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.
My next article for Computer Weekly will look at the best storage options for virtual servers, including SAN, NAS and hyper-converged infrastructure (HCI).
Specifically, the piece will ask:
- What kind of storage requirements virtual servers and their data have?
- What are the characteristics of a) SAN b) NAS and c) hyper-converged storage?
- What are the pros and cons of SAN vs NAS for virtual machine storage? What are the management and performance issues?
- What about scale? Is a SAN, NAS or HCI better suited to large or smaller deployments?
- What impact do workloads have on storage choices? Are all virtual machine workloads created equal in I/O terms?
- What other factors affect storage choices, such as the applications being used, scale of the deployment and even skills on the IT team?
First and foremost I am looking for background information, analyst research/ technical papers and case studies which will help to answer the points above. If you or your client has expertise in this area, please contact me by email in the first instance. The deadline for input is Monday, 13 May.
My next article for Computer Weekly will be on “Scale-out NAS in the age of cloud”. The outline for the piece is below.
Scale out NAS is a very important storage technology for those that want to store large amounts of file and unstructured data.
But in recent years it has had to fight off other methods of storing large amounts of unstructured data, namely on-premises object storage and the rise of the cloud providers and their (often object storage-based) storage offerings.
So, who are the key scale-out NAS providers now? What do they offer in terms of products? And how are they meeting the challenge of the cloud?
If you or your client has expertise in this area, please contact me by email in the first instance. The deadline for pitches and initial input is Monday, 25 March.