We feel that there aren’t sufficient hours in a day. It’s a problem. Additionally, if you are a data analyst and you work with data, you know how the 8 hours workday can extend to 9 or even 10 hours work shift easily when you are debugging and problem-solving with heaps of amount of data.
However, there is light at the end of the tunnel for us. Here are the things data analysts can do to make themselves as efficient as possible with the normal working hours.
Check Record Counts and Test Queries
Data analysts waste so much time in waiting to download datasets, just to realize that it is not correct or not the data that is needed as per the scope of the project. If you are working with a large dataset, it is important to either check the record count in the table or evaluating your queries on a subset of the available data before downloading.
Here are the steps that one should take before using an ample of time in downloading datasets:
- Run a query at the first 10 rows of the dataset to decide whether you need it or not.
- Run the query to count the number of records in the table. Then you can compare it with the source system the data is coming from (if possible).
- If the query is going to be complex, then one can limit the results to 10 or so records. Then grouping by certain values or whatever else will make sure that the designed query is correct.
Also, depending on the data tools that you are going to use for analysis, embedding creative business logic into your database query will make your analysis more efficient.
Leverage Parallel Processing
When a person coins the term ‘Leverage parallel processing’, it is much broader term than just in the machine learning modelling sense. The way one can ‘parallel process’ is normally by executing workflows on the cloud and on the analyst’s local machine. This way, if I have a workflow that takes a conservable time to run, I can set it up on the cloud and then do the respective tasks on the computer.
A much better method would be to fully automate the processes can run on a server when the analyst is not even logged in to the computer, but that is not even possible, and won’t be even helpful while the data professional is still cleaning / analysing code.
Optimize and Organize Along the Way
Developing a documentation surely helps for organizing small projects or work that a data analyst to reference later. For bigger projects, analyst’s data team might already have an organization standard or system that you can learn from.
Here are some other things that help one stay organized:
- Just make sure to give file descriptive names.
- Create an archive folder that you might want to reference later. Down the line if you don’t have any need for those files, then don’t be afraid to delete them.
- Organize emails with folders with the name of each project or topic you work on to make referencing communication easier.
- Commenting inside the code helps not only to understand the analytical process, but also for organize it as well.
I know these are simple suggestions, especially for the professionals, but even seasoned professionals sometimes ignore these tips, which results in having messy and unorganized work at hand.
If we talk about perfecting, people get towards the end of the project and then think “how can I make the data processing efficient? “My theory is that if the data professional thinks about efficient at every stage of the project, then it would make his life much easier.
Consult Team Members Before Debugging by Yourself
There are times when one struggles through a problem at work, which normally gets resolved after debugging it for hours. But if that same person is up against a deadline, it’s better to not waste time.
Sometimes, all it takes to contact your co-workers within your organization and explaining the process – either by sharing the screen, sending a specific snippet of code, or even by discussing the code.
Regardless of how you share your problem, your team members are an asset and can help you learn and correct your mistakes much faster.
At the end of this informative article, I expect that at least one tactic I summarized in this piece can help you get more time back in your working day and help you finish project on time.
