Did you know that data collection is one of the most time-consuming steps in the process of data science? But it's definitely not as terrifying as data cleaning.
The explosion in data production is making all organizations more data-driven.
Collecting data is the new trend in the market and this post is an informative piece on everything you should know about data collection.
Here's what you can expect to learn today:
- The idea behind data science
- What is data collection in data science?
- The process of data collection
Before we get into the details of the data collection process, let me briefly introduce you to the idea behind data science.
If you're already familiar with data science, skip to the next section.
The idea behind data science
If I were to pick one of the most defining periods of technological advancements, it would be the Big Data era.
Data science became a thing due to two important reasons:
- Acceleration in the power of data processing through the introduction of the graphics processing unit (GPU)
- Production of massive amounts of data
Did you know that the human species is producing 2.5 quintillion bytes of data every single day? Every time you surf the web, hit a "like" on Instagram, or share a cat meme, you produce data.
What's the smart thing to do with so much data? Process it? Extract insightful information and correlations?
Data science is the art of extracting insightful information from data.
Have you ever wondered why Google throws ads at you about what clothes to buy? The reason is that Google collects data about which websites you interact with, what products interest you, and so on. It then targets you with ads relevant to your interests.
That's how you use data to grow businesses, and that's why most organizations are becoming more data-driven by the day.
However, the process of extracting insightful information is not as straightforward as the example I gave above. You have to be able to identify a problem statement, collect relevant data, and then go about cleaning, processing, analyzing, and extracting useful data.
Today we will focus on data collection.
What is data collection in data science?
Data collection is the process of accumulating data that's required to solve a problem statement.
What do I mean by a problem statement?
All data science projects (all projects really) start with a problem that needs a solution. There's always something you can solve or improve.
Step-by-step guide to data collection
Data collection gets done in steps, and it's important to understand that this is an iterative and repetitive process, meaning that after the first round of collecting data, you probably need to repeat what you did.
In the below sections, you can read about the steps you can take to collect your data.
Identify a problem statement
The most vital step is to identify and pinpoint the exact question that needs to be answered.
For example, let's say your online cat food business is not producing enough sales. Your problem statement would be: find ways to attract more customers and improve your sales.
You can work backward once you briefly identify your problem and solution. In this case, you can start off by taking a look at the audience you are targeting.
Maybe you need to target a wider age group, or you may want to learn more about what type of cat owners shop online, such as their geographic location, gender, ethnicity, and so on.
Collecting more data is often about collecting the right type of data. Thus, the first step is to understand what problem needs solving and how you can go about solving it.
Determine what type of data is needed
The next step is to consider what type of data you must collect.
Is it quantitative or qualitative?
Accessing and processing quantitative data is easier because it involves raw numbers and digits. On the other hand, processing qualitative data, such as customer reviews or feedback, is more complex.
Segregating the different types of data from the moment of data collection can be useful while performing data processing down the line.
Decide on your data sources
Once you have an idea about what data you need, start looking into whether the data is within your organization or if you'll require third-party or external data.
In most cases, the smart thing to do is to acquire external data. This acquisition will keep you on par with your competitors, who will probably also invest in third-party data. You must be willing to buy data and keep your legal team close.
At this point, it's important to draw your attention to the ethical issues relevant to data collection and data privacy.
Make sure your audience is fully aware of the data you're collecting about them. You don't want to fall into a data scandal, such as the one in which Facebook and Cambridge Analytica were involved. If your organization is buying data from another corporation, your legal team must be careful to consider all data privacy clauses.
In addition to that, collecting data from government organizations is also common. Some data scientists also use surveys to collect data.
Another practice is to build a user persona based on existing data. For instance, your organization has insights into the type of people who buy sports gear. Such information can get used to create a user persona for people with varied interests. This process is common when there is not enough data available.
Create a timeline
Now it's time to identify the time frame within which the data is most useful.
For example, do you need end-to-end data about how a customer lands on an e-commerce website? Or do you need relevant parts about the user's search history, geography, and background?
Identifying the timeline is key to getting the exact type of data you need to solve your problem statement.
A potential lead may generate data at different stages, and it's your job to effectively evaluate which data is most relevant.
Collect your data
To effectively collect data, devise a plan that addresses all the questions relevant to securely collecting data.
If you're collecting data from a third party or a stakeholder, make sure all requirements and privacy issues get considered.
Additionally, create a plan for how you will store the data. Make sure your organization has the right tools and infrastructure to manage and process the data.
You also need to establish a systematic approach for storing all the different types of data so that you can later combine and further process them.
For example, storing transactional data can be relatively easier since there are tons of tools that arrange such data in a tabular format. On the other hand, unstructured data can be relatively difficult to manage and store due to its loose format.
Therefore, you must devise a plan to collect your data and make the processing simpler.
Panoply can help!
Panoply offers a simple solution to the problems involved in collecting data by offering a hassle-free data collection service. You can go through their free demo to check out how you can store, manage, and access your data.
Collecting our thoughts
During the process of data analysis, you'll get new revelations about additional data that's required, thus making data science an iterative process.
The act of retrieving useful insights requires identifying and collecting the right kind of data. Once your organization has the right data, it becomes easier to process and analyze it.
Data collection can play a vital part in helping businesses grow.
By understanding what data collection is and the various steps you should consider while collecting data for your data science project, you can gain valuable insights into how to apply the information for future growth and development.