Having a data lake reaffirms an organisation’s need to invest more effort in data governance and data quality, helping you to understand the content that resides within it. However, both in our experience, and as revealed by Gartner in their report on data lake failure, if data lakes are misunderstood in the context of a wider data strategy, this can lead many initiatives to flounder.

Should I attend CanSecWest? Yes, if you want the latest updates on developments in the security industry and turn up prepared for extremely technical seminars. On the other hand, if the aim is to network with vendors or do non-technical marketing for your company then this conference is not for you.

It’s vital to ensure that your AI is kept fit and effective. The first step in doing so is understanding the need to consistently curate your AI models. To do this, you need an effective ModelOps team. AI is evolving quickly and businesses need to ensure that their teams evolve to deliver against business and regulatory needs, whilst empowering their people too.

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.