Data Engineering and AI Collaboration – Why Is It Essential for Making AI Investments Profitable?

Despite AI technologies being in existence for over half a century [1], the AI buzz has yet to die down. From generative pre-training transformers to live translators and generative photo editors, artificial intelligence has always been a hot topic. And from the look of things, it’ll remain that way for many years to come.

At the heart of this technological revolution lies the intersection of AI and data engineering. It wouldn’t have been in existence without the presence of data engineering professionals who ensure that data is collected, cleansed, and structured appropriately, enabling AI systems to perform accurately and efficiently.

Data engineers, on the other hand, also benefit from AI since it makes their work a lot easier. By leveraging the latest trends in data engineering, organizations can effectively tap into AI’s potential to streamline business operations.

Read on as we shed more light on data engineering and AI integration and why it is essential for making AI investments profitable.

Data engineering and AI collaboration – is it a popular combination?

Data engineering is one of the fundamental building blocks that make AI what it is today. To better understand the combination of these two disciplines, we’ll have to first look at each one individually.

What is data engineering?

Data engineering is a discipline that centers on the creation, implementation, and maintenance of data systems, pipelines, and infrastructure. These data pipelines and infrastructure are necessary for processing and transforming large data sets. In doing so, it allows for seamless collection, storage, and accessibility of data for analysis.

In addition to that, data engineering also enables professionals and researchers to gain valuable insights from the data. Without data engineering, it would have been impossible for organizations to make sense of the huge data they’re collecting.

Here are some of the most common tools and technologies that data engineering professionals must be proficient with:

• Data pipelines: Depending on the organization’s needs, data engineering professionals can construct pipelines using ETL or ELT format. The ETL format involves extracting data from the source, transforming it into a standardized format, and loading it into the storage destination. The ELT format, on the other hand, involves extracting data, and loading it into a centralized respiratory, before transforming it into the required format.
• Data storage solutions: Data engineers should be proficient with various data storage solutions such as cloud computing services, relational databases, NoSQL databases, and more.
• Programming languages: They should be proficient in various programming languages such as SQL, python, java, and Scala.

What is artificial intelligence?

Artificial intelligence (AI) is a technology that allows computer systems to mimic human-level intelligence like learning, comprehension, problem-solving, and summarization. AIs can learn from new information, adapt to changing environments, and get better with every interaction. Generative AI systems, like ChatGPT and Gemini, are among today’s most popular types of AI. These models typically accept prompts to generate text, images, audio, or video.

Some of the technologies that enable AI systems include:

• Computer vision: Enables computers to identify people and objects in pictures
• The Internet of Things: This is a network of physical objects embedded with sensors and software to be able to connect and exchange data with other devices.
• Natural language processing (NLP): Allows computers to understand human language

Where do these two fields intercept?

Every AI system must be trained on quality data for it to work effectively. It’s the work of data engineers to refine and organize this data. This usually involves gathering, refining, and organizing the data to create pipelines that feed and train the AI systems continuously. These systems then learn to use the data to make predictions, classify information, and a host of other applications.

Data engineers are responsible for the robust data architecture behind the biggest AI models today. They do the heavy lifting in the background, powering generative AIs, autonomous vehicles, and other artificial intelligence technologies.

However, it’s worth noting that the relationship between the two fields isn’t “one-sided,” with artificial intelligence systems getting all the benefits. AI is also reshaping data engineering in many ways. For instance, AI systems are stepping in to make the work of data engineers easier by automating routine or menial tasks. These systems can also generate synthetic data or improve existing data. This helps provide crucial insights that data engineers can use to achieve their bottom lines.

Some of the common ways data engineers can use artificial intelligence include:

• Data collection: This is the extraction of relevant data from reputable sources and databases. This is accomplished via a network of APIs working in synchrony to acquire data from different sources while facilitating smooth communication between systems.
• Data storage: Here, data scientists and engineers use state-of-the-art storage facilities, including warehouses and data lakes, in conjunction with software like Spark and AWS to safely store all the data collected in an unstructured or structured format.
• Data processing: Data scientists and engineers must transform raw data into a clean and readable format for analysis. This process removes inconsistencies, redundancies, and downright erroneous data using software like Talend. Next, they’ll load the data into analytical platforms based on generative AI systems.
• Data governance and quality assurance: Proper data processing workflow results from having complete, accurate, up-to-date, consistent, and intelligible data. Manually reviewing the data is cumbersome, so data scientists and engineers use AI systems with validation algorithms that can identify errors and understand the context to fill in missing values. These systems also ensure conformity with regulatory norms.
• Data integration: The above steps result in siloed data banks that don’t interact with each other. Using the help of artificial intelligence and integration tools, data scientists can then create transparent ecosystems with clear relationships between these data silos. That way, the database can operate as a holistic unit with data freely moving between branches and departments.
• Feature engineering: With well-established data workflows and relationships between data banks, data scientists can innovate new features to improve the predictive power of AI models. This involves selecting and creating specific features and data pipelines that enhance AI algorithms. These features are then implemented in the generative AI models, which recognize patterns and make predictions or classifications based on the features.
• Continuous improvement: Data engineers can use AI systems to continuously refine data pipelines, ensuring that they adapt to changing external environments. They’ll then establish feedback loops that monitor the performance of these models.

