What is Raw Data? And How to use it
- Jeffrey Agadumo
- January 31, 2023
In this article, we’d be exploring raw data, why it’s important, and how you can use it.
Raw data is like a treasure trove of information just waiting to be discovered, full of insights and potential.
Come, let’s explore the exciting world of data together, shall we?
What is Raw Data?
In this global village that we currently live in, over 1.145 trillion megabytes of data is generated every day!
It might seem like an outrageous estimate, but consider how everything around us generates data: from your likes and tweets on Twitter to surveys and polls, even the Google search that brought you to this article!
Raw data refers to data that hasn’t been processed in any way and is commonly called source or primary data.
It also describes:
- Original figures recorded before narrations are added, or any conclusion is drawn.
- A collection of numbers, facts, or statistics recorded to be referenced in the future.
Examples of raw data include:
- Survey responses from a mobile app test launch.
- Sensor data such as pressure levels or temperature readings from a home thermometer.
- Raw audio or video files before they are edited or transcribed.
- Raw data from a database, such as CSV or Excel file, before it is imported into a data visualization tool or statistical software.
- Raw text from a document before processing it for text analysis or natural language processing.
- Raw data from social media, such as tweets or posts, before it is analyzed for content or sentiment.
- Raw data from a survey or poll before it is organized for analysis.
Naturally, this is not an exhaustive list, nor can we have a thorough list, because data is everywhere and in everything.
What’s the Difference Between Raw Data and Data?
Let me guess, two minutes into reading this; you’re probably thinking, “If information is processed data, isn’t raw data just data?”
There are many articles on data, but not many clearly highlight the difference between raw data and data. Is this another case of potato potatoes?
Well, let’s elaborate on the definition to bring some clarity.
Raw data is usually:
- difficult to read.
- difficult to navigate.
- offers little to no actionable insights.
On the other hand, data is what you’d typically get after organizing raw data:
- It is readable.
- It is easier to navigate.
- You can derive actionable insights after productive data analysis.
Why is raw data important?
As with any building, if the foundation is not solid, you’re bound to end up with another leaning wall of Pisa.
Simply put, raw data is the building block of concrete information, and here are some reasons why:
- Integrity of data: Since it has not been manipulated in any way, its integrity is intact. Which is crucial in extracting error-free information and making data-driven decisions.
- Freedom: Raw data affords you more freedom in data transformation as it gives preliminary visibility to the dataset.
- Data Backup: With raw data, you have a backup to refer back to when you encounter problems after processing and analyzing your data.
- Input for Complex Systems: Raw data is used to track and predict financial trends in complex systems such as business intelligence (BI) tools.
- Advanced technology can utilize raw data to create models that analyze the data’s performance and generate alerts. These models can also be used in machine learning to develop artificial intelligence.
Also, although it gives no actionable insight, it has the potential to become high-quality information.
And with the decrease in the cost of storage in recent years, companies now opt to store raw data in data lakes for later use.
Now that you know why raw data is important, how can you use it?
How to Process Raw Data
Various ways of using raw data depend on the data type and the analysis’s goal.
But for whatever you have in mind, raw data has to be cleaned, organized, and transformed into a readable format.
Here are a few steps to transform your raw data:
Datasets are represented using columns and rows , with the columns representing the variables and rows describing observations.
With raw data, the datasets this is not the case and has to be rearranged.
For small-size data, this can be done manually.
But when working with large datasets, this process is automated using both simple analysis tools like Microsoft Excel and more complex ones like business intelligence (BI) tools.
With the data sets properly arranged, you can move on to filtering.
Data filtering is the process of removing inaccurate or irrelevant observations from a dataset.
With filters or queries, you can select only the data that meets the criteria you want to work with.
This removes irrelevant data, such as duplicate rows or outliers.
With the errors eliminated, you can organize the data into groups and summarize those groups to create a more meaningful and manageable dataset.
And this can be achieved in a couple of steps:
- Define groups : Define the attributes of the data that should be considered for grouping, which may include time frames, categories, or regions.
- Apply grouping: Using the defined attributes, implement the groupby() function in a programming language, like pandas or SQL, to organize the data.
- Perform calculations: carry out statistical computations like mean, sum, count, and standard deviation on each group of data.
- Pivot the data: Use pivot tables to reshape the data and create a new table with different rows and columns.
- Data Encoding: This involves converting data into a format that can be stored or transmitted. It might include encoding data into a specific file format such as CSV, compressing data for storage, and encrypting it for transmission.
Realistically, a substantial amount of time is spent cleaning data before it can be used.
Steve Lohr, an editor for The New York Times, tried to put a number to this, estimating that about 50% – 80% of the time used is spent on data preparation.
It is not wasted time, though, and there are some tips to improve productivity while maintaining quality.
Transform Data using Datameer
With the varying expertise and skills in any organization, being able to manipulate and interpret data as required by each team without IT assistance might pose a challenge.
Datameer has taken this on and offers an interface that smoothly adapts to the varying skillset in your organization.
By providing no-code , low-code, and SQL options for data transformation!
Its easy-to-use interface has made producing data science-driven insights from data at your disposal an easy, everyday task.
It also offers a platform to store, transform, and analyze your data in one place.
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