For Computer Weekly I am writing a feature on tape.
As a data storage medium, tape has been on the verge of obsolescence for decades. But the format endures. Why are IT and data managers continuing to choose tape?
The piece will look at:
- The limitations and benefits of tape in today’s data centric environments
- New and emerging tape formats and technology enhancements, such as software defined tape
- How tape works with other storage media, including the cloud
- Key use cases for the various tape technologies currently on the market.
The deadline to suggest interviewees or to share research is Wednesday, 5th August at 1700 BST. Initial submissions by email please.
For Computer Weekly, I’m looking at the compliance issues around gathering, storing and processing unstructured data.
This article will examine the likely compliance risks in unstructured data, and suggest potential solutions. It will ask:
- What is unstructured data? How does it compare to structured and semi-structured data types?
- Why is compliance an issue at all?
- Why is achieving compliance of unstructured data potentially problematic?
- What are the key steps to achieving unstructured data compliance?
As businesses gather ever greater volumes of unstructured data, and develop new ways to process and analyse the information, compliance becomes increasingly important. This is especially the case when organisations start to combine data sets, and use advanced analytics to search for insights in the information. Does the original consent to hold and process the data carry over to this type of application? And what happens when unstructured data is mixed with other data sets?
For the piece I am keen to have comments from data scientists, compliance experts, academics, lawyers and end user IT organisations. As the deadline is quite short, please send pitches, initial comments and leads to me by 1200 London time, June 13th by email please.
Data storage is an often-overlooked part of machine learning and other AI deployments.
This article will appear in Computer Weekly. It will cover:
- Definitions of machine learning/deep learning
- Its storage requirements including
- Sizing, capacity, performance (to match compute)
- Media (SSD vs HDD, hybrids of the two)
- Throughput vs IOPS
- Locations – including use of the cloud
For this article we are open to comments from vendors, as well as analysts, consultants and other experts. Examples of ML use cases and how systems were designed to run it are most welcome.
Initial pitches and leads by Wednesday May 29th by email please.