What’s the Difference between Big Data, Data Science & Data Analytics

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The most important thing in business nowadays is data. Therefore, in this data-driven environment, various technologies, processes, and systems have been developed to process, transform, analyze, and store data.

Regarding the essential components of Big Data, Data Analytics, and Data Science, there is still a lot of misunderstanding. In order to better comprehend each technology and how it interacts with one another, we shall demystify these notions in this article.

While these technologies complement one another, they can also each be employed independently. Large data sets can be stored using big data, and information can be gleaned from smaller datasets using data analytics techniques.

Big Data

A survey by Forbes found that more than 95% of companies face some kind of need to manage their unstructured data. Big data simply refers to extraordinarily huge data sets, as the term suggests. 

These data sets can now outperform those of conventional data management technologies thanks to their size, complexity, and ongoing evolution. As a result, data warehouses and data lakes have surpassed the capabilities of conventional databases to become the go-to solutions for handling big data.

We can categorize the following data sets as truly big data:

  • Stock market data,
  • Social media,
  • Sporting events and games,
  • Scientific and research data.

Big Data Characteristics

These are some of the most common characteristics of big data technology:

  • Volume: Big data is vast and significantly exceeds the capability of conventional data processing and storage techniques. The amount of data affects whether it qualifies as big data.
  • Variety: Large data sets are made up of different types of data rather than just one type of data. Regardless of data structure, big data includes a variety of data types, including tabular databases, pictures, and audio data. 
  • Velocity: A measure of how quickly data is produced. In big data, fresh data is continuously produced and often added to the data sets. When working with constantly expanding data sources like social media, IoT devices, and monitoring services, this is very common.
  • Variability: Due to the size and complexity of big data, some inconsistencies in the data sets are unavoidable. Therefore, in order to handle and process big data effectively, variability must be taken into account.
  • Value: The value of Big Data resources. Big data analysis output’s value might be rated subjectively and according to certain company goals.

Data Science

So, what is data science? Data science cannot be confined to a certain role or industry. It is a multidisciplinary approach that combines the following to extract knowledge from data:

  • Scientific approaches,
  • Statistic and math,
  • Programming,
  • Advanced analytics,
  • AI and ML,
  • In-depth learning.

The main goal of data analytics is to extract valuable insights from the underlying data. Data science will deal with everything, from evaluating difficult data to developing new analytics algorithms and tools for data processing and purification to even designing potent, practical visualizations. Its breadth is much beyond this goal.

Data Science Tools & Technologies

Programming languages like R, Python, and Julia fall under this category. These languages can be used to develop novel algorithms, ML models, and AI techniques for big data platforms like Apache Spark and Apache Hadoop.

Data science tools can also include data processing and purification tools like Winpure and Data Ladder, as well as data visualization tools like Microsoft Power Platform, Google Data Studio, Tableau, and visualization frameworks like matplotlib and ploty.

Any tool or technique used in big data and data analytics can be employed in the data science process since data science encompasses everything relating to data.

Data Analytics

Analyzing data to glean useful information from a particular data collection is known as data analytics. Although they can be used with any data source, these analytics approaches and procedures are typically applied to big data.

Data analytics’ main objective is to assist people or organizations in making well-informed decisions based on patterns, habits, trends, preferences, or any other kind of valuable information that can be gleaned from a set of data.

Businesses might, for instance, utilize analytics to pinpoint client preferences, purchasing patterns, and market trends before developing plans to address them and adapt to changing market conditions. From a scientific perspective, a medical research institution can gather information from clinical trials and accurately assess the efficacy of medications or therapies by doing so.

Data Analytics Types

Although there are numerous analytics approaches and procedures, there are four types that may be used for any piece of data.

  • Descriptive: Understanding what has occurred in the data set is meant by this. The descriptive analysis will aid users in comprehending the past as the first step in any analytics procedure.
  • Diagnostic: The next phase of descriptive analysis is diagnostic, which builds on the descriptive analysis to determine why something occurred. It enables users to learn the precise details of the underlying causes of earlier occurrences, trends, etc.
  • Predictive: Predictive analytics, as the name suggests, foretells what will happen in the future. In order to forecast future trends, patterns, difficulties, etc., ML and AI techniques will be combined with data from descriptive and diagnostic analytics.
  • Prescriptive: Prescriptive analytics expands on the predictions made by predictive analytics by examining how they will come to pass. This can be regarded as the most significant type of analytics because it enables consumers to comprehend future events and customize tactics to properly address any projections.

In Final Words

In the end, big data, data analytics, and data science all assist people and organizations in dealing with massive data collections and obtaining useful information from them. Data will become important elements in the technology world as their significance increases exponentially.

Finding what works best for your organization does not have a set formula for success. Every business will have distinct use cases and workloads that could call for various solutions, and these things will change and develop over time.

Here is where emphasizing open, clever, and adaptable solutions can significantly impact your future success. You must thoroughly research your use cases and solutions, concentrating on the most effective means of enabling and empowering data users as well as realistically assessing fundamental capabilities to maximize their efficacy.