• As unstructured data is growing much faster than structured data, it is essential that enterprises learn how to extract value from it, and quickly.
  • AI will be crucial in using unstructured data to resolve business problems and find new opportunities.

With organizations, communities, businesses, and products becoming more intelligent, data-generating endpoints have also rapidly increased. Data significantly impacts our day-to-day activities – at work, at home, and general. But to make the most of it, we must be able to gain actionable data insights, which rests crucially on our ability to comprehend that data in highly specific and specialized ways. This means that data ought to be organized and structured.

Across industries – from consumer products to healthcare – much of the data being generated today is unstructured. For example, data generated from internal messaging platforms don’t fit into traditional analytics models. But if their potential ought to be realized, we must redefine what it means to access the appropriate information at the right time and use it to improve outcomes.

Unstructured and semi-structured data constitute opportunities worth millions and hold the potential to offer new levels of access, services, and insights. Many organizations are already deploying Artificial Intelligence (AI) across unstructured datasets, which has helped them put vast amounts of unstructured data to good use. The insights gained from the analysis of unstructured data are then used to create recommendation engines, fake-news detection tools, and dynamic pricing models.

Several hurdles must be crossed to realize the potential of unstructured data truly. In this blog, we will cover the following:

  • Which data can be categorized as unstructured data?
  • How can we analyze unstructured data?
  • AI’s role in unstructured data.
  • Limitations of AI and unstructured data.

Making sense of unstructured data

Unstructured data is different from structured data in many ways. Most importantly, while the latter is more organized and formatted, the former does not have a predefined format – it can be stored in the form of sequences, point clouds, images, irregular meshes, and so on. It can also take different shapes, including multi-resolution, multi-channel, non-tabular, and sparse. This makes it difficult to collect, process, and analyze. Hence, traditional methods and tools cannot be used to analyze and process it. For this reason, unstructured data finds a purpose in BI and analytics.

Analyzing unstructured data

The methods to analyze data are broadly statistical. With so many entries, algorithms identify patterns/relationships between them. They may also apply an additional layer of structure to the data source – the process is often referred to as embedding the data or building an embedding.

To cite an example, a text can be searched for the 10,000 most common words that may not have anything in common in other books or sources. It can also be broken into different sections. This rough structure forms the base for statistical analysis.

Developing these embeddings is as much an art as it is a science — data scientists involved in this process design and test different strategies to develop a draft embedding.

Unstructured data in big data environments are analyzed using various techniques and tools. Other techniques used for unstructured data analytics include data mining, machine learning and predictive analytics.

Using AI on unstructured data

AI will be crucial in using unstructured data to resolve business problems and find new opportunities. Adaptability will be required across system architectures, storage and analytic services to bridge the gap between unstructured data’s inherent problems and AI’s current maturity.

For instance, to deliver better analytic results at the requisite speed and accuracy, technology must do a better job processing varied volumes of data at different scales. This includes the creation of highly specialized services that prioritize performance and scalability. In short, we require a platform that considers the what is, what if, what else, and what could be aspects of search.

Some critical features will be needed for the optimal solution to manage unstructured data at the required scale, size, and complexity level, namely:

  • Capability to host and serve data in many forms.
  • Must allow AI algorithms to search for patterns in the hosted data.
  • Must support a query language for database retrieval (exact search), Machine Learning-based pattern search (approximate search), and user-defined functions (domain-specific search).
  • Provide programming interfaces for database operations that are simple to use.
  • Should run on a variety of new server architectures (shared-memory, distributed-memory, or fabric-attached-memory technologies).
  • High-performance computing frameworks are included that can mature to manage ever-increasing data quantities and scale up to reduce time to insight.

Early results from firms that have started experimenting with unstructured data look promising. The level of detail with which they comprehend consumers, processes, and the firm as a whole suggests that there is a lot of room for growth. However, the widespread adoption of high-performance systems is yet to occur. It’s critical to rethink existing modalities and interfaces as we go toward increasing the integration of AI and unstructured datasets.

What artificial intelligence and unstructured data can’t accomplish

The quality of the data determines how well an algorithm will perform with that data. The data may often fall short of providing enough correlation for a definitive response to a query. This problem is exacerbated by the fact that unstructured data is more likely to contain useless information and much more noise. This makes it even more difficult for the algorithms to sift through data and remove useless parts.

On top of that, even when the algorithms are successful, some unstructured data analysis is ineffective because success is too rare. Detecting an event that occurs infrequently does not generate a lot of profit.

Poorly defined queries can yield unclear findings. Looking for insights into unstructured data will be fruitless because, without well-established definitions, the results can be just as unclear. For many unstructured projects, specifying a clear goal so the models may be trained effectively is a major difficulty.