The data you need to use in the research process usually comes from different sources. It also comes in different formats, too. As a researcher, you have to perform the data extraction process and then clean that data from unwanted errors and omissions to use it in your PhD dissertation. Sadly, today’s students, particularly students in the computer science field, face difficulties in data extraction and writing their computer science dissertations.
Keeping this in mind, today’s article is about the rules to follow when extracting data for your dissertation. There will be a description of the meaning of data extraction and its importance, along with the rules to follow. So, let’s start today’s discussion with the following question.
What Is Data Extraction, And Why Is It Important?
In its simplest definition, it is the process of retrieving information from a source. The researchers do this to process the data further or store it for later use. The extracted data may be in poor form or unstructured form. By using different techniques of extracting data, you consolidate, process, and refine data so it can be stored for later use, i.e., transformation. The location of this process has always been under question. So, for your knowledge, the location for this process can be on-site, cloud-based, or a combination of both.
Extracting large amounts of data is important because it allows you to consolidate and process all the information in one go. However, if you want to store the data, it also lets you store and unify the whole data sets in one place. Simplified sharing is another thing that makes the extraction of different data important. Organisations can share their data with other companies, and data extraction helps them do so. So, keeping all these points in mind, data extraction is an important step for research. Meanwhile hiring a PhD dissertation writing service is also helpful in this regard.
10 Rules To Follow While Extracting The Data
After reading the information above, you have got a full idea of the meaning of extracting data and its importance for organisations and research. Now, you are ready to perform the process of extracting the data for research. But stop for a while, dear student. Still, you do not know the rules which are necessary to consider when doing this. Hence, a brief description of the top 10 rules is as follows:
Define The Process You Want To Analyse
First things first, determine which process you want to analyse for extracting the required. Indicate the scope of the process, i.e., where it starts and where it ends. It does happen that people have a different process scope in mind, but they do not know its name. It is important to note that if you are extracting data for someone else, ensure all the stakeholders agree to the defined process.
Determine Questions About The Process
In the next step, define 3-5 questions that you have in mind about the selected process. Always try to include questions that relate to the process flow. For example, one question could be, where do I need to work more during the whole process? To define the questions about the process, you can also include the manager in the discussion.
Select The IT Systems
After defining the questions, the next step is to determine the IT systems that you desire to use in the data extraction process. To select the right system for this purpose, do not forget to take help from experts who use different systems. Pay special attention to Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP) systems.
Giving Case ID
The minimum requirement that your data needs to fulfil as per the guidelines of The Minimum Requirements for an Event Log is that it must contain a case ID. Case ID is a unique number which is allotted to each data element in the process. Extracting one data element is the execution of the process.
Activities In The Process
The activity is the second minimum requirement that you must fulfil. The activities are the steps or changes in the status that happen during the process of extracting data. You’re selected IT system record the activity information along with interesting debug information. Make sure that you capture all the activities during the process.
Having The Timestamp
The timestamp is the third minimum requirement that you need to fulfil. You need at least one timestamp for each of the activities that your IT system performs. Having the timestamps allows you to bring the events for each activity and each case in the right order. Timestamps also help you calculate the time duration of the activity.
Incorporate Other Attributes
The 7th rule of data extraction tells you to incorporate all other attributes in the process which are necessary and not included in the minimum requirements. To decide on which attribute to include, it is important to have goals in your mind. The additional attribute gives birth to a new perspective in your data extraction process.
Selection Method
This rule is about which method you should go with to run the process. It depends heavily on the amount of data you want to extract. Determine whether you can extract all the data in one go, or if you need to select cases based on start time and end time. If you select the latter, write down which start or end activity you will use.
Measure The Timeframe
Before running the actual extraction process, you must measure the time that the process will take to complete. This determination of the timeframe is dependent on the selection method defined above. So, write the start and end times of your activity.
The Format Of The File
The file format is of utmost importance in the data extraction process. Each row must correspond to the activity that happened in the process. Mostly, the researchers use Comma Separated Values (CSV) files.
Conclusion
Conclusively, the above-mentioned are the 10 rules you need to follow while performing data extraction for your PhD dissertation. Defining the right IT system is the most important rule. So, pay close attention to it and extract the right type of data.
