1 Introduction

The emergence of digital technology in recent years has significantly changed the face of journalism. Innovative approaches with unique opportunities to access, analyse and present information have emerged, some of which are data analytics and visualisation tools. Data journalism has become a fundamental approach-that analyses data to add context to stories-to address prevailing issues of our time, from political tendencies to environmental concerns. It relies on the increasing generation or availability of data today using analytical techniques that help identify meaningful patterns and insightful trends to enrich or add context to reporting. A very important aspect of data journalism is the use of visualisation tools aimed to make data 'look' comprehensible for audiences. Turning raw data into visual formats, such as charts, graphs and interactive maps, has become an increasingly used technique in journalism due to its capacity to turn complex pieces of information into accessible and visually appealing messages. This ability to add valuable depth to journalistic pieces becomes an important function in an era where information overload may disorient readers who are looking for clear and contextualised information. Despite the advantages of data journalism, it poses considerable challenges and ethics. Until the accuracy of data is ensured and the appropriate context is added, there is a risk for this approach to be instrumentalised. There is also a need to address ethical dilemmas around privacy and the risk of bias in use of data [1]. This paper explores these issues by examining the use of data journalism techniques and its applications, challenges and responsibilities for those working in the field. It examines these elements-data analytics and visualisation tools-through case studies and technological developments.

2 Techniques in Data Journalism

2.1 Data Collection

Data collection involves getting the data to inform or support a story. It is a crucial first step in data journalism, and journalists need to find the relevant data. They work with open data portals, government databases, public records and so forth to collect data for their stories. Sometimes journalists scrape data from websites and social media. Scraping involves extracting data from the websites or platforms to see what information is available. This helps them understand what is trending, what people are talking about and so forth. The data journalist needs to be clear about what story she wants to tell, and what data she needs to tell it. She needs to look into the credibility and reliability of the data source [2]. The data collection process can take a long time. It involves having the skills and tools to collect the data, and sometimes even the additional ones to clean it.

2.2 Data Analysis

This step is the process of examining and interpreting data to find patterns, trends and associations that could contribute to developing narratives. In this step, statistical methods, machine learning algorithms and data mining methods are employed to get meaningful insights from the raw data. It is common to see this step is conducted by a researcher who collaborates with journalists for rigorous data analysis. Tools like Excel, R and Python are commonly used to analyse data. With the help of these tools, the data journalist can process large and unstructured data for further analysis and get in-depth insights into his or her data. As stated, this step forms a pivotal part of the practise of data journalism for the following reasons: 1.data analysis allows the journalist to go beyond what is observed on the surface but find deeper implications; 2. with this step, it becomes possible to discover a trend and pattern that would otherwise not have been identified [3]. Data analysis, for instance, can be used to show, for the first time that the poorer sections of the population are the most at risk from environmental harms in Africa. Table 1 gives an overview of how this step is conducted to extract meaningful information and narratives in journalistic practise.

Table 1. Data Analysis Techniques and Insights

2.3 Data Visualization

The first step of data journalism is data visualisation-turning complex data into images and graphs that are easy to understand and analyse. Visualisation is an essential skill in the field because it allows journalists to communicate their findings in an interesting and accessible way to audiences. Websites such as Tableau, D3.js and Flourish can be used to create almost any type of visualisation, from simple pie charts and bar graphs to interactive maps and dashboards. With these tools, journalists can help their readers spot key observations and trends within data. Visualisation is a critical step in democratising journalism because it helps break down complex findings and allows readers to easily grasp and remember information. This leads to better, more informed decisions. Strong reporting is not sufficient for communicating an important story. Data also needs to be presented in a clear and concise way so the audience can perceive insights and draw relevant conclusions [4].

3 Applications of Data Journalism

3.1 Political Reporting

Likewise, a basic understanding of data journalism is vital to political reporting. Election trends, voters' behaviour, and the effects of certain policies are often explored with data analytics, which is consequential for political reporting. To analyse polling data, campaign finance records and voting patterns, journalists use data-analytics tools to reveal the underlying trends and insights that help their political stories. With these analytic techniques, data produced by pollsters, campaign managers and government agencies can also be presented in a way that's interesting, accessible and insightful to a wider audience. Visualisation tools enable journalists to turn data into infographics, maps and charts to illustrate election results, district trends and other demographic patterns in the political landscape. Just for instance, through visualising election results in 2020 on an interactive map across the US, it's much easier to see which districts voted for Trump or Biden, as part of the post-election reporting. It has become a common practice for political reporters to use data journalism techniques because the results are critical in holding politicians accountable [5]. For example, data-analytic tools can expose inconsistencies in what politicians say and what they do, providing a deeper understanding of political figures and their actions.

