Artificial intelligence

Why Organic Data Is Prefered For A Successful Data Science Model


Thanks to Internet search engines that build data sets with every entry, Twitter generates tweet data continuously, traffic cameras digitally counting cars, and internet sites capturing and storing mouse clicks. Our digital society is assembling data on massive amounts and are self-measuring in increasingly broad scope. Organic traffic is a metric that’s enabling you to measure how many visitors came from searches made on search engines. Text data is one of the largest forms of unstructured data and is ever-growing. And text analysis is the process of analysing unstructured and semi-structured text data for valuable insights, trends and patterns. 

One of the biggest challenges while working with text data is you need a large training data set to build robust models. It is essential to ensure that the training data is organic, implying that it is rich, robust and reliable. A learned data science professional is a best fit for resolving dynamic business issues.

Here are 5 reasons to be extra-cautious while collecting training data for conducting supervised training ML models:

  • Consistency in Subjectivity

There can be many instances where you encounter a subjectivity conflict regarding the meaning of a certain text to a variety of users. An example of credit related sentiment analysis, where a problem of defining a negative vs positive sentiment in an earnings call transcript may arise. An overlap of training data analysis can help check reliability and ensuring consistency in labelling subjective language. Maintaining consistency in training data prevents coexistence of multiple conflicting ground truth values for similar texts, which can introduce confusions in ML model.

  • Apply an Unbiased Approach

When starting to build a new supervised ML model that involves identifying and classifying a novel text data, you need to accumulate training examples to train the data. The data thus collected by using existing search bars or data queries and used keywords inherits the pattern that has already been used to search for data. This introduces bias to the supervised model training. The resulting model will be an underfit as it will rely heavily on the used keywords as well as other strong co-occurring words and will not be robust as if it were trained on thoroughly randomized data. 

  • Random Data is the Key

Building a randomised data set is the key to a strong model. This reduces the burden of collecting training data and provides a direction for construction of an organic training data set. By obviating the need to use search bars to find data and allowing the team to proceed directly to the next step of combing through a spreadsheet to appropriately label the randomized data. This iterative and collaborative process provides clarity as multiple rounds of text data randomization and labelling gives higher insight.

  • Early Error Detection 

The time invested in above measures help in understanding the data better and saves time in the later stages of model-building processes. Overlooking subtle yet key details in the training data in the beginning of the model training may cause a bias or variance error and lead to a poor model performance. This would eventually lead to spending excessive time tuning the model later or even in worst cases, shelving the project due to suboptimal model performance. A trained data science professional with the best data scientist certification can help avoid this big hurdle by applying expert knowledge to the developmental stages of the model.

  • Stringent Data Management

Large data science projects involving longer development time can be severely impacted by any change in the team members, any alterations of labelling definitions as the model evolves or any shift in the project scope. The training data thus collected on first day of the project can be entirely different than what was collected on say day 50. This clearly impacts the quality of the original training data and introduces systematic disturbance in the model.

There are a number of data science certification programs, to name a few- CDSP™ from USDSI™, Google Data Analytics Certificate, and others; that could help leveraging the benefits of this advanced skill and land you a great data science role in this rising industry. As we have seen in the above parameters, training data needs to be homogeneous for robust modelling. Strict training data management is needed throughout the model-building process by controlling and mediating the influence of multiple stakeholders. The conclusion is a clear yes towards building healthier models with organic training data. Keeping the above pointers in mind, you can definitely design a successful ML model.

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