Benefits of using AI and data engineering

The integration of artificial intelligence and data engineering has far-reaching benefits that stretch beyond the two disciplines. Here are some of the most common benefits of using AI and data engineering:

Automates repetitive tasks

Manually doing redundant, mundane tasks is not only time-consuming but can easily wear you down. By fusing artificial intelligence with data engineering, data engineers can reduce their workload since AI systems take on the bulk of the work. For instance, by letting autonomous algorithms clean, integrate, and organize data, engineers can focus on creative or strategic work, resulting in improved productivity and faster delivery time.

Better data quality

Human beings are prone to errors and inaccuracies, especially when fatigued or when working with large data sets. Integrating artificial intelligence and data engineering is one way to eliminate the error factor from data science and other workflows. AI systems can quickly spot inconsistencies, anomalies, and inaccuracies and address them in real time. This results in more accurate predictions and analyses.

Faster data processing

The absence of anomalies, inaccuracies, and redundant data means faster data processing for artificial intelligence systems. Data engineering makes data processing even faster by organizing data into structured formats and ensuring streamlined pipelines. Faster data processing means AI models can make predictions or respond to interactions with external environments in real time, making them capable of applications such as autonomous driving and fraud detection.

Predictive analytics

Predictive analytics using generative AI models would be impossible without data engineering. That’s because these models tap into past data to identify patterns and trends that let them make accurate predictions. Data scientists, with the help of engineers and IT technicians, cleanse and categorize fragmented data so these models can find patterns and make predictions with pinpoint accuracy.

Enhanced scalability

As organizations grow, data engineers have to deal with increasing data volumes. In some cases, the data may be too much for the existing system infrastructure or workforce. Artificial intelligence, in conjunction with data engineering, can provide solutions for scaling up and down to accept new data sources without extensive capital investment.

More satisfied customers

Better insights into customers’ preferences and spending habits could help businesses serve customers better. With better data quality and clear data pipelines feeding the AI system, business owners can understand their customers better. They can then use artificial intelligence technologies to automatically generate personalized shopping experiences, including tailored customer support. The result is improved customer satisfaction and higher profit margins.

Better tools for data engineers

The advent of artificial intelligence has ushered in a wave of new tools to make data engineering a lot easier. For instance, they can now use Machine Learning Operations (MLOPs) [2] tools to handle the operationalization and automation aspects of artificial intelligence while they focus on gathering, cleansing, and structuring high-quality data to train artificial intelligence models.

Read more: Data Engineering with Databricks

Challenges of data engineering and AI

Despite the dynamic and complementary synergy between data engineering and AI, the integration of the two disciplines comes with a few challenges, including:

Integration and management issues

As mentioned, creating data pipelines for artificial intelligence models involves using vast amounts of data, usually from multiple sources. Harmonizing this data into easy-to-understand formats is easier said than done.

The process usually requires extensive resources in terms of computing resources and technical labor. For instance, data scientists and engineers must combine disjointed data, which is sometimes structured or unstructured, into well-organized and categorized forms.

These tasks typically involve manually writing and reviewing multiple lines of code. The more data sources, the harder and more frustrating the work is. Also, a lack of proper due diligence compromises the data quality, resulting in flawed AI models.

Compliance with data regulation policies

Data compliance regulations like the General Data Protection Regulation (GDPR) and the Health Insurance Performance and Accountability Act are major stumbling blocks to data science and artificial intelligence. While everyone deserves their right to privacy, sometimes these policies are misinterpreted or stand in the way of efficient data gathering. Remember, data collection is the first step in data engineering for AI.

Besides ensuring data collection practices align with these policies, organizations must also put in place measures to ensure the safety and protection of the data they use. Failure to do so can result in legal ramifications and the prohibition of artificial intelligence systems critical to operations. As such, data engineering professionals must ensure continuous compliance monitoring of the AI systems they develop.

