By 2025, the amount of data created, consumed, and stored is expected to exceed 180 zettabytes. For those trying to grasp this mind-boggling number, one zettabyte is expressed as 1021 (1,000,000,000,000,000,000,000 bytes), a billion terabytes, or a trillion gigabytes.
But all of this data doesn't mean a thing if it's not cleaned and shaped into usable forms.
As the amount of data rapidly increases, so does the importance of data wrangling and data cleansing. Both processes play a key role in ensuring raw data can be used for operations, analytics, insights, and inform business decisions.
The differences between the two are more subtle. By the end of this post, you'll understand how data cleaning and data wrangling are just two of several steps needed to structure and move data from one system to another. And you'll see a simple way to automate these historically manual processes without writing a line of code.
Data wrangling is the process of restructuring, cleaning, and enriching raw data into a desired format for easy access and analysis. It can be a manual or automated process and is often done by a data or an engineering team.
Wrangling data is important because companies need the information they gather to be accessible and simple to use, which often means it has to be converted and mapped from one raw form into another format. This process requires several steps, including data acquisition, data transformation, data mapping, and data cleansing.
In a large organization, data wrangling is part of managing massive datasets. An entire team may be responsible for wrangling, organizing, and transforming data so it can be used by internal or external teams. Small organizations may dedicate a data scientist, an engineer, or an analyst to the task, especially if the company isn't using an automated data wrangling tool.
The goal of data wrangling is to prepare data so it can be easily accessed and effectively used for analysis. Think about it like organizing a set of Legos before you start building your masterpiece. You want to gather all of the pieces, take out any extras, find the missing ones, and group pieces by section. All of this organization makes it easier to create the project you're working on. (In this case, building a data pipeline).
But throughout the wrangling process, it's important to ensure the data is accurate.
Data cleansing, or data cleaning, is the process of prepping data for analysis by amending or removing incorrect, corrupted, improperly formatted, duplicated, irrelevant, or incomplete data within a dataset. It's one part of the entire data wrangling process.
While the methods of data cleansing depend on the problem or data type, the ultimate goal is to remove or correct dirty data. This includes removing irrelevant information, eliminating duplicate data, correcting syntax errors, fixing typos, filling in missing values, or fixing structural errors.
Finding and correcting dirty data is a crucial step in building a data pipeline. That's because inconsistencies decrease the validity of the dataset and introduce the chance of complications down the line.
Let's say you're an eCommerce company that wants to set up a custom email campaign for customers. You need to pull data from your product catalog, customer profiles, and inventory to recommend the best products for each person. If you're using dirty data, it won't be easy to automatically pull data for your campaign. Differences in product formatting, misspellings of name or email addresses, and inventory information can make it difficult to populate the data. This means your team has to manually sort through and clean data to ensure it's accurate, increasing the time and effort needed for the campaign—and, ultimately, reducing the revenue.
Not only does dirty data use up your team's time, but it also decreases the credibility of your data. If you're constantly recommending the wrong products to people or sending them duplicate emails, you're going to lose customers.
To keep customers (and datasets) happy, it's important to have clean and usable data in order to get accurate insights for your company and customers.
Insights are only as good as the data used to discover them. Having consistent, accurate, and complete data improves analysis, but it also trickles down to other business activities. Using a clean dataset helps eliminate errors, which can decrease costs and increase the integrity of the dataset. When decision-makers use high-quality data to inform business decisions, it enhances accuracy and reduces the risk involved in high-stakes decisions.
Manually wrangling and cleaning data takes a lot of work. At Osmos, we know that engineering and data teams' time are best spent on building products and analyzing data. So we created an AI-powered data transformation engine that lets you validate, clean up, and restructure your data to fit the destination schema and format, without having to write code.
Our no-code engine has six modes to automate data clean up and transformation:
Osmos AI-powered data transformations do more than save your team time. It gives your team the capacity to highlight inconsistencies, removes duplicate information, and restructures data without the need to write any code. Ingesting clean data frees up your team's time so analysts can focus on gathering insights and forecasting, and engineers can concentrate on building products.
As more organizations increase the number of data sources, the need for data wrangling and cleansing will only grow. Those using innovative no-code data transformation solutions to clean complex datasets and resolve errors will be able to make the most of their data—leading to error-free data ingestion process and with less resources.
You’ve likely heard about ELT — Extract Load and Transform… the Modern Data Stack’s evolution on ETL. This is a game changer by nature in that it enables organizations to ingest raw data into the data warehouse and transform it later. ELT gives end-users access to the entirety of the datasets they need by circumventing downstream issues of missing data that could prevent a specific business question from being answered.
A majority of business leaders believe data insights are key to the success of their business in a digital environment. However, many companies struggle to build a data-driven culture, with a key reason being the lack of a sound data democratization strategy.
Just like data mesh or the metrics layer, active metadata is the latest hot topic in the data world. As with every other new concept that gains popularity in the data stack, there’s been a sudden explosion of vendors rebranding to “active metadata”, ads following you everywhere and… confusion.
Do you know the current status — quality, reliability, and uptime — of your data and data systems? Not last month or last week, but where they stand at this moment. As businesses grow, being able to confidently answer this question becomes more important. That’s because data needs to be clean, accurate, and up-to-date to be considered reliable for analysis and decision-making. This confidence comes through what’s known as data observability.
In the past years, organizations have been investing heavily to convert themselves into data-driven organizations with the objective to personalize customer experiences, optimize business processes, drive strategic business decisions, etc. As a result, modern data environments are constantly evolving and becoming more and more complex. In general, more data means more business insights that can lead to better decision-making. However, more data also means more complex data infrastructure, which can cause decreased data quality, a higher chance of data breaking, and consequently erosion of data trust within organizations and risk of not being compliant with regulations. The data observability category — which has quickly been developing during the past couple of years — aims to solve these challenges by enabling organizations to trust their data at all times. Although the category is relatively young, there are already a wide variety of players with different offerings and applying various technologies to solve data quality problems.
Data governance is more than just having a strategy – it is about establishing a culture where quality data is achieved, maintained, valued, and used to drive the business. Modern-day businesses are supported by data and information in many ways and forms. In recent years, data has become the foundation for competition, productivity, growth, and innovation. We are seeing successful organizations shift their focus from producing data to consuming it, and data governance strategies becoming increasingly important to support their crucial business initiatives. Executives and shareholders are starting to realize that data is a strategic asset and data governance is a must if they want to get value from data.
I started my career as a first-generation analyst focusing on writing SQL scripts, learning R, and publishing dashboards. As things progressed, I graduated into Data Science and Data Engineering where my focus shifted to managing the life-cycle of ML models and data pipelines. 2022 is my 16th year in the data industry and I am still learning new ways to be productive and impactful. Today, I am now the head of a data science & data engineering function in one of the unicorns and I would like to share my findings and where I am heading next.