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5 Important Steps We Take When Collecting Data

Regardless of the industry or field your organization is in, data collection is an essential part of every system that is subject to constant change.

Data collection is the process of accumulating data for use in better decision-making, data analytics applications, machine learning and AI tools, planning and many other purposes.

When done right, it provides information that is needed to answer questions, analyze performance, identify problems before they appear, analyze possible failures and even predict future events and scenarios. In this piece, we would like to talk about how we tackle data here at Medius.

1. Gathering the data

Any event or piece of data in your business process can be used to your advantage. When we start gathering data, we go all-in and try to include data from any available software or applications from the existing system in order to collect it in one place. This centralized approach allows us to analyze data more effectively in the following stages.

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2. Handling missing data

When it comes to handling big data using multiple software solutions (custom or off-the-shelf) data can get lost, which can cause misinterpretations that affect the end results. To avoid this, we need to work smarter.

We use statistical and machine learning techniques to maximize and balance the datasets to reduce potential bias and invalid conclusions.

3. Taking data further

When we start to see the first opportunities for improvement and potential issues we know we have reached a turning point in the process and we can act accordingly. At this point we start to look at data through visualizations and the first useful insights start to show up.

By using a custom software solution such as Gamayun, the options for data analysis are much broader because the software is built on top of your existing system and technologies.

4. Deciding what is important

Depending on business goals, strategy and desirable outcomes, you can focus on the things that stand out.

Even if you think that you have crystal clear goals, new data processed by machine learning algorithms may uncover something that was hidden before.

This kind of insight can change the course of your future operations and impact your business the way that off-the-shelf products can’t. On the other hand, you still have all the freedom to ignore the data that’s not relevant to your business goals and strategy. This is the beauty of the testing part that comes next.

5. Put data to the test!

In order to get the most out of your data, you have to look at it from different perspectives and test it out. Data used correctly will help your business immensely and empower the team to take the best possible steps in order to reach your long-term business goals.

Without the testing, you’ll just be taking a leap of faith, which is contrary to the idea of bulletproof systems we want to build.

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