Data Analytics 101: A Beginner's Guide

In the age of information, data has become a crucial part of our lives. We often hear about how helpful it is, or how it's necessary to do, frankly, anything these days. Those are really intense allegations; why does it feel so intimidating if it's so important in every walk of life?

We understand that data can feel complicated and overwhelming, but it shouldn't have to be. That's why we're here. On our blog, we talk about all kinds of ways to use data to your advantage and exactly how to do it. But for this post, we're going back to basics. We're going to go over the basics of data in our own Data Analytics 101.

Let's get into it.

What is Data?

When you're looking at data, a simple rule of thumb is 'if you can count it, you can use it'. This could be anything from the number of users on your platform, the number of posts made in a day, to the amount of time users spend on your site. The key is to turn this data into something useful, turning inputs (data) into outputs (information). In every walk of life, data on its own isn't enough; the purpose of collecting data is to do something with it.

What is Data Analytics?

Data analytics is the process of turning the data we talked about into something useful. This can include examining, cleaning, transforming, and modeling data to discover pertinent information, draw conclusions, and support decision-making. It's about turning raw data into something meaningful that can help you understand trends, patterns, and insights that can drive better decisions.

Data can help you in various ways. It can help you understand your audience better, measure the impact of your actions, identify growth opportunities, and much more. The key is to know what data to look at and how to analyze it effectively.

Common Definitions

When you're learning about data, sometimes, people will use unfamiliar terms that can throw of your understanding of the whole conversation. Here's a quick index of some common terms that will help you.

Median and Mean: These are measures of central tendency. The median is the middle value in a data set while the mean is the average.
Z-Score: This is a statistical measurement that describes a value's relationship to the average of a group of values in a dataset.
API: This stands for Application Programming Interface. It's a set of rules that allows different software applications to communicate with each other.
Integration: This refers to when a data collection tool is automatically connected to a data analysis tool through software. Integrations are helpful because they help you avoid the complicated process of trying to manually extract data.
Big Data: This refers to extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations.
AI and Deep Learning: AI, or Artificial Intelligence, is the simulation of human intelligence processes by machines that has the capability of learning from itself and its inputs. Deep Learning is a subset of AI that uses neural networks to analyze various factors with a structure similar to the human brain.
Data Mining: This is the process of discovering patterns in large data sets.
Data Warehouse: This is a large store of data collected from a wide range of sources used to guide management decisions.
Algorithm: This is a process or set of rules to be followed in calculations or problem-solving operations.
.csv: This is a simple file format used to store tabular data, such as a spreadsheet or database. You'll sometimes see this acronym when people are talking about where they're storing their data.
Dashboard: This is a tool used to visualize and present data understandably and interactively. Dashboards often have several charts and graphs to help tell a meaningful story.

Clipboard and phone on computer, showcasing data visualizations

Common Tools

There are various tools available for data analytics. Our platform, Spontivly, is designed to be simple and easy to use. It connects all your favorite community platforms like Slack, LinkedIn, Meta, HubSpot, Discord, and many more in one place. With data dashboard tools like Spontivly, data becomes easily accessible and understandable.

You might hear people talk about tools they use to build dashboards. These tech giants lack the agility and flexibility that Spontivly brings; they having steep learning curves and only provide value to highly technical users. If everyone needs to harness the power of data to make their decisions, why shouldn't everyone be able to work with it?

Tools like Power BI, Tableau, and Domo can't keep up with the pace that you need your data at, plain and simple. Accessibility and efficiency are imperative for data empowerment, and that's what we offer that they don't.

Resources to Get to Know Data Analytics Better

There are lots of resources out there that can help you become more familiar with data analytics and what harnessing them can do for you.

We've sprinkled a few resources throughout this post, but here are a few more:

Common mistakes to avoid
What types of charts are used for what purposes
Podcast: Making Data Simple
Short-Form Video Content: Hanalytx
Long-Form Video Content: CareerFoundry

Next Steps

Now that you have a basic understanding of data analytics, the next step is to start applying this knowledge. Start by identifying what data is most relevant to you, then use tools like Spontivly to analyze and visualize this data. Remember, data is a powerful tool that can help you make informed decisions. So, don't be afraid to dive in and start exploring the world of data analytics.

Data Analytics 101: A Beginner's Guide

What is Data?

What is Data Analytics?

Common Definitions

Common Tools

Resources to Get to Know Data Analytics Better

Next Steps

About the author

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Data Analytics 101: A Beginner's Guide