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Why graph analytics is the “next big thing” for data
Tue, 31st Jan 2023
FYI, this story is more than a year old

All kinds of organisations depend on data for business-critical operations. It plays an instrumental role in everything from managing production lines and supply chains to defining growth strategies to preventing fraud, etc. 

The role of chief data officer (CDO) is relatively new, but it’s becoming more prevalent across organisations. Today, some 21% of companies have a chief data officer globally, and the trend is expected to be reflected in the APAC region, demonstrating the growing importance (and volumes) of data within companies. If you’re working as a CDO or in a similar role, you know that high expectations from management combined with these increasing volumes of data can make for seriously challenging work. 

One tool that can make the lives of CDOs easier is graph analytics, or network analytics. According to predictions by Gartner, graph technology solutions from companies like Linkurious will be used in 80% of data and analytics projects by 2025. We’ll take a quick look at what graph analytics is, and explore how this type of technology can address some of the main challenges and concerns of today’s chief data officers.

Chief data officers and the data silo challenge

Chief data officers are the executive owners of data within their organisation. Some of their main tasks include managing a company’s data resources to address complex business challenges and building a data-driven culture within their organisation. According to a recent survey, 65% of large, data-driven companies have now appointed a CDO, an increase from 12% in 2012. 

The role of CDO is not without its challenges. Some of these are rooted in the fact that many CDOs today are only the first or second person in that role, with much of the scope of their work to be defined. The role also often carries demanding expectations from management.

Many of a CDO’s challenges are closely related to the data itself, however. They may walk into a job and be confronted with existing legacy systems that are costly and time-consuming to change. Data in many organisations is spread across multiple silos. When important data is stored in multiple databases, which may be separated by region or by department, it’s impossible to get a 360° view. And without that complete view, the value a CDO can derive from the data is limited. 

Breaking down data silos is critical for organisations to get a complete picture, but the process isn’t always straightforward. 

Graph databases can help address all these data challenges.

Graph analytics 101 for CDOs

A graph data model consists of nodes and edges. Each node represents an entity, and each edge represents how two nodes are linked to each other. For example, person A might own bank account B. Nodes and edges also have properties: additional information associated with them. All this information is stored in a graph database.

When you’re working with large datasets or multiple data sources, the relationships within the data can be highly complex. By analysing data as a graph, it’s possible to determine the closeness of different entities, as well as how entities are connected. Graph analytics provides algorithms that help data-driven analysts answer questions or make predictions using graph data. 

By comparison, traditional analytics are more focused on individual data points, either considered separately or aggregated. It can be time-consuming and computationally demanding to analyse relationships within data using traditional methods.

Five reasons why graph technology is an asset for chief data officers

Graph technology not only addresses many of the challenges that today’s chief data officers face. It also helps derive more insights from your data more quickly. Here are some of the advantages of graph technology for CDOs.

1. Accelerate insights discovery

Finding simple connections within relational data is generally fast. But when your work involves analysing and exploring networks to understand complex connections or dependencies, querying relational data comes at a high computational cost and becomes much slower. On the other hand, since both data points and the connections between them are stored in the same database, querying graph data is blazing fast.

2. Get a 360-degree view of your data

Graph technology helps eliminate data silos since you can combine multiple data sources into a graph database. Instead of hopping from one data source to another, hoping you don’t miss important insights, you can explore and analyse everything in one place.

3. Flexibility and scalability

Unlike relational databases that have rigid data models, graph databases have a high level of flexibility in how data is stored. This means it’s easier to adapt graph data as your organisation’s needs evolve. It’s also easy to add more data from additional sources as needed, making it an easier solution to scale. This is an important consideration at a time when organisations are dealing with huge volumes of data.

4. Quickly detect complex patterns and anomalies

Being able to spot patterns and outliers in your data is essential for many businesses. It can alert you to fraud, a security breach, or a potential supply chain disruption. Graph technology is built to easily find patterns and anomalies, so by using graphs, you can anticipate problems and react quickly when issues do arise.

5. Use graph visualisation to easily find hidden insights

A picture is worth a thousand words, and that goes for data, too. Visualising data as a graph can help you better understand the patterns and insights within, since the human brain processes visual information much more quickly than written text. It’s also easy to share a graph visualisation to communicate key information to teammates or stakeholders.