Tag: ai

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Upcoming articles: compliance for AI projects

I am writing two features for Computer Weekly looking at AI and compliance.

The first is AI and compliance: What are the risks? (deadline for contributions: 1200hrs, Friday 25th April).

This piece will spotlight some of the most common compliance risks around AI projects in the enterprise and will look at:

  • what are the most common AI deployments in the enterprise right now?
  • what potential problems exist with source data for AI?
  • how important is data quality and data verification?
  • what potential problems exist with outputs from AI?
  • what issues do enterprises face, around the right to move data to where it is being trained, or where it is used?

The second article is AI and compliance: How to stay the right side of laws and regulations? (deadline for contributions: 1200hrs, Thursday 1st May).

The piece will look at:

  • the context of enterprise AI deployments, and the compliance and regulatory framework around this
  • the laws and regulations that govern AI (focused on UK/EU)
  • likely future legislation and regulation
  • the key steps an enterprise can take to ensure AI compliance

To contribute to either piece, please email with details in the first instance noting the deadlines above.

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Computer Weekly features: AI storage options

I am currently researching the following two features for Computer Weekly, and I am keen to receive background information on deployments and use cases as well as vendor-neutral comments:

NAS vs SAN for AI storage   – deadline 11/04/25 12:00 BST         

  • What kind of data – file, block, object – makes up data processed in AI workloads?
  • What implications are there for the storage used?
  • Is AI data mostly stored on NAS or SAN?
  • Why does file storage dominate in AI?
  • Can you store AI data on a SAN, and when is that likely to be better?
  • Is it always better to store file data on a NAS?
  • What other options are there besides NAS and SAN for AI data, such as object storage

Cloud storage for AI – pros, cons, options – deadline 18/04/25 12:00 BST

  • What kinds of storage does AI generally require, in terms of performance, protocol etc?
  • What kinds of storage are available in the main clouds?
  • What kinds of cloud storage are required for AI processing in the cloud?
  • What kinds of cloud storage can be used for AI processing on prem? And in what situations?
  • Looking at the different kinds of cloud storage available, what are the pros and cons of each?

To contribute to either piece, please email with details in the first instance noting the deadlines above.

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Upcoming article: Small Language Models

For an upcoming piece for Computer Weekly, we’ll be covering small language models.

We’ll look at the emergence of AI at the edge and how a new breed of hardware and software powers AI inference applications on any devices. And discuss now SLMs are developing. Large language models attract most of the attention, but could SLMs be more useful to enterprises?

The focus of the piece will be on actual or potential use cases, so I’m very keen to hear from businesses that have implemented them – or even replaced LLMs with SLMs.

Deadline for all contributions is Friday 21st March.

Please email with details in the first instance.

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Upcoming articles: Computer Weekly, April 2024

I am working on two storage-related features for Computer Weekly, with April deadlines.

When is cloud data storage the answer?

Has there been a cooling off in enterprise enthusiasm for cloud storage?

In this piece we will look at some reasons firms should move their data storage to cloud infrastructure.

The piece will follow up on this from earlier in the year: Cloud is not always the answer: Five reasons why

We aim to cover

  • When does on site storage fall short and how cloud can be better?
  • Performance / availability / operations:
  • IT management:
  • Costs
  • Agility and responsiveness
  • Data protection and compliance

The deadline for leads is 1700, Thursday 28 March.

How does AI affect data storage

The second looks at the impact of AI workloads on storage. This includes how data are used in training models, how AI analytics are used, where data are sourced and where they are stored. The piece will also cover compliance, which is a growing concern for anyone using AI.

The piece will not be limited to LLMs or generative AI, but all the different forms of AI being used by enterprises.

On the technology side, we’ll cover IO, the types of storage being used, where the data processing bottlenecks sit, how to feed GPUs and where to store the outputs from AI systems. But that’s not the whole picture.

I’m looking for analysts and consultants with direct experience of AI projects.

Deadline for leads: Wednesday 10th April, 1700hrs.

To contribute to either piece, please contact me by email in the first instance.

