11.21.2022 Executive Data Bytes – Is all Big Data built the same?

Executive Data Bytes

Tech analysis for the busy executive.

Welcome to another edition of Executive Data Bytes! You probably already know something about big data, but are you aware of its different types and their uses? This week, we are getting into the nitty-gritty of Big Data.

Focus piece: “Big Data Definition”

Executive Summary

Big Data analytics spans across almost every industry to change how businesses are operating on a modern scale. So, you definitely want to know about Big Data. This article from BuiltIn answers the questions, “What is Big Data?” and “How does it affect me?”

Key Takeaways

• Big Data requires systems that can process its various structural and semantic differences. NoSQL databases provide the flexibility needed to cohesively analyze seemingly disparate sources of information.
• Big Data analytics is used in nearly every industry to identify patterns and trends, answer questions, gain insights into customers and tackle complex problems.
• Roughly 80 to 90 percent of all Big Data is unstructured, meaning it does not fit easily into a straightforward, traditional model. Everything from emails and videos to scientific and meteorological data can constitute a Big Data stream, each with their own unique attributes.
• Big Data tools can analyze large data sets and identify patterns in real-time, saving time, money and energy.

Focus piece: “What are the Types of Big Data?”

Executive Summary

The Internet age has created an unfathomable amount of data. Big Data analytics can provide virtually any kind of insight an enterprise could be looking for. Read this SelectHub article to learn more about the types of Big Data analytics you're working with.

Key Takeaways

• Big Data analytics is based on three terms: extract, transform, and load. These terms explain how data is harvested, formatted to be readable by an application, and then stored for use.
• Structured data is the easiest type of data to analyze because it requires little to no preparation before processing.
• Not all data is as neatly packed and sorted with instructions on how to use as structured data is. The consensus is no more than 20% of all data is structured. So it’s naturally called unstructured data.
• The hardest part of analyzing unstructured data is teaching an application to understand the information it's extracting. This involves translating the data into some form of structured data, which is often done using text parsing, natural language processing and developing content hierarchies via taxonomy.

Focus piece: “What is Big Data?” 

Executive Summary

Data is one of the prime factors of any business purpose and Big Data is the most widely used technology these days. This article from IntelliPaat dives into the characteristics of different Big Data types, and how you can use them to drive performance in your business.

Key Takeaways

• Big Data is technically applicable at all levels of analytics, but the ETL process for each data structure varies.
• The ETL process stores the finished product in a data warehouse. However, structured data makes up only a slim minority of all data.
• Apart from the three main types of data, there are subtypes of data that are somewhat pertinent to analytics, such as social media, machine (operational logging), event-triggered, or geospatial data.
• To extract value from data, you need to mine the data, clean the data, and analyze the data to find out insights, results, etc. that were not possible earlier.
• Walmart is the biggest retailer in the world with the most revenue. Consisting of two million employees and 20,000 stores, Walmart is building its private cloud to incorporate 2.5 petabytes of data every hour.

Let's talk data!

Who We Are

Data Products partners with organizations to deliver deep expertise in data science, data strategy, data literacy, machine learning, artificial intelligence, and analytics. Our focus is on educating clients on varying aspects of data and modern technology, building up analytics skills, data competencies, and optimization of their business operations.