A short guide on data analysis

If you are diving into a project associated with massive data volumes, data analysis will become a serious obstacle in your way. No worries, we got you! Today we are taking this bull by the horns and breaking down the basics of data analysis and how AINSYS can help you deal with it.

Data analysis adheres to a strict, sequential process, just like any method of processing information. Each step calls for a unique set of abilities and knowledge. However, it’s crucial to comprehend the process as a whole in order to achieve goals.

What problem are you solving through data analysis?

First, you will need to make a data decision and identify the issue you’re looking to tackle through data analysis.

You will need to set very specific goals. We have put together several strategies for identifying the problem at hand — you can read them here.

Collecting data

To analyze data, specialists go through the grueling task of collecting it from sources first, namely internal and external ones. When dealing with all matters data, you can rely on AINSYS.

Our extraction tool will present data in a properly structured Google spreadsheet. Not only will our system collect all data necessary, but it will automatically update your spreadsheet whenever new information is received. This way you can keep all your records up to date, facilitating the whole analysis process.

When numerous data sources are merged, data can be duplicated or incorrectly categorized. So, before the actual analysis, you will need to clean up data: fix or remove all incorrect, corrupted, poorly formatted, duplicated, or incomplete data within your dataset. AINSYS templates will help you visualize and sort data in any way necessary.

The actual analysis part

There are several types of analysis:

• Text analysis means deriving high-quality information from text sources;
• Statistical analysis includes pinpointing the most common patterns for removing bias from evaluating. It includes both inferential and descriptive types;
• Predictive analysis, as you might have guessed, will help you focus on predicting what results you should anticipate in your work;
• Prescriptive analysis aims to determine which content appeals to your audience the most.

These types of analysis might be used together and separately. If you get misleading results, make sure to test a different approach to analyze your data.

What comes after?

Data interpretation is the final stage of data analysis. You must return to your original task after you have finished going through data. Only then develop conclusions from your results.

Verify your conclusions are accurate and correct. You must be able to go back and rerun the analysis using templates because separate factors may point to different issues.

You can assist your employees and stakeholders in understanding your findings by visualizing the data in the form of graphs, maps, reports, charts, and dashboards. Even if it’s not necessary, doing so will make it easier to interpret your data’s story and use it to guide business decisions.

Working with data doesn’t end here. Make sure all of your apps communicate properly to ensure that data is present where it should be and isn’t redundant or conflicting. Data transfer between apps can be automated by AINSYS, allowing you to concentrate on other creative tasks that will help your company grow.

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