Harnessing the Darkness ( Dark Data )

Dark data is any data that is collected, stored, but never used or analyzed. It can be found in a variety of forms, such as sensor data, customer records, and financial data. Dark data can be a problem for businesses because it represents a missed opportunity to improve efficiency, reduce costs, and make better business decisions.

Consider a Hollywood studio is producing a new science fiction movie. They use dark data to analyze the social media posts of their target audience. They find that the audience is most interested in movies with realistic and believable special effects. They use this information to invest more money in the special effects budget. As a result, the movie's special effects are critically acclaimed and the movie is a box office success.

Dark data can be difficult to identify and quantify because it is often unstructured, without any context and difficult to access.

Here are some specific examples of dark data in a steel plant:

• Sensor data from machines that is not monitored or analyzed.
• Customer data that is collected but not used to improve product development or marketing campaigns.
• Financial data that is not used to identify trends or make predictions about future performance.
• Historical data that is not used to learn from past mistakes or identify new opportunities.

However, there are a number of tools and technologies that can help steel plants to identify and manage their dark data.

1. Identify your dark data. The first step is to identify the dark data that exists in your organization. This can be done by auditing all of the data that is collected and stored. You can also use data discovery tools to help you identify dark data.
2. Classify and organize your dark data. Once you have identified your dark data, you need to classify and organize it. This will make it easier to access and analyze the data. You can classify your dark data by type, sensitivity, storage location, and format.
3. Analyze your dark data. Once your dark data is classified and organized, you can start to analyze it. This can be done using a variety of tools and techniques, such as data mining, machine learning, and artificial intelligence. The type of analysis that you perform will depend on the specific goals of your organization. For example, you might use dark data to identify trends, make predictions, or develop new insights.
4. Implement the findings of your analysis. The final step is to implement the findings of your analysis. This could involve making changes to operations, developing new products, or launching new marketing campaigns.

Benefits of managing dark data in the context of steel industry

• Improved efficiency: By analyzing dark data, steel plants can identify areas where they can improve efficiency and reduce costs. For example, a steel plant might use dark data to identify machines that are not being used efficiently or processes that can be streamlined.
• Reduced costs: By identifying and reducing waste, steel plants can save money on energy, materials, and labor. For example, a steel plant might use dark data to identify areas where they can reduce scrap rates or improve energy efficiency.
• Better decision-making: By analyzing dark data, steel plants can make better decisions about product development, marketing, and operations. For example, a steel plant might use dark data to identify new customer markets or to develop new products that meet the needs of their customers.

Some examples of how steel plants can use dark data to improve their operations:

• Identify trends in furnace temperature and performance. Steel plants can use dark data to identify trends in furnace temperature and performance. This information can then be used to optimize furnace settings and reduce energy consumption.
• Predict machine maintenance needs. Steel plants can use dark data to predict machine maintenance needs. This information can then be used to schedule preventive maintenance and avoid costly downtime.
• Improve product quality. Steel plants can use dark data to identify patterns in product defects. This information can then be used to improve the manufacturing process and reduce scrap rates.
• Reduce waste. Steel plants can use dark data to identify areas where they can reduce waste. For example, steel plants might use dark data to identify areas where they can reduce the amount of water they use or to recycle more scrap metal.
• Improve customer satisfaction. Steel plants can use dark data to identify customer needs and preferences. This information can then be used to develop new products and services or to improve the customer experience.

[ The views expressed in this blog is author's own views and some research leveraging Google Bard and it does not necessarily reflects the views of his employer, JSW Steel ]