DEMYSTIFYING DARK DATA: THE UNTAPPED POTENTIAL

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As the world becomes increasingly digitised, organisations are generating more data than ever before. But did you know that up to 80% of that data remains untapped? This structured/unstructured, unprocessed data is known as dark data, and it has the potential to be a goldmine of insights for organisations.

What is Dark Data?

Dark data refers to data that organisations generate but don’t use. This data is typically unstructured and stored in various formats, such as emails, documents, images, videos, and social media posts. It’s often ignored because it is difficult to process and analyse, requiring advanced analytics tools and techniques to extract meaningful insights. However, with the right approach, dark data can be a valuable source of information that can help organisations make better decisions and gain a competitive advantage.

Where is Dark Data Found?

Dark data can be found in many areas of an organisation, including customer feedback, product reviews, employee emails, and social media mentions. By analysing this data, organisations can uncover hidden patterns and insights that can help them improve their products and services, enhance customer experiences, optimise operations, and reduce costs.

Dark Data in Action

Dark data can be used in virtually any industry to improve business outcomes. For example, an FMCG company can analyse customer reviews on social media and product forums to identify common complaints or issues. This data can be used to improve product design, customer service, and marketing strategies. Similarly, a healthcare organisation can analyse patient data to identify potential health risks, improve treatment plans, and optimise resource allocation.

Analysing dark data requires a different approach than traditional structured data analysis. Machine learning and artificial intelligence can be used to process large amounts of unstructured data and extract meaningful insights. This technology can be used to categorise data, identify patterns and anomalies, and extract sentiment from text and other data.

How to Leverage Dark Data?

To leverage dark data effectively, organisations need to establish a data strategy that includes data governance, data quality, and data privacy. They need to ensure that the data they collect is accurate, complete, and secure, and that they comply with regulatory requirements. Additionally, organisations need to invest in the right tools and technologies to extract insights from dark data.

There definitely some challenges with dark data is to extract valuable insights from it, but there are strategies you can use to put it in action:

  1. Identify your goals: Before you begin to analyze your dark data, it is essential to identify your business goals. Understanding what you want to achieve will help you determine what data to focus on and what insights you need to extract.
  2. Collaborate across teams: Dark data is often spread across different departments within an organization. Collaboration across teams can help you identify opportunities for using this data and uncover insights that might have been missed.
  3. Make your data consumable: Once you have identified the data you need, the next step is to make it available. This doesn’t mean that you need move your data in a single location as there are plenty efficient architectures to prevent data duplication. Making your data available in an agile and flexible way, will enable you to process and analyze it more efficiently.
  4. Use data analytics & AI/ML: The right tools can help you mine valuable insights from your dark data. There are various tools available, from simple data visualization software to sophisticated machine learning algorithms.
  5. Implement data governance: Finally, it is essential to have proper data governance in place to ensure that your dark data is used appropriately. This includes establishing data quality standards, data retention policies, and data security protocols.

Conclusion

Dark data represents a vast untapped resource for organisations seeking to gain a competitive advantage. By analysing this data, organisations can uncover valuable insights that can help them make better decisions and improve their operations. With the right strategies and technology in place, organisations can demystify dark data and unlock its full potential. It’s time to start exploring the dark data lurking within your organisation and turn it into a competitive advantage.

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THE LINK BETWEEN ESG AND DATA: TRANSPARANCY FUELED BY MEASUREMENT & REPORTING

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In recent years, Environmental, Social, and Governance (ESG) has become a buzzword in the corporate world. Investors and stakeholders are increasingly concerned with a company’s commitment to sustainability, social responsibility, and ethical practices. As a result, many companies are now incorporating ESG factors into their decision-making processes. But what is the link between ESG and data? In this blog, we explore how data plays a critical role in ESG transparancy.

Tracking ESG Factors with Data

Firstly, it’s essential to understand that ESG encompasses a wide range of factors, from environmental impact to labor practices to corporate governance. Companies must be able to track and measure these factors accurately to report on their ESG performance. This is where data comes in. By collecting and analyzing data, companies can gain insights into their ESG performance and identify areas for improvement.

Using Data to Report on ESG Performance

For example, an organization may collect data on its carbon emissions, water usage, and waste generation to assess its environmental impact. It may also collect data on employee turnover, diversity, and working conditions to assess its social impact. Finally, it may collect data on board composition, executive compensation, and shareholder rights to assess its governance practices. Once a company has collected this data, it can use it to report on its ESG performance.

The Importance of Data for Benchmarking ESG Performance

Reporting is an essential part of ESG because it allows investors and stakeholders to evaluate a company’s ESG practices and make informed decisions. ESG reporting typically involves disclosing data on a range of metrics, such as carbon emissions, employee diversity, and board diversity. Data is also crucial for benchmarking ESG performance. Companies can compare their performance against industry peers and ESG standards to identify areas for improvement.

Benchmark Data for ESG Investing

This benchmarking process often involves the use of ESG ratings and rankings, which are based on data collected from multiple sources. By using these ratings, companies can identify areas where they may be falling behind their peers and take steps to improve their ESG practices. Finally, data is critical for ESG investing. ESG investors use data to identify companies that are committed to sustainability, social responsibility, and ethical practices. They often look for companies with strong ESG ratings, which are based on data collected from multiple sources. By using data to identify these companies, ESG investors can make informed investment decisions that align with their values.

Conclusion

In conclusion, data plays a critical role in ESG. Companies must collect and analyze data to measure and report on their ESG performance accurately. Data is also crucial for benchmarking ESG performance and for ESG investing. As ESG continues to grow in importance, companies that prioritize data collection and analysis will be better equipped to meet investor and stakeholder expectations.



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