Any information being collected is termed as data. It can be a fact or figure or anything that is collected from a source and stored in a place in one format or the other. Data can be classified into following categories:
Structured data: Highly organized data which is present in a database or an excel or a csv file or any other data source. This data is organized in such a way that it is easily accessible and appropriate for queries and computation. This is the most desirable kind of data for anyone who is responsible to process or read it. Handling this type of data is relatively easy and hence it is easy to process them at various stages.
Unstructured data: As the name suggests, this is nothing but any raw data present in any system. It lacks any organized content structure thus makes it difficult for further processing. Common examples are audio-video clips, images, documents, etc. This is the most challenging kind of data to be studied especially when it needs to be processed rigorously to give desired results.
Semi-structured data: This is in middle of structured data and unstructured data. Common examples include metadata, emails, json from different sources. This data is comparatively easier to study that any unstructured data as some parts of this data is already structured.
Importance of data:
Data is a very important part of software life cycle. It can be considered as the very backbone as the whole functionality of a software is based on data being collected, stored, processed and then shown in a suitable manner to end user. But valuation of data is nothing without processing. For better processing, we need better structures to store data. If data is unorganized, then the time to analyze it will be increased a lot. The better the structure, better are the chances to get the optimum output through it. Once processed, this data can further be refined based on end user requirements.
Data in school:
A lot of data is created every day in a student’s life. The difference is that most of it is not collected and stored for future use. A student data can be classified into following common categories:
Attendance records: Daily attendance of all students to find out number of days each student has skipped school.
Academic Performance track: Monthly / Quarterly collection of student’s performance data stored at some place to assess the student in each subject at the end of semester.
Extracurricular Performance check: Daily collection of data to check the interest of student in various fields.
The first one is the kind of data that is regularly possessed by most of the schools to keep track of students who are attending classes at regular basis.
But there is major scope of improvement in the latter two categories. Most of the schools either don’t or poorly maintain this data. The final purpose of any data is to be able to alert end user of its outcomes and make him /her act accordingly for better results in similar situation. Let us try to find an optimum solution for these two categories. “Academic Performance Tracks” are not only to decide whether a student is performing up to the expectation. Rather, they should act as a tool to open a new direction to a student for improvement.
If a common spreadsheet is maintained by a whole section of students in which each student categorically mentions the level of understanding he / she possess after being taught in class, then monitoring of a student’s progress can be done efficiently. This must be checked by a teacher regularly to determine what needs to be done as future course of action. Suppose a topic is not clear to many students then another lecture needs to be scheduled for the same. Similarly, various other parameters can be added in this common spreadsheet. Assignment completion, subject interests, grievances and various other things can be put into this spreadsheet. Also, this is not only for a teacher to understand whether a student is facing some problems but also for students to self-assess themselves. If any student sees that the assignment completion is a major challenge for him / her as understood by last month’s entries into the spreadsheet, then student needs to reschedule the time table for the day so that more time can be given to complete assignment. This spreadsheet is a source of learning for both students and teachers as actions need to be taken by both if results are not as expected.
The above example shows us that a lot can be done if data is well preserved and studied upon. Especially for a student, when he / she needs to re orient himself / herself, this can serve as a major tool. In the above process we equipped students with a simple way to collect data. But the most important factor is the way this data is structured. Students need to define what parameters should be set on which want to be judged. Obviously, some parameters will be set by teachers as well as they know better the assessing criteria. But if this collection of data is not standardized, then it will only create hassles. Imagine a situation where each student is making entries into the spreadsheet on the basis of his / her own set of parameters that are completely irrelevant for some or all of the remaining students. That is where structured data come into existence. If data is standardized in advance at optimum level then only best results will be achieved. This example is a very good way to understand importance of data as well as of structuring data in a desirable way.
We can conclude from above discussion that data can play a very important role when collected properly. Of, course it needs to be very well structured to attain bet result but the importance of data is not reduced even when it is unstructured or semi structured. The only difference among them is the ease of processing. The result, however, can be better only if to way analysis of data is happening. A data that is just stored and not used is of no use. Data collection, in itself, is useless unless processed and used by someone. If one thinks that performance will improve just by keeping track of activities of student’s life then it is very big mistake. The whole purpose of data creation is to yield a result which was earlier not possible. The trends which could not be predicted earlier can now be seen and studied with the help of data. These trends can predict the outcomes if corrective measures are not taken at right time and hence should be observed very carefully.