Unstructured Data Extraction
The increase in digitization of information, mixed with multiple transactions has resulted in a flood of data. The consistent increase in the speed of digital information has led the global data to double in very short time intervals. As per Gartner, around 80% of data with organization is unstructured data, which is comprised of data from emails, social media feeds and customer calls. This is in addition to information logged by the user devices. While it would be frightening to even make an appropriate analysis from organized data, it is even tough to make proper sense of this unstructured data.
Unstructured Data Analysis Information Extraction
Analyze semi- structured and unstructured data sets for improved business decisions
As an outcome, organizations have to analyze semi- structured and unstructured data sets to extract structured data insights to make improved business decisions. These decisions include shaping customer sentiment, finding customer needs and identifying the offerings that will relate more to the customer requirements.
While filtering big amounts of data can look like a tedious work, there are benefits. By analyzing large data sets of unstructured data, you can categorize connections from unconnected data sources and find specific patterns. And this analysis enables the discovery of business as well market trends.
Unstructured to Structured Data Conversion
There are seven steps to analyze unstructured data to extract structured data insights as below
First analyze the data sources
Before you can initiate, you need to analyze what sources of data are essential for the data analysis. Unstructured data sources are in found in different forms like web pages, video files, audio files, text documents, customer emails, chats and more. You should analyze and use only those unstructured data sources that are completely relevant.
1. Know what will be done with the results of the analysis
If the end result is not clearer, the analysis may be unusable. It is key to better understand what sort of outcome is required, is it a trend, effect, cause, quantity or something else which is needed. There should be clear road-map defined for what would be done with the final results to use them better for the business, market or other organization related gains.
2. Decide the technology for data intake and storage as per business needs
Though the unstructured data will come from different sources, the outcomes of the analysis must be injected in a technology stack so that the outcomes can be straightforwardly used. Features that are important for selecting the data retrieval and storage totally depends on the volume, scalability, velocity and variety of requirements. A prospective technology stack should be well assessed against the concluding requirements, after which the data architecture of the whole project is set-up.
Certain examples of business needs and the selection of the technology stack are:
Real-time: It has turned very critical for E commerce companies to offer real-time prices. This requires monitoring and tracking real- time competitor activities, and offering offerings based on the instant results of an analytics software. Such pricing technologies includes competitor price monitoring software.
Higher availability: This is vital for ingesting unstructured data and information from social media platforms. The used technology platform should make sure that there is no loss of data in real- time. It is a better idea to hold information intake as a data redundancy plan.
Support Multi-tenancy: Another important element is the capability to isolate data from diverse user groups. Effective Data intelligence solutions should natively back multi- tenancy positions. The isolation of data is significant as per the sensitivities involved with customer data and feedbacks combined with the important insights, to meet the confidentiality requirements.
3. Keep the information stored in a data warehouse till the end
Information should be well stored in its native format until it is really estimated beneficial and required for a precise purpose, maintaining storage of meta-data or other information that might help in the analysis if not now but later.
4. Formulate data for the storage
While maintaining the original data files, if you require to enable utilization of data, the best option is to clean one of the copies. It is always better to cleanse whitespaces and the symbols, while transforming text. The duplicate results should be detached and the out of topic data or information should be well removed from the data-sets.
5. Understand the data patterns and text flow
By using semantic analysis and natural language processing, you can use Parts- of- Speech tagging to fetch entities which are common, like “person”, “location”, “company” and their internal relationships. By doing this, you can build a term frequency matrix to better understand the data patterns and the text flow.
6. Text mining and Data extraction
Once the database has been shaped, the data must be categorized and properly segmented. The data intelligence tools can be utilized to search similarities in customer behavior when targeted for a particular campaign or classification. The outlook of customers can be resolute using sentiment analysis of feedbacks and the reviews, which assists in better understanding the product recommendations, market trends and offer guidance for new products or services launch.
You can utilize Social Media Intelligence Solutions to extract the posts or the events that customers and prospects are sharing through social media, forums and other platforms to improve your product and services.
7. Implement and Influence project measurement
The end results matter the most, whatever it might be. It is vital that the results are provided in a required format, extracting and offering structured data insights from unstructured data.
This should be handled through a web data extraction software and a data intelligence tool, so that the user can execute the required actions on a real-time basis.
The ultimate step would be to measure the effect with the required ROI by revenue, process effectiveness and business improvements.
Conclusion
The actual value can be derived when structured, semi- structured and unstructured data analysis is combined for a 360-degree outlook.
To know how you can mature your business outcomes utilizing DataCrops web data extraction solutions and data intelligence platform, connect for a free consultation with one of our experts today.
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