Part 11/12:
Helen stresses that robust data engineering forms the backbone of successful marketing tech solutions. Building efficient data pipelines, ensuring data quality, and monitoring for anomalies are essential steps. She notes that garbage in, garbage out remains a fundamental principle—without clean, accurate data, insights and predictions will be flawed.
A typical pipeline includes:
Identifying KPIs and defining data models
Designing data transformation processes
Implementing data quality checks and alerts
Orchestrating processes to run automatically
She concludes that effective data engineering is vital for turning raw data into business value, underpinning all strategic marketing initiatives.