Brian Seery, Singlepoint’s technical consulting director, was interviewed this week’s edition in The Sunday Business Post by Emmet Ryan about Singlepoint making its mark on British market. Read the transcript for this interview below.
Dublin-based IT consultancy firm Singlepoint plans to create 150 jobs over the next 18 months, as part of its expansion into the British market. The business, which has clients such as Facebook, Vodafone, Aon and Telefonica, will grow to 350 staff in total on the back of the move. The news comes after Singlepoint recently hired an additional 50 staff over the past year. The new roles will be heavily focused on technical consulting and delivery.
Brian Seery, co-founder and director of technology consulting at Singlepoint, told The Sunday Business Post that the growth was being spurred by increased interest in machine learning and cloud solutions, coupled with a shortage of skilled staff in these sectors. “If you look at how companies that are using technology are looking to scale, they are experiencing a shortage in the market when it comes to finding skilled people. More and more, they are looking for partners to take that issue away from them,” said Seery.
“A lot of the work that we have been doing in-house is building our own solutions in areas that we see high growth, so we can come in and just switch it on for clients. “Because all these technologies are so new, it’s a struggle to find people who have been through it, have the war stories, and the expertise to deliver. Where you do find them, they come at a premium. A lot of what we’re doing is building that capability and automating it so we can get people moving quickly.”
In addition to the geographic growth plans on its radar, Singlepoint is also looking at expanding the technologies it specialises in as it looks to scale internationally. “Chatbots are an area of particular interest to us. We’ve built a number of them and we see a big opportunity for them in terms of cost optimisation. Already, it’s clear that a lot of people are happy to use bots if they get the answer they need. The typical call centre activity is going to change to higher value work,” said Seery.
“Insurance is a big area where we are already seeing that, freeing up the logjam that goes into call centres. We’ve also worked around developing for the likes of Alexa technology, looking to be able to use the chatbots through multiple channels.” Seery sees a shortage in staff around developing technologies as offering his business an opportunity to grow its partnerships with existing clients.
With large companies having substantial graduate intakes every year, Singlepoint is looking at means to reduce the strain on resource for its clients. “As the technology is changing, the skill sets and the way teams are set up need to change. You hear a lot about being agile; that’s a process for how you work but if you don’t have the tools and technology to support that, then you won’t be agile,” he said.
“We’re building core capabilities to package up for customers and our own teams. We’re looking at the market, particularly around entry-level graduates. There’s a timeline for ramping them up to be ready. A grad might come out of college having done four years of computer science. When they come into a job for the first time, they’ve never worked with an agile team end to end in a commercial environment.”
Within that move to get staff ready for market is the drive for more people with machine-learning skills. The issue for businesses is not just in finding people capable of developing models around it but in having these staff adapt to existing corporate demands. “It’s a hot term right now. The typical people you think about around it are data scientists or working in quants. That’s not what we are. One of the challenges we see is that companies will have a team working on machine learning and build models, but they won’t have thought about how they will deploy that model,” said Seery.
“It’s one thing to have a sample data set and do analysis on it, but taking that data and delivering it into an app or putting it in a report to sell is another matter entirely. The technology to support that is new, and that’s where the market shortage comes in. That’s the gap where we are coming in, to build that data end to end, and manage the infrastructure.”
This issue comes back to how the type of hire being made now is different to previous IT rollouts for firms. Traditional policies that dictated how a company operates can be difficult to implement when the type of hire being made is radically different to when those policies were drawn up. “The types of people who are building infrastructure now are not your typical network engineers. They are people who can build code but they don’t think with the same mindset as an operational person,” said Seery.
“A lot of organisations find when they move to cloud for the first time find that, all of a sudden, they have a lot of things running and managing that becomes a challenging. They have to rebuild departments that they wouldn’t necessarily have internally anymore to manage that.” There’s also the small matter of the magic bullet problem, where the hot new tech thing is seen as a panacea. Seery said what firms did before they deploy a machine-learning model was as important as the data they input.
“The one thing machine learning needs is data and processing power. As companies move to it more, it’s making more decisions. What’s not really thought about is what’s making those decisions,” he said. “Traditionally you would have had some data as a management team, done some analysis, made decisions, and it was pretty clear. When you are trusting machines to drive that decision-making, you have to understand why the machine came up with the reasoning to make those decisions.
“The legal and financial sectors are looking to address this in particular, thinking about getting the machine-learning models they build to output the reasoning. A machine-learning model is only as good as the data it receives.”
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