Why distinction matters in Big Data and Data Science?

by August 12, 2017

Data has become a resource of interest globally, and harnessing its true potential is becoming important to organizations. According to IBM, 2.5 quintillion bytes of data is created every day. This means that data never sleeps. The increase in data requires the use of different tools and techniques to meaningfully extract insights. Let us first understand how the use of data is defined in the big data and data science industry.

Defining data by Work

The big data and data science industry terms and definitions overlap and interweave with one another in the analytics field.  However, these are still distinct and are used based on the nature of work.

Data science comprises a number of disciplines. These include business intelligence, computer science, data engineering, and statistics, among others. Data science involves processes to collect, clean and analyze both structured and unstructured data. It makes use of the following:

+ Cleaning raw data to make it ready for analysis.

+ Use of advanced mathematics and statistics.

+ Finding patterns in the data and helping decision makers in day-to-day business problems.

Data science involves discovering hidden patterns within the data through dependencies between different variables. It is used in different industries to make better decisions by understanding and improving the existing business models.

On the other hand, Big Data analytics deals with the processing of a large volume of both structured or unstructured data which cannot be processed with the traditional methods. Big data is characterized by 3Vs: the volume, the variety and the velocity at which the data is processed. The key enablers for the growth of big-data are the increase of storage capacities, an increase of processing power, and the availability of huge amount of data.

How is data analyzed?

Big data and data science help organizations to understand their consumers, and identify new opportunities. Let’s understand how these are applied in real-world situations.

A data product may include sales forecast, stock market predictions, health diagnosis and target advertising, among others. Data science involves using existing/historical data to draw conclusions and make predictions based on the below methods of reasoning:

Hypothesis-based reasoning: The hypothesis-based reasoning helps in formulating hypothesis about relationships between variables. It requires experimenting with data to test hypothesis and models.

Pattern-based reasoning: The pattern-based reasoning helps to discover new relationships and the analytical path from the data. It involves drawing inferences based on probability. The conclusion reached from this technique is reasonable, probable and believable.

On the contrary, big data analytics involves the following steps.

Data Integration: Big data analytics starts with ingesting data from different sources. This is the first step towards the analysis. It requires integrating all types of structured, unstructured and semi-structured data. Examples include databases, mainframe, social media, file systems, SaaS applications, and XML.

Discovery: The step involves understanding the data sets and how they relate to each other. The process consists of exploration and discovery of data.

Iteration: Uncovering insights from data is an iterative process as the actual relationships are not known. Industry experts suggest small defined-projects to enable learning from the iterations.

Classification and Prediction: Once the right data is collected, we go ahead for classifying and predicting the data. Classification models predict categorical data, and prediction models predict continuous data.

Qualifications matter

A critical component of any organization is its team. Both data science and big data require a diverse set of skills. Data scientist or big data analyst are the hottest job titles in the IT industry. Data scientists are highly educated with 88% have master’s degree and 46% have PhDs. They need to possess an in-depth knowledge of statistics with programming languages such as SAS, and R.

Big data analysts must have technical knowledge along with the skills possessed by a data scientist. These include SQL databases and database querying languages, Python, Hadoop, Hive & Pig and cloud tools like Amazon S3.

However, in both the fields, domain expertise significantly contributes to the understanding of where the problem lies and how the problems could be measured.

Closing Thoughts

Big data continues to occupy our day to day lives.  When properly infused and analyzed, big data analytics can provide unique insights hidden inside the data. Both data science and big data tools and techniques require a significant investment of time across an array of tasks. The dynamic nature of the field makes its necessary for organizations to understand both the terms. However, no matter, how many the differences are, one cannot be successful without the other.