Digital View presents the unique thoughts, ideas and experiences of a Digital Public Service team member. Our first subject in this series of blog posts is Elisa Capecci, a data scientist who works as part of the Business Intelligence team.
Who I am
I’m originally from Italy, but I’m a Wellingtonian now. I came over 9 years ago on a sort of adventure — you know, the other side of the world, so exotic — but when I arrived here, I felt at home. Here at the Department of Internal Affairs (DIA), I feel included and supported. Those are big things for a woman and a foreigner.
I did a PhD in Computer Science, specifically in machine learning. My thesis was in spiking neural networks, which are used in applications for machine learning and artificial intelligence. In machine learning, you use some data to train algorithms. You can learn from structured and unstructured data and find patterns where you don’t see them. You can use supervised or unsupervised learning, discovering what is hidden there in the data. It’s really interesting.
In academia, it often takes time to see the end goal of what you produce. A lot of times, what you’re doing might be used in the future, but that gets frustrating. I wanted to feel like what I was doing could have an impact right now, and I wanted to use my skills and passion to influence changes that can help the population. I think government is the place where I can do that.
Video transcript
(Welcome page on white background with blue and yellow triangles on top left and bottom right of screen. A centred black logo features the New Zealand coat of arms and words ‘Te Tari Taiwhenua Internal Affairs’.)
The subject — Elisa Capecci, a woman wearing a bright red top — is seated in an office in front of a grey-patterned glass wall.)
Elisa: My name is Elisa Capecci and I’m a data scientist.
(The subject — dressed in blue jeans and a grey jersey, with an oven mitt on her right hand – pulls a heavy pot out of an oven and sets it down on the kitchen bench. With a spoon and spatula, she pulls out a loaf of sourdough bread and leans it on the edge of the pot.)
I’m originally from Italy although I’m a Wellingtonian now. And I came to New Zealand to do my PhD in Computer Science, specifically in machine learning.
(The subject — is in the original setting, sitting in the office.)
For me, technology has always been a passion. It surprised me to see for some people it could be a barrier. Well, I’m here to support them.
(The subject — dressed in blue jeans and a grey jersey – walks down an office staircase, the hand-held camera following her from behind.)
(The subject — dressed in blue jeans and a grey jersey – sits at a desk in front of a computer and 2 monitor screens, showing lots of code and numbers. She turns to the camera, laughing.)
Everything is data — sounds, music, words, text. The emails we exchange, the documents we create.
(The subject — is in the original setting, sitting in the office.)
Here at DIA, definitely I feel included and supported. These are big things for a woman and a foreigner.
(Outro page on a blue tiled background, and a centred white band with black logo featuring the New Zealand coat of arms and words ‘Te Tari Taiwhenua Internal Affairs’.)
What I do
I work in the Business Intelligence (BI) team for the Digital Public Service (DPS) branch. My team is quite new, and our main purpose is to provide actionable insights to support the branch in delivering evidence-based advice. Using data helps the branch set its priorities, inform its strategy and shape its decisions.
At the DPS, our goal and mandate is to achieve the outcomes of the Strategy for a Digital Public Service. One of the focus areas is to explore new ways of working to deliver better service for New Zealanders.
Presently, I’m helping the Marketplace team improve the timeliness of the highly repetitive 2-stage onboarding of prospective information and communication technology (ICT) suppliers. Reviewing the content of submitted supplier documents is a critical step to ensure they are accurate and sent to us under the right Marketplace service definitions.
This repetitive process involves several members of the Marketplace team. I am applying mature text mining and algorithms in natural language processing to automatically read and classify the submitted documents to give a level of independent assurance — this speeds up the manual validation process and, more importantly, allows the team to use their time and skills on more productive activities.
This is only the beginning. I’m excited by the prospect of applying machine learning techniques to improve other Marketplace processes, but also to meet the needs of other business units within DIA.
How machine learning works
The first thing to understand about this is: data is not just numbers. Text mining and natural language processing have been around for decades. We use them every day — how good is text prediction, even when we write an email?
I mine text from the supplier applications, where words are used to describe a service. I input these words into my classifier, so these words are my data. I train my classifier to recognise the data. The more documents it reads, the more it learns, and therefore the better it functions. Every new document that comes is assigned to one of the learnt categories.
Video transcript
(Welcome page on white background with blue and yellow triangles on top left and bottom right of screen. Centred black logo featuring New Zealand coat of arms and words ‘Te Tari Taiwhenua Internal Affairs’.
The subject — Elisa Capecci, a woman wearing a bright red top — is seated in an office in front of a grey-patterned glass wall.)
There are lots of questions that the branch has and some of them cannot just be answered through descriptive analytics or totals and sums.
(The subject — dressed in blue jeans and a grey jersey — sits at a desk in front of a computer and two monitor screens, showing lots of code and numbers. She turns to the camera, laughing.)
Are we doing a good job? How can we measure this?
(The subject is in the original setting, sitting in the office.)
Are we achieving the values of the strategy? Are we achieving our objectives? This can be forecasting and prediction. It’s not just about looking at what happened in the past but also what the future could look like, how can we optimise it. These are all machine learning techniques.
(Outro page on a blue tiled background, and a centred white band with black logo featuring the New Zealand coat of arms and words ‘Te Tari Taiwhenua Internal Affairs’.)
Digital dashboards
People are used to seeing results from data as bar charts and line graphs, but a digital dashboard gives us the chance to deliver clients a tool they can interact with. You can highlight part of it, you can change the parameters — it’s a big advantage over simply printing it out.
At the moment, we’re providing only descriptive analytics, such as summary views, on data we have collected over a certain period. By adding insights from machine learning techniques, we can go deeper, we can optimise our results, we can predict and forecast. Machine learning is powerful because we can personalise the results we populate to dashboards.
The future
I believe that one of the BI team’s main tasks is to influence the growing data culture in the branch. Data can really help us to improve our work, the way we make decisions and the evidence we provide. It can also help us to better plan the work we do.
I know in the branch that people have great ideas and great questions they want answers to. Questions like:
- are we doing a good job
- how are we measuring this
- are we achieving the outcomes of the strategy?
This is where forecasting and prediction can be helpful. It’s not just about looking at what happened in the past, but also what the future could look like and how we can optimise it.
