1. Solution Value
In the intricate landscape of modern data management, where information is strewn across cloud services and on-premises storage, the adoption of data fabric architecture stands out as a game-changing solution. This approach knits together diverse data sources, enabling a seamless flow of information. By implementing a data fabric, organizations can achieve unprecedented levels of data accessibility and integration, making it simpler to manage and utilize data across various platforms and systems.
The true value of data fabric lies in its ability to provide a holistic view of an organization’s data assets, breaking down the barriers posed by data silos. This ensures that data is not just accessible but also actionable, facilitating advanced analytics and real-time decision-making. Moreover, data fabric supports governance and compliance efforts, ensuring data quality and trustworthiness across the board.
By leveraging data fabric, organizations can significantly reduce the complexity of their data landscapes, streamline operations, and enhance agility. The result is a more dynamic and responsive data ecosystem, capable of supporting innovative data-driven strategies and delivering insights that drive competitive advantage. In essence, data fabric is not just a technology solution; it’s a strategic enabler for the data-driven enterprise, offering a scalable and sustainable path to digital transformation.
SAP Datasphere eases the burden of data management by offering a unified, scalable architecture that simplifies the integration, distribution, and management of data across diverse landscapes. Unlike the fragmented DIY approach, which necessitates a complex mix of tools and platforms, SAP Datasphere stands out with its integrated, cost-effective solution that streamlines data processes, ensuring real-time, trustworthy data access.
With its semantic layer and knowledge graph, SAP Datasphere provides a differentiated solution—a business data fabric. This kind of data fabric is a data management architecture that focuses on delivering an integrated, semantically-rich data layer over underlying data landscapes to provide seamless and scalable access to data without duplication.
2. Urgency and Risk
The future of data management isn’t just coming; it’s here, requiring an integrated approach that unites our legacy data landscape into a single, cohesive catalog ready for business use. Delay adoption of a unified strategy like data fabric, and organizations risk falling behind, unable to leverage the full power of their data assets. This urgency underscores the need to act now, ensuring data isn’t just managed but truly harnessed for growth.
The increasing complexity of data landscapes and the imperative for real-time data access is driving an important shift toward integrated data fabric architectures. SAP Datasphere is particularly crucial for organizations looking to quickly adapt to market changes, ensure data governance, and drive innovation without the overhead of managing disparate systems. Driving business semantic analytics is urgent for organizations that seek to decode complex data into clear, actionable insights, thereby driving informed decision-making and unlocking strategic opportunities in real time.
Risk
Transitioning to a data fabric architecture involves challenges, including the integration of existing systems and the potential need to upskill the workforce to adapt to new technologies and processes. However, these risks are mitigated by the long-term benefits of improved data management and decision-making capabilities.
While SAP Datasphere offers significant benefits, organizations transitioning from a DIY approach may face challenges:
- Integration complexity: Migrating existing data systems and processes to SAP Datasphere requires careful planning to ensure business continuity.
- Change management: Organizations must invest in training and change management to maximize the adoption and benefits of SAP Datasphere.
- Loss of context: Organizations that fail to preserve data lineage and business semantics risk creating a system with a lot of data but lacking the context needed to make the data valuable and actionable.
3. Benefits
The adoption of data fabric architecture offers numerous benefits, including:
- Enhanced data accessibility: Ensures data is easily discoverable and accessible, facilitating efficient decision-making.
- Smoother data integration: Automates integration of diverse data sources, reducing manual efforts and errors while preserving lineage and context.
- Efficient data processing: Utilizes in-memory and parallel processing to speed up data analysis and optimize resource use.
- Scalability and flexibility: Adapts to growing data volumes and supports multi-cloud environments, offering operational flexibility.
- Preparation for AI: Data fabrics are your gateway to generative AI value. Data fabrics preserve and present the most valuable things in your business data in a way that is ideal for the best results from Generative AI and large language models. They centralize the focus and allow you to address the risk and compliance in one place rather than disparate systems across your organization.
Organizations that leverage SAP Datasphere experience significant advantages, as revealed in a GigaOm Benchmark report that compared a SAP implementation against a similar DIY implementation. Among the advantages:
- Cost efficiency: Steep reductions in TCO for enterprises of all sizes, with DIY implementation costs that are more than 2x greater than SAP Datasphere, according to the recent GigaOm Benchmark report, “Data Fabric Field Test: SAP vs. DIY.” (Figure 1)
- Operational efficiency: Streamlined data management processes, resulting in time savings and simplified operations.
