Harmony News
Comparing Apples to Oranges: How to Analyse Two Completely Different Surveys So, you’ve created a survey (or multiple surveys), convinced people to take part and answer your questions, and have received the results. Great job! Yet, the real test comes when it’s time to make sense of these survey findings. Today, we’ll take a look at why you need surveys, how to design effective surveys, how to analyse survey data, how to analyse survey data with multiple responses, and how to do data harmonisation when you’re dealing with multiple surveys.
Data Harmonisation Tools – Overview Organisations typically collect data from multiple sources and for many different reasons. This data comes in various forms and formats – for example, it may be coming from market research, customer research or inter-organisation departments. Data harmonisation, an advanced technique used to make sense of all the raw data collected and uses for research purposes, becomes necessary in this case, but unless it is incorporated effectively, organisations might miss the full, holistic view of business performance that they wish to gain.
In the digital age, where data is as valuable as currency, marketing professionals face the challenge of navigating through vast oceans of information. The key to unlocking the potential of this data lies in harmonisation - a process that not only streamlines disparate data sources but also unlocks profound insights for targeted and effective marketing strategies. Data harmonisation is a critical process in the marketing world, enabling organizations to standardize, integrate, and leverage data from diverse sources for strategic decision-making.
About Harmonised Data and The Harmony Project If you’ve ever heard of data integration then you can easily understand what harmonised data or data harmonisation is: where disparate data sources are brought together into a single, unified location. But where harmonisation goes a step further is that it reorganises data according to the parameters set by a single schema. Let’s say you want to combine surveys or questionnaires on mental health with different kinds of wording for similar questions, like “anxiety” vs.
Upcoming Tech Talk: at the AICamp AI Meetup (London): AI, Generative AI, LLMs Update: you can download the slides from the presentation here We’re pleased to announce that the AI tool Harmony will be showcased at the upcoming AICamp AI Meetup in London on 27 March. You are invited to AICamp’s monthly in-person AI meetup in London. Join us for deep dive tech talks on AI, GenAI, LLMs and machine learning, food/drink, networking with speakers and fellow developers
Introduction In the realm of healthcare, integrating and harmonising data across public and private records is crucial for advancing patient care, research, and policy-making. Data harmonisation in healthcare is a pivotal process for integrating diverse data sources, ranging from public health records to private medical data. This endeavor aims to standardize and unify data for more comprehensive insights, enhancing patient care, research capabilities, and policy-making. This blog explores the concept, significance, and methodologies of data harmonisation in healthcare, referencing key sources and projects in the field.
