Data Analytics vs Big Data Analytics vs Data Science – What is the Difference?

Data Analytics vs Big Data Analytics vs Data Science

Data can be fetched from everywhere and grows very fast making it double every two years. Studies by IBM reveal that in the year 2012, 2.5 billion GB was generated daily which means that data changes the way people live. Let’s find out what is the difference between Data Analytics vs Big Data Analytics vs Data Science.

There is an article written in Forbes magazine stating that data is rapidly growing than ever before and by 2020, almost 1.7 MB of new information in every second would be created for everyone living on the planet. This makes is essential for one to know the rudiments of the field since this is where the future lies.

Today, with the help of digital economy many doors have been opened in the landscape of big data. Various experts in fields such as data engineering, data mining, data analytics, data science and many others work hand-in-hand but have their individual functions. Although people mistakenly interchange these concepts, there certainly are differences.

A strong confusion exists amongst the concepts data science, big data and data analytics with job seekers opting for a job role totally different from the skills they have acquired.

We shall look into Big Data Data Science vs Data Analytics by understanding what they are, where they are used, the skills needed for you to become an expert in the field of data possible salary and other areas as well.

Data Analytics vs Big Data Analytics vs Data Science definitions

Data Science:

This is a field comprising of everything that has to do with preparation, cleansing, and analysis, dealing with both structured and unstructured data. Data Science combines mathematics, statistics, capturing data in intelligent ways, programming, problem-solving, data cleansing, knowing how to look at things from a different view, preparing and aligning the data.

We can simply say that it is the combination of several techniques used when trying to get information and insights from data.

Big Data:

Big data is known to be the massive volumes of data that can’t be processed properly using the traditional techniques. Big Data processing starts with non-aggregated raw data and is not really possible to store in the memory of a just one computer.Daily, Big data inundates businesses by using a buzzword to describe large volumes of structured and unstructured data. It is something that is used for analyzing insights which aid better decision making and business moves that are strategically planned.

According to Gartner, the definition of Big Data is “High-volume, and high-velocity and/ or high variety information assets that need forms of information processing that are cost-effective and innovative and can allow enhanced decision making, insight and process automation.”

Data Analytics:

This involves the application of algorithmic or mechanical processes in deriving insights. For instance, looking for reasonable correlations between data sets by running through a certain number of them. Data Analytics is used by several industries to allow them to make better decisions and verify and disprove existing models and theories.

Data Analytics focuses mainly on inference, which is the act of deducing conclusions that majorly depend on the researcher’s knowledge.

Data Analytics vs Big Data Analytics vs Data Science Applications

Applications of Data Science:

1.) Recommender systems

These systems add so much to user experience and also make it easy in finding relevant products from so many products that are available. Several companies use recommender systems for promoting their suggestions and products according to users’ relevance of information and demands. The recommendations depend on the previous search results of users.

2.)  Internet Search

Data science algorithms are used by many search engines so as to deliver the best results in just a split second.

3.) Digital Adverts

The whole digital marketing ecosystem makes use of digital science algorithms spanning from display banners down to digital billboards. This is the major reason why digital ads get higher CTR than the conventional forms of advertisements.

Applