How Data Engineering is the Core of Business Strategy?
For supply chain leaders, speed is everything
The faster you can understand your data the faster you can act on it and make a proactive decision. What you will see today is that, although overflowing with a rich amount of data, supply chain leaders are still not able to make the right decision at the right time.
Why? Because it's not the lack of data that affects their decision, it's the chaos! The fragmented data, data silos, latency, and poor-quality pipeline, all these factors contribute to inefficient decisions leading to supply chain disruptions.
So, how do businesses know they’re making the right decisions? That’s where data engineering comes in.
At Syren, we are experts in finding solutions to supply chain disruptions and, in this blog, we have curated 5 strategic ways data engineering is transforming decision-making to build a resilient and robust supply chain.
Before we dive in let’s talk about what data engineering solves for supply chain leaders.
Data Engineering Brings Clarity to Chaos
Reliable data empowers organizations to predict market shifts, identify opportunities, and finally drive profitable outcomes.
By analyzing raw, distinct data into an insightful actionable asset, data engineering lays the foundation for data-backed decision-making. It bridges the gap between information and insight, giving an organization an asset to act with precision.
At the end of 2024, Gartner predicted that 75% of organizations would shift from piloting to operationalizing artificial intelligence (AI), driving a fivefold increase in streaming data and analytics infrastructures. The shift is here, and it demands robust, well-engineered data systems.
4 Strategic Ways Data Engineering Transforms Decision-Making
1. Data As a Single Source of Truth
Fragmented data is the biggest challenge that organizations face today. This fragmentation nearly always results in inconsistencies, blind spots, and lost opportunities. Data engineering counters this problem by consolidating data sources into a single repository.
These repositories maintain all data, independent of its source — saved, processed, and accessed reliably and consistently.
The outcome? One single source of truth that eliminates data silos, giving a comprehensive overview to act faster.
2. Enhanced Data Quality
According to a Gartner study, it was found that poor data quality costs organizations at least $12.9 million a year on average.
Bad data causes bad decisions.
Incomplete, inaccurate, or inconsistent information eventually results in wrong decisions and can be costly in the current market dynamics. Data engineering addresses this by maintaining strict data quality standards using advanced tools for cleansing, standardization, and validation.
3. Advanced Analytics
Advanced analytics are necessary for deeper insights into operations, markets, and customers. With AI and ML, companies have already started achieving this efficiency. Data engineering ensures that the foundation for these technologies is rock-solid, with robust systems for collecting, storing, and processing the datasets they require.
By delivering high-quality, readily available data, data engineering powers predictive models, trend analyses, and prescriptive insights that enable businesses to stay ahead of the curve.
4. Increased Data Governance and Compliance
Regulations can change overnight; it is extremely important that every business is aware of and abides by these regulations to stay compliant. Data governance ensures data is managed securely and responsibly, while compliance guarantees adherence to global standards.
Data engineering provides the frameworks, tools, and policies needed for good governance. From access and data lineage tracking to retention policies, it empowers enterprises to safeguard sensitive information, maintain compliance, and build trust with stakeholders.
Conclusion
Data is the backbone of innovation and growth. As businesses continue to embrace advanced analytics, AI, and real-time data-driven strategies, the demand for robust, scalable, and reliable data engineering systems will only intensify. By centralizing data, enhancing its quality, streamlining processing, enabling advanced analytics, and ensuring governance, data engineering services and tools become a strategic advantage.
An IDC white paper highlighted that data-driven organizations are over 3X more successful than those with less mature data practices.
At Syren, our data engineering experts are ready to scale your business operations.
Speak with our experts to see how you can eliminate silos, accelerate analytics, and improve decision-making across your supply chain from our data engineering services!
