What is big data? According to Wikipedia, “In business, big data refers to a new development in information science and data analysis that combines traditional statistical methods with high-speed electronic data collected from scientific experiments, clinical trials, demographic studies, and surveys.” Big data has become a buzzword in the business world in recent years as a result of advancements in Internet marketing, financial services, consumer preferences, and commercial activity.
Data analytics or data mining is the extraction of statistically significant facts from large databases. Big data is also known as Hadoop and Big Data. Data analytics aims at improving business performance by efficiently managing information from diverse sources. Data mining aims at finding previously unutilized data sources and using them to support business decision making. While both concepts are used, they work in different ways.
The main difference between data mining and predictive analytics is their underlying methods. Data mining involves the use of algorithms to make predictions. On the other hand, predictive analytics makes use of mathematical machine learning to generate and make predictions. With predictive analytics, the process is supervised and results are often test cases which show the effectiveness of the predictive model. In contrast, predictive analytics relies on an unsupervised approach, where no actual machine learning or statistical method is used. Machine learning uses traditional algorithm and optimization techniques to achieve high accuracy and speed on small inputs.
Data mining can be applied to various types of structured data sources such as text, images, videos, and audio. However, the best examples of structured data are temperature and humidity sensor readings, and online stock quotes. Examples of unstructured data sources include cityscapes, travel route planner information, weather reports, and scientific experiment data. Both unstructured and structured data can be processed using modern machine learning algorithms. However, with unstructured data, training data is needed for the system to learn how to classify, group, and rank the data in order to make effective predictions.
The challenge with big data analytics is that the source must be unbiased and well-documented. It is also important that the training data for the machine learning algorithm can be obtained from multiple sources and then used to support the machine learning models. This is one of the main hurdles to overcome for most companies. Fortunately, there are several ways for a company to obtain this training data. One popular option is to hire outside consultants to obtain insights from experts on what industries are providing valuable data.
Another way to obtain data sets is to rely on publicly available social media feeds. The quality of these feeds needs to be carefully reviewed before relying on them to support big analytics. Additionally, some industry groups may be reluctant to release large quantities of data sets, especially if it will not be independently verified. Therefore, it may take an entire conference or teleconference to gain access to data sets from a large number of stakeholders.
There are two additional challenges to consider with the use of big analytics tools in the data analytics stage. First, it can be difficult to evaluate and measure the progress of an algorithm. Second, if you are working with multiple domains and Datasets, it can be difficult to apply methods that are appropriate for each domain without being overkill.
Beyond these challenges, data mining has many advantages. Unlike traditional analytics, data mining requires no formal training to operate. Also, it is typically more efficient, since it exploits previously available information. Finally, predictive analytics makes the most of available data sets by allowing business managers to predict trends before they happen.