Effective data governance

Besides complying with data regulation policies, data engineering professionals must also set up frameworks to ensure they use high-quality data in the correct formats. They must establish controls and minimum requirements for data quality and formats. That’s the only way to prevent hallucinations, where the artificial intelligence systems give wrong or distorted output, compromising their effectiveness. Technicians can avoid this issue by validating and testing data before feeding it into the AI systems to preserve data quality.

Lack of enough qualified workforce

The adoption of artificial intelligence into an organization requires skilled and competent staff who are well-versed in IT and data science. There’s a shortage of such skilled technicians in the market despite the high demand. This shortage means organizations must pay hefty salaries to attract and retain these skilled technicians.

Furthermore, the rapid development of artificial intelligence and data engineering technologies keeps data engineering professionals on their toes, and some cannot keep up, further hampering the organizations’ competitiveness.

According to the University of California, over 40% of companies believe the lack of qualified data scientists hinders their competitiveness in today’s market [3]. In fact, 60% of these companies choose to train their staff in-house, regardless of whether the staff members possess college degrees.

The future of data engineering and AI collaboration

The integration between data engineering and AI collaboration is rapidly evolving. While some organizations and companies are just getting their foot in the game, some are already anticipating what the future has to offer. As data processing capabilities improve, so does the field of data science. Here’s what we expect from this union in the future:

The lower threshold to entry

The future holds immense promise for the non-tech audience interested in data science and engineering. That’s because AI-powered chatbots are likely to replace conventional SQL and BI dashboards, which are restricted to users with technical expertise. These chatbots will be able to understand input in natural conversational language, allowing a broader range of users to leverage artificial intelligence and data science and engineering for various applications.

Better integration with IoT devices

Data engineers have uncovered a consistent stream of high-quality data from IoT devices. The future of data science is set to become more geared towards these devices as engineers bring their data processing systems closer to the data sources. This translates to more efficient use of bandwidth and an overall reduction in latency for improved processing and better functionality of AI systems. It’s also likely to improve data quality across the board.

Adoption of cloud and software as a service (SaaS) technologies

On-premise data processing systems and infrastructure are set to be pushed to the back burner as cloud and SaaS technologies become the norm. Adopting these technologies means organizations won’t have to expend plenty of resources to install and maintain on-premises infrastructure. Instead, they can outsource to cloud and SaaS companies for a fraction of the price. Embracing these technologies also allows data engineering professionals to focus on their core activities rather than configuring these systems.

AI-powered data governance and compliance

Earlier, we talked about how data governance and compliance are major hurdles to integrating artificial intelligence and data engineering. This issue will soon be resolved almost entirely as artificial intelligence takes the wheel.

Data engineers will leverage AI systems to ensure full compliance with data regulations to align with privacy frameworks. The artificial intelligence systems will also check for anomalies and inconsistencies in data and implement real-time security protocols to safeguard user privacy and data.

Self-optimizing pipelines

Data pipelines sit at the core of the symbiotic relationship between data science and artificial intelligence. As the demand for more efficient artificial intelligence systems grows, these pipelines must handle large-scale and real-time data processing. Self-optimizing data pipelines capable of feeding refined, structured, and error-free data automatically for artificial intelligence systems will help ease the burden on data scientists and create efficient and effective AI systems.

Final thoughts

The integration of artificial intelligence and data engineering is the reason why much of the world enjoys artificial intelligence technologies. Organizations and companies can utilize this relationship to advance their goals and get an edge over the competition. But first, they must commit to hiring skilled and experienced data scientists and couple them with a robust IT workforce to reap the full benefits.

As generative AI technology becomes more readily available, we can see even small players making their mark with innovative artificial intelligence applications. The biggest beneficiaries of these developments, however, are the data scientists and engineers who not only get to refine their skills but also benefit from a lighter workload. On the flip side, however, the market will get more competitive as the non-tech audience enters the scene. They’ll have to step up their game to remain competitive and relevant in the fast-evolving field of artificial intelligence and data science.

References

[1] IBM.com. What Is Artificial Intelligence (AI)? URL:
https://www.ibm.com/topics/artificial-intelligence. Accessed on September 10, 2024
[2] binariks.com. MLOps, and Data Engineering: Opposition or Synergy? URL:
https://binariks.com/blog/data-engineering-vs-mlops-engineering. Accessed on September 11, 2024
[3] engineeringonline.ucr.edu. Data Scientist Shortage: Current Demand and Future Job Outlook. URL:https://tiny.pl/t14q740b. Accessed on September 11, 2024