3.2 Environmental Journalism

Data-driven approaches are highly useful in environmental journalism too. Reporters can quantify the effects of climate change, pollution and natural resource depletion, and signal the urgency of these issues to readers by reporting on data trends. Data analytics could, for instance, aggregate temperature records, air quality indices and forestry rates, revealing patterns and trends to unveil environmental problems. For example, visualisation is a valuable practice in environmental journalism, as it makes inherently abstract data more readable. Interactive maps showing the spread of wildfires or the impact of rising sea levels on coastal communities can help environmental journalists provide information on their real-world consequences [6]. Data journalism also enhances the capacity of environmental reporters to tell stories that connect with environmental issues and drive engagement among audiences.

3.3 Health and Science Reporting

Data journalism can help health and science reporters tell stories about complex topics through advanced data analyses and data visualisation techniques. Data journalists use data analytics to explore patterns in medical research, public health data and scientific studies, which is then translated into evidence-based insights into current and emerging health trends and scientific discoveries. Data visualisation tools are increasingly used to help journalists describe these insights in dynamic ways that audiences can better understand complex scientific concepts. For instance, data visualisations could illustrate how infectious diseases spread, the efficacy of different medical treatments, or the outcomes of scientific experiments. Data journalism techniques can help health and science reporters share accurate and timely information with readers, allowing them to make informed decisions about their health and wellbeing [7].

4 Challenges in Data Journalism

4.1 Data Quality and Accuracy

A core concern for data journalism is, of course, data quality. Often, it might be the quality of data first collected by others. Before using data or reporting on data, journalists should check the reliability, credibility, and accuracy of their sources. Data can be factually inaccurate because it is internally inconsistent or because facts were misreported by purveyors of 'alternative' or misinformation sources and viral content. This includes errors from data collection, including faulty formatting errors and clumsy interpretation of data that results in biased versions of its meaning [8]. To address data quality issues, journalists should engage in rigorous data validation and verification by leveraging the expertise of data scientists and analysts to ensure its accuracy before reporting. Maintaining high standards of data quality will help maintain the credibility and trustworthiness of journalists' reporting.

4.2 Technological Barriers

Moreover, technological barriers can be an obstacle: enormous datasets might be difficult to obtain or analyse because the journalist lacks technical skills or access to the proper tools to work with them. Last, even if data is readily available, journalists might have to develop a certain level of technical expertise relatively quickly, following the fast and constant pace of technological change. This includes keeping up with new tools and standards for data analysis and visualisation [1]. Overcoming these obstacles can, of course, be made easier, for instance by teaming up with data scientists and/or technologists. But news organisations can support their journalists in enhancing their data skills by providing the proper training, or through simply investing in the necessary resources.

5 Future Directions in Data Journalism

5.1 Advanced Analytics and AI

Technologies including natural language processing and automated content generation can help to analyse vast amounts of text and surface relevant information in faster and more efficient ways than ever before. Processing and interpreting data at a never-before-seen pace, automation-assisted reporting can surface insights that would otherwise be difficult to pick up manually. For example, machine learning algorithms can track social media trends and sentiment in real time, providing insights into shifting public opinion and hot-button issues. Further, AI can be used to automate the production of data-driven reports, freeing up journalists to delve into more complex and investigative research. All of these technologies have the potential to reshape how journalists collect, analyse and communicate data [9].

5.2 Cross-Disciplinary Collaboration

Cross-disciplinary collaboration between journalists, data scientists, designers and technologists will become even more important in the future of data journalism. These professionals can work together to create richer and more impactful stories, innovating new types of storytelling forging a seamless integration of data analysis and visualisation in the reporting process. For example, data scientists can help journalists focus on trends and patterns of interest in large and voluminous data, designers can develop beautiful graphics from data, and technologists can help provide a back-end for editorial teams to manage stories. Collaboration between different disciplines not only generates reporting that is more accurate and powerful, but it also facilitates creativity and innovation. As newsrooms evolve, teams will be assembled to best harness the power of data journalism and deliver powerful and impactful reporting to audiences everywhere.

