providers , without the proper training of the company's staff the effectiveness of
, the system will be limited. A question AbbVie should ask themselves is ‘How able is the
ARCH system process to still ensure effectiveness as the number of clients and data volume
increases?’.
Companies always have to make sure that when obtaining large data sets they can manage
the complex and vast information with the systems they have in place. The lack of
standardised format can lead to inconsistencies in organisation. In obtaining large data sets
its a challenge to create effective and user-friendly data tools to retrieve data without
complexity. A security challenge would be being able to create a robust security system put
in place to protect the large amount of stored data. As the amount of data obtained is so
vast it is possible their is a loophole in security measurements.
Explaining how AbbVie processes and records large data sets of scientific information and
how it is transformed into useful forms for customers
The sources of data that AbbVie gathers are from lab experiments evaluate the safety and
the ability of the medication or therapy that then later are referred to clinical trials. Data
from the lab experiments are then brought to the clinical trials, the data is used to do
preclinical research to understand the effects and mechanism of the drug or therapy. More
data that is processed and recorded in large sets would be insurance claims, patient details
and health records that the company would use to analyse the real-life safety and real-life
efficiency of the drug or therapy. Omics data is data that is based on the study of various
“Omes” of an organism, this information is gathered by technologies like proteomics. These
technologies are made to understand disease mechanism and pinpoint therapeutic targets.
Machine learning algorithms and statistical analysis are used to search for patterns,
relationships, and insights in the data. Large datasets are safely organised, stored, and
retrieved by AbbVie using sophisticated data management systems. Data consistency,
quality, and regulatory compliance are all guaranteed by data governance policies.
Cross-functional cooperation is facilitated by the consolidation of heterogeneous data
sources using data integration techniques. Cleaning, converting, and standardising raw data
are processes in the preprocessing process that get it ready for analysis. =To find patterns,
connections, and insights in the data, statistical analysis and machine learning algorithms are
used. Large-scale datasets can be processed rapidly using high-performance computing
infrastructure by utilising distributed and parallel computing techniques.
Type of data collected where its stored and how it meets customers' needs and ensures
traceability
As AbbVie is a global pharmaceutical company it collects Research data for pharmaceutical
drugs using biological and genetic data. Medical records are collected for clinical trial
patients which is a predominant process needed to trial the companies' pharmaceutical
drugs and therapies. Clinical trial data which is collected to know the outcomes of the
clinical trial and the patients. Regulatory data is collected as for the drug to be approved
Introduction
I am a lab technician working in the development department for pharmaceutical company
that develops and produces new drugs. The lab stores confidential information relating to
drug development. It also stores personal and confidential information relating to volunteers
who are used for drug trials. My manager has asked me to produce a report evaluating the
challenges in storing and communicating the range of information recorded and processed
in the laboratory, comparing the systems in the development laboratory to those in the
company’s manufacturing department.
AbbVie
AbbVie is the company I work for, this is a pharmaceutical company mostly known for its
ability to treat autoimmune diseases including Crohn's disease, plaque psoriasis and
rheumatoid arthritis.
Explaining how useful scientific information is obtained from large data sets
Large data sets are often collected from different sources like experiments, in this companies
case it is medical trials and experiments. AbbVie has a vast amount of scientific data related
to their drug discoveries and developments. Before the data is analysed it needs to be
processed and checked for any missing values, duplicates, anything to make it more
manageable when reading.
AbbVie uses a tool called ARCH to extract knowledge from data, it pulls different data sets
together into one place. The data is then summarized, for example patterns and
relationships between different variables in the data are identified, this helps scientists
extract meaningful information. In this company scientists may need to evaluate
experiments in where they went well and wrong so they can better the experiment or batch
of new products. This tool Arch allows AbbVie scientists to identify problems in the data,
they will research what is causing that problem. Validating the findings from large-scale data
analysis is essential. This entails applying methods like holdout validation, bootstrapping,
and cross-validation to evaluate the validity, accuracy, and generalizability of findings.
Effective interpretation and communication of the analysis's findings are required after it is
finished. Complex discoveries can be communicated in an easily understood way by using
visualisation techniques like plots, charts, and graphs. It is common practice to examine
huge data sets iteratively, using the lessons from previous analysis to guide subsequent
actions. To further improve their understanding, researchers can adjust analysis methods,
gather more data, or improve theories.
There can be some issues found in the ARCH systems like duplicates in electronic health
records , gaps in patients data records which can lead to inaccurate assessments. The ARCH
system process is quite complex which obstructs the successful use by patients and
healthcare providers , without the proper training of the company's staff the effectiveness of
, the system will be limited. A question AbbVie should ask themselves is ‘How able is the
ARCH system process to still ensure effectiveness as the number of clients and data volume
increases?’.
Companies always have to make sure that when obtaining large data sets they can manage
the complex and vast information with the systems they have in place. The lack of
standardised format can lead to inconsistencies in organisation. In obtaining large data sets
its a challenge to create effective and user-friendly data tools to retrieve data without
complexity. A security challenge would be being able to create a robust security system put
in place to protect the large amount of stored data. As the amount of data obtained is so
vast it is possible their is a loophole in security measurements.
Explaining how AbbVie processes and records large data sets of scientific information and
how it is transformed into useful forms for customers
The sources of data that AbbVie gathers are from lab experiments evaluate the safety and
the ability of the medication or therapy that then later are referred to clinical trials. Data
from the lab experiments are then brought to the clinical trials, the data is used to do
preclinical research to understand the effects and mechanism of the drug or therapy. More
data that is processed and recorded in large sets would be insurance claims, patient details
and health records that the company would use to analyse the real-life safety and real-life
efficiency of the drug or therapy. Omics data is data that is based on the study of various
“Omes” of an organism, this information is gathered by technologies like proteomics. These
technologies are made to understand disease mechanism and pinpoint therapeutic targets.
Machine learning algorithms and statistical analysis are used to search for patterns,
relationships, and insights in the data. Large datasets are safely organised, stored, and
retrieved by AbbVie using sophisticated data management systems. Data consistency,
quality, and regulatory compliance are all guaranteed by data governance policies.
Cross-functional cooperation is facilitated by the consolidation of heterogeneous data
sources using data integration techniques. Cleaning, converting, and standardising raw data
are processes in the preprocessing process that get it ready for analysis. =To find patterns,
connections, and insights in the data, statistical analysis and machine learning algorithms are
used. Large-scale datasets can be processed rapidly using high-performance computing
infrastructure by utilising distributed and parallel computing techniques.
Type of data collected where its stored and how it meets customers' needs and ensures
traceability
As AbbVie is a global pharmaceutical company it collects Research data for pharmaceutical
drugs using biological and genetic data. Medical records are collected for clinical trial
patients which is a predominant process needed to trial the companies' pharmaceutical
drugs and therapies. Clinical trial data which is collected to know the outcomes of the
clinical trial and the patients. Regulatory data is collected as for the drug to be approved
