Getting started with your new data set is one the most challenging part while embedding data analysis as per your beat. A simple error at the beginning results in a cascading effect, which leads to scrambled columns and unintelligent field names.
In a day-to-day analysing, a data analyst needs to establish a valid workflow and need to get him/herself comfortable with the data sets.

TEN FACTORS TO CONSIDER DURING DATA ANALYSIS:

1. No need to overestimate the meaning of the data collected:
All the data collected have their own limited. As a data analyst, one needs to draw the line about the productivity and limitation of the data. Sometimes collected data may or may not tell you as per the expectations of the data analyst.
The reliability of data lies by the methodology used for collecting them. Most data analysts especially neophytes must learn that all numbers are not ironclads. After all, its human who procures the data and humans sometimes tends to make mistakes.
Before trusting the data completely, it’s better to check the source of data from crucial database fields and make sure it comes from a reliable source. When it comes to the collection of demographic data, one must opt for self-analyses racial categories, as they are more accurate than third-party observations.
While analysing data, which are less than 100, entries, analysts must maintain extra carefulness as a small mistake drastically alters the findings. Always stick with accurate data while analysing. Analysts who notice any limitations regarding the authenticity of a field’s data, then he/she must avoid using the data. If necessary, analysts must explain the limits of the data to the readers for sure.
A data analysed must always rely on “real world check” findings while working a beat. It’s better to avoid any third party data and rely on first-hand data while preparing a data project. While preparing your report, if you encounter shocking findings then do your best to ensure the reliability of your data by cross-verification through a different source.
2. Always check the format of the file
If crucial for a data analyst to check the size and extension of the data file, making it easier to choose the right program. Most of the time, data usually comes with an excel spreadsheet (.xlxs) with a size less than 700MB. Ms Excel is a perfect program for data entry due to its user-friendly environment and advanced features. Data sets with a size larger than 700MB works perfectly in Microsoft Access. Analysts can use any database programs that run on SQL for bigger size files.
Comma-Separated values or .csv files works fine in Excel as well, but it does have a little limitation. When working with multiple sheets in .csv format, Users have to change the file type to an excel workbook before saving the file to avoid loss of all other extra excel sheets within the file. However, programs as MySQL asks operators to change a workbook file into CSV before uploading them in MySQL.
Sometimes analysts get their data as a plain text file (.txt) which doesn’t include any columns and rows. However, analysts can open that file from Excel and save it in (.xlsx) or (.csv) format. Data downloaded in PDF format may need some extra effort from the part of analysts. To break the data into rows and columns, reports can use special converter tools such as Tabula. Most of the tools come free of cost. All it requires is some quick editing and modification to make it work.
3. Clean the data properly
Waiting for a prolonged period to get hands on a new data set is quite tempting for any data analyst. With such zeal, most analysts start working on the new data without considering its usability. To make sure the new data is usable; one must spend the first few hours to clean up the data. To clean up the data one can use internet-based open-source tools such as OpenRefine to remove all small discrepancies within the data.
One of the most common things to do while cleaning data sets includes segregating the first and last name into separate fields. Other tasks involve splitting full addresses into more readable fields. An analyst can use the different Excel formula to execute any of the tasks.
4. Indexing the fields properly
The database provided to analysts often comes organised and sorted. Some are sorted alphabetically while some are by date etc. however, based on the necessity of the analyst, one has to sort the data in a way that is more productive.
To reorder and analyse your data, the sort feature in Excel comes as a boon. Besides, one must first index the fields in the data before sorting them to avoid messing-up of data. To prevent any mess, create a new column on either side of the data and label it ‘index’. Now fill in the data from 1 till the end of the rows. In case one wants to undo the sort, he/she should sort the new column from smallest to largest.
5. Making sure about the meaning of field names
Even a simple looking data set may carry complex data and its crucial for every data analyst to request for a ‘data dictionary’ from the data source. A data dictionary includes a list of all the fields, their name and the type of information in it. The information could be about dates, numbers or even phrases. If a data dictionary is not available, then call the agency or office of the data provider and take all the information regarding the database.
Sometimes entirely accurate looking data could be deceptive. So it’s better to double check everything about the fields before working on it. One might either be right about their first impression about the field, but still, it’s better to be sure.
6. Make copies of every significant change
In data analysis, the chances of irrecoverable damages go way up that any other types of analysing. Analysts may lose their hard-worked data just by pressing save after making a mistake. So to avoid such dire situation, it is better to make copies of the projects, then allot date and number to each version of your data.
The common mistake many data analysts do is making changes in their original data set. It’s better to use a clean version for every task so that analysts can come back for references in future. Also, make sure you know the location of the original file which the agency gave it to you. It will help you to resolve disputes arises in the future if the agency accuses you of unfairly modifying the data.
7. Work slowly and with precision
Double checking data entries is a tremendously burdening task. It’s crucial to take frequent breaks and work slowly so to avoid any mistakes. The standard break time is 10 minutes in every 1 hour but also the necessity of every individual matter as well.
Hurrying in completing a task often invites several mistakes, which one may realise minutes later or may realise at the works end. In such scenario, finding a quick fix is hardly possible. No matter how much expertise one holds in their works, some situation is far away from being salvaged. The only option that left is to start the task once again from the start or an earlier saved version. Instead of letting things come to such a point, it’s better to work slowly, think fast and refresh the mind.
8. Maintain good coordination with the editor
An Editor plays an integral role in the performance of a data analyst in data analysis. An editor may not show interest during the interviews or watch their analyst shifting columns in excel for hours, but they do play a crucial role in data-driven project work.
An ideal data analyst must note down their work details every day for future references. Data analysts can show those notes to their editors during the reviewing process. Noting down work details also helps in do a ‘logic-check’ with colleagues ensuring the project is going as planned.
Every major phase of work requires proper planning. Most data analysts draft their ideas on whiteboards, formulate a strategy and take valuable suggestion regarding tackling the complicacy of the project. Frequently taking reviews from Editor keeps everyone involved in the project. It also keeps everyone within the loop when it comes to deadlines and facing potential roadblocks. It also helps in getting exciting ideas from other data analysts who may or may not be directly involved with the data.
9. Equally focus on Visualisation as well
Most data analysts only focus on the calculation parts like finding the mean, medians, ranges etc. in the data set. Even after calculating the numbers, figuring out the next task is complicating for some data analysts. The best way for data analysis is to create a story using the visualisation with Excel program. Data Analysts can use various cool graphs and charts to produce a valid point in their data analysis process. Visualizations help data analysts in seeing the trends in their data which one cannot see just by reading the numbers. With proper implementation of visualisations, one can improve their analytical skills and can also publish them with their story too.
10. Don’t hesitate in asking for help
Data analysts must be upfront in asking for help when he/she stuck on a project. Data analysts can ask for help from their colleagues. Data analysts can use online tutorials and forums to get advice from other users. Online discussions of Excel, MySQL are quite, and one can get a prompt reply from other Users.
Data Analyst seeking help related to the tech-based question can ask help from other analysts on Twitter through direct messages.
Data analysis is indeed a crucial task for any successful organisation. Every organisation needs to be proactive as per the regular shifting of marketing trends, and data analysis helps those organisation in realising their current position. Keeping businesspersons informed about the health of their organisation is the primary task of any valuable data. It’s also important to dig deeper into the data rather than focusing only on a bigger picture in mind. However, one should not rely entirely on data and ignoring one’s own conscious. Data can be deceptive as well as productive, based on how they are gathered. Businesses should make their ultimate decision based on data but also keep in mind that the information in the data is not set in stone.

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