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While empowering the professionals specialized in data and business analytics can be effective, it cannot work without the active support of the companyu2019s leadership team. As a data specialist, linking the benefits of data analytics with business objectives is likely to ensure that more resources and data access are available for you.
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What is unstructured data and how is it different from structured data?
While structured data refers to the type of data that can be stored in traditional database systems, unstructured data refers to data (without any predetermined format) such as social media posts, E-mail messages, images and videos, online searches, product reviews, and many more.
According to the International Data Corp. (IDC), Big data is estimated to grow from 33 zettabytes in the year 2018 to 175 zettabytes by the year 2025. IDC also estimates that almost 90% of this digital data is already unstructured data.
Small and large business enterprises are gradually becoming aware of the massive amounts of unstructured data that they possess and are trying ways to derive maximum value from them.
Extracting unstructured data So, why is unstructured data so important? By industry estimates, a nominal 10% increase in data accessibility for a Fortune 1000 company can net an additional $65 million in the company’s net income.
As compared to the limits of structured data, business enterprises can use a variety of data analytics tools to extract valuable insights from the large volume of unstructured data, which in turn, can drive productive business decisions. These insights include identifying product or service-related issues, market trends, or customer expectations from a brand.
As an integral part of machine learning, natural language processing or NLP-based solutions are being deployed by enterprises to interpret human language and sentiments (positive or negative) from available data on social platforms.
Machine learning tools are designed to analyze and interpret multiple variables at the same time and provide more better insights to the business. An example of this is in the games development industry where machine learning is improving games programming and the overall player experience.
Integrating structured and unstructured data Business enterprises can derive value from their data by combining their structured and unstructured data analysis rather than simply focusing on either of them
While structured data represents the “what” of customer understanding (example, purchase histories, demographic data), unstructured data represents the “how and why” of the customers (examples, product reviews, feedback comments).
Organizations need to build a business case for big data analytics that addresses its overall business objectives This process primarily comprises of the following steps: Defining the overall challenges faced by the business. Drafting the main questions that need to be answered to meet each challenge. Identifying and leveraging the key data points that can resolve each of these questions.
While empowering the professionals specialized in data and business analytics can be effective, it cannot work without the active support of the company’s leadership team. As a data specialist, linking the benefits of data analytics with business objectives is likely to ensure that more resources and data access are available for you.