5.3 Interactive and Immersive Storytelling

As well as being a great trend in data journalism, interactive and immersive storytelling could become a dominant trend in journalism in general as it offers audiences new ways of 'reading'. Interactive visualisations and data-driven narratives let the reader explore information at their own pace and discover information in a more enjoyable way. This is especially true for scrollytelling storytelling-a data journalism technique that changes the visualisations or adds narrative elements as the reader scrols down the page. It's a data journalism royal flush that combines the benefits of data exploration with those of narrative journalism. Wealth of Nations itself offers lots of examples of scrollytelling and other techniques that will create a more engaging experience for the reader [10]. The power of VR and AR technology are being increasingly used to tell immersive stories that let the reader/viewer experience data and 'immersive data journalism' is becoming a viable technique for breaking down complex issues into a simple and comprehensible experience.

6 Implications for News Organizations

6.1 Organizational Change and Training

Ultimately, if the potential of data journalism is to be fully exploited, news organisations would need to undergo organisational changes, and invest in retraining its workforce. This could involve creating a culture that recognises the value of data-driven storytelling, and supports journalists in learning new skills. This could involve committing to structured training programmes and workshops on data analytics tools, visualisation tools, and emerging technologies-allowing journalists to stay abreast of developments in the industry. News organisations would need to become fluid in their structure, and invest in transdisciplinary collaboration, allowing for opportunities to think creatively and innovatively.

6.2 Technology and Infrastructure Investment

One of the biggest challenges facing news organisations looking to support data journalism is the lack of investment in technology and infrastructure. Data journalism practitioners must be empowered with the tools required to handle and process data efficiently. This requires investment in data analytics and visualisation tools which are more powerful and easier to use than ever before. News organisations should also invest in cloud infrastructure and systems that can support the storage, processing and analysis of data, enabling journalists to run a proactive strategy for data analysis at scale [11]. Furthermore, an investment in technology and infrastructure can facilitate partnerships with technology companies and data providers who can give something extra back to newsrooms in terms of tools or datasets. By investing in technology and infrastructure, news organisations are putting their journalists in a position to conduct impactful and innovative data journalism.

6.3 Audience Engagement Strategies

It is important to design strategies for maximising audience engagement. News organisations can use digital platforms and social media to achieve these outcomes by reaching out to a wider audience than before and encouraging user engagement with data stories. Interactive storytelling, personalised content recommendations and community engagement strategies can play a significant role in audience engagement and develop a loyal community of readers. Second, news organisations should practise greater transparency and trust in their audiences. They can explain the methods behind data findings and provide context for the data stories. As far as possible, they could open their work to reader participation and feedback. By developing a dialogue with its audience, a news organisation can create a smarter and more engaged public.

7 Conclusion

Using data analytics and storytelling tools, journalists can transform data into illuminating narratives and interactions that add value to their stories, both in terms of telling them, and understanding them. In this paper, we have looked into the techniques and applications of data journalism, and how journalists are using data to enhance reporting, communicate complex issues, and tell compelling stories. We looked into the increasing use of data in political reporting, especially around elections in countries such as India and the United States. We also discussed how data is being used by journalists to report on the environment and its changes; as well as how data journalism is transforming how we report on health and science. Comparatively little work has been done on how journalists are reporting on social issues and problems using data, such as poverty, homelessness or inequality. As discussed in this paper, one of the main challenges is the quality of the data being used by journalists. With more and more information being created all the time, it is important that this information is valid, and not distorted by algorithms and unwieldy analysis. This gets into questions of being data literate, and how new technological platforms could assist journalists in being more data literate. This is even more complicated given the fact that many different disciplines, from statistics to computer science, contribute to the science of data. Even for data scientists and mathematicians, analysing and working with large data sets is difficult. Right now, most journalists are clueless about how to deal with large data sets, let alone how to use data analysis and storytelling tools. Nonetheless, room for growth exists. News organisations need to adapt as this process starts to take hold. They will need to invest in technology, infrastructure and training their staff. They will also need to figure out how to best engage their audience and build a relationship with them that enables the presentation of data-driven stories effectively. As analytics, artificial intelligence and immersive storytelling technologies advance, more sophisticated tools will become available. If journalists learn to use them in the coming years, there is no limit to what kind of discoveries our community can make, how much we can learn about the world, and how much greater knowledge and understanding might flourish from data journalism.