5 Reasons Graph Data Projects Fail

Bringing graph data technology to an organization is not for the faint of heart. You are constantly juggling your budget, schedule, and requirements. Expectations collide with the reality of deploying a graph platform inside an organization. We’ve worked with teams worldwide who are bringing the power of graph data to their organizations, and there are a few roadblocks we see over and over again.

Let’s dive in: 

#1. Technical Requirements Don’t Match Business Requirements

When implementing graph technology at your company, avoiding a haphazard approach and adopting a more focused strategy to achieve results faster are crucial. Identify specific areas where graph technology will be applied, the problems it will address, and the metrics it aims to improve. Define the expected outcomes and interactions from the perspective of business users or end-users of the graph data application.

Business requirements represent a software project’s high-level goals and objectives, encompassing the necessary features and capabilities. These are typically expressed in non-technical language and concepts, sometimes ambiguous or subject to change.

Once business requirements have been established and agreed upon, they have to be translated into technical specifications. This detail outlines the actual features and functions the application must have to fulfill the business needs. It’s best to use a structured approach by breaking down business questions into data requirements and mapping them to application features.

Effective communication between subject matter experts on both the business and technical sides is crucial to ensure engineers understand end-user needs and that the targeted end-users provide the most straightforward explanations of what they expect to see when the app goes live.

To address this issue:

  • Clarify business requirements before starting work: Coordinate with stakeholders, subject matter experts, and future end-users to ensure clear and well-defined requirements.
  • Involve technical experts early: Ensure experts understand requirements and can provide input on the feasibility and limitations of the technology.
  • Use templates and documentation extensively: Create use cases, process flows, and data models to map out how the application will meet business and technical requirements.
  • Validate, test, and re-test: Test technical requirements with prototypes or proofs-of-concept to ensure accuracy and feasibility, involving stakeholders and end-users.
  • Collaborate and communicate: Schedule regular meetings and updates between stakeholders and the technical team to ensure requirements align with business needs and maintain a common repository for documents and data.

#2: Data Quality Is Low, Data is Hard to Access, and Data Modeling Takes Forever

Data sources and quality are critical to a graph data project’s success. Ensuring that applications can connect to the required data sources and maintain high-quality data is essential.


Preparing data for a graph database differs from traditional relational databases. It’s essential to structure, ingest, and process the data correctly and model it to work with the graph. To do this, you must normalize the data and perform ETL (Extract, Transform, Load) processes compatible with the graph database. Often teams need to learn a specialized query language or use third-party tools to ingest the data effectively. Some Configuration may be required to recognize the entities or nodes and relationships in the data and create the connections between them. High-quality data ingestion and modeling are crucial for success.

Key indicators this problem is rearing its ugly head:

  • Stalling out to get data right: You are six months into the project, and your team is still struggling with data preparation.
  • Too much time writing connectors: Your team is overwhelmed with writing connectors and parsers for data sources.
  • Non-normalized data: Storing redundant data can lead to inconsistency and maintenance difficulties.
  • Poor connectors: Inadequately designed connectors may result in data inconsistencies or loss, such as mishandling data types.
  • Inadequate ingestion: An improper ingestion process can cause data quality issues.
  • Clunky data modeling: Incorrect data modeling can result in inefficient queries, redundant data storage, and performance degradation.

How to fix this issue:

  • Identify entities: Determine the objects or concepts in your data that will be represented as nodes in the graph database.
  • Identify relationships: Determine the connections between entities that will be represented as edges in the graph database.
  • Normalize the data: Break down the data into smaller subsets to eliminate redundancies.
  • Ensure unique identifiers are unique: Create unique identifiers and properties for each entity and relationship.
  • Test data connectors regularly: Optimize connectors with better hardware, upgrades, or other fine-tuning.

#3: Learning Curve Discourages End Users

Too often, graph implementations are well underway before stakeholders realize that some graph platforms require end users to learn a whole new set of skills or scripts or a coding language to operate the planned application. Many teams don’t incorporate this potentially long learning curve in their project plans and schedules. 

Applications that require a lot of training and are difficult to use will not endear end users. End users are busy and want to get going and find what they want.

How you know you have this problem: 

  • A few highly technical users get a lot out of the app, but everyone else has to beg them (or engineering) for help.
  • End users balk at the learning curve and abandon the app altogether, and this could even be your more “technical” users who don’t want one more thing they have to learn on top of the pile of technologies, tools, and frameworks they’re trying to stay current on.
  • No one wants to learn a query language like Cypher or Gremlin. This additional learning curve can discourage end users and slow down the adoption of the application.

How to fix this problem:

  • Invest in front-end development: Customize the interface to meet the specific needs of the business, making it easier for non-technical users to find, enter, and manipulate data. A well-designed front-end can also provide security features that ensure only authorized users access the database, protecting sensitive data.
  • Keep what’s under the hood, under the hood: Shield end users from the complexity of the database and application, allowing them to focus on solving their business problems. Simplifying user access to data can reduce errors and increase productivity.

#4: End Users Can’t Share Graphs or Collaborate on Analysis

The most incredible graph application in the world can give an end user a startling analysis that could change the direction of the business. But if that analysis can’t be easily shared with others, that limits its reach. Many graph data packages are currently available as desktop or client-based, single-user applications, which can make collaboration and sharing views with colleagues difficult.

When evaluating a solution or application for graph data projects, whether custom-built or from a third party, examining its support for role-based access and its capacity for creating and sharing knowledge is essential. Ideally, users should be able to capture “snapshots” of their work and share them with others through shareable links, similar to Google Drive’s sharing feature. This functionality aligns with users’ expectations of the applications they use daily.

Another crucial aspect to consider is data enrichment. Allowing users to incorporate additional data or context will enhance the graph, enabling faster and more effective problem-solving, and considering these factors when developing or working on graph data projects will lead to a more collaborative and efficient experience for all users.

How to fix this problem:

  • Prioritize features that facilitate sharing specific projects and views of data sets with shareable links and snapshots, ideally including view/edit roles. While custom coding may be required, this approach will encourage broader app usage, as graph viewers will be more inclined to use the app frequently.
  • Consider additional features that allow users to add supplementary data sources to a graph for further enrichment and a more comprehensive view.

Check out how Gemini Explore lets you share snapshots of graphs and add new data sources to enrich. 

#5: Analysis Takes Too Much Time

We outlined above how there’s a long learning curve for people to get used to using graph technologies due to specialized query languages and the learning hurdles of learning different processes and experiences to learn to get the most out of a graph data application. 

With the recent advancements in generative AI like OpenAI’s ChatGPT and Google’s Bard, graph data has become even more accessible to more people. The hurdle in learning a query language has been dissolved.  Previously, users had to learn specific query languages and methodologies to use certain tools effectively. Integrating with generative AI allows users to ask questions using human text input and receive graph data output with context as answers without needing to master the tools.

Graph databases are highly effective in managing intricate and interlinked data, making them particularly well-suited for training generative AI. On the other hand, traditional relational databases primarily rely on tables, rows, and columns and may face difficulties when dealing with intricate relationships. In simpler words, graphs provide a versatile, efficient, and easily understandable structure for organizing information, ideal for training AI models to comprehend complex connections. As a result, graph databases serve as an excellent basis for AI projects.

You can see how Gemini Explore solves this problem with our natural language search that uses human data input and responds with graph data output in our blog post, Generative AI, ChatGPT, and the Future of Graph Technology

Stay Vigilant, Stay Focused

Introducing graph data technology to your organization is a multifaceted endeavor that requires careful planning, clear communication between the technical and business teams, and a thorough understanding of the organization’s data needs and resources. It’s vital to mitigate the challenges posed by the learning curve for end-users, manage data quality and access effectively, and ensure smooth collaboration and information sharing. Incorporating generative AI can drastically reduce the learning curve and open up the potential of graph data to a broader audience. Implementing a graph database, despite its challenges, can unlock tremendous value and insights, enabling organizations to handle complex, interlinked data efficiently and providing powerful, accessible tools for decision-making. The path may be challenging, but with strategic planning and execution, the rewards can be game-changing.

See firsthand how Gemini Explore lets graph data teams leapfrog over the usual stumbling blocks to get broader adoption, faster time-to-value, and increased ROI. 

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