In this conversation, Gideon, Nathan, and Sanne explain how BearingPoint’s business technology and analytics teams work closely together, and guide the full process, all the way from identifying the business pain points – their problems – to designing and implementing full-stack graph solutions.
Julia Astashkina: Welcome. For people not that familiar with graph technology, could you tell us what your definition of graph technology is?
Gideon Pappie: Graph databases are a new way of storing your data. In traditional databases, we focus on storing data entities, but in graph databases we also explicitly store the relation between those data entities.
For example, in a normal database you would store in a couple rows, Nathan, Sanne, myself – and we can say we all work at BearingPoint. But in a graph database, you also store the relationships between us, for example, that we are colleagues. This way of storing data provides a lot of benefits.
Julia Astashkina: And what are some of the benefits that clients might experience?
Gideon: For example, by storing the relationships between your data entities, you can get better insights into how all your data is connected, seeing relationships that you wouldn’t have seen if you had stored all your data separately.
Graph databases also make it easy to connect all the data that you have, and they are very scalable. It also changes the way you retrieve data. With a graph database, because you already store those relationships, it’s very easy and quick to retrieve data and create a 360-view of, for example, your customers. There are a lot of applications for graph technology, and it is easily getting insights from the data you have.
Julia: What expertise does BearingPoint have to make these kinds of implementations successful?
Nathan Meibergen: Well, first and very important, it has strong business expertise and is therefore able to one, identify the real business problem and two, organize an implementation. Next, we have the technological and analytical expertise to build a data model, implement Neo4j in your architecture, create working software, and perform advanced analytics on the graph technology.
So at BearingPoint, the business technology and analytics teams work closely together, and therefore we are able to guide the full process, all the way from identifying the business problem to designing and implementing full-stack graph solutions.
Julia: In which situations have you already used a graph database?
Nathan: We have guided multiple clients with their implementation. I can highlight two of those. One of our clients, a knowledge institute, was looking for a way to better structure, gain insights, and provide better and personalized search results on all their textual data.
What we created for them is called a knowledge graph, a graph database for all your knowledge. This is exactly what a knowledge institute needs. And then on top of this graph database, we built a web-based application for the end user to ask questions of this database.
Another great example is our resourcing graph. The power of the graph is that it can easily provide you a 360-degree view on any object, for example, on your employees for HR purposes. The goal of this resourcing graph is to provide insights into your people, their skills, their competencies, the projects they are working on, the connections between them, and so on.
Among other things, this can help you better staff projects, identify scarce skills and critical employees based on their expertise, and shape your recruitment policy.
Julia: How do you handle these kinds of projects?
Sanne Legtenberg: First of all – it’s important at BearingPoint – we’re always working together with the different service lines in the different business perspectives they have. In the end where we’re a business consultancy that wants to add value to a client. That also means we see technology as a means to an end.
We will always start with identifying a problem or the goal of a client. We identify pain points and opportunities in your organization and we find a matching solution, which in turn could be a graph database. We then align with the client on expectations about the potential users. We design use cases and figure out which data sources are relevant.
Based on all that information the client gives us, we are able to work toward an MPP (massively parallel processing) version where we leverage our technology team, for example or our data and analytics team who are able to connect all these different data sources and write the actual scripts to make it work.
So that’s the way we really align business and technology. We bundle the knowledge together in order to reach a business objective that really adds value to the client.
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