Smarter use of city data is helping bustling metropolitan areas be better places to live. From generating useful insights in complex areas, to developing new public services, data is creating a new kind of infrastructure that’s growing to be as essential to successful city living as roads and utilities.
On the 17th May, Mastodon C, with a group of fellow urban innovators, will be sharing insights into the data products and services that have been built over the past three years to address the problems towns and cities face. As well as sharing our stories, and showcasing what we have created, the event is a chance to explore what the future might hold for data driven innovation in cities. We’d love it if you joined us. Space is limited so book your place here.
We’re looking for a Sales Development Representative to help us attract, educate and win clients for our new city data tool, Witan.
Data science made a noticeable impact on the public sector in 2017. And it’s no longer just a small group of innovators who are putting data to work in their organisations. Projects such as the GLA’s datastore are helping the sector build a data infrastructure that will benefit us all.
We’re looking for a Product Marketing Manager to help us attract, educate and win clients for our new city data tool, Witan.
We know that making smarter use of data in the public sector is important to transformation and effectiveness - we’ve seen data put to work, by us and others, for purposes from detecting fraud to modelling the impact of new policies. But how does this process happen in practice? We’ve put together an infographic that illustrates a six-stage process that is common to data projects that have impact.
The excitement about data needs to be balanced with an uncomfortable truth. Most data projects fail. Perhaps as many as 85%. This doesn’t mean your data modeling project or big data analysis tool is doomed. But it does mean you need to know how to swing the odds back in your favour. In this article we look at the reasons many data projects don’t deliver value. We also suggest practical steps you can follow to make your project a success.
There is great potential and enthusiasm in the public sector for data-driven innovation that improves services and reduces costs. This was the key message that the Mastodon C team took away from a round table discussion with senior figures from the public sector this week. Yes, challenges remain around data skills, data governance and shaping the right approaches for data projects, but some clear patterns are emerging that show that data makes an impact at a local level.
Data governance is about giving your people and partners timely but compliant access to data. Whether it’s driving collaboration across departments or engaging external suppliers, an effective approach to data governance gives you control over data access and usage, whilst making sure you stay on the right side of policy, regulations and the law. All whilst being able to prove that you’re doing it right. With new regulations like GDPR making jail sentences possible penalties for data owners, that’s more important than ever.
Data exploration is perhaps one of the most important elements when designing a data science project. Exploring the data allows you to better plan your project and answer important questions - like whether your data can actually answer the questions you’re trying to answer. In this short guide we outline how we approach investigating a new dataset, step by step. Typically, with a new data science project you will be working with a new dataset. Investigation of the data (it’s content and composition), will help to inform how the project should proceed and whether the data can answer the question you are aiming to address.
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