- Strategic advantage: Enhanced decision-making capabilities through real-time, integrated data insights.
Figure 1. Three-Year Data Fabric TCO (from GigaOm Performance and TCO Analysis Report)
4. Best Practices
To maximize the benefits of data fabric, organizations should engage in thorough planning, stakeholder engagement, and continuous learning to ensure smooth integration and widespread adoption:
- Thorough planning: Ensure a smooth transition by meticulously planning the integration of existing systems and data sources.
- Stakeholder engagement: Engage key stakeholders early and often to align on objectives and drive adoption.
- Continuous learning: Invest in ongoing training and support to empower users and maximize the value of SAP Datasphere.
5. Organizational Impact
Adopting data fabric transforms data management, enhancing operational efficiencies and enabling a more agile response to market demands, thereby impacting the entire organization. SAP Datasphere impacts the entire organization by fostering a culture of data-driven decision-making, improving operational efficiencies and enabling a more agile response to market demands.
People Impact
Adopting a data fabric architecture significantly impacts the workforce involved in data management and utilization. This transition requires a holistic approach to staff training, skills development, and potentially redefining roles to align with the new data management paradigm.
- Staffing levels and skills acquisition: The implementation of data fabric architecture may necessitate adjustments in staffing levels, particularly in IT and data analytics departments. As the architecture promotes automation and integration of data sources, there might be a shift towards roles focused on strategic data analysis rather than traditional data management tasks. Organizations may need to invest in upskilling their workforce to handle advanced data fabric tools and technologies, including understanding cloud-based data management, data virtualization, and real-time analytics.
- Training and team structure: Comprehensive training programs are crucial to ensure that both IT professionals and end-users across various departments can navigate and leverage the data fabric architecture effectively. This might include specialized training for data scientists and analysts on utilizing advanced analytics features and broader training for non-technical staff on accessing and interpreting data through self-service portals.
- Budget impacts: The transition to a data fabric architecture can have budget implications, particularly in the short term, due to the costs associated with training, potential hiring of specialists, and purchasing or subscribing to new software solutions like SAP Datasphere. However, these costs should be weighed against the long-term efficiencies and potential cost savings resulting from more streamlined data management processes and improved decision-making capabilities. There are also potential cost savings in deprecation of legacy technology and elimination of technical debt.
- Cultural shift: Embracing data fabric architecture often requires a cultural shift within the organization, fostering a more data-driven mindset. This includes encouraging collaboration between departments, breaking down silos, and promoting a culture where data is viewed as a valuable asset for every aspect of the organization’s operations.
In conclusion, the “’people impact” of transitioning to data fabric architecture is profound, requiring careful planning and consideration to ensure a smooth transition and to fully leverage the benefits of this modern approach to data management. Organizations that invest in their people—through training, appropriate staffing, and fostering a data-centric culture—will be well-positioned to thrive in this new environment.
Investment Outlook
Investing in data fabric aligns with strategic objectives to leverage data as a key asset, offering clear cost advantages and efficiency gains that justify the expenditure.
The investment in SAP Datasphere aligns with the strategic vision of leveraging data as a key asset, with clear cost advantages and efficiency gains justifying the expenditure. Organizations should consider the broader benefits of enhanced decision-making capabilities and competitive advantage in their ROI analysis.
6. Solution Timeline
Deploying a data fabric involves a structured approach to ensure seamless integration and maximum value from your data assets. Here’s a simplified plan:
Plan (1-2 Months):
- Define objectives: Identify key business challenges and opportunities that data fabric will address.
- Assess current state: Evaluate existing data infrastructure, including cloud and on-premises data sources.
- Design architecture: Outline the data fabric architecture, including data integration, orchestration, and governance frameworks.
Test (2-3 Months):
- Develop prototype: Build a scalable prototype to test the data fabric concept with a subset of data sources and use cases.
- Iterate based on feedback: Conduct thorough testing, gather user feedback, and refine the prototype accordingly.
