In this interview, we talked to Nic Arnold, CEO of the startup Stitiched.io, who told us all about their newly built platform, which is also called stitched. The platform has the ability to manage skills and capabilities and collect valuable data that can then be used in a more effective way to help reduce costs and enable companies to add additional metrics to their current capabilities.
Nic, on your website, you talk about powering organisational insight. Could you explain to us in more detail, what your solution entails?
Nic: Organisational data is often top down, bottom up – with management information systems providing lead, lag or real-time cost, revenue and risk performance data across multiple verticals or horizontals in a hierarchy. Our solution simply adds capability to the information system; this then allows our customers to see the capability and apply additional metrics to that capability.
In a lot of companies the insights available are typically constrained by availability and the reliability of data in the systems, top down organisational structures, and the effort to produce those insights. What data there is typically goes up the chain and decisions come down it. But a business demands more decisions and at a faster rate than those available from the data provided at the top of the chain. Stitched provides capability data dynamically across the enterprise because we score the skills of the work and people first, and then apply the organisational data afterwards.
As we drive our capability skills, scoring off both structured and unstructured data sources used in the execution of a customers’ day to day business and the people assigned to it, we are not bound by systems or organisational constraints. In fact, as we build a skills universe for a customer, constraining it to the traditional hierarchy – department X, department Y is counterintuitive.
What technology is your solution based on, and how does it work?
Nic: The stitched platform uses semantics, natural language processing software and machine learning python algorithms to identify, calibrate and score skills information found in the work a company does and the people who do it.
There is a triangulation between scoring the work – past, present and future, scoring the peopleand the time people have spent or are spending using specific skills. We then overlay the organisational data, department, cost, employee type, seniority, and other business data such as client, service, line of business to support aggregation for business insights or simply searching.
We use a graph database – neo4j – that supports the user access via a ReactJS-based desktop or mobile UI. Customers also access the skills data by connecting their HR systems or BI tools via our API.
The platform runs distinct customer instances on public and private cloud – Linode and AWS, allowing us to physically place the data in any country or region.
Whom is your solution for? What problems are you aiming to solve?
Nic: The first obvious port of call for us are service providers, i.e. external and internal shared services. These are customers that directly sell skills and capability and where success, indeed survival, is directly linked to their ability to provide the capabilities their clients need whenever and wherever.
In order to win business, they need to know if they have credibility (capability history), capability (skills), capacity (capability available) and that it is cost effective to deliver. As we drive skills data off work demand, work doing and work done we are able to expose these insights.
The key here is increased competition on adaptability, in skills terms the pipeline doesn’t always look like the business completed or in progress, there’s a skills gap and its shape is changing all of the time. We help these customers manage that in their resourcing, talent management and business management processes.
In parallel to this, we target large enterprises to support their internal mobility strategies. These strategies have a dual outcome of employee value and retention, but also waste reduction and efficiency. The stories are known to all of us that have worked in large companies; one department hiring for skills already available in another department next door, or worse, one department cutting staff numbers when those skills are being actively hired next door. A key feature of our platform unlocks this opportunity by having the ability to link skills by similarity..
So, our solution targets capability management – for resourcing, workforce planning and internal mobility needs, and we’re able to address the key challenges they traditionally faced; not having access to the data, having to spend a huge amount to get it, and maintaining its relevance and objectivity. Our solution is aimed at these specific challenges.
Do you think machine learning will revolutionise the sector you work in? Maybe even our society?
Nic: It already has. The question is really whether the pace of change will continue or accelerate further. It isn’t so much a blanket on/off as groups of diverse applications moving at pace based on maturity, need or ambition. Until recently, the drivers in our sector have been financial, – predicting customer behaviour and applying resultant sales actions, or predicting market behaviour and security – scanning vast data sets to piece together threats, but as our sector learns from successes it will accelerate the application of this technology rather than invention. The key is how to work with the data and what to do with the legacy approaches, and the resistance to those changes in such an accelerated environment.
In our example, it is one thing to unlock the capabilities of your organisation, but how do you recycle and re-shape the hundreds of processes that were trying to achieve the same in the past?