If you are looking to start an AI initiative, your enterprise should understand data science and the importance of this highly specialized field.
The future of successful business depends on artificial intelligence and machine learning. According to a recent IDC Perspective, “How Does Data Culture Drive Enterprise Intelligence,” 83% of CEOs want their organizations to be more data-driven. 87% of CXOs shared that becoming an intelligent enterprise is a top priority.
Data scientists could be your key to unlocking the potential AI and ML—but what do data scientists do? What should you consider when seeking Data scientists? Let’s discuss this further.
In short, a data scientist’s job is to analyze data for actionable insights, but there is more to it.
Data Scientists have several day-to-day responsibilities:
Data scientists must have in-depth knowledge of math and statistics. Being a naturally intuitive and curious person is also pertinent. When data scientists view data, they think, “What can I do with all the data?” The data scientist’s goal is to find the potential in the data.
Data scientists are also highly educated. According to an industry survey by Google’s Kaggle, 51% of the 2,675 employed data scientists have a master’s degree, while 24% have a bachelor’s.
The short of it, all industries can and will benefit from data scientists. Data science plays an important role in nearly all aspects of business planning, review, and strategies. Data science can provide insight into the clientele that helps a business create stronger marketing campaigns and targeted advertising to increase sales. It can assist in weighing financial risks, detecting fraudulent transactions, and preventing equipment breakdowns in manufacturing plants and other industrial settings. It helps block cyber-attacks and other security threats in IT systems.
Data science teams often have limited access to text data, let alone annotated data. Generating synthetic text is an option but can be challenging without the right tools. Data collection on its own can take away many valuable hours yet is critical for fine-tuning a model properly. To remain competitive, data scientists need to simplify the processes of prototyping, building, and releasing models.
Intelligent automated solutions to support data collection and extraction can swiftly and automatically gather specific information. Automating data extraction tasks with AI can augment teams with modern technologies and streamline data-gathering. Data science teams can extract data from documents (PDFs) and turn it into a CSV file for model building. This is just one example of how AI can support a data science team, there are endless possibilities.
Using Amazon Textract and Mantium, we can provide an AI platform for Intelligent Data Processing (IDP). Documents are processed through Textract to extract the necessary data from the document. Large language models (LLM’s) perform a wide range of cognitive tasks like classification, translation, question and answering, text generation, search, and summarization. Mantium leverages the power of LLM’s, to provide several industries with the ability to perform robust natural language processing (NLP) tasks such as personal identifying information (PII) detection, document classification, and data extraction to make better use of the data living within your documents.
With Mantium and Textract, users gain accuracy and security and improve their data science team morale, freeing their time to build and fine-tune models.
Most recent posts