3 Big Advancements in Unstructured Data Analytics

Unstructured data analysis has become a buzzword in recent months. Advances in the field have opened the door to a new way of doing business, one which puts IT professionals in a position of thought leadership within their organizations. This article will take a look at 3 major advancements in the field of unstructured data analytics that are changing the way that organizations forecast and make decisions.

Structured data is information that is organized to a high degree. It is most often found in databases and can be searched with a basic search engine algorithm. The opposite of this is unstructured data, which can be scattered across an organization or the entire internet. Some examples of unstructured data are:

• Email correspondence
• Social media posts
• Video, sound, or image files
• Web browsing behavior

Due to its very nature, unstructured data is difficult to analyze. In the past, analysts were not able to do much with it. Computers lacked the power and sophistication to work with large, unstructured datasets.

Unstructured Data: Just Another Buzzword or a Topic Worth Watching?

In recent years, unstructured data has begun to generate much more interest among enterprises and analysts for a couple of reasons. One is that there is now a vast, unstructured gold mine of data out there, filled with nuggets of insight that could help businesses understand their customers and deliver better products and services. According to IBM estimates, 90% of the world’s data was created within the past 2 years. Most of this data is unstructured.

The other reason for the surge in interest in unstructured data are several recent advancements that are making it possible to collect meaningful insight from large sets of unstructured data. These insights can be used to make intelligent business decisions. Here are 3 big advancements in unstructured data analytics.

3 Advancements in Unstructured Data Analytics

New Tools for Intelligent Analysis

At a high level, the best way to analyze unstructured data is to look for patterns. There are several new tools available that use artificial intelligence to analyze just about any kind of unstructured data.

Some of these tools include:

• Cognitive computing
• Natural language processing
• Machine learning

These advancements will automate the process of curating and managing data. Analysts are freed to focus more on analysis and prediction. These new tools will also speed up the cycle of data curation, preparation, and analysis. Enterprises will be able react to changes in their markets in real-time.

New Methods of Data Processing

Big data requires a lot of computational power. In many cases, it requires more power than one machine can handle. This problem has caused engineers to look for new approaches to handling tremendous amounts of data. The advent of cloud computing is making it possible to handle large data sets using several networked devices to create vast computing grids.

Unlike traditional parallel processing, this ‘device mesh’ can distribute and analyze information across several end points. Consensus algorithms are then used to optimize the results. The model learns by using past data with the expectation that the device mesh will work better on future data. This method could be employed to create a smart SPAM filter or bank fraud alert system, for example.

New Opportunities for Business Insight

The rise of big data analysis is creating new business opportunities. Enterprises can analyze all datasets, unstructured and structured, from sources both inside and outside of the organization. They can use the resulting insights to make informed decisions to drive growth and innovation.

Advances in data storage, networking, and computing speed translate into a much shorter window between analysis and value delivery. In addition, they provide deep insight into all aspects of an enterprise, allowing for much more precise forecasting and decision-making.

The Rise of the Insight Driven Organization

The age of big data analysis has given rise to a new kind of enterprise: the Insight-Driven Organization (IDO). It is characterized by a combination of processes, people, and strategies that leverage technology to drive business decisions.

Those Who Hesitate May be Lost

Some enterprises are hesitant to adopt these new methods of collecting and analyzing business data. However, many are recognizing that to be successful, marketing intelligence has to be delivered in real time. Recent advancements in analytics, cloud computing, and machine learning are making it possible.