The increasing importance of data engineering: Insights from the 2023 Dresner Study
In today's fast-paced and ever-changing business world, data engineering has emerged as a crucial field for organizations seeking to gain valuable insights from their data. As the demand for efficient data workflows and analytics capabilities continues to grow, it is critical to explore innovative solutions that streamline and enhance data engineering processes.
The 2023 Wisdom of CrowdsⓇ Data Engineering Market Study Report™ reveals how organizations face growing demands for analytical data and struggle with fragmented data, all while data engineering emerges as a key driver of success. In this blog post, we will dive deeper into the key findings from the study and explore the technical and strategic value of data engineering.
While 77% of survey respondents now consider data engineering to be critical or very important, only 26% rate their current data engineering approach as highly effective.
Growing Importance and Adoption of Data Engineering for Diverse Use Cases
The importance of data engineering is rapidly growing and being widely embraced. A significant 77% of survey respondents now consider data engineering to be critical or very important. This increased importance can be attributed to the growing demand for access to analytical data juxtaposed with the rising fragmentation of data sources and targets.
Furthermore, the study highlights that 65% of organizations currently utilize data engineering capabilities. And an encouraging 27% of respondents have expressed their intentions to implement data engineering tools within the next 12 to 24 months, indicating a rising demand in the year 2023.
Organizations utilize data engineering tools for a range of business intelligence (BI) use cases, including data integration, cleansing, and transformation workflows supporting dashboards and reporting. These tools play a crucial role in ensuring that data from various sources can be consolidated, cleaned, and transformed into a unified format, enabling effective analysis and reporting.
Key Features of Data Engineering Workflows
- Data Aggregation, Grouping, and ETL/ELT Workflows to efficiently consolidate and transform data from diverse sources for streamlined analysis and reporting.
- Efficient Workflow Management allows for seamless management of data processing with features such as alerting/job monitoring, execution plans, and time/event-based schedulers to minimize errors and enhance productivity.
- Support for Kafka and Apache Big Data Services enables streaming data processing and handling large-scale datasets, complementing other essential features.
Enhancing Workflows: Leveraging Primary Sources, Object Stores, and Third-Party Data Integration
Primary data sources and targets are crucial components in data engineering workflows. These sources encompass various platforms, including relational databases, file systems, and applications, serving as the fundamental pillars for acquiring data in data engineering processes. To further enhance these workflows, object stores such as Amazon S3 and Google Cloud Storage emerge as key players, offering invaluable support as offline storage and staging repositories. Their reliable and scalable nature makes them ideal for efficiently managing and storing vast amounts of data.
It is worth noting that approximately 35% of organizations occasionally augment their data engineering workflows with third-party data sources. The integration of external data enriches the overall data engineering process by adding valuable context, expanding the scope of information, and enhancing its quality. This collaborative approach elevates the effectiveness and impact of data engineering efforts.
Effectiveness of Data Engineering Approaches & Impact on BI Initiatives
Only 26% rate their current data engineering approach as highly effective, showcasing successful implementation, this indicates a portion of organizations have managed to optimize their data engineering practices, leading to positive outcomes. However, the study uncovered that 57% of respondents perceive their approach as only somewhat effective, leaving ample room for improvement. Only 18% consider their approach to be somewhat or totally ineffective.
These findings emphasize the importance of enhancing data engineering strategies to further enhance the effectiveness of BI initiatives. It’s clear that the effectiveness of data engineering approaches plays a pivotal role in influencing the overall success of BI initiatives, making it a critical area of focus for organizations seeking to unlock the true potential of their data.
Harness the Potential of Data Engineering and Supercharge Your Business Intelligence Initiatives
To delve deeper into the insights provided by the 2023 Dresner Advisory Services Data Engineering Market Study and learn more about the growing importance of data engineering, download the full report.
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