The document provides an introduction to pharmacy informatics focused on pharmaceutical data processing, outlining the data processing cycle which includes stages like data collection, preparation, input, processing, output, and storage. It highlights various types of data processing methods such as batch, real-time, online, multiprocessing, and time-sharing, along with their specific applications in sectors like banking, education, logistics, and healthcare. The objective is to equip students with a comprehensive understanding of data processing to improve their utilization of information in various organizational contexts.

1. Drug Information Service
Lect 1b: Introduction to Pharmacy Informatics Ib
Presented by:
Heng Wei Kai
BPharm (Hons), MSc (Pharmacy), RPh. (Mal), APh (Mal), mMPS
Faculty of Pharmacy
AIMST University

2. Acknowledgement
• Dr. Dinesh Kumar Upadhyay for the construction of the previous
version of lecture slide.

3. Topic 1b:
Pharmaceutical data processing

4. Topic Learning Outcome
At the end of the topic, the students shall be able to:
• describe data processing and its different types (CLO3)
• outline the data processing cycle and its application (CLO3)

5. Data Processing
Data Processing:
• Data processing occurs when data is collected and translated into
usable information.
• It is usually performed by a data scientist or a team of data scientists.
• Data processing starts with data in its raw form and converts it into a
more readable format (graphs, documents, etc.), giving it the form
and context necessary to be interpreted by computers and utilized by
employees throughout an organization.

6. Data Processing Cycle
There are six stages of data processing:
1. Data collection
2. Data preparation
3. Data input
4. Processing
5. Data output/interpretation
6. Data Storage

7. Data Processing Cycle

8. Data Collection
• Collecting data is the first step in data processing.
• Data is pulled from available sources, including data lakes and data
warehouse.
• It is important that the data sources available are trustworthy and
well-built so the data collected (and later used as information) is of the
highest possible quality.

9. Data Preparation
• Once the data is collected, it then enters the data preparation stage.
• Data preparation, often referred to as “pre-processing”, is the stage at
which raw data is cleaned up and organized for the following stage
of data processing.
• During preparation, raw data is diligently checked for any errors. The
purpose of this step is to eliminate bad data (eg: redundant,
incomplete, or incorrect data) and begin to create high-quality data for
the best business intelligence.

10. Data Input
• The cleaned data is then entered into its destination (perhaps a CRM
software or a data warehouse), and translated into a language that it
can understand.
• Data input is the first stage in which raw data begins to take the form
of usable information.

11. Processing
• During this stage, the data inputted to the computer in the previous
stage is actually processed for interpretation.
• Processing is done using machine learning algorithms, though the
process itself may vary slightly depending on the source of data
being processed (data lakes, social networks, connected devices etc.)
and its intended use (examining advertising patterns, medical
diagnosis from connected devices, determining customer needs, etc.).

12. Data output/interpretation
• The output/interpretation stage is the stage at which data is finally
usable to non-data scientists.
• It is translated, readable, and often in the form of graphs, videos,
images, plain text, etc.
• Members of the company or institution can now begin to self-serve the
data for their own data analytics projects.

13. Data storage
• The final stage of data processing is data storage.
• After all of the data is processed, it is then stored for future use.
• While some information may be put to use immediately, much of it
will serve a purpose later on.
• Plus, properly stored data is a necessity for compliance with data
protection legislation.
• When data is properly stored, it can be quickly and easily accessed
by members of the organization when needed.

14. Types of Data Processing:
There are different types of data processing based on the source of data
and the steps taken by the processing unit to generate an output. There
is no one-size-fits-all method that can be used for processing raw data.
1. Batch Processing
2. Real-time Processing
3. Online Processing
4. Multiprocessing
5. Time-sharing

15. Types of Data Processing:
Type Uses
Batch Processing Data is collected and processed in batches. Used for large amounts of data.
Eg: payroll system
Real-time Processing Data is processed within seconds when the input is given. Used for small amounts of
data.
Eg: withdrawing money from ATM
Online Processing Data is automatically fed into the CPU as soon as it becomes available. Used for
continuous processing of data.
Eg: barcode scanning
Multiprocessing Data is broken down into frames and processed using two or more CPUs within a single
computer system. Also known as parallel processing.
Eg: weather forecasting
Time-sharing Allocates computer resources and data in time slots to several users simultaneously.

16. Application of Data Processing
• In the banking sector, this processing is used by the bank customers
to verify their bank details, transactions and other details.
• In educational departments like schools, colleges, this processing is
applicable in finding student details like biodata, class, roll number,
marks obtained, etc.
• In the transaction process, the application updates the information
when users request their details.
• In a logistic tracking area, this processing helps in retrieving the
required customer data online.
• In hospitals patients, details can be easily searched.