Getting Started With Unstructured Data

Data is a huge source of competitive advantage, but it's also a massive challenge for companies. About 80% to 90% of all data is unstructured, and many companies struggle to organize it, according to Deloitte’s recent survey.

There are a variety of ways that businesses use unstructured data, such as for customer service, product development, and sales and marketing. For example, retailers and manufacturers use unstructured data to learn how consumers feel about their products or services through sentiment analysis. Similarly, producers use sensor data to monitor equipment performance and make predictive maintenance decisions.

When used with analytics tools that work at compute speeds and automatically learn from users' activity, unstructured data can help companies improve their business intelligence. For example, natural language processing (NLP) and pattern recognition are common big data analytics tools that can help organizations understand their customer feedback through sentiment analysis and text-mining algorithms.

Getting Started With Unstructured Data

To get started with unstructured data, businesses should first define their goals. Defining these goals helps them determine what kind of data they need to collect and how they want to use it.

Ultimately, the information they collect will help them understand how to best serve customers, meet regulatory compliance requirements and enhance their business operations. It can also be a way to understand how competitors and other entities are reacting to this product or service.

The next step is to identify where to store the data and what tools you need to analyze it. For example, if the unstructured data is in the form of text, you might need to use a natural language processor (NLP) tool to extract and clean it. If it is in the form of images, you might need to consider a visual analytics tool.

Preprocess Your Unstructured Data

Before you begin analyzing your unstructured data, you should pre-process it to remove noise and cut it into manageable segments (like opinion units). This makes it easier for machines to analyze.

Analyze Unstructured Data With Machine Learning

Using machine learning, analysts can quickly learn to recognize the patterns and trends in data that they have collected. This allows them to make more informed business decisions, which can lead to more successful outcomes.

For example, a company that is heavily regulated can use analytics tools to examine its email threads and chatbot conversations for potential noncompliance issues. This insight can help a company avoid costly fines, litigation and lost business.

Unstructured data is a growing resource for companies that are willing to take the time and energy to use it properly. But it can be difficult to get started if you don't know what your goals are.

Taking the time to define these goals will allow you to focus on what you need to do with your unstructured data and help you create a plan to achieve those goals. It will also help you find the best tools for analyzing your unstructured data so that you can gain valuable insights and make better business decisions. For more info, check out this related link: https://en.wikipedia.org/wiki/Data_structure.