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Lessons You Can Learn From Unstructured Data

Files, photos, spreadsheets, emails, and social media posts all contain unstructured data. It does not have a set format. As a result, moving such data into a target system becomes difficult. Moving your unstructured data to a data lake via ELT (Extract, Load, and Transform) processes is the simplest way to process it. Most organizations have extensive structured or unstructured data collections, but the connections between them are not well-defined or even made. Nevertheless, the data may have value, as is evidenced by a recent study that mapped employees’ home addresses to their retention data and parking pass assignments to their employee’s parking status. The study also found that commute time played a significant role in determining voluntary turnover.

Unstructured data

The majority of data is unstructured. This data can take many forms, from text and documents to audio and video recordings. It can also be tied to associative data and spatial events. You can gain a competitive advantage by using unstructured data to your advantage. Let’s take a look at seven great lessons you can learn from unstructured data. Below are seven of the most important lessons you can learn from unstructured data.

When working with unstructured data, blend it with transactional data. The best organizations analyze both kinds of data to discover opportunities and challenges. Unstructured data can be both operational and strategic. By blending it with structured data, leaders can improve their decision-making. They can find opportunities to improve their company’s processes by analyzing the data. From entry-level employees to the CEO, leveraging this information is the most effective way to improve your bottom line.

Unstructured data is more difficult to interpret than structured data. As a result, previously developed methods don’t work well with them. For example, AI algorithms trained to detect objects in 2D video data will not work well with 3D video data. In both cases, however, they represent the same real-world environment. These AI algorithms can help build more accurate virtual environments or improve genome-based precision medicine.

Use unstructured data to drive new product development and marketing efforts. Smart cities are the next frontier, so why not get your hands on the data that will shape them? For example, the technology behind smart cities, such as those in Smart Cities, will greatly benefit all kinds of industries. For instance, landlords can improve the quality of life for tenants by monitoring the health and safety of their properties with data. In addition, governments can make better decisions by combining structured and unstructured data.

As a result, unstructured data is a powerful complement to structured data. Examples of unstructured data include video analysis of foot traffic, social sentiment, influencer behavior, and product placement. Even if structured data is the backbone of your organization, unstructured data can provide valuable insights into how to improve it. But how do you tap the potential of unstructured data? Unfortunately, it’s not that easy.

Semi-structured data

If you’re working in Human-Computer Interaction, semi-structured data is essential for understanding your interaction design problems. Qualitative methods help to understand user needs, as well as to evaluate existing designs. Although qualitative methods vary, semi-structured data studies share many of the same data gathering and analysis methods. 

 

 

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