The world of analytics is very broad and extensive. It has many aspects and terms that no one has yet discovered and explored. For any business or even application, analytics is an important thing to keep a close eye on. If you are an app developer or a business owner, you know how you can get and generate millions of pieces of data in only a matter of days. All of this data needs regular analysis using different techniques in order to help you optimize all the business processes and operations. That is why, in this article, we will demonstrate a very important comparison between traditional analytics vs big data analytics. Throughout this article, you will explore the definition of each type and how you can use both to enhance your business or applications.
What Is Traditional Analytics?
Let us start our traditional analytics vs big data analytics comparison with the traditional way of analyzing data. As the name implies, traditional analytics is a conventional way of gathering, organizing, and analyzing structured data to gain insights and make informed decisions. Statistics, data mining, and other business intelligence tools serve as the foundation of the traditional analytics techniques that have been in use for decades. It serves all types and sizes of organizations. So whether your business is small, mid-sized, or large, you are going to need the various implications of traditional analytics.
Traditional analytics is based on very structured and centralized sets of data that will, later on, be analyzed. These sets of data are usually kept in precise and fixed formats. Which is maybe why it took the name traditional analytics. This type of analytics includes various techniques; however, visualization is usually the main one.
Main Characteristics of Traditional Analytics
Organized and Structured Data
The main characteristic that has always set traditional analytics apart from any other type is structured and organized data. Statistical and visualization means making traditional analytics able to create structured sets of data that are always present in an organized manner and format. This allows data analysts to easily deal with, analyze, and store these sets of data.
Emphasis on Accuracy
If it is not 99.99% accurate, then it is not traditional analytics. Accuracy is a major characteristic of traditional analytics. Since various-sized organizations employ traditional analysis, traditional analytics undergoes many tests to prove the precise accuracy of each set of data and avoid any errors.
Limited Predictive Abilities
Traditional analytics is all about analyzing data to find out the pitfalls and areas of improvement, so it is mainly for describing and diagnosing the data of your business and application. Thus, it is safe to say that the predictive abilities that traditional data possess are very limited. However, it is ideal if you are looking for powerful and robust descriptive abilities
Human Interpretation
Lastly, traditional analytics is infamous for being heavily reliant on human interpretation. Analysts perform traditional analytics with the help of a wide range of software and applications, but these will never be sufficient or as precise as they could be. Traditionally, analysts have relied on their own knowledge and experience to make sense of data. Human analysts are usually responsible for the ultimate analysis and decision-making in traditional analytics.
What Is Big Data Analytics?
Next up in the traditional analytics vs. big data analytics comparison is big data analytics. Big data analytics is the process of handling large and major sets of unstructured data for all organizations. The process of big data analytics includes the precise and proper examination and extraction of all valuable insights from very large datasets. The ability to analyze major sets of unstructured data was a huge difficulty for any organization back in the day. However, with the many technological advancements arising, tools and software, it is now possible through the advanced method of big data analytics. It often includes the employment of many technologies like machine learning and natural language processing, which make the process easy and swift.
Main Characteristics of Big Data Analytics
Volume
Volume majorly defines big data analytics. The exponential growth of data produced and gathered is a direct result of the widespread adoption of the Internet and other digital tools. Information is gathered from various sources, such as social media, sensors, mobile devices, etc. Big data analytics tools and approaches usually manage and make sense of massive amounts of data. Businesses may learn a lot about customer habits, changes in the industry, and more by studying massive data sets.
Velocity
Velocity is another key characteristic of big data analytics. The pace at which data is generated and must be handled is another defining characteristic of big data. Processing real-time data streams from social media, scanners, and other sources efficiently and quickly is essential for gaining valuable and actionable insights.
Variety
Variety is the third quality of big data analytics. Structured, semi-structured, and unstructured data are all collectible and manageable data types. Structured data, like that found in a spreadsheet, is neatly arranged and simple to research. Semi-structured are like emails and social media posts, which are organized but difficult to find. Images and videos are examples of unstructured data that is not easily searchable due to its lack of structure.
In order to be effective, big data analytics can easily process and draw conclusions and insights from any data set. We can pretty much say that big data analytics support and can process any type of data
Veracity
The reliability and veracity of big data analytics is yet another crucial feature. Due to the sheer volume of data collected, its authenticity and consistency must be guaranteed. Incorrect data can have a negative effect on businesses since it can lead to inaccurate insights and decisions. To ensure data validity, it must be checked for completeness, consistency, and accuracy. They usually accomplish this by collecting data from trustworthy sources and employing rigorous data cleaning and validation procedures. For businesses to make smart choices using the information gleaned from big data analytics, accuracy is essential.
Value
The true value of big data analytics comes from being able to draw conclusions and act appropriately based on the findings. By filtering through piles of information, companies can spot trends, interconnections, and other patterns that might otherwise go unnoticed. This can aid businesses in making smarter decisions, enhancing their operations, and gaining a competitive edge. Big data analytics can reveal useful information, but only if that information is put to use. In order to fully benefit from big data analytics, businesses must be open to making adjustments depending on the results.
Traditional Analytics vs Big Data Analytics: Key Differences
Traditional analytics and big data analytics are two distinct approaches to data analysis that differ in several key aspects. Here are the main differences between traditional analytics and big data analytics:
The first major difference between traditional analytics vs big data analytics is the type of data analyzed. As we mentioned previously, traditional analytics emphasize gathering and examining only sets of structured data. As it hardly can deal with any unstructured large sets. Big data, on the other hand, refers to vast collections of unstructured data. The advanced technologies that it possesses are very much capable of interpreting these sets and extracting all the useful insights.
The data processing approach that both types use is also very different. Here you’ll find traditional analytics often rely on batch processing. This means that data is collected over a period of time and analyzed periodically. Big data analytics, on the other hand, emphasizes real-time or near-real-time processing of streaming data. It involves processing data once generated to enable timely insights and actions.
The volume of data processed is also a major difference. Due to big data analytics capability to deal with large sets of data, this made the volume of data it can analyze is pretty much limitless. Big data analytics can interpret data as large as Petabytes, Zettabytes, or Exabytes. If you don’t know how much this is, let me tell you that the smallest unit here is a petabyte, which is equivalent to 1000 terabytes. In contrast, the amount of data that traditional analytics can handle is pretty much smaller in comparison to what big data can handle. The maximum volume of data that traditional analytics can handle is terabytes. Pretty petite, right?
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Which Is Better to Use for Your Business: Traditional Analytics or Big Data Analytics?
Your needs as a business owner will determine which option is best for you. Big data analytics and traditional analytics have noticeable differences that become more apparent when compared to one another. Organizations have benefited greatly from utilizing statistical methods and business intelligence technologies for doing traditional analytics on structured past data and records, benefiting from descriptive abilities. However, the emergence of big data has left organizations with the hard challenge of real-time management of massive quantities of data of varying forms. Big data analytics makes use of cutting-edge methods like machine learning and predictive modeling to glean insights from vast databases. This made it be viewed as a powerful and invaluable option. Strategic decision-making and innovation, offered by big data, are both fueled by the predictive and prescriptive insights made possible by real-time analytics, the discovery of usually undiscovered patterns, and the extraction of such insights.