- Validate compliance and security: Ensure the data fabric meets all regulatory compliance and security standards.
Deploy (3-4 Months):
- Rollout phase: Gradually expand the deployment to include all identified data sources and integrate with existing systems.
- Training and adoption: Facilitate training sessions for teams and encourage adoption through clear documentation and support.
- Ongoing optimization: Continuously monitor performance and user feedback to optimize the data fabric and add new features or data sources as needed.
Specifics for SAP Datasphere:
When it comes to SAP Datasphere, consider the following specifics during your data fabric implementation:
- Integration with SAP ecosystem: Leverage SAP Datasphere’s native integration capabilities with SAP and non-SAP data sources for seamless data flow.
- Leverage business semantics: Utilize SAP Datasphere’s business semantics layer to simplify data models and enhance data accessibility for business users.
- Scalability and flexibility: Take advantage of SAP Datasphere’s cloud-native architecture for scalable and flexible data management solutions that can grow with your organization.
- Open data ecosystem: Simplify integration with leading data and AI vendors that have built tailored integration for SAP Datasphere—Collibra, Confluent, Databricks, DataRobot, and Google Cloud.
- Advanced analytics and AI: Explore SAP Datasphere’s advanced analytics and AI capabilities to enrich your data fabric with predictive insights and automated decision-making processes.
Remember, the key to a successful data fabric implementation, especially with complex ecosystems, lies in thorough planning, iterative testing, and strategic deployment, ensuring that the solution is tailored to your organization’s unique needs and objectives.
Future Considerations
As data landscapes continue to evolve, SAP Datasphere’s roadmap includes enhanced AI and machine learning capabilities, deeper integrations with emerging technologies, and continuous improvements in usability and performance. These advancements will enable organizations to remain at the forefront of data management innovation.
7. Analyst’s Take
In the recent GigaOm Performance and TCO Analysis report, “Data Fabric Field Test: SAP vs. DIY,” SAP Datasphere produced sharp cost advantages compared to DIY across all aspects of adoption—data fabric infrastructure, initial migration/build, CI/CD pipeline, and administration. Looking across all organization sizes, Figure 2 shows that the overall three-year TCO for SAP Datasphere is less than half that of a DIY implementation (42%). Looking at individual aspects, the cost advantage for SAP ranges from 3.2x for infrastructure costs to 1.7x for administration costs.
Figure 2. Comparing Relative Cost of SAP Datasphere vs. DIY Implementations (from GigaOm Performance and TCO Analysis Report)
Beyond cost, data fabric architecture is a strategic evolution in data management, offering a robust foundation for leveraging data assets effectively. Supported by technologies like SAP Datasphere, it positions organizations for future innovations and competitive advantage.
SAP Datasphere represents a significant leap forward in data management, offering a viable, efficient alternative to the traditional DIY approach. Its integrated, scalable architecture not only reduces TCO but also empowers organizations with real-time insights and data-driven decision-making capabilities. As the data landscape continues to evolve, SAP Datasphere’s forward-looking approach positions it as a key enabler of future innovations.
8. About GigaOm
GigaOm provides technical, operational, and business advice for IT’s strategic digital enterprise and business initiatives. Enterprise business leaders, CIOs, and technology organizations partner with GigaOm for practical, actionable, strategic, and visionary advice for modernizing and transforming their business. GigaOm’s advice empowers enterprises to successfully compete in an increasingly complicated business atmosphere that requires a solid understanding of constantly changing customer demands.
GigaOm works directly with enterprises both inside and outside of the IT organization to apply proven research and methodologies designed to avoid pitfalls and roadblocks while balancing risk and innovation. Research methodologies include but are not limited to adoption and benchmarking surveys, use cases, interviews, ROI/TCO, market landscapes, strategic trends, and technical benchmarks. Our analysts possess 20+ years of experience advising a spectrum of clients from early adopters to mainstream enterprises.
GigaOm’s perspective is that of the unbiased enterprise practitioner. Through this perspective, GigaOm connects with engaged and loyal subscribers on a deep and meaningful level.
9. Copyright
© Knowingly, Inc. 2024 "CxO Decision Brief: Data Fabric Solutions and SAP Datasphere" is a trademark of Knowingly, Inc. For permission to reproduce this report, please contact sales@gigaom.com.