How to Achieve Self-Service Data Transformation for AI and Analytics

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Read more about author Raj Bains.

Data transformation is the critical step that bridges the gap between raw data and actionable insights. It lays the foundation for strong decision-making and innovation, and helps organizations gain a competitive edge. Traditionally, data transformation was relegated to specialized engineering teams employing complex extract, transform, and load (ETL) processes using highly complex tooling and code. While these have served organizations well in the past, they are proving inadequate in the face of today’s growing desire to democratize data to meet the evolving needs of the business. 

The limitations of these approaches resulted in a lack of agility, scalability bottlenecks, the need for specific skill sets to leverage, and an inability to accommodate the growing complexity and diversity of data sources. As enterprises seek to lower the barriers to their data assets and accelerate the path to business value, a new approach is needed – one that embraces self-service, scalability, and adaptability to keep pace with the dynamic nature of data. 

The Evolution of Data Transformation

To reveal its true value of providing actionable insights and complete data for machine learning, data in its raw form requires refinement. Today, businesses need to clean, combine, filter, and aggregate it to make it truly useful. Cleaning ensures data accuracy by addressing inconsistencies and errors, while combining and aggregating data allows for a comprehensive view of information. Filtering, on the other hand, tailors datasets to specific requirements, enabling business subject matter experts (SMEs) and other stakeholders to conduct more targeted analysis.

Relational operational databases, popularized in the late 1970s and widely adopted in the 1980s, lacked analytics capabilities, leading to the emergence of relational analytical databases. Since then, a major process challenge still remains: migrating up-to-date data over to these analytical databases, then combining, preparing, and putting it in the right structure for fast analytics. As organizations grapple with the vast troves of data at their disposal, many factors are driving the evolution of data transformation:

  • Increasing demand across diverse user bases: Data analysts and scientists need to be able to self-serve the data they need, whenever they need it.
  • Growing scale and variety of data: The exponential increase in data sources, data volume, and data types (e.g., structured databases, unstructured streams, etc.) makes it harder to efficiently prepare data at scale. 
  • Pipeline development, deployment, and observability: To enable the efficient flow of data, activate the pre-defined sequence to flow within the operational environment, and ensure its reliability and efficiency are all addressed.
  • Time allocation: Despite technological advancements, a staggering 80–90% of engineering time is still dedicated to data transformation activities which pulls them away from other high-valued tasks.

It’s clear that there is a critical need for a comprehensive, unified solution to truly democratize data transformations for all data users across the enterprise.

Options: Visual ETL or Code?

Visual ETL tools have been a data transformation stalwart for decades. These legacy tools provide visual representations that simplify complex transformations, making them accessible to a broader audience, including business SMEs. This approach often boasts a user-friendly interface, fostering collaboration across teams, and facilitating quicker development cycles. However, there are constraints as they typically lack the customization required for complex data transformations, and they cannot handle large-scale data operations.

On the other hand, code-based methodologies provide a level of precision and flexibility that appeals to data engineers and other programming users. Code allows for intricate customization, making it ideal for handling complex transformations and scenarios where fine-tuned control is paramount. Additionally, code-based approaches are often seen as more scalable for diverse data sources.

Unfortunately, the need for coding proficiency limits a business SME’s ability to surface and analyze data. This is because code lacks intuitive visual representations, making it nearly impossible for all stakeholders to understand the transformations, hindering collaboration. What’s needed is a consolidated solution that keeps the advantages of both while eliminating the disadvantages.

How a Unified Approach Handles the Three Primary Scales Challenge

Organizations need a comprehensive method that seamlessly integrates the user-friendly nature of visual tools with the power of code, putting them in a better position to handle the three primary scales found in most large organizations: users, data, and pipelines. This is because neither visual ETL nor code is individually up to the task of handling the three basic scales that all enterprises need. 

As a result, organizations are looking to apply a complete solution that combines a visual modern user interface with the customizable power and flexibility of code to replace legacy ETL systems. With this approach, all stakeholders can work within an environment that is both user-friendly and powerful, which allows enterprises to more effectively modernize their ETL processes and:

  • Scale users with self-service: Enterprises have an ever-increasing number of users who need to access and transform data. With a visual, self-service interface, they can increase the demand for data transformation from a diverse user base – from data users within engineering to data analysts and scientists. The key, however, is to select a tool that is open in nature to avoid vendor lock-in and ensure data users can develop high-quality pipelines using the same standards as their engineering team counterparts. 
  • Scale data sizes: Data continues to increase exponentially as new data sources are born out of rapid technological advancements. This increasing scale and variety of data is making data preparation more complex. What’s needed is a tool that can automatically generate high-quality code that is native to cloud-based distributed data processing systems like Databricks and avoid losing the ease of use a visual interface provides. 
  • Scale the number of pipelines: As data transformations scale to the thousands, it’s imperative that standards are put in place for repeatable business logic, governance, security, and operational best practices. By developing frameworks, engineering teams can provide the building blocks for business SMEs and data users to easily leverage visual components to build and configure data pipelines in a way that is both standardized and easy to manage.

So, What’s Next? Key Considerations to Finding the Ideal Solution

Self-service is the future of data transformation, with a shift toward increased automation, better analytics, and enhanced collaboration. As organizations strive for greater autonomy in their data transformation processes, there will be a rise of intuitive interfaces, automated data profiling, and augmented insights to enable users to engage in more sophisticated data activities without having to rely heavily on central engineering teams.

Organizations must also be prepared to leverage the latest innovations like generative AI and large language models (LLMs). These capabilities, sometimes branded as “co-pilots,” are revolutionizing the way data is transformed and analyzed and are empowering systems to automate aspects of data transformation and enhance natural language interactions within the data transformation process. 

However, when taking the next steps toward a more self-service approach to data transformations for AI and analytics, it’s crucial to consider key factors for optimal efficiency, agility, and performance. Start by looking for a solution that enables greater productivity across all data users, while also helping avoid vendor lock-in. Next, prioritize extensibility so data engineers can import and create pipeline standards and then put them into the hands of business SMEs. Lastly, consider a platform that supports the entire data lifecycle to reduce infrastructure complexity and simplify pipeline maintenance at scale.

The imperative is clear: Fostering a unified approach that seamlessly combines the intuitive appeal of visual tools with the precision of code is key to catering to the diverse needs of both engineering data users and business subject matter experts and stakeholders. The era of unified visual and code technology is here and it promises a paradigm shift, empowering organizations to efficiently unlock the full potential of their data in an agile and collaborative environment.