DATA SCIENCE VS BIG DATA VS DATA ANALYTICS

Individuals, businesses and organizations generate and use data on a daily basis. And with the world being in a digital age, it is unsurprising that thousands of companies are engaging the services of professionals for technical data generation, analysis and application.

In the business of digital data management, data science, big data and data analytics are essential fields with professionals highly sought after by service companies.

Generally, data science, big data and data analytics are all facets of the data-specific field of science and in the simple sense, they share certain features. However, in the broad sense, they are different from each other and this is why reading this article further will help you understand the differences between these three data-specific fields particularly with respect to their use cases.

An Overview of Data Science

Data science is a field concerned with the integration of programming, mathematics, statistics, etc. for the purpose of generating information and insights from data. Broadly, data science incorporates a range of activities including data preparation, data cleansing, data alignment, data analysis and expert ways of capturing data.

As a scientific field, data science necessitates the ability to solve data-related problems involving both structured and unstructured data.

Use cases of data science

• On search engines, data science algorithms help execute the swift delivery of effective results for search queries entered by search engine users. What this implies is that data science is very useful in internet search
• Another use case of data science is in digital marketing where the adoption of data science algorithms results in comparatively impressive click-through rates
• Data science facilitates the use of recommender systems which enable companies to identify how they can best meet the demands of their product users. Precisely, the recommender systems track users’ past internet search results and based on these results, the systems provide companies with recommendations on how product delivery can fit into users’ demands.

An Overview of Big Data

Big data is a technical term which refers to volumes of (structured and unstructured) data so large that conventional apps in use cannot process them. Essentially, big data is useful for the analysis of insights needed for smart decision making.

Broadly, big data is an aggregate of information assets, of high velocity, variety and volume, which require innovative and less expensive information-processing strategies. Ultimately, such strategies are expected to yield improved insight, process automation and decision making.

Businesses can apply big data for strategic decision making. However, a business could be overwhelmed with big data and while big data processing is important, the processing starts with scattered raw data that could be difficult for the storage of a single computer to hold.

What are the common use cases of big data?

• Big data is generously used in telecommunications where service providers need to effectively analyze an abundance of day-to-day customer/machine-generated data. This effective analysis of abundant data is very essential for service providers in the telecommunications business to achieve such goals as customer retention and subscriber base expansion.
• Big data is useful to institutional investment banks, insurance firms, retail banks, credit card companies and other service companies. Particularly, big data helps such service companies in solving the problem of abundant multi-structured data found in many different systems. To facilitate their varying analytical tasks, the stated companies use big data for customer analytics, operational analytics, fraud analytics and compliance analytics.
• The usefulness of big data in online retail business lies in the effective analysis of the multivariate data that online retail companies handle on a daily basis. Such data may include customer transaction data, loyalty program data, credit card data, weblogs, etc. Effective analysis of the stated data is a great recipe for understanding customers’ needs. Ultimately, such understanding can aid the delivery of services that can keep a retail company ahead of the competition.

An Overview of Data Analytics

Data analytics is concerned with the mechanical or algorithmic process of generating specific inferences through the examination of raw data. Specifically, the algorithmic process aids the generation of insights as well as the identification of essential correlations through the examination of various data sets.

It is worthy of note that data analytics is majorly concerned with the process of generating conclusions. Otherwise known as inference, this process implies the generation of conclusions intended to validate or reject a researcher’s prior knowledge.

While data analytics is a widely applicable science, organizations leverage its use in making strategic decisions and in confirming/refuting theories.

Use cases of data analytics

• Data analytics is useful in the travel business due to its ability to help generate customized travel recommendations. Meanwhile, travel websites require such recommendations in delivering services that should meet clients’ preferences. When based on analysis of social media data, data analytics can assist travel websites in generating insights into how they can optimize customers’ buying experience.
• Some software developed by data analytics outfits is now needed by hospitals and other healthcare facilities that are willing to reduce healthcare expenditure by augmenting efficiency gains. Increasingly, hospitals are bent on spending less while still delivering effective healthcare to as many as required patients.
• Some companies are leveraging data analytics for effective energy management. Specifically, these companies utilize data analytics for the management of service outages and the control of network devices. Furthermore, data analytics is useful for achieving optimal results in the areas of energy distribution, automation building, energy optimization and smart-grid management
• The application of data analytics also cuts across the gaming industry where (gaming) companies rely on data analytics for the generation of insights into their users’/customers’ preferences. Frankly, this has the advantage of placing such companies in a better position as regards doling out games and gaming features that will meet users’ preferences.

Conclusion

Each of data science, big data and data analytics has excellent use cases, making it a relevant profession with a satisfactory salary scale. However, data science holds the highest average salary among the three data-related professions.

The information published on Glassdoor reveals that the salaries of professionals in the three professions are as follows: $113,000 (as a yearly base salary for the average data scientist), $62,453 (as a yearly base salary for the average data analyst) and $103,000 (as a yearly base salary for the average big data specialist).