BASIC SEMANTICS, Blog

3 main problems with big data management

The problems with big data depend on scale, and perspective. The bigger the data, the greater potential for problems.

Although being able to take advantage of the great amount of unstructured information available to any organization (news feeds, social media, online content, documents and reports on the intranet etc.) may be challenging, big data represents a unique source of strategic knowledge that companies and government agencies must use. The ability to “make sense” and effectively manage big data provides important opportunities to any organization. Customer care optimization and better decision-making support are just two of the most powerful areas where it can be applied.

To understand the main problems with big data management and identify how to exploit big data, we’re going to analyze the three most significant issues:

1. Problems with big data: Insufficient time

Given the enormous amount of information that is created every day, too much time is required to intelligently manage all of this knowledge, integrate and relate data in different formats and extract all of the useful information. We need a technology that can help us read and automatically analyze the content of thousands of text documents. This helps us understand what a document is about without opening it, and allows us to easily search (and retrieve) the specific document we’re looking for (and not hundreds of documents).

2. Ineffective information access

To solve the problems with big data, we need a fast technology, but speed alone is not enough: the technology also has to be intelligent. Traditional technologies quickly analyze documents but treat the text only as a series of keywords or using a statistical approach based on frequency. These systems don’t understand the meaning of the text they’re analyzing because they employ superficial processing. This results in returning too many irrelevant results… and the problems remain unsolved. We can access the information but we can’t find the precise information that we’re looking for. Only an intelligent technology, able to read and comprehend language the way people do, can help organizations find and exploit information more effectively.

3. Limited integration capabilities

The problems with big data are rarely sorted out with a single technology: In many cases we need to combine different systems and platforms into a structure to leverage the true potential of big data. If a big data analysis technology doesn’t offer a comprehensive integration capability, the whole process of big data management can collapse. When we choose a new technology, it’s worth it to evaluate how well the technology integrates with your current systems, as well as the leading platforms. Successful integration between solutions can expand your overall capability to respond to your specific data analysis needs. By incorporating different and flexible systems (ETL platforms, data warehouses, analysis engines, etc.), we can take on the complex challenge of big data management.

In summary, only a semantic technology gives us what we need to solve the problems with big data: time (it reads automatically), precise results (it reads the way humans do), increased capabilities (it can be easily integrated with other applications).


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