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Data may be entered by humans through their online activities or generated by machines. Structured data can be found in relational databases or RDBMS where the fields have a pre-defined length. Some examples are like the social security numbers, automobile registration numbers, zip codes, etc. It is easy to analyze this data because it fits a set pattern. Your algorithm can be designed to easily recognize this data via markers or identifiers.

When data deviates a little from a formal structure but is still relevant in its context, it is called semi-structured data. This data is recognized by the relational databases with the help of identifiers or tags to place the data under hierarchical categories. Only then can it be analyzed with the help of data management tools.

EXAMPLES OF SEMI-STRUCTURED DATA

One cannot say that semi-structured data never fits a preset data model or schema. Some examples of human-generated semi-structured data are:

  • Text data like powerpoint presentations, word processing files, etc.
  • Emails which do to conform to a specific internal structure except through the metadata, tags, and keywords.
  • Social media updates, tweets, etc.
  • Images and videos on YouTube, Pinterest, Instagram, and photo sharing websites.
  • Online communications like chats and video calls.

Some examples of the machine-generated semi-structured data are:

  • Digital surveillance imagery and video footage, oil and gas exploration, spatial imagery, etc.
  • Sensory data like traffic, weather, seismography, oceanography, etc.
  • Satellite imagery for defense purposes.

PROBLEMS ENCOUNTERED WHILE ANALYZING SEMI-STRUCTURED DATA

Data Analytics algorithms cannot follow rigid format while analyzing semi-structured data. Email is the best example of a semi-structured data format.

Let us say that a business owner’s emails are usually addressed to potential customers in her contacts list. They will usually have some specific attributes like date, time, size of the file, product details, etc. Yet, there will be many variables like changing prices, new product names that your algorithm may not recognize.

As against emails, spreadsheets are not among the list of human-generated semi-structured data. In most cases (maybe not all); since the data on spreadsheets is already arranged in predefined cells and can be associated with specific identifiers or markers in your algorithm, they cannot always be called semi-structured.

10 WAYS TO DEAL WITH STRUCTURED AND SEMI-STRUCTURED DATA

Despite the vast amount of online data that machine learning tools have to analyze, your Business Intelligence model can source real-time data, identify it as structured or semi-structured, quantify it to make it scalable, and interpret it accurately to produce the best results.

These are 10 effective ways to deal with structured and semi-structured data:

1. Using lexical analysis: Lexing is the process of arranging a set of characters into tokens or strings with assigned values so that they can be easily identified. Also called tokenizing, your algorithm should incorporate a parameter that tokenizes a string of characters into identifiable entities.

2. Seeking out identifiers: In very large and unrelated schema (or rather schema-less data) the best way your machine learning tool can recognize relevant information is with the help of semantic text classification. Most data scientists and agencies use automated document classification labeling for data to make it scalable by the machine learning tools. In short, there are certain keywords, metadata, alt tags, etc. that will explain the context of the content.

3. Normalizing the data: Your algorithm must be designed to normalize the semi-structured and unstructured data into a structured format which makes sense. Normalization involves three steps:

  • Ensuring that similar or overlapping data does not produce erroneous outputs,
  • That your algorithm has made provisions to incorporate all key parameters that would influence the output,
  • Making sure that isolated but essential data is not missed out. You will have to make a provision for such data to be tagged along with similar data before it is analyzed.

4. Analyzing sentiment: This is a process of tracking social media activities of all kinds that will help your algorithm to track and analyze customer opinions.

5. Web scraping: To know whether your machine learning model works at the highest level of efficiency, the best way to test it is by using semi-structured or unstructured data. With the help of web scraping, you can collect and store real-time data and use that as a test sample to check the efficacy of your model.

6. Natural-language processing (NLP): This aspect of Artificial Intelligence deals with creating software that acts as an interpreter between human language and machine language. A lot of important data is shared via audio and video calls. Your algorithm should have the ability to understand natural language and interpret it for accurate predictions.

7. Pattern sensing: Semi-structured data can also be analyzed with the help of tracking trends and patterns in customer behavior. Give your machine learning tool an antenna which recognizes particular signals that might help businesses (your clients) to improve their sales strategies.

8. Predictive analytics: Making predictions about future events in machine learning may seem farfetched. But in reality, it is a complex but an almost-accurate science. Based on the current behavior of customers, an algorithm can be designed to accurately predict their future choices.

9. Avoid over-fitting: Semi-structured data will definitely contain a large volume of irrelevant information. Regularize your data model so that it automatically eliminates irrelevant data.

10. Prevent wrong population: Designing a hybrid machine learning tool will reduce the chances of the data being populated with the wrong information. Sometimes web users enter wrong details. Your algorithm must be capable of recognizing such instances and ignoring them during data analysis.

To sum up, whether the data is structured or semi-structured, algorithms with a comprehensive set of parameters can successfully deal with both types of data. As long as you follow these 10 effective ways to deal with structured and semi-structured data, you will have the near-perfect machine learning tool.

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