Short answer
Prioritize the development of integrated systems that manage the full data lifecycle, enabling robust analysis for informed strategic decision-making.
- Field
- Innovation & Design
- Source
- Serdica Journal of Computing (2018)
- Method
- Systematic Literature Review
- Evidence
- Strong effect
Leveraging comprehensive data lifecycle management and advanced analytical functions is crucial for developing effective, data-driven solutions that inform strategic business decisions. This innovation & design research insight is drawn from a 2018 study published in Serdica Journal of Computing. Using Systematic literature review, researchers explored how this design variable affects real-world outcomes. The key design takeaway: Prioritize the development of integrated systems that manage the full data lifecycle, enabling robust analysis for informed strategic decision-making.
Big Data Integration is Key to Strategic Decision-Making
Leveraging comprehensive data lifecycle management and advanced analytical functions is crucial for developing effective, data-driven solutions that inform strategic business decisions.
Serdica Journal of Computing · 2018
Key Findings
- 01Big Data presents significant opportunities for applications across various domains.
- 02There is an urgent need for end-to-end, data-driven solutions to guide strategic decisions.
- 03Advanced data functions are required for structuring, cleaning, storing, aggregating, modeling, processing, and analyzing Big Data.
- 04Runtime adaptations across the complete data lifecycle are essential for Big Data Value Chain solutions.
Application
Design takeaway
Prioritize the development of integrated systems that manage the full data lifecycle, enabling robust analysis for informed strategic decision-making.
How to apply
When designing any data-intensive product or service, map out the entire data journey from ingestion to actionable insights, ensuring each stage is robust and interconnected.
Project actions
- 01Consider how your design project will handle data, even if it's not 'Big Data'.
- 02Think about the entire lifecycle of the data your project will generate or use.
- 03Explore tools and techniques for data analysis relevant to your project.
Method & Evidence
Variables
Strengths & Limitations
Strengths
- +Provides a comprehensive overview of Big Data challenges.
- +Highlights the importance of end-to-end data solutions.
Limitations
The scope of 'Big Data' can be overwhelming; focus on a manageable subset of data challenges relevant to your specific design project.
Reliability & validity
The reliability of a systematic literature review depends on the rigor of the search strategy, inclusion/exclusion criteria, and synthesis methods. Validity is enhanced by the breadth and depth of the literature reviewed.
Think critically
How can a design project, even on a smaller scale, incorporate principles of robust data lifecycle management and analysis to achieve better outcomes?
Design Principles
"Design for the complete data lifecycle, ensuring robust analytical capabilities to drive strategic insights."
In today's competitive landscape, organizations that can effectively harness and analyze vast amounts of data gain a significant advantage. This research highlights the necessity of robust data infrastructure and analytical capabilities to unlock the true business value hidden within 'Big Data'.
What This Means for Your Design
To make good business decisions, you need to be able to collect, clean, store, and analyze a lot of data really well, and have systems that can change as the data changes.
How to use in your project
- 1.Reference this study when discussing the importance of data management and analysis in your design process.
- 2.Use the findings to justify the need for specific data handling features in your design proposal.
Add to My Project
Quick Cite
Paragraph starter
The integration and analysis of 'Big Data' are critical for informed strategic decision-making, necessitating end-to-end, data-driven solutions that manage the complete data lifecycle. This requires advanced functions for data structuring, cleaning, storage, aggregation, modeling, processing, and analysis, with mechanisms for runtime adaptation to ensure ongoing relevance and value.
Source
Serdica Journal of Computing
Big Data Research and Application - A Systematic Literature Review
journal · 2018
View sourceQuestions About This Research
- What does the research say about big data integration is key to strategic decision-making?
- Prioritize the development of integrated systems that manage the full data lifecycle, enabling robust analysis for informed strategic decision-making. Evidence: Serdica Journal of Computing (2018).
- Why does "Big Data Integration is Key to Strategic Decision-Making" matter for design?
- In today's competitive landscape, organizations that can effectively harness and analyze vast amounts of data gain a significant advantage. This research highlights the necessity of robust data infrastructure and analytical capabilities to unlock the true business value hidden within 'Big Data'.
- How can designers apply this research?
- Prioritize the development of integrated systems that manage the full data lifecycle, enabling robust analysis for informed strategic decision-making.
- What were the main findings?
- Big Data presents significant opportunities for applications across various domains.. There is an urgent need for end-to-end, data-driven solutions to guide strategic decisions.. Advanced data functions are required for structuring, cleaning, storing, aggregating, modeling, processing, and analyzing Big Data.. Runtime adaptations across the complete data lifecycle are essential for Big Data Value Chain solutions.
- What research method was used?
- Systematic Literature Review.
- How strong is the evidence?
- Evidence strength is rated Strong effect, based on a 2018 journal from Serdica Journal of Computing.
- What should I do differently in my next project?
- When designing any data-intensive product or service, map out the entire data journey from ingestion to actionable insights, ensuring each stage is robust and interconnected.
- What are the limitations?
- The review focuses on existing literature and may not capture emerging, unpublished research or proprietary industry solutions.