We help build your digital factory

If you want to go beyond familiar knowledge to new insights and innovations, you will need tools that allow you to go where your questions lead you - with speed and agility. We do that by building a Flywheel of Data - where insights can be built on insights - thereby producing the necessary compounding effect of knowledge creation.

Define

We begin the process of building the Flywheel by starting with simple questions about your data. What is it? What does it measure? Where is it located?

Defining this rigorously and from the ground up is vital to building a robust and reliable data architecture.

Contextualise

How is the data used? What does it affect? Why is it important? What can it tell us?

There is a common misconception that context is limited only to definition and description of data. In our view context is more about the relationships and networked linkages between data points so that they can tell you something. Much of the data we work with is very raw and lacking meaning but can be incredibly useful and valuable for our clients.

Our approach to context is multi-dimensional, going far beyond the typical narrow hierarchical (parent-child), folder-like structure. We do this by using network theory and knowledge graphs to enable the mapping of many relationships between data points.

Explore

What do we need to know? Why did something happen? How can we understand something better?

Thred's approach recognises that knowledge and improvement is usually acquired through a process of exploration and curiosity. And that requires a method that is both flexible and fast enough to keep up with the pace and dexterity of thought.

Thred's tools allow users to rapidly connect dots, create expressions (calculations) and produce insights. This can be done for a wide variety of use cases - from problem solving, process mining, reporting and analysis.

Validate

Is the data reliable and true? Does the hypothesis check out? Is the insight permanent or ephemeral? Can we scale the insight?

Thred's process of validation ensures all learnings and insights are captured and secured as part of the overall knowledge graph. This in turn allows for the ability to have control over data quality through understanding lineage, providing data observability, and enabling power tools such as anomaly detection over your data.