How to make data analysis more productive, more reliable and more relevant
Data analysis is a vital part of any enterprise, but often times, it’s just not what you’re used to.
Here’s a checklist of things you should know before you start analyzing your data, to make sure that you’re doing it right and not going too far off the mark.1.
What data are you analyzing?
It depends on the data.
For example, let’s say you have a data set of tweets about an upcoming event.
You might want to analyze tweets from one week to the next, so you can determine which tweets were shared more and to what audience.
Data that you don’t have to keep track of in the first place could also be useful.
You may want to collect the Twitter activity for each event you are analyzing, for example, to see how tweets are being shared across multiple platforms.2.
What type of data are being analyzed?
Data analysis tasks like data visualizations and text analysis can be useful in the field of data.
These types of data analysis tasks can be very useful for understanding how a particular piece of data relates to a wider audience.3.
How do you organize your data?
You may have already decided that you want to work with aggregated data.
The most common approach for this type of analysis is to categorize the data by key terms.
For instance, you may want a Twitter activity chart to show the top tweets about a particular event, and then you might want another chart that shows the top 100 tweets about the same event, with the top tweet being the one that was shared most often.
If you have data that you need to sort and filter out, you could start with the most common results first.4.
What kinds of data do you need?
Data that you are aggregating can often have data points that are very different than the data you are using in the analysis.
For these types of datasets, it is helpful to understand the data types you are looking for, and what types of features are needed.5.
What kind of tools are available to you?
There are tools that are geared towards helping you analyze data in a more efficient and cost-effective way.
For this type, you can use the tools provided by your platform to sort, filter, and export the data into a CSV file.
If the data is too large, you might consider downloading the CSV file and sorting the data in Excel.
The data can also be exported into the Google Sheets and other Excel or other data visualizers.6.
What are the limitations of data analytics?
Data analytics are not perfect and you will have to adapt to some of the limitations in the data, especially in areas like social media, which are often quite complex.
You can always use a data scientist to assist you in making better decisions.
For social media data analysis and analysis of tweets, it would be a good idea to get a professional data scientist.
For more data analysis tips and tricks, visit the data analysis blog and follow the blog for more data analytics tips and tools.