The Rise of Machine Learning to Manage Dark Data
Dark data, in layman’s terms, comprises of every click and move made by an organization while conducting business. However, organizations fail to utilize this dark data beyond the immediate requirement. Often companies collect dark data for regulatory and compliance purposes as a precautionary measure which lies redundant in their storage forever. Since storing dark data is cheaper than analyzing it, organizations find it convenient to store dark data indefinitely, resulting in loss of valuable insights over time.
The age-old quest for exploration of unknown realms forms the fundamental basis of scientific research and development. Research primarily involves three key steps: observation, recording, and inference, with inference being the most significant element of the entire process.
For years we have been wasting time and resources pushing aside the benefits of dark data, unconsciously dismissing the great potential it can offer a business or industry.
Dark data is a type of unstructured, untagged and untapped data that has not yet been analysed or processed. With 80% of data being classified as dark data, there is undoubtedly enough information for companies to leverage to their advantage. Therefore, it is now time to bring light to the unknown gold mine of dark data.
For many businesses, understanding the sheer amount of dark data can be overwhelming and time consuming to manage. Businesses may use excuses like legality issues, workflow disruption or architectural costs as to why it has been reluctant to harness dark data. It may also fear that getting access to dark data can invade valuable time which could be used for other tasks and disrupt employees with new ways of operating. Of course, disruption can be kept to a minimum when implemented correctly with the right tools.
The dawn of machine learning
For most companies today, transforming unstructured data into readable assets involves processes that are mostly manual. To create better value, businesses need to automate these processes and free up resources from mundane tasks, and this is where machine learning comes in. Machine learning is an application of artificial intelligence (AI) that provides systems with the ability to automatically learn and accomplish the equivalent of continuously running programmes in a fraction of the time.
Businesses can utilise machine learning to build models that work in the specific business function and industry. In the case of dark data, the process of learning begins with data observations, in order to look for patterns and make better decisions in the future based on previous examples. Typically the system alerts business users to exceptions, and remembers each time they address those exceptions so that it can offer a solution the next time a similar event occurs. If users keep accepting the recommended solution, the system will learn automatically.
Structural changes are required when implementing machine learning, which costs time and money. But it’s worthwhile long term and the business benefits will guarantee a high return on investment.