Examples of Dark Data

• Media: Audio, video and image files oftentimes will not be indexed, making them difficult to gain insights from. Contents of these media files, such as the people in the recording or dialogue within a video, will remain locked within the file itself.
• Social Data: Social media analytics have improved drastically over the last few years. However, data can only be gathered from a user’s point of entry to their exit point. If a potential customer follows a link on Facebook, then send the visited website to five friends in a group chat, the firm will not realize their advertisement had 6 touchpoints, not just the one.
• Search Histories: For many companies, especially in the financial service, healthcare, and energy industries, regulations are a constant concern. As legal compliance standards change, firms worry that they will end up deleting something valuable.

As analytics and automation improve, more dark data is beginning to be dragged out into the light. AI, for example, is getting far better at speech recognition. This allows media files to be automatically tagged with metadata and audio files to be transcribed in real time. Social data is also starting to be tracked with far better accuracy. In doing so, companies will be able to better understand their customers, their interests, and their buying habits. This will allow marketers to create limited, targeted ads based on a customers location that bring in more revenue while reducing cost.

The explosion of data we are currently seeing is only the tip of the big data iceberg. As IoT and wearable devices continue their integration into our daily lives, the amount of data we produce will only grow. Companies are looking to get ahead of the curve and ensure they can gain as much insight from this data as possible. If these firms do not have a plan to create actionable insights from this currently dark data, they ultimately could fall behind and lose out to competitors with a bigger focus on analytics.

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