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data visualisation

Data visualisation

Written by ew_admin on . Posted in Glossary. No Comments on Data visualisation

The goal of data visualization is to present data in a way that is easy to understand and analyze, making it easier for users to identify trends, patterns, and relationships within the data. By transforming data into visual representations, data visualisation can help users communicate insights and findings more effectively, and can lead to better decision-making.

For a business to be able to visualize their data means they can represent their data graphically and in a way that is easily understandable. It can help businesses to quickly identify patterns, trends, and outliers in their data. With data visualization, businesses can create interactive dashboards, reports, and charts that allow them to explore their data and gain insights.

Data visualization can enable businesses to make better decisions by presenting complex data in a more accessible and intuitive format. By visualising data, businesses can quickly identify relationships and trends that might not be immediately apparent from a table of numbers. This can lead to more informed decision-making and can help businesses to identify areas where they need to make improvements or take corrective action.

Moreover, data visualisation can help businesses to communicate their findings and insights more effectively. By presenting data visually, businesses can make it easier for stakeholders to understand the significance of their data and to see the impact of their decisions. This can be particularly important when presenting data to non-technical stakeholders who may not be familiar with the underlying data.

Take the Covid 19 tracking map, for example. Obviously the start of the pandemic was an incredibly challenging time for governments and private sector companies alike.

The New York Times created an interactive map that allowed readers to see the spread of COVID-19 around the world in real-time. The map used data visualisation to display the number of cases and deaths in each country, allowing users to easily see which areas were most affected. The map quickly became one of the most popular features on the Times’ website, and was praised for its clear and informative design. The COVID-19 tracking map was created by Johns Hopkins University and is one of the most well-known examples of data visualisation in the context of the COVID-19 pandemic. The map provides an interactive, real-time display of COVID-19 cases and deaths around the world, using data from various sources such as the World Health Organization (WHO) and national health agencies.

In the UK, there are a number of similar COVID-19 tracking tools and dashboards that provide visualisations of data on cases, deaths, and other metrics related to the pandemic. For example, the UK government’s coronavirus dashboard provides daily updates on the number of cases, deaths, and tests in the UK, as well as breakdowns by region and age group. Other organisations and media outlets have also created their own COVID-19 dashboards and visualisations to help people understand the impact of the pandemic on different communities and sectors.

The COVID-19 tracking map is indeed an excellent example of data visualisation, as it takes complex data sets from various sources and presents them in a way that is easy to understand and engage with for users. The map utilizes interactive visualizations such as color coding and scaling to represent the number of COVID-19 cases and deaths in different countries, making it easy to quickly see trends and patterns.

The visualizations on the COVID-19 tracking map help users to better understand the global impact of the pandemic and make informed decisions about how to respond. The map has been widely praised for its clarity and accessibility, and has been used by government officials, healthcare professionals, and members of the public to track the spread of the virus and develop strategies for mitigating its effects.

In the UK, there are similar COVID-19 dashboards and tracking tools that use data visualisation techniques to make information about the pandemic more accessible to the public. These visualisations help people understand the impact of the virus in their communities and make informed decisions about their health and safety.

Data visualisation really can be considered a bridge between complex, abstract data and easy-to-grasp concepts that the average professional can understand. By presenting data in a visual format, it becomes easier to identify patterns, trends, and relationships that might not be immediately apparent when looking at raw data. This can help businesses and professionals make informed decisions based on data, rather than relying on intuition or guesswork. In addition, data visualisation can help to communicate complex information and ideas more effectively, making it a powerful tool for presenting findings to those interested in a company and its decision-makers.

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Data analytics

Written by ew_admin on . Posted in Glossary. No Comments on Data analytics

The goal of data analytics is to gain valuable insights into data and identify trends and patterns that can inform decision-making and improve business performance.

Data analytics involves using statistical and mathematical techniques to analyse data, while data visualisation involves presenting data in a visual format, such as charts, graphs, or maps. By combining these two approaches, companies can quickly and easily see patterns and relationships in their data, which can help them to make informed decisions and improve their performance.

Data analytics are significant parts of business, as they aid companies in:

  1. Making informed decisions: By analysing and visualising data, firms can easily identify trends, patterns and connections within their data, which can guide decision-making and enhance performance.
  2. Improving operational efficiency: Data analytics and visualization can help companies streamline their operations and identify areas for improvement through analysing metrics such as production times and resource utilisation.
  3. Enhancing customer experience: By analysing and visualising data, companies can gain a deeper understanding of their customers, enabling them to improve the customer experience and meet customer needs more effectively.
  4. Gaining a competitive advantage: Companies can gain insights into their operations and customers through data analytics and visualization, which can inform their decisions and help them compete better in their market.
  5. Increasing profitability: By using data analytics and visualization to spot areas for improvement, companies can raise their profitability and boost their bottom line.
  6. Monitoring and tracking performance: Data analytics and visualization assist companies in monitoring and tracking their performance over time, helping them identify areas for improvement and make informed decisions.
  7. Identifying new opportunities: By analysing data, firms can discover new opportunities such as market trends and customer preferences, which can shape their strategy and support their growth.

Data analytics enable companies to gain the insights and information needed to make informed decisions and enhance their performance. Through analysing and visualising data, firms can acquire a deeper understanding of their operations, customers and market, aiding them in succeeding in today’s competitive business environment.

A food delivery company was facing challenges in managing their restaurant partnerships and ensuring that their customers received hot and fresh food. They had a large volume of data on delivery times, driver locations, and customer preferences but struggled to make sense of it.

The company hired a data analyst who specialized in data analytics and visualisation to help them better understand their restaurant partners and customer preferences. The data analyst collected data on delivery times, driver locations, and customer preferences such as food type and preferred delivery times. They then used data analytics to identify patterns in the data and created dashboards to help the company understand the data more easily.

The analysis revealed that some restaurant partners were consistently delivering food late, causing customer dissatisfaction. The translation of the data into something tangible showed the delivery times and customer preferences, making it easier for the company to see where the problems were occurring.

With this information, they were able to work with their restaurant partners to improve delivery times and ensure that their customers received hot and fresh food. They also identified areas where they could improve their delivery process and make it more efficient. It was not only able to improve customer satisfaction and reduce complaints, the business also gained a better understanding of their restaurant partners and customer preferences. The company was now better equipped to meet the demands of their customers and compete in the market.