Deep Learning is a machine learning approach using artificial neural networks with multiple layers to automatically learn hierarchical representations of data.It's called "deep" because of the multiple layers of processing units stacked on top of each other, creating depth in the network architecture.
What is Deep Data Management?
Deep Data Management is an approach that enables businesses to extract value from unstructured data. It does this by respecting its context within your organisation.
It is called “deep” because it looks at the data across multiple dimensions of its lifecycle. Specifically, where it has come from, where it is now, and where it is going to. By understanding the data at rest (the storage dimension), the data pipeline it belongs to (the data pipeline dimension), and the applications in play (the application dimension).
Why is Deep Data Management Important?
Unstructured data is the heaviest form of data. It also represents a significant overhead to organisations.
As organisations scale, unstructured data becomes exponentially more costly to store and difficult to manage.
The end result is over-provisioning of storage estate, or data systems that keep data in the wrong place. This ultimately impedes an organisation’s productivity.
Deep Data Management acknowledges the fact that all unstructured data has a lifecycle. To maximise its value while reducing it as a cost overhead, a new way of thinking is needed.
By taking a holistic approach to data management; we look at where data has come from, where it is now, and where it is going to. By doing this, we reduce the number of data and technology siloes, and ultimately cost centres that exist in an organisation.
How does Deep Data Management Work?
Unstructured data is packaged conveniently in files and folders (compared to structured data like databases) but organisations and users have huge amounts of freedom in the way that this data is organised.
Most unstructured data pipelines leave breadcrumbs on storage of the data underlying processes and applications that are taking place.
Deep Data Management is the process of building a picture of unstructured data using metadata (both absolute, and derived from machine learning). It then triangulates its context in terms of the data itself, the data pipeline it belongs to, and the applications that are in play.
This enables a more business-relevant understanding of unstructured data for organisations that cannot be achieved by traditional, one-dimensional methods.
How is Deep Data Management Different
Modern data platforms (Snowflake, Databricks, etc.) pitch ROI and revenue generation heavily as part of their offer.
Data governance tools are primarily risk/compliance-driven. They do have features that aim to add business value and cost optimisation to justify their price tag.
The majority of data migration and metadata management tools are vendor lock-in by design, licensed as “data under management”. Therefore, organisations pay twice; once for the underlying storage infrastructure to store their data, and another time to centralise metadata and build a picture of their data estate.
Other approaches exist to make sure the right data is in the right place at the right time. However, they’re expensive and often siloed, so they don’t solve the root problem.
Our Deep Data Management approach turns data on existing storage infrastructure into operational intelligence. No moving data or changing workflows, just insights and value creation in-place.
Who is Deep Data Management for?
Unstructured data is used in all verticals.
However with strong Media & Entertainment domain expertise, our focus is bringing Deep Data Management to this industry first.
We are gathering like-minded executives in the M&E industry. These individuals want to think bigger picture than just endlessly purchasing storage.
To coin a metaphor; they could tidy their garage or buy a bigger garage. We want the tidiers.