Machine Learning Will Convert Your Unstructured Data into Structured Data for Usable Sources of Insight

Machine Learning Will Convert Your Unstructured Data into Structured Data for Usable Sources of Insight

Each second, huge amount of data is created and collected as billions of people interact, shop, study or order online through the power of social media people stream movies, find jobs, send texts, share pictures and learn new skills. These interactions create data and businesses have a data problem; specifically, an unstructured data to structured data problem to generate intelligent insights.

Most of the data that organizations collect are unstructured, which does not easily conform to an existing data model like structured data or even semi-structured data can fit into. For many organizations, unstructured data is, more or less, useless until resources are engaged to transform them into clean and insightful information.

Imagine you are looking for a style change and are excited to shop. You order some shirts and pants online as finished products. Imagine if the shopping boxes are filled with lumps of fabric, cotton, some thread and some disassociated buttons. Technically these are the materials that would have been the inputs for your clothes, but it is useless until they are clubbed together to form a finished product. It would take a lot of time, tools and an efficient mind to convert these inputs into your requirement shirt and pants. That happens with unstructured data too; it is useless and no useful insights can be drawn just like getting lumps of fabric, cotton, some thread and some disassociated buttons instead of a well-tailored shirt and pants.

Integrating Machine Learning and Unstructured Data

Giants like Facebook have time and again discussed the importance of data. Nav Kesher, head of data sciences for the Facebook Marketplace Experience asserts, "For organizations to get use out of such data requires significant time and money investment." Addressing at the AI Summit in San Francisco, "in the technological era about 80% of the digital data is unstructured, and while businesses have, in the past, ignored or forgotten about such data, that is slowly starting to change".

As the computation power becoming cost-effective, organizations are empowered to achieve cost-effectively algorithms required to convert unstructured data into structured data for intelligent analysis. The algorithms have become more advanced, with special focus and funding going to emerging technologies like AI and machine learning tools.

The Worthless Unstructured Data

Machine learning models, after being trained, can be deployed automatically and efficiently to label and categorize unstructured data. This is a continuing process, certainly expensive and time-consuming, using well-trained resources to change unstructured data to structured data in a quest to business excellence.

It is a ripe time to invest in technology for businesses looking to make use of their unused data with the help of machine learning tools. The initial step, i.e. getting started at the business level, can be as simple as setting a business goal to an organization.

The Initial Steps to Data Cleaning

Initially, when organizations start to tackle their unstructured data problem they should begin the process by setting business goals which should answer questions like, does the organization need classification or needs clustering. This answer will ultimately set the course of the processes how the data sources should be evaluated. This will enable companies to move fast and be smart, and pick out data that is relevant to the goal, prioritizing steps to drive business insights from unstructured data to structured data.

This gives admins and organization heads an opportunity to responsibly evaluate analytic methods; log analytics tools and data storehouse platforms. This will keep organizational goals a priority analyzed through different systems and vendors.

After setting the business process to drive unstructured data into structured, the next step is data cleaning. It is the process to identify and fix errors in the data, like typos or formatting issues, which can be very tedious and time-consuming. Analysts should look for broad errors in the data and apply a machine learning model to automatically correct those errors. The whole experience can be frustrating and monotonous but it does feel good when the model runs.

Applying Data Model and Visualization Tools

After the analytical model removed data errors and redundancies, the next step in changing the unstructured data to structured data is data modeling. Analysts' study data relationships and mark co-relations in what could be a lengthy process, but a very important one, as these data relationships give an insight into what intelligence a business can drive from them. Data modeling differs from case to case and client to client. Organizations have to figure out for themselves the accuracy they need.

The final step is data visualization, driving insights from the now structured data through visual aids like Tableau or Qlik Sense. There are numerous graphs and charts organizations may deploy to visualize data, so an intelligent evaluation is important with the choices of data visualizations tools at hand.

Ultimately, data science is not just about building models, it is about the process of data transformation from raw information to make them mean something to someone. Data Science is all about the art of being simple and making other people understand the power of data.

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