Technology Reseller v20 2019

continued... AI 01732 759725 28 and social data as well. So, how do we ingest and break down those data silos in sales, marketing, HR, purchasing and manufacturing? 2 Next is transform: once you’ve got that data, how do you transform it into something more valuable i.e. information? 3 The third piece is store: how do you store that information compliantly, how do you make sure you are not infringing GDPR, how do you make sure that over time you destroy that data programmatically, so you don’t hold on to it beyond the scope of the different data projects you are running, how do you make sure that data is available everywhere you need it? 4 The last piece is explore: how do we make that data available to as many decision-makers as possible? Previously you might have had one person with an Excel spreadsheet running some formulas, but if you have ever picked up someone else’s spreadsheet and tried to understand what they were doing you will know how difficult that is. The portability of Excel spreadsheets is terrible. It would be really valuable if instead we provided you with some visualisations that could inform the widest possible cohort of people in their decision-making. That is what we call the Arrow Framework for Data. If a partner says ‘I am just not ready for AI’, we would go to them with our data strategy. We would say ‘All your customers want to talk about data. Here is how we can help you develop a story around data. You start the story here, then when you get here, you can say: ‘Now you’ve got your data in order; now you’ve got an industrial data pipeline that lets you start to do more intelligent things with data, we can start to talk about AI’. TR: Is there a lot of demand from channel partners for help in developing stories to take to their customers? DW: Absolutely, it’s probably the single biggest thing we get asked for, net incremental today. If I am asked what our partners ask for over and above what we have been doing for them for many years, it is absolutely around this. Partners that think there’s still huge value in selling a product with very low-end services like rack and stack and initial installation are going to have difficulty going forward because the reality is end users are not as bothered about the brand of technology as before. Technology has become so ubiquitous that today it’s a case of ‘I need a solution that delivers this outcome and it needs to be under this SLA and it needs to be pay per month or scale up/scale down or fit this budget. Everything else I don’t care about’. I always use the analogy that we need to be selling cars, not engines, and I think a lot of our partners are still trying to sell engines. I have opened a couple of events recently by asking ‘Who in this room drives?’. Everyone puts their hand up. I then ask: ‘Who knows the manufacturer of their brakes?’ Nearly everyone puts their hand down – being an IT crowd there are always a couple of geeky guys who do know. ‘Why don’t you know that, they save your life every time you get in the car?’ All of a sudden the cogs start to whir and people say ‘Actually that’s a good point. I don’t want to be a brake manufacturer as a partner. I want to be Ford or Toyota; I want to deliver the outcome. I want customers to say I want it red, I want it to be fast or economical or to carry lots of stuff’. It massively ups their value from being a brake manufacturer to selling the whole car. fiveyearsout.com www.arrowecs.co.uk/vision2019 and repeat; when what we are trying to deliver isn’t a snowflake. The problem with AI is that every project is a snowflake to a degree, because people want it to do very specific tasks, which are ever so slightly different to the way others do them. What we try to do is develop solutions that are repeatable and then work with service organisations to deliver the specialisation. Currently, we are working on one with Microsoft that uses the Vision AI camera developed by our global component colleagues. Essentially, this lets us take the cameras, train modules and provide the whole lifecycle management for a vision solution, which could be used to track people, say, or monitor production lines. Because it’s built on the cloud, it can scale to thousands of cameras and gateways. TR: Presumably, AI is still a step too far for many of your partners and their customers. DW: Yes, and that’s why we have a whole strategy about how to prepare for AI. We call it the Arrow Enterprise Data Strategy and it provides a framework for big data that can help you break down data silos in organisation, which is necessary before you can adopt AI at any scale. If you just turn around and say ‘I‘ve got all this data in an Excel spreadsheet, in an Oracle database, in a Microsoft database’, you are going to spend more time trying to refine that data than delivering a valuable outcome. Our framework for big data is really simple. It has four steps: ingest, transform, store and explore. 1 Customers start with ingest: what data do you need and how do you get it? If you work in sales and are trying to figure why customers aren’t buying, it is really useful to ingest marketing data ...continued Partners that think there’s still huge value in selling a product with very low-end services like rack and stack and initial installation are going to have difficulty going forward