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Computer Weekly: data analytics and enterprise applications coverage, and a piece on cloud ERP

I am writing some additional pieces for Computer Weekly, in the areas of data analytics and BI, and in enterprise applications.

A couple are already in the editorial pipeline, so do keep an eye open for them.

Meanwhile I am looking for input for a piece on what organisations should consider, when moving core ERP to the cloud. This is less a pros and cons piece than a breakdown of the process. Some points we will cover are:

  • Why move ERP to the cloud?
  • How to do it (lift and shift, vendor-managed cloud, cloud native ERP perhaps?)
  • What functionality or other improvements will they see?
  • What’s the value to the business?
  • What are the risks?

I’m interested in the process firms go through when making this decision, and understanding best practice for a migration. I am keen to speak to analysts, consultants and CIOs who have made the journey.

I would prefer not to receive pre-written comment in the first instance, but a brief paragraph on who you are putting forward, and why. I’m also happy to receive white papers and other research.

The deadline for leads is Monday 5th February, with a view of completing all interviews that week or early the week after.

Please email your submissions in the first instance.

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Upcoming feature: Storage requirements for AI, ML, and analytics

For Computer Weekly, I am writing a feature looking at the storage requirements for AI, machine learning and analytics technologies.

This will include:

the demands these technologies make on storage infrastructure

The types of storage are best for the varying workloads in these areas (file, block, object, cloud

What storage vendors recommend for AI/ML/analytics use cases, and by workload

The deadline for leads is Wednesday, 16 March, with interviews the week after.

Please get in touch via the usual email address.

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Upcoming commission: robotic process automation

For Computer Weekly, I am looking into this developing area of enterprise applications and workflows.

A brief outline is below.

In the first instance, I am keen to hear from experts in the field. Please email with your credentials and background in RPA, and links to relevant research or case studies. I will then follow up with a questions or an interview request.

Click here to email, no later than Friday 12th March.

Robotic process automation promises to seamlessly handle arduous workflows, linking disparate business processes, which normally require human intervention. Simpler process flows can be automated this way but there are few manual processes that only require someone rekeying information into systems that should really have been more tightly integrated. There is a level of intelligence, which cannot easily be shifted to a machine. While RPA is deterministic, an AI is probabilistic. We look at how RPA and bots that follow predetermined scripts are being made more intelligent.

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Upcoming feature: Storage for AI, ML and analytics

For Computer Weekly my next feature will look at the specific demands placed on storage architecture by artificial intelligence, machine learning, and analytics.

The piece will ask:

What different approaches to providing storage are there for these technologies? 

What limits, performance considerations and bottlenecks exist with the different approaches?

What ways of providing storage for analytics are we likely to see in future?

The article will cover both on-premises and cloud-based storage, where relevant. I’m keen to include some real-world use cases if possible.

I am open to comment from industry professionals, consultants, analysts and CIOs working with AI. ML and analytics.

Deadline for leads: 1700hrs BST, Tuesday 23 June. As ever, please email in the first instance.

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Upcoming articles: Compliance and Object Storage

I am writing two articles for Computer Weekly’s storage section, one on storage and data compliance for the enterprise, and the other on the growing field of high-performance object storage.

Data compliance

This piece will look at the top 5 UK compliance concerns in 2020.

What are the five key laws/regulations that must be adhered to by UK organisations in 2020, including both current and upcoming legislation. For each we will look at the implications of the law/reg for storage, backup, and archiving.

This could, for example, include legal search and e-discovery, or the Right to be Forgotten under GDPR.

We will also look at how the cloud fits in.

High performance object storage

Object storage has been known as a good way of storing lots of unstructured data, but with less emphasis on performance.

But AI and analytics workloads are prompting storage architects to look at performance too. The feature will cover:

  • Where object storage is heading in performance terms and what’s driving it.
  • Which performance metrics matter
  • How have object storage vendors improved performance?
  • Who are the key object storage vendors that are tackling the challenge of better performance and what do they offer?

The deadline for leads for both articles is Friday 20th March, please contact me by email if you can help.
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