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    Gen AI: Shaping the Future of Data Engineering in Digital Product Innovation

    Data Engineering

    Data engineering is the art of acquiring and organising data for in-depth analysis. When combined with generative AI, digital product innovation will be seamless and proficient. With this combination, you can create outstanding AI systems with the capability of generating outstanding insights and knowledge.

    The importance of data engineers for scaling the potential of gen AI is relatable to what coders do for the software. The future of data engineering relies on the way professionals integrate and use generative AI for their benefit.

    As you know, data engineers have been the underrated heroes for modern-day businesses. Most of the achievements associated with the businesses of the present time have relied on the work of data engineers behind the scenes. In addition to that, they maintain the databases, data infrastructures, and data pipelines!

    With gen AI, they have something more to add to the digital product innovation as well as data management processes. Alongside network services and management, businesses also pay emphasis to data engineering and ways to scale it to advance their digital product efficiency.

    The impact of Gen AI on the data engineering aspects is quite humongous. It promises to evolve the way you approach data analysis, utilisation, and processing! In this article, you will get a more detailed insight into how gen AI is scaling data engineering efforts.

    What is the Ultimate Significance of Gen AI for Data Engineering?

    Gen AI is considerably the latest breed of AI technology. It has the capability of generating original content depending on the structures and patterns learned from large volumes of current data. One of the evident examples of Gen AI is GPT-4 by OpenAI.

    There are various gen AI models in practice, most of which are in the visual form. The most obvious benefit to data engineering is that professionals will be able to produce detailed graphs, reports, or charts from the data set. In addition to that, they won’t need any analysts or designers.

    Businesses have been operating on the AS400 I Series for efficient database operations, data management, CRM, business intelligence, financial management, and much more. A data engineer who is proficient with this IBM solution has a complete understanding of how to use the dedicated features for solving big data problems. Moreover, they can also create high-end data processing solutions for optimal business management.


    As data engineers have the potential to develop, test, evaluate, and maintain data solutions for businesses, they also have the liberty to utilise the latest technologies, such as gen AI, to advance end outcomes.

    The purpose of data engineering has always been to define the meanings and trends within the data set. Therefore, Gen AI adds to the potential that not only helps identify the meanings of the data but also presents them with utmost clarity.

    But, the creativity of data engineering with gen AI is much more than utilising IBM AS400 I Series for basic features. The purpose is to enhance the functionality of the existing IBM data management system and add productive benefits to the business growth.

    Gen AI models are becoming smarter and more advanced over time. They will develop the ability to tackle all forms of complex data engineering activities, starting from schema generation to feature engineering. Even now, gen AI has automated much of the technical aspects associated with the data work, such as coding or system maintenance.

    Thus, the data engineers are free enough to dedicate their time to doing some high-value work. Thus, it will enhance the business productivity further.

    In What Ways Does Gen AI Help Scale the Data Engineering Efforts in Product Innovation?

    Apart from helping the data engineers manage the data flow better, Gen AI can also help create fresh ideas. Some of the ways in which gen AI is helping businesses utilise the new data for driving growth and deciding better on digital product innovations include:

    1. Data Augmentation:

    The fundamental goal of a data engineer is to produce a complete dataset. The use of GPT-4 has the capability to produce human-seeming and lifelike text, which showcases the ML techniques that the gen AI models leverage. Some of those ML techniques include VAE (Variational Autoencoders) and GANs (Generative Adversarial Networks) for generating high-quality and realistic data samples.

    Moreover, you can hire IBM i remote managed services to train the ML models with several neural networks in collaboration with one another. This way, the output generated will be refined until it becomes indistinguishable from that of the missing data.

    With such an innovation, you can expect complete eradication of manual data imputation. It will greatly streamline the entire process of data engineering. Thus, it will reduce the overall time you spend on data preprocessing and cleaning.

    2. Data Anonymization:

    This is the age where data privacy is a big concern, and regulations such as CCPA and GDPR are very stringent about it. Businesses must follow these standards to ensure that sensitive user data doesn’t get compromised.

    Data engineering, when embedded with gen AI models, can create synthetic data that will retain the statistical aspects of the real data but remove any other information that is personally identifiable. Thus, the synthetic data can then be ideally used for proper data analysis and other purposes. And there will be no need to violate privacy regulations!

    3. Predictive Analytics

    Suppose the businesses find it beneficial to monitor and analyse the past or current data for their productivity. What do you think they can do when they get the data of the future? It will definitely be a winning move for the businesses!

    With gen AI and data engineering being integrated together, it is possible for you to acquire informed predictions based on your past or current data. Thus, you will be able to make informed predictions about market dynamics, operational performance, or customer behaviour.

    You can always look out for IBM i remote managed services to integrate your current data management or utilisation system with gen AI and scale its feature proficiencies.

    Megamax Can Help You Leverage the Next Level of Data Engineering with Gen AI

    At Megamax, we are a team of managed service providers, experts in handling IBM’s IS400 iSeries system for clients and helping them scale on their profitability. We have proficient service experts in the Big Data and analytics domain. They will help you integrate Gen AI with data engineering applications for accounting high-end returns.

    We can help you integrate your existing data stored within the IBM I AS400 systems with that of the modern analytic platforms using gen AI. This will provide you with optimal decision-making capabilities. Not only that, but we also have proficient data engineers to organise and keep your data safe throughout your contract tenure with us.

    Starting from network services and management to utilising gen AI for data engineering, we have the right tools and expertise to offer all-round managed services for the business operational systems.

    If you have any queries regarding our services associated with data engineering and gen AI, connect with our team of experts and get your queries attended to right away!

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