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Blog / 6 Data Challenges That Generative AI Could Help Solve for Marketers

6 Data Challenges That Generative AI Could Help Solve for Marketers

In the last year, large language models like ChatGPT have swept the globe, disrupting industries with promises of efficiency. 

By now, you’ve probably had a go at drafting up some content with ChatGPT. In fact, according to one study, 48% of marketers are now using AI to assist with generating content. While its capabilities are impressive, it’s important that marketers don’t become blinkered. The full potential for this powerful tool goes far beyond copywriting. There’s still a large part of the marketing workflow where the potential for using generative AI is largely untapped: managing marketing data. 

While collecting, cleaning, and transforming data with ChatGPT might not sound as fun as writing a social campaign with the press of a button - that’s actually good news for marketers. By shouldering the burden of these more technical, mundane tasks, generative AI can free up marketing teams to focus on more creative pursuits where they can add value. 

In this blog, we’ll explore how Generative AI, which includes models like ChatGPT and Large Language Models (LLMs), can address data challenges and open new avenues in this field.

 

 

6 data challenges that Gen AI could solve for marketers

The volume of data is growing at an unprecedented rate, and the complexity of managing it is increasing as well. Generative AI holds the potential to transform data engineering and analytics. Here are six marketing data challenges that generative AI could help to solve.

1. Simplifying data pipeline construction


Getting a single source of truth for all your marketing data manually involves a lot of time-consuming, error-prone tasks, such as logging into multiple platforms to extract data, and copying all your data into one place. So, marketing teams often rely on tools that automate the workflows for exporting and loading data by connecting to all their different platforms through APIs. This way, they can automatically create a single data set from all their platforms.

Setting up these automated connections can take a while, but generative AI tools could play a big hand in simplifying the automation of these mundane and time-consuming data-gathering tasks by designing and implementing new data pipelines. 

For example, imagine if you could upload the API documentation of your chosen data source to a tool like ChatGPT that’s integrated into your tech stack, and create new data connections quickly and easily with the help of AI. 

In short, automating all the grunt work of getting marketing data from all the sources you use into one place would get much easier — and that means more efficient and scalable data pipelines.

 

data pipeline construction

Generative AI tools could design and implement new data pipelines.
 
 

2. Simplifying data integration and transformation


Using gen AI could massively lower the barrier to entry for advanced data integration and transformation. Integrating gen AI into the tech stack opens the door for non-technical marketers to create data transformations and integrations by asking for them in plain English, without any regex or Python knowledge. 

So for example, you could ask a gen AI tool to standardize your field names by data mapping all the names for cost metrics from across different platforms (spend, cost, ga:adCost) to one term. Or, you could convert all your currencies across a campaign into USD using up-to-date conversion rates so you can compare spend across regions. This would mean non-technical marketers have much more control and access when working with their own data, and can uncover more valuable insights without relying on data analysts.

On top of this, generative AI has the potential to review and optimize data integrations by intelligently identifying data relationships, mapping schemas, and harmonizing data formats. Similarly to how it could offer up a blueprint for building data pipelines, gen AI could recommend best practices and efficient architecture for data transformation.

 

3. Simplifying data analytics


By integrating generative AI tools into the tech stack, gen AI could also lower the barrier to entry for advanced data analysis. Again, this would allow non-technical marketers to get more hands-on with their data analysis, carrying out advanced queries through gen AI tools, instead of relying on analysts.

Using gen AI, marketers could analyze marketing data through a simple conversation, receiving plain answers, graphs, or reports. 

4. Improving data quality


Ensuring data quality is a perpetual challenge in data engineering. Poor data quality can lead to inaccurate analyses and decisions. Generative AI tools can enhance data quality by automating data cleaning and validation processes. These tools identify anomalies, inconsistencies, and errors within the data, saving valuable hours of manual inspection. They also help establish data lineage and migration challenges.

5. Optimizing data storage architectures


Generative AI tools can help design optimal data storage architectures. By analyzing metadata, access patterns, and scalability requirements, they generate recommendations for data partitioning, indexing strategies, and storage formats, ensuring efficient resource utilization. This minimizes wastage, reduces costs, and ensures that resources are efficiently allocated.

6. Improving data security and compliance


Data security and compliance are paramount in the world of marketing data. Generative AI tools can assist in automating data governance processes by capturing and documenting metadata, lineage, and data quality metrics, ensuring that data remains governed, well-documented, and traceable throughout its lifecycle. 

This helps organizations maintain compliance with evolving regulations and mitigate potential risks. Generative AI introduces both opportunities and challenges in this context. It can help identify and mitigate potential security risks but also necessitates careful handling of sensitive data and guarding against algorithmic bias.

 

data privacy gdpr lock on keyboard

Data privacy compliance could be made simpler by generative AI.
 
 

Bonus: Streamlining data migration


Migration projects entail transferring data from one technology to another, such as moving from on-premises systems to the cloud - they can be really complex and time-consuming. 

Data engineers play a vital role in designing and implementing data pipelines for extracting, transforming, and loading data into the target technology. Generative AI tools can simplify this task by analyzing existing data structures and generating migration scripts. These scripts streamline the extraction, transformation, and loading (ETL) processes necessary for migrating data to new platforms. As a result, data engineers can expedite the migration process while maintaining data precision.

  • Writing migration code: Generative AI tools facilitate the automation of the data migration process by analyzing existing data structures and generating migration scripts. These scripts streamline the extraction, transformation, and loading processes required for migrating data to new platforms, expediting migration while ensuring precision.
  • Code conversion: Generative AI tools can automatically translate code from one language or framework to another, drawing insights from existing codebases to generate equivalent code in the desired format.
  • Deployment: Generative AI tools can help generate deployment scripts or platform-specific configurations, minimizing errors and ensuring consistency. Furthermore, they can conduct deployment environment analysis and provide recommendations for optimizing scalability, performance, and cost-effectiveness.
  • Testing: Generative AI tools automatically generate test cases and data sets based on various scenarios and edge cases, improving testing efficiency and coverage. These generated test cases help identify potential issues or performance bottlenecks.

Conclusion

The adoption of Generative AI tools in data engineering empowers data engineering teams to enhance productivity, efficiency, and accuracy. This integration brings numerous benefits to both business and IT teams. 

From a business perspective, employing Generative AI reduces project time and costs, enabling faster time to market and improved profitability. It provides accurate and high-quality data for analysis, leading to better decision-making and unlocking valuable insights, ultimately driving revenue growth and enhancing customer satisfaction.

 

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