The Ultimate Guide to Big Data for Your Business
Big data refers to a huge and complex chunk of data that is difficult to process using traditional methods. It is generally an amalgamation of structured, unstructured, and semistructured data that is collected by organizations on a daily basis. This data can be used to mine information and for machine learning, predictive algorithms, and many more analytical applications. The amount of data is not necessarily important, but how the organization uses that data is critical.
Types of Big Data
Big data is mainly classified into three categories:
- Structured Data has some predefined properties that make it easier to search and analyze. These types of data are backed by a value like type, length, size, etc. For example, “units produced a day” is an entry that has defined a product type and amount.
- Unstructured Data is just the opposite of structured data with no predefined properties. This type of data makes up the major part of big data. Social media posts, videos, and phone call transcripts are some examples of unstructured data.
- The line between unstructured and semi-structured data has always faded. At first glance, the semi-structured data looks unstructured. These types of data do not have the information in a traditional way similar to structured data. One of the examples is NoSQL documents. These documents do not have predefined properties but have some keywords that can be used for data extraction.
Importance of Big Data for Businesses
A need to understand trends, patterns, and preferences gave rise to the big data concept when a huge database began accumulating from the interaction of people with different systems. By analyzing big data, organizations can figure out the most valuable trends and customers. It also helps organizations create new services, experiences, and improved products.
Big data mining and using that information to improve their service is crucial for some organizations to outperform the competition. Big data is used in almost every sector and industry including IT and healthcare.
Categories of Big Data
Big data is generally characterized by several V’s. Doug Laney, an ex-Gartner analyst, defined the first three V’s as Volume, Variety, and Velocity in 2001. Nowadays, the list has expanded to five V’s.
- Volume: There is no minimum size unit that forms big data. Generally, big data is measured in terabytes.
- Variety: Big data includes various forms of data including documents, texts, and more which are processed and stored together in the same system.
- Velocity: Big data is typically generated at a very fast pace in real-time.
- Veracity: This category defines the accuracy and trustworthiness of different data sets.
- Value: Organizations derive value from big data by optimizing their services and improving products.
What Are the Uses of Big Data?
Big data has a wide application across the business world beyond simply in technology. It is used in:
Product Making: Information derived from big data is used to analyze the upcoming trend of products. Data from past products can be combined with current data to analyze whether the newer product will work or not. Pricing of the products is also decided by analyzing the best-selling product costs.
Testing: Big data can be used to analyze millions of bug reports, readings from the sensors, specifications of the hardware, and previous changes to recognize potential break-down points in the system even before they can take place. This analysis helps the maintenance team prevent the fault and system down.
Marketing: Marketers use the previous marketing campaign data to optimize future campaigns to garner improved outputs which is also a crucial part of digital marketing strategies. Big data helps fine-tune forthcoming campaigns by combining retailers and advertisement data and analyzing subtle preferences like image types, word choices, and colors.
Healthcare: Medical professionals use analysis from big data to find the side effects of drugs and catch early symptoms of illness. For example, imagine there is a new condition that is affecting people at a fast pace without early warnings.
However, the affected people reported headaches in previous checkups. This can be flagged by using big data analysis that all the previous headache-reporting patients are gonna have a similar illness. This analysis is not possible to do manually due to the big number of patients, different times, and locations.
Customer Experience: Analysis of big data is done by the product team after the launch of products to find out customer experience and product reception. Through these analyses, we can find out social media mentions, reviews posted on several platforms, and feedback found under product videos to find out what customers are liking about the product and what problems are they facing.
Machine Learning: Machine learning and artificial intelligence modules feed on big data to improve their accuracy and resilient predictive algorithms.
Recruitment & Staffing: Talent acquisition and HR professionals rely on recruitment tracking software to gather and understand key recruiting metrics that help optimize their business performance and hiring strategies. Recruiters can use this data to get insight into how they can improve their current processes, what is working for them, and even predict future revenue opportunities.
What Advantages do Businesses Draw From Big Data?
- Predictive analysis is one of the largest advantages of big data. Analytics tools use previous data to predict future outcomes more accurately, thereby, helping organizations and businesses make superior decisions. This also helps them improve operational efficiencies and reduce risks.
- Businesses are harnessing data from social media platforms to streamline their marketing strategies. Big data introduces businesses to the pain points of consumers and helps improve products and services.
- To produce actionable insights with great accuracy big data integrates the pertinent data from multiple sources. Around 43% of the companies do not have the required tools to extract relevant information from the bulk of collected data which costs them millions to do the job. Although, big data tools can hash out useful data, ultimately saving money and time.
- Businesses and marketers are leveraging big data analytics tools to understand the performance of their products and services. Analytics helps them generate more quality leads which ultimately boosts the revenue. This data also helps companies understand the response of customers toward their services and products and manage the investment of their valuable money and time.
- Big data insights always keep marketers and brands a step ahead of their adversaries. Brands can monitor the promotions and offers provided by their competitors and come up with better ones.
