In today’s fast-paced and data-driven business landscape, making informed decisions is crucial for achieving success. Businesses are constantly seeking ways to gain a competitive edge to drive growth and executives play a pivotal role in guiding their organizations towards growth and profitability. As organizations battle against escalating volumes of data and yearn for meaningful insights, the relationship between data technology and AI becomes increasingly self-evident. Before we dive deep into this relationship, let’s quickly cover both topics at a fundamental level –
What is Data Engineering?
Data engineering focuses on setting up required procedures for creating, developing, and overseeing the systems, infrastructure, tools, and architecture needed to gather, store, and process massive amounts of data. Data engineering aims to build a dependable, vast, and effective infrastructure for data collection, processing, and analysis. There are 3 major components of the data engineering infrastructure –
- Data Ingestion: The process of collecting data from various sources and transferring it to a storage system. It involves setting up integration to multiple systems within the organisation to enable completed data collection via real-time streaming/batch processing.
- Data warehousing: Determining the appropriate storage solutions for data types (structured, semi-structured and unstructured data) such as relational databases, NoSQL databases, data lakes, or data warehouses.
- Data Processing: Transforming and cleaning raw data into a format suitable for analysis. This includes functions such as data cleaning, aggregation, and normalization.
How can Organisations leverage AI in Data Engineering?
Organisations can leverage AI capabilities in multiple data engineering processes such as-
- Data Definition – Warehouse and schema recommendation, element nomenclature and data type standardization/recommendations, auto-identification of relationships, define and recommend constraints.
- Data Transformation – Generate custom ETL templates/code snippets, schedule transformations, combine and standardise data sources, preparation of data marts etc.
- Data Management – Optimizing storage and access, Indexation, data profiling and anomaly identification.
- Quality Assurance and Governance – Prepare documentation, track resource usage and compliance tracking, perform security audits etc. This can help data engineers automate repetitive labour-intensive activities and facilitate rapid scaling of the solutions.
Generative AI and Machine learning models (subsets of artificial intelligence) can equip systems to learn from data patterns, adapt, and make predictions or decisions without explicit programming. They can further simplify pattern recognition, and predictive modelling and can even assist in data imputation using the synthetic data generation capabilities of GenAI.
Generative AI provides a unique value proposition for business users as well where data engineers can design self-serving analytics solutions that leverage NLP (Natural Language processing) to query data from data marts without the need to learn data query languages such as SQL, or Python for day-to-day needs, hence reducing the reliability on data engineers/analysts.
However, it is always recommended to use Gen AI solutions with adequate human expertise to ensure that automation is aligned with business goals by making continuous improvements, monitoring bias and fairness of learning algorithms and adherence to all regulatory requirements. The combination of artificial intelligence (AI) and data engineering holds great potential for breakthroughs in the areas of autonomous decision-making and instantaneous analytics, ushering in a new era of intelligent data extraction.
Saarthee remains committed to harnessing the unlimited possibilities of AI in data from collection, and engineering to analytics and is making continuous headways. We would love to connect and explore collaborative opportunities.