Essential Tips for Processing and Analyzing Data on an Analytics Platform

Why analytics platforms simplify data science workflows by covering all you need to know on processing and analyzing your data using an analytics platform.

Data is a powerful tool for helping you make important decisions for your business. In addition, data gives you insight into what might happen in the future. But how do you properly process and analyze data using an analytics platform? When faced with the task of using an analytics platform for the first time, many people struggle with this. 

On this page, we will help you further understand why analytics platforms simplify data science workflows by covering all you need to know on processing and analyzing your data using an analytics platform. To do so, we will provide tons of tips and advice for doing this successfully below.

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It All Starts with the Quality of the Visuals


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Right off the bat, the aesthetic quality of your charts and graphs will be one of the most critical factors in determining whether or not your audience understands the data correctly. Poorly designed visuals will often confuse people and make it harder to know what they are seeing. Therefore, spend some time thinking about how to improve the quality of the charts you create. If you need help, we have prepared a guide for using the proper visuals for your data.


Start Monitoring the Right Data


In addition to having aesthetically sound charts and graphs, you need to be tracking data that is most relevant to your business KPIs, mission statement, and general model. If you aren’t sure how to choose which data to monitor on an analytics platform, consider what your business is all about.

For example, if you are a new startup that sells high-quality, handmade hats for dogs, then your business model will revolve around metrics such as the number of items sold per day, the total number of sales per month, and revenue. Companies like this should focus on tracking data related to sales volume.

However, if your company invests in different types of marketing, then you should focus on metrics like the number of clicks per ad, total impressions for each channel, and click-through rate.


Determine What Goals You Have For Your Data


You will never correctly analyze and process your data if you don’t know what you hope to gain from it. In other words, you need a goal for the data before any processing or analysis can begin. Once you have established your goals, find metrics that will help you track the progress of your goals.

Imagine that you are running a marketing company, and you want to get more customers. You might decide that your goal is to increase the number of website visits per day on your website and track metrics like average session duration, bounce rates, search engine clicks, and so on to track your progress.


Create a Data Collection & Processing Blueprint


After you have decided what your goals are and which metrics will be most helpful in tracking them, it’s time to create a blueprint of the steps you will need to take in the analytics platform for fully processing your data. This blueprint should list the exact steps you need to take for your data to be processed and analyzed correctly.

For example, suppose you want to track the number of website visits per day. In that case, your blueprint might include steps like adjusting the date range, viewing the new daily visitors, and then making sure information updates in real-time to using the analytics platform’s data connections. Without specific steps for processing your data, you may always feel like something is missing.

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Final Tips for Analyzing Your Data


Now that we have broken down thoroughly how you should be collecting and processing your data, let’s finish up by exploring some final tips on how to properly analyze your data.


Go Back to Your Initial Goal


When you first decide to track data, you established a goal or set of goals. To best analyze your data, it is essential to go back and remember these goals so that you don’t lose sight of what matters most.


Create a Hypothesis for Your Data Analysis


After you remember fully why you made the switch to an analytics platform, it’s time for the real work of data analysis. Create a hypothesis that summarizes what you are expecting to find in the dataset. Then, put your pre-existing beliefs and biases to test by using the help of charts and graphs on your analytics platform.

For example, if one of your goals is to increase daily website traffic by 20 percent, your hypothesis might be that adding new pages will increase the traffic. Therefore, you could start tracking how many new pages you make and see if there is a correlation over time between the total number of visitors on the website and the number of pages created each day.


Try Your Best to Prove Your Hypothesis Wrong


As a budding data scientist, you know that correlation does not imply causation. Therefore, do anything and everything possible to try and prove your hypothesis wrong. Especially if you have already started spotting potential trends. If you act on a hypothesis before fully confirming it, you can end up making a bad decision.

Back to our example: imagine that you stop adding new pages each day and notice that new website traffic peaks and then levels. Then, once you start making new pages again, the traffic continues to spike. This would be further evidence that the hypothesis mentioned in our example above is true.


Make Sure Your Data is Clean


One of the tricks in analyzing data efficiently is to make sure your data is clean. You should have clean and organized files that make up your dataset. If necessary, spend some time cleaning through your data to make it easier for the analytics platform to process and read.


Investigate Data Points that Seem Odd


When you start using an analytic platform, you will inevitably run across data points that seem odd. For example, you might notice one day when looking at your website’s traffic report that the total number of visitors dropped by a third from one day to the next.

In scenarios like this, it is vital to try and investigate what could have caused this drop in numbers. Most importantly, if there was a singular cause for this change, several factors, and what you can do to address the problem immediately.


Once You Have Established Trends, Make Changes to Fix the Problem


After you have established trends in your data, you need to make changes to fix the problem. For example, let’s say that you noticed that adding new pages led to a sharp increase in website traffic. If your goal was to increase traffic by 20 percent, you will need to add in new content continuously until you reach your goal. Therefore, your final data-based decision would be to invest more money in content creation since your data analysis has proven this to be worthwhile. 


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