Fix Your Data To Take Back 40-60% of IT Spend
- By Sumit Pal, Brandon Richards, Doug Kimball, and Michael Atkin
- April 08, 2024
Most enterprises struggle with a "Bad Data Tax" caused by tangled integrations and inefficient data management. This hinders deeper, faster, and better insights and decisions. The problem is not a lack of data or technology but a lack of context, relationships, and structure. There is a better way to unlock the true value of data.
The solution to the data dilemma
This can be solved at a fraction of the cost organizations spend on data integration workarounds. It doesn’t require rip-and-replace but requires building a semantic graph layer mapped to data assets across data silos to connect the dots and restore context.
Shifting from a relational to a graph paradigm is advocated by Gartner, who advises that “using graph techniques at scale will form the foundation of modern data and analytics” and “graph technologies will be used in 80% of data and analytics innovations by 2025.”
While this benefits most use cases, it doesn't fix the root causes behind the "Bad Data Tax”. Until executives take a strategic approach with graph technologies, they will struggle to deliver needed insights for a competitive edge.
Modernize the data environment
Most organizations still manage data using relational technology in a world dominated by technology. Data is isolated and copied across repositories and silos due to technology fragmentation. These silos, built on proprietary data models, are hard to comprehend, discover and adapt. This liability diverts resources from business goals, extends time-to-value, and leads to business frustration.
Graph technologies and web standards that share data across federated environments between interdependent systems can fix this. The approach is built on open standards and granular concepts with reusable building blocks. This removes ambiguity, facilitates automation, and reduces the need for data reconciliation.
Strategic graph deployment
Many successful Fortune 500 organizations leverage graph and semantic standards to traverse relationships and connect dots across silos. However, this is implemented case-by-case, covering one business area and focusing on isolated applications. While this results in faster time-to-value for a singular use case, it fails to address the foundational data layer resulting in another silo, missing the benefits of reusability and interoperability.
The key to adopting a strategic approach to semantic standards and a graph-based paradigm is buy-in across the C-suite. This increases the likelihood of support across stakeholders in managing an organization’s data infrastructure.
Forming a Graph Center of Excellence has a significant impact. It provides a dedicated team tasked with building a foundation and prioritizing use cases. The team must evangelize and execute the strategy, score incremental wins to demonstrate value and leverage best practices and economies of scale. Key benefits from this approach include the ability to start small, deliver quick wins, and expand.
Scope of investment required
Knowledge graph advocates agree that a long tail of investment is necessary to realize its full potential. Enterprises need basic operational information, including an inventory of the technology landscape and a roadmap of data & systems to be merged, consolidated, eliminated, or migrated. Organizations need to have a clear vision of systems of record, data flows, transformations, and provisioning points. They need to be aware of costs associated with platform acquisitions, triple store databases, pipeline tools, and components to build the foundational layer of the knowledge graph.
Additionally, organizations need to understand underlying content that supports business functionality like reference data about business entities, agents, and people. Concepts, taxonomies, and data models are the foundations of the semantic approach.
These are not exciting but critical scaffolding for everything else.
Initial approach
Graph-enabled applications can take 6-12 months from conception to production. Significant time needs to be invested in getting data teams aligned and mobilized, underscoring the essential nature of leadership and the importance of starting with the right set of use cases. The application needs to be operationally viable and solve a real business problem.
With the right strategic approach, the first delivery is infrastructure plus pipeline management and people. This gets the organization an MVP, including an incremental project plan and rollout. The second delivery should consist of foundational building blocks for workflow and reusability.
Business case summary
Organizations are paying a “Bad Data Tax” of 40%‑-60% of their annual IT spend on data integration. This is because data is traditionally stored in 2-dimensional tables, which completely overlook context, semantics, and relationships to extract insights.
Adding a semantic graph layer is a non-intrusive solution to connect the dots, restore context, and provide what data teams need to succeed. While “Bad Data Tax" alone quantifiably justifies the cost, it scarcely scratches its full potential. The lost opportunity cost is no less significant, with graphs enabling new capabilities. These include better AI and data science outcomes, increased personalization and recommendations for driving revenue, holistic 360 views through data fabric, digital twins, processes and systems for what-if analysis.
Aligning functionality for reusability allows teams to shift efforts from technical components to incremental functionality, reducing costs by 30% and enabling faster rollout. The long-term cost efficiency of an Enterprise Knowledge Graph results in better management of silos, potentially saving millions for large enterprises, enhancing data science capabilities and meeting regulatory obligations for risk management.
While most organizations have begun deploying graph technologies in isolated use cases, they have yet to apply them foundationally to fixing underlying data problems. The C-suite must support this to overcome organizational inertia. Creating a Graph Center of Excellence focused on strategically deploying a semantic graph foundation must be essential for the best outcomes. The mantra to success is starting small, delivering quick wins with incremental value, and effective communication across all stakeholders.
Conclusion
The business case is compelling – the cost to develop a foundational graph capability is a fraction of the amount wasted on "Bad Data Tax". Addressing this is easier and more urgent than ever. Failing to incorporate the capabilities of graph technologies can put organizations at a significant disadvantage, especially with accelerating AI capabilities.
The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends. Image credit: iStockphoto/PashaIgnatov
Sumit Pal, Brandon Richards, Doug Kimball, and Michael Atkin
Sumit Pal is the strategic technology director at Ontotext. He is an ex-Gartner vice president analyst in data management and analytics. Sumit has over 30 years of experience in the data and software industry, having performed various roles spanning companies, from startups to enterprise organizations, in building, managing, and guiding scalable software systems across the stack.
Brandon Richards is the general manager for APAC at Ontotext. He has spent the last 8 years helping hundreds of enterprises across APAC on their graph technology journey at both Ontotext and Neo4j. During that time, Brandon has focused on shifting the "graph" conversations to C-level and senior executives for a more strategic approach to their digital transformations.
Doug Kimball is the chief marketing officer at Ontotext. He is a business and technology evangelist focusing on advancements that can be applied to e-commerce, customer centricity and insights development in data management. With more than 20 years of full-cycle B2B marketing experience at data, retail and supply chain providers across various industries and regions, he has developed a passion for enabling digital business.
Michael Atkin is the managing director at Content Strategies LLC. He has been an analyst and advocate for data management since 1985. His experience spans from the foundations of the information industry to the adoption of semantic technology. He has served as an advisor to financial institutions, global regulators, publishers, consulting firms and technology companies.