Data engineering is a profession focused on making raw data usable for analysis by data scientists, analytics applications, and data consumers. Data engineers construct data pipelines that gather and refine external and operational data to support applications, including decision support, business intelligence (BI) systems and machine learning (ML) models.
Why is Data Engineering Important?
Data engineers are critical for every business because they are the primary enabler of fact-based decision-making. They enable the digital data it produces to guide the operational decisions that propel its growth.
Data engineers use readily available data analytics to help a business learn what changes can make its operations more efficient and decision-making more effective.
A growing consumer of data pipelines is machine learning models that become more effective as they consume more high-quality training data and provide better predictions about market changes and customer behavior as they happen.
Citizen data analysts depend on data engineers to organize data for easy analysis, set up BI dashboards, and develop metadata to help them find relevant data.
Examples of Data Pipelines Data Engineers Create
Data Engineers create data pipelines to support decision-making across many business departments. Below are some examples of data flows.
Real-Time Corporate Governance Analytic Dashboards
At the highest levels, business intelligence dashboards use data pipelines fed by customer relationship management (CRM) systems to stay current with operations. For example, a business can benchmark its performance against established key performance indicators (KPIs) for corporate performance management. BI dashboards can highlight situations when metrics drop below predefined thresholds, such as sales management over-discounting to the level at which profit margins for the quarter come under pressure. A sudden decline in profitability can trigger a fall in investor confidence and subsequent negative management actions.
Online Retail Engagement
Machine Learning models can use browser histories combined with past purchasing activity to personalize shopping experiences for active shoppers by providing real-time recommendations.
Navigation Systems
Transportation systems such as shipping use data pipelines with sensor inputs about current wind and ocean conditions. Combined with weather forecasts, they can prescribe the safest and most fuel-efficient course settings.
Healthcare Diagnostics
Data Engineers create data pipelines that train machine learning models to study the patient symptoms, medications and clinical trial data to predict likely outcomes. This data advises doctors about a given patient’s most effective treatment plans.
Stock Trading
Machine learning models can provide buy and sell guidance based on current trading patterns, regulatory filings, the latest analyst rating changes and news feeds to suggest trades.
Risk and Fraud Analytics
Financial institutions and insurance companies have to monitor for potential fraud continuously. AI Models use data pipelines from current and past transactions to help them anticipate fraudulent transactions.
The Benefits of Data Engineering
Data engineering has been one of the fastest-growing technical job functions in recent years because of its proven benefits to organizations. Many of these benefits are listed below:
- Data Engineering is responsible for harvesting operational and market data that makes it possible for businesses always to be aware of changes in business conditions that they need to respond to.
- Data pipelines help to optimize decision-making by predicting outcomes and learning from past successes and mistakes.
- Data engineers automate data workflows that can be used to extract immediate value from operational data that in the past would have languished in a data lake,
- Data Engineering is a fast-growing career path that can draw candidates from data analysts and create a pool of potential Data Architects and Data Scientists.
- Data engineers are at the forefront of creating data-driven organizations by providing actionable insights from raw operational data. Using data to justify decisions accelerates approval processes, making a business more responsive and competitive.
- Artificial Intelligence-driven models enable businesses to detect subtle correlations between decisions and outcomes that would have been impossible without data engineering’s role in making data available for decision support.
Actian Data Management for Data Engineering
A fully managed hybrid data platform like the one from Actian simplifies complex data integration challenges and gives data engineers the flexibility to adapt to evolving data pipeline needs. With Actian, you can get started with DataConnect, a standalone, hybrid data integration and quality tool with more than 200 connectors or Actian Data Platform, a unified data integration, warehousing, and analytics platform. Either way, you’ll be confident knowing you have the toolkit to work with a variety of data sources and formats to maintain reliable data pipelines with ease.
Data engineers should start by downloading a free trial of the Actian Data Platform. From there, they can load their data and explore what’s possible within the platform.