What is Big Data Analytics?

Big data analytics is the process of collecting and analyzing large and varied data sets in order to unearth useful trends and insights.

What Do I Need to Know About Big Data Analytics?

Organizations have always analyzed data in order to gather insights for more informed decision making. However, with the arrival of the internet, data exploded in terms of volume, variety, and velocity. According to estimates, around 4 petabytes of data is generated on Facebook alone every day.

Traditional systems that relied on relational databases were ill-equipped to handle this surge in data. For instance, relational databases can only process structured data. Thus, unstructured data, such as that produced by audio and video streams, is wasted. Moreover, such a huge volume of data cannot be processed by a single machine.

Big data analytics solves the problem by breaking down big data into smaller chunks and sending it to multiple servers for concurrent processing. It is done through a file system architecture known as Hadoop Distributed File System (HDFS).

Why Do I Need Big Data Analytics?

Big data analytics can help identify important patterns in customer behavior, supply chains, and even the spread of epidemics and pandemics. Some common uses of big data analytics include:

  1. Fraud detection. Banking and financial institutions use big data analytics to identify abnormal patterns in trading and transactions. This helps mitigate fraud. Other kinds of organizations are increasingly analyzing big data to identify bots in real-time.
  2. Personalization. A growing number of companies around the world use machine learning algorithms to personalize their marketing messages and customer experiences. Machine learning and artificial intelligence rely on big data analytics.
  3. Supply chain optimization. With the help of sensors and the ensuing data, companies can track inefficiencies in their supply chain. Big data analytics can also be used to identify the buying behavior of customers to better inventory management.
  4. Healthcare. Data can be immensely useful in identifying and tracking the spread of new virus strains, such as COVID-19 and Ebola, and effectively mitigate the spread of pandemics.
  5. 360-degree marketing. Big data analytics relies on centralized data warehouses, where data from different sources can be combined to get a 360-degree view of consumer behavior. This can be used for better marketing strategies as well as research and development.

Are There Different Kinds of Big Data Analytics?

Broadly speaking, the field of big data analytics can be broken into the following types:

  • Prescriptive analytics. By taking into account historic and current data, systems can suggest the best course of action for the future. A common use of prescriptive analytics is dynamic ads that change in real-time based on performance.
  • Predictive analytics. Systems can predict future events based on data, statistical modeling, and machine learning algorithms. A common example of predictive analytics is forecasting demand for a certain product.
  • Descriptive analytics. The opposite of prescriptive analytics, it analyses outcomes of an event and gives insights for a future course of action. For example, insights gathered after a Facebook ad campaign has ended is an example of descriptive analytics.
  • Diagnostic analytics. While descriptive analytics tells you what happened in the past, diagnostic analytics deep-dives into the reason behind the occurrence. For example, descriptive analytics might tell you that your Facebook ad campaign didn't resonate with women, while diagnostic analytics will give you plausible reasons behind the trend.