10 Tips for a Successful Graph Data Science Project

Get 10 project tips to drive your graph data science project to its production.

Are you wondering how you can get started with graph data science (GDS)?

In this blog post, we’re going to give you 10 project tips and some resources to guide you from the beginning of your graph data science project to its production. As opposed to technical details, here we’ll help you figure out how to successfully drive your graph project forward within your organization. Let’s dive in.

1. Investigate Use Cases and Get Comfortable with Concepts

Graph technology is applied across industries in various use cases. To get up to speed with graph data science, start by reviewing different use cases. Read case studies of how people use GDS and review material that sets GDS in a larger context to expand your knowledge.

2. Identify and Engage a Spearhead Team

Graph technology is still new to many people, so assemble a small team that can become your experts in translating business needs into technical requirements and the application of GDS. Provide your developers and data scientists with technical resources – such as the Graph Algorithms book, the Graph Databases book, the Graph Data Science For Dummies book and the Neo4j Sandbox.

3. Evaluate Your “Graphy” Problem

Graph technology is useful for problems that depend on a lot of connected, interdependent information. When you look into what areas of your business you want graph data science to solve, start with an intersection of ideas between users, business and technology. Define your stakeholders’ needs, create connections-related questions, story-board possible solutions and identify key challenges and opportunities with your cross-functional team.

4. Assess the Current State

After you have a target use case in mind, start with documenting your current state. Consider existing problems and find out how your organization or business sponsors view this use case. Be as specific as you can. Also remember to consider external market factors such as customer or transaction growth, competitive factors, emerging opportunities or productization opportunities.

5. Map the Value of the Proposed State

Although your first graph project may spawn many new ideas and future projects, direct the near-term graph project to business values. Consider the current state and pain points and how your graph target use case can help with business concerns such as cost savings, increased revenue, new market opportunities, time to market, risk mitigation and the like.

6. Measure ROI

For each of your value areas, determine how you plan to measure your return on investment (ROI) or success. Compare the soft and hard costs of maintaining existing processes to your graph project. If you’re unable to audit your existing state, be more conservative when estimating incremental saving or revenue opportunities. Add qualitative analysis when necessary.

7. Align Stakeholders

Eventually, you need cross-functional agreement on the goals and requirements of your graph project. This process is iterative and different teams may have alternative views on the project vision, key ROI and even the role of graph technology. Getting alignment on the goals of the project and how success is measured are essential – and you may want to consider a process for dealing with conflict or dissenting opinions.

8. Get Your Project Approved

Taking advantage of new technologies like GDS requires your stakeholders and approvers to be comfortable trying something unfamiliar. Document stakeholder assumptions about business value. You may be asked about the competitive landscape as well as alternatives and the opportunity costs. Your work to target the right use case, map values, and estimate ROI needs to come together in a concise story that aligns with your company’s motivations.

9. Conduct a POC and Plan for Production

Larger projects, especially if the technology is new to a team, often require a proof of concept (POC) before approval and deployment. In GDS, your data model and algorithm choices are highly dependent on the questions you’re trying to answer. Make sure your data scientists and subject matter experts are involved to ensure the right assumptions are made and your IT teams raise any red flags from end-users. Take advantage of vendors that provide POC services to help accelerate your project with their graph experience.

10. Get Connected and Continue Your Journey

Applying GDS is a journey. You may start with one focused project and find yourself answering questions you never knew you had. We highly recommend your team connect and engage with the graph community, which shares new ideas and helps with specific, and sometimes unusual, questions. Getting educational support and certifications helps your team be successful with its first graph project and expands the value of your graphs over time.

For more information on the commercial applications of graph data science and underlying technical components offered in Neo4j, download your free copy of Graph Data Science For Dummies.

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