Big Data Analytics: What It Is and Why It Is Important

How Big Data Analytics Works

Big data analytics is a process that uses data science with special software and algorithms to help businesses make sense of all this data.

This software can partition the data into manageable chunks, which makes it easier to analyze. 

The algorithms then identify patterns and trends in the data that can help businesses make better decisions about their products and services.

1. Data Collection

   Data collection is the first and most important step, but the process looks different for every business.

   Businesses can collect structured, semi-structured, and unstructured data from various sources such as cloud computing and storage, mobile apps, Internet of Things (IoT) gadgets, supply chain software, and other sources.

   Some data will be stored in data warehouses where business intelligence tools and solutions can easily access it. Raw data that is too complex for a warehouse can be stored in a data lake and assigned metadata.

2. Data Processing

   After you’ve collected and stored data, you must organize it to ensure accurate results from predictive analytics and other queries. This becomes increasingly important as data sets become larger and are unstructured.

   The available data businesses have for decision making is growing rapidly, which makes data processing more challenging.

   Businesses can use batch processing, stream processing, or a combination of the two. The way you process data influences how useful the insights from it become.

   • Batch Processing

     Batch processing is a technique used in data processing to speed up the execution of a task by dividing it into a series of smaller tasks that can be executed concurrently.

     This technique is often used when the task involves I/O operations, such as reading or writing data, or when the task requires access to resources that are shared among several processors.

     Batch processing allows tasks that are I/O-intensive to be executed on multiple processors simultaneously. This can improve performance by reducing the amount of time required to complete the task.

     Another benefit of batch processing is that it can improve resource utilization by allowing multiple tasks to share resources, such as memory and CPUs.

     Batch processing can also improve reliability by allowing tasks to be executed in parallel. If one task fails, the other tasks will continue to execute.

   • Stream Processing

     Stream processing is a type of data processing that deals with data streams as they are generated. In other words, the data is processed as it comes in, in real-time.

     This makes stream processing well-suited for applications that need to respond to changes in data as they happen, such as financial trading or fraud detection. Stream processing can also be used to quickly aggregate and process large amounts of data.

3. Data Cleansing

   No matter the amount of data you have, it requires regular cleaning or scrubbing to improve quality. Your data needs to be formated correctly. Duplicate and irrelevant data needs to be removed or otherwise accounted for. “Dirty” data can result in poor insights that mislead you and

4. Data Analysis

   • Data Mining

     Data mining is a process of extracting valuable information from large data sets. It is used to find patterns and trends that can help businesses make better decisions. Data scientists use various techniques, including statistical analysis, machine learning, and artificial intelligence, to extract insights from data.

     Data mining can be used to identify customer trends, predict future behavior, and improve marketing strategies. It can also be used to detect fraud and other security threats. By analyzing large data sets, data scientists can find correlations that would otherwise be impossible to detect.

     The benefits of data mining can be seen in a wide range of industries. Banks use it to identify fraudulent transactions, retailers use it to determine what products to stock on their shelves, and healthcare providers use it to improve patient care.

     The potential uses of data mining are endless and continue to grow as new technologies are developed.

   • Predictive Analytics

     The term predictive analytics is used to describe a number of different analytical techniques that allow businesses to make predictions about future events.

     These techniques can be used to predict everything from the likelihood that a customer will defect to the probability that a particular product will be returned.

     Predictive analytics is made possible by advanced analytics techniques such as machine learning, data mining, and artificial intelligence. These techniques allow businesses to analyze large amounts of data in order to identify patterns and correlations. Once these patterns have been identified, businesses can use them to make predictions about future events.

   • Deep Learning

     Deep learning is a subset of machine learning that utilizes artificial neural networks to learn from data. It has been shown to be more effective than traditional machine learning methods in many cases.

     Deep learning algorithms are able to learn feature representations of data that are much more accurate than those learned by other methods. This makes them better at tasks like classification and prediction.

“Big data compiles all your company’s data sources into a central location for processing and analysis.”