The amount of data created, captured, copied, and consumed globally increased dramatically from 2010-2020, with data creation expected to grow to 181 zettabytes by 2025.
That's a hell of a lot of data!
In marketing, processing large data volumes can help advertisers analyze data sets and generate business intelligence for better reporting, decision-making, and campaign management. However, this process is difficult — really difficult — when data exists in a multitude of different formats.
Data transformation can solve this problem. It involves modifying data by cleaning it, structuring it, and converting it into the correct format for data warehouses, data lakes, and business intelligence platforms. But this involves lots of coding with programming languages like SQL — skills many marketers lack.
Thankfully, there are low-code and no-code data transformation tools that can do all this hard work for you. This means you can modify data with little or no coding knowledge. Sounds good, right?
Learn more about how to transform all your advertising data without SQL below.
Data transformation for no-code marketing data
Think about all the marketing data in your organization. You might have:
- Ad platform data
- Analytics data
- CRM data
- Customer data
- E-commerce sales data
- Demographic data
The problem is that all this data originates from different platforms and is probably in different formats — CSV, JSON, Open File Format, or something else. Moving your data to a central repository, like a data warehouse, won't work unless you modify data to the most appropriate format for the repository system and business intelligence tool. That's where data transformation comes in!
Structured query language (SQL) is a programming language that plays a huge role in data transformation. It can manipulate data in a relational database, which stores marketing data in tabular form, and prepare it for no-code data analytics. It does this by:
- Fixing structural errors
- Removing irrelevant data
- Removing duplicate data
- Managing missing data
- Aggregating data
- Harmonizing data
- Standardizing data
- Validating data
Say you have data in a relational database that you want to analyze for an upcoming advertising campaign. One of the best ways to do this is to move the data to a data warehouse and run it through business intelligence tools like Tableau and Looker. However, for this process to happen, you need to build data pipelines that push data from the relational database to the warehouse.
You can use SQL in this use case. The programming language will help you standardize (or normalize) the data in your relational database, comply with data governance frameworks in your region, such as GDPR and CCPA, and load data in a warehouse of your choice.
But what happens if you have little knowledge of SQL, or you have never used this language before? How can you possibly move advertising and marketing data to a warehouse for analytics? You could hire a data engineer with SQL knowledge to do the hard work for you. But that costs money, with the average data engineer salary per year in the United States being $131,516 as of May 2023. What happens if you need two or more data engineers to transform large volumes of advertising data in your organization?
Low-code tools and no-code tools come to the rescue here. More specifically, SQL tools with no-code graphical user interfaces (GUIs). These platforms let you interact with your relational database — and query, manipulate, and transform the data inside it — with little or no knowledge of SQL itself.
The challenges of SQL-based data transformation for marketers
SQL data transformation isn't rocket science, but it's not the easiest task in the world, either. Here are some of the challenges of transforming data with this programming language:
Technical barrier to entry
You can't just use SQL to create data pipelines if you've never utilized this language before. Like all languages, SQL has a unique syntax you must learn before writing code. Then there are multiple SQL commands you need to master, such as SELECT, INSERT, UPDATE, and DELETE.
Time-consuming learning curve
Learning SQL can take as long as six months if you do it on your own. It can then take another few months to become confident using this language. There are loads of SQL courses out there, but these cost money, sometimes thousands of dollars. If you want several people on your team to learn SQL, it's going to eat into or even wipe out your budget.
Difficulties in collaborating with non-technical team members
Say you have the SQL training budget, and three or four team members master this language. There will still be other people on your advertising team with no SQL knowledge, making communication and collaboration between the SQL people and non-SQL people difficult.
Maintenance and troubleshooting complexities
OK, so what if you have hundreds of thousands of dollars to spare, and everyone in your advertising department learns SQL? That's not the end of it. You'll still need help from an SQL expert to fix advanced SQL errors, which requires an outlay.
No-code platforms for data transformation
In an SQL context, "no-code data transformation tools" might include low-code tools — which require a basic understanding of SQL — or tools that don't need any SQL knowledge whatsoever (true no-code data transformation tools). Both types streamline data transformation by reducing data validation, management, standardization, cleaning, governance, and other tasks associated with coding SQL queries in relational databases.
Some no-code tools streamline data transformation from all data sources, not just relational databases. These tools include:
- Google Sheets and Excel, which have basic data transformation capabilities. You can use these no-code tools to normalize and transform advertising data. However, they can be error-prone and make it difficult to truly achieve your transformation objectives.
- Extract, Transform, and Load (ETL) tools move data from data sources such as relational databases, transactional databases, customer relationship management (CRM) systems, SaaS tools, and apps. ETL tools extract data from a source, transform that data into the right format for low-code and no-code data analytics, and load the data into a central repository like a data warehouse.
- Marketing data hubs are no-code tools that connect, store, organize, and share data from sources used by your advertising team. These tools differ from ETL tools because they don't move data to a central repository but store it inside the platform. Funnel is an example of a marketing data hub that requires no coding whatsoever, making it an awesome fit for your in-house marketers and marketers at media agencies!
Benefits of using no-code tools
Data transformation tools that are true "no-code," like Funnel, provide the following benefits:
- Accessibility and ease of use
- Faster implementation and iteration
- Enhanced collaboration across teams
- Automated workflows
- Better execution of marketing strategies and marketing operations
- Scalability and adaptability
- Reduced dependency on IT or data teams
- Get more insights about your target audience, landing pages, marketing videos, marketing apps, and other aspects of your campaigns
- Improve the customer experience, lead management, and business processes
These no-code tools gather data from any source, securely store it, clean and harmonize data, and let you share data with business intelligence platforms — all without writing a single line of code or hiring a data engineer! You can generate a single source of truth for all the marketing data in your organization, no matter your skill level or experience. The result? You spend more time analyzing data than transforming it.
Recommended reading: Data transformation examples
How marketing teams can get started with no-code data transformation
Here's how to start your data transformation journey with marketing tools and improve marketing initiatives:
Assess your organization's data transformation needs
Determine why you want to transform data, such as generating customer analytics in a BI tool. You should then review the customer and marketing data you want to transform and consider any obstacles to successful data transformation, such as missing or duplicated data sets.
Evaluate and select the right no-code tool
The perfect data transformation tool should have a simple drag-and-drop interface and learning curve. It should also protect all the data in your company, adhere to data governance legislation, and follow data security standards like ISO and SOC 2.
Implement no-code solutions in your marketing workflow
Your no-code tool should align with your long-term marketing goals and integrate well with your existing workflows. It shouldn't make marketing any more complicated than it already is and benefit every member of your team. Not all data transformation tools help you achieve your goals, so do your research.
Access training and support resources for no-code platforms
Learn more about your chosen no-code tools by reading all available documentation and resources for data transformation. That will help you get more value from no-code platforms and achieve successful implementation.
The final word on no-code tools
When advertising data exists in multiple formats, no-code tools can standardize it and ensure it's ready for successful analysis with little or no code. For example, removing the need to write SQL code when moving data from relational databases to a second location. Data transformation tools that require no code help marketers with no programming skills, allowing them to improve data management and analysis. Explore the best no-code solutions for your digital marketing use case and remove the pain points of transforming data in your marketing campaigns!
Takeaway: Learn how to perform data transformation on your advertising tool without SQL knowledge in this new guide from Funnel.