Getting more insight and value from a business’s data, for example through machine learning, first requires a considered view of the data estate. An increasingly popular method is to build a knowledge graph of all data assets in the business.

A graph database stores information as relationships between entities, and represents this data using nodes and edges, instead of using rows and columns in tables. But what are some of best use cases for graph databases?

Graph databases and graph abstractions enable views to be curated over multiple and varied data sources. There is rarely a single graph “view of the world” and the aim of the exercise is not therefore to create a monolith master database, but to curate and mark-up the connections between business data silos as metadata.

Have you ever had one of those days in your Business Analyst (BA) career where it feels like everyone wants a piece of you? You’re overwhelmed, it feels as though there aren't enough hours in a day and your precious time is split precariously across numerous projects.

As agile teams scale, or as developers roll on and off a team, even the more diligent development teams encounter the issue of tech debt which is left behind for new people to absorb. What is the best way to deal with tech debt?

As Blockchain matures in the enterprise setting, the need to integrate more and more artificial intelligence (AI) into the transaction will increase, and we will start to see the promise it holds – in particular, when it comes to Digital Business Process Management.