How Do Data Analysts Discover Meaningful Patterns in Data?

Learn how analysts prepare data and discover meaningful patterns to help companies grow

Interested in a career in Data Analytics but have questions? You’ve come to the right place!

This blog will go through how analysts find and prepare data, how they discover patterns in data and some of the common challenges that they face when looking for those patterns in data.

Data Preparation

Before you can analyze your data for patterns, you must first gather and prepare it. Data collection takes some time, but if you follow this 6-Step process, it can be done efficiently and effectively.

Step 1: Data Collection

The first step of data preparation is the collection of data
This entails retrieving data from a variety of different sources, including data warehouses, data catalogues, and more

Step 2: Data Assessment

The next step is to profile and assess the data
This step is about understanding the data you have collected and determining its intended use

Step 3: Data Cleansing

After that is complete, the next step is data cleansing
At this stage, any errors, faulty data sets, or inconsistencies are removed or amended

Step 4: Data Structuring

In the next step, the data is structured and organized in order to meet analytical requirements

Step 5: Data Transformation

Following that, the data is then transformed into a usable format that is easy to access and understand

Step 6: Data Validation

Finally, the data is validated for its consistency, completeness, and accuracy, and stored in a data repository where people can easily access it

Discovering Data Patterns

Recognizing repeating patterns is something we do every day, whether we are conscious of it or not. It is also a key component of being a data analyst. There are a variety of different ways you can interpret and recognize these patterns, so here are just a few.

1. Data Visualization

Firstly, you can use data visualization. This involves the use of charts, graphs, and other visual aids to see the data represented and the relationship between a set of data and its comparable variables.

2. Statistical Analysis

Another way to spot patterns within data is through statistical analysis. Statistical analysis consists of using mathematical formulas and equations to evaluate data sets and determine the numbers that represent their relationship with other data.

3. Machine Learning

One last way to find patterns in data is machine learning. Becoming increasingly common, machine learning will automatically scan data and pick out patterns it finds. It can often be inaccurate, so you still must vet the data yourself for errors, but it is certainly a helpful place to start when trying to find patterns in data.

Challenges in Data Analysis

Being a data analyst is a great job. However, just like any other job, it comes with its fair share of challenges. Here are just a few of the major ones that you may face when searching for patterns, and how to overcome them.

Data from Multiple Sources

One such example is analyzing data for patterns that come from multiple sources. This can be incredibly time-consuming, and the patterns could be mispresented if not enough balanced data is collected. Using software to assist you in collecting and combining data is a major help in this department.

Low-Quality Data

Another common issue that you may encounter as a data analyst looking for patterns is the obstacle of low-quality data. Data that is inaccurate or incorrect can distort patterns and reports, making them harder to spot and possibly influencing bad decisions. This can be avoided by using software to enhance data quality by removing asymmetric data.

Data Inaccessibility

One final problem that you can encounter in this endeavour is data inaccessibility. When analysts are not given access to up-to-date information and data, it can seriously inhibit their ability to spot patterns. With the right software and safeguards, you can easily make data accessible to everyone in the organization who needs it.

We hope that this has illuminated the role of data analysts, how data is prepared, and what goes into discovering patterns in data. If you are interested in a career in data analytics, click the link below to learn all about our exciting Diploma in Data Analytics Co-op Program.

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