Deep data refers to big data that is of high quality, relevant, and actionable. Data experts have processed it for the use of all employees in an organization.
Deep data is typically broken down into smaller sections for more efficient handling. All unnecessary or unusable information is removed, ensuring relevance.
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Deep Data versus Big Data: Is There a Difference?
Deep data is a subset of big data. As such, they share similarities. Both use the same information collected daily by businesses worldwide.
Big data and deep data undergo analytics to help organizations predict industry trends and develop more effective operational strategies. Their primary difference lies in how they are analyzed.
Big data refers to the entire collection of information an organization has access to. It includes even irrelevant details. While users can still identify trends and analyze it, they often have to filter the relevant from the irrelevant data first.
Deep data is what’s left when experts are done taking out the irrelevant information from big data. All users are left with then is usable information.
Here’s an example: Let’s say that a clothing shop with multiple locations worldwide collects all of its customers’ past purchase data. That would allow it to predict what products to offer and how many around the same time each year. All of their information (e.g., product purchases, personal details, etc.) gets stored in a database (i.e., making up big data). To make more accurate decisions, the company needs to know in which stores each purchase was made. That way, it can stock up more products in stores that sell more instead of providing the same number in each store. It can save more on shipping costs should a better-selling store run out of stocks.
In sum, deep data can help by segmenting the big data by purchase location. Instead of just knowing the total number of products to produce and distribute to stores, the company would know how many stocks to put in each store.
Challenges with Deep Data
As shown by the example, deep data is more beneficial than big data. Using it comes with challenges, though, such as:
Variations in Data Quality
Transforming big data into deep data can be challenging because information quality can vary widely. In most cases, some relevant information can get lost during data gathering. Sometimes, it can be inconsistent with other information stored in a database, and so are left out. When that happens, users can miss out on relevant data.
Inability to Convert Data
Another challenge lies in organizations’ inability to convert information into actionable data. It often takes ages for users to successfully convert information into measurable inputs, which slows down data analysis.
The delay often stems from using multiple platforms within an infrastructure. All systems and platforms collect and store data in different formats. For deep data to work, all information, regardless of source, must take the same format without losing their quality.
Access to the Right Tools
Part of making data formats and gathering, and analytical processes consistent is choosing the right analytics tool. In most cases, that requires increasing the analytics budget to use the right tools.
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While deep data usage has challenges, recently launched technologies can help organizations benefit from it. If you are already using big data to enhance operational efficiency, shifting to deep data may be the next step.
