Radian6 and Sprout Social These are two examples of paid social media monitoring services for companies that need more robust tools. Here are some ways in which you. Kies de juiste social media monitoring tool voor jouw organisatie. Download white paper met stappenplan, checklist en factsheets van 21 social media monitoring tools. Issuu is a digital publishing platform that makes it simple to publish magazines, catalogs, newspapers, books, and more online. Easily share your publications and get. This series is supported by Gist. Gist provides a full view of the contacts in your professional network by creating a rich business profile for each one. COURSE Curriculum WHO SHOULD ATTEND Marketing Professionals Sales Professionals Business Owners Entrepreneurs Digital Marketing Professionals Students. Compare top Customer Relationship Management CRM Software in the UK with our reviews, free demos and pricing. Salesforce-Social-Studio.png' alt='Radian6 Engagement Tool' title='Radian6 Engagement Tool' />Content Marketing Institute 2 Chief Content Officer Job Description Participation in the hiring and supervising of contentstory leaders in all content verticals. Its that time of year again a time for trying and reviewing, for testing and reporting, and for the list of the best startups Ive personally. Discovery understanding user needs Open Policy Making toolkit Guidance. An introduction to discovery. Definition. The discovery stage should be used to till research and knowledge gaps with insight and evidence from user research and data science. Following a users journeys through a policy or service, experiencing their lives and using data to understand how user needs and challenges at a deeper level will help provide insight. Radian6 Engagement Tool' title='Radian6 Engagement Tool' />This insight should give you a full picture of what a user needs from a policy or service that you can develop into solutions and policies. What you should achieve. User needs. You should finish discovery with a full picture of how people experience a policy and the service, and the needs that any solutions you think up need to answer. An understanding of the policy context. Understanding the world in which people experience a policy is fundamentally important to the future success of a policy project. There are many push and pull factors that might affect how people experience a policy or service that might need to also be included in a policy challenge. Final project challenge. You should use any data insights or user research to finalise the aim and challenge of your project. This will be key in the next stage of the project development. Data science. Contents. Introduction. When to use data science. How to start using data science. Tips for data science. Ethics of data science. Data science an introduction. Data science uses advanced software, computer power and artificial intelligence to analyse and visualise big and complex data to provide useful insight that can improve an understanding of a problem and design better policy. Data science analyses any form of data, from large quantitative numerical data sets, to unstructured qualitative descriptive data that doesnt fit in a standard database. This can take the form of free form text, interviews, consultations, images or phone calls, to name a few. It can even analyse real time data that is constantly changing and point out trends, views, themes, sentiments, characteristics or any kind of finding you can think of. Examples from around government. The London Fire Brigade created a prototype data visualisation to help them see where they needed to improve reaction times. This used data available from various fire brigades and visualised it using a heat map and google maps to easily show where the service needed to focus its money. The Foreign Office allowed policy makers to visualise their international connections on twitter accounts. This used visualisation tools to showcase where any communications they wanted to spread could be best targeted and enabled quicker sharing of information around the globe. The Government Digital Service have been using feedback to a services webpage can be used to spot problems with a service before it becomes a serious problem. When to use data science. The wide variety of techniques within data science can be tailored to answer a policy question. If policy makers are aware of the types of things data science can do, it can provide new evidence, ideas and insights at different stages of policy development. How to start using data science. Data science is a specialist skill and requires policy makers to work together with data analysts or scientists. A policy maker needs to understand what is possible so they can commission data science and identify new and alternative data sources for the data scientist to use. Using techniques like data science tool cards can help to inspire policy makers about what data to use and why. If you are considering a data science project or would like to explore the policy questions you have and the data you hold, contact the analysts in your department. Policy lab can also help organise data science as part of a policy project with them. You can contact policy lab at policylabcabinetoffice. Tips for data science. There are a number of issues non data scientists can consider and discuss with analysts to understand the potential of data for their policy area. Read the glossary of common terms in data science. Try and make the best use of existing data. Do you have data of particular value that you would like to understand better Can you explore your data assets to see if there is untapped potentialComplex free text data e. Some older data therefore can now become more useful. To take complaints data as an example, the data science approach would look at more than just summary statistics of how many complaints have been received or dealt with. It would analyse the text, examining trends to understand demand fluctuations on a service, or looking at the language people use through sentiment analysis to see how they are interacting with a service. This data may have previously been available but unused but data science allows you to explore that data and make use of it. Combine multiple data sources. Your data might be more valuable when combined with data from other sources. The Department of Energy and Climate Change DECC has a target of ensuring vulnerable customers get access to schemes such as the Green Deal to install energy efficiency measures e. DECC already held data on physical property characteristics like energy consumption, but when this was combined with Department for Work and Pensions welfare data this gave a richer insight into households that need energy efficiency measures. Present data better. Datasets sometimes come in spreadsheet form with thousands of rows of complex figures. For a policy this is often incomprehensible. Visualisation data lets you to see the data and findings, share them with interested parties and ministers and make better policy. The Foreign and Commonwealth Office built a prototype of visa demand to show a story with the data. Ethics of data science. The Cabinet Office has created Data Science ethics to help policy makers and data specialists to work together. The ethics is an ethical framework that brings together the relevant parts of the law and ethical considerations into a simple document that helps Government officials decide what it can do and what it should do. Start with a clear user need and public benefit this will help you justify the level of data sensitivity and method you use. Use the minimum level of data necessary to fulfill the public benefit there are many techniques for doing so, such as de identification, aggregation or querying against data. Build robust data science models the model is only as good as the data it contains and while machines are less biased than humans they can get it wrong. Its critical to be clear about the confidence of the model and think through unintended consequences and biases contained within the data. Be alert to public perceptions put simply, what would a normal person on the street think about the project Be as open and accountable as possible Transparency is the antiseptic for unethical behavior. Aim to be as open as possible with explanations in plain English, although in certain public protection cases the ability to be transparent will be constrained. Keep data safe and secure this is not restricted to data science projects but we know that the public are most concerned about losing control of their data. Read the full ethical framework. Data visualisation. Contentsintroduction. When to use. Examples of data visualisation. How to visualise data. Data visualisation an introduction. Unattended Install Windows 7 Answer File there. Data visualisation can be used with data science, open data and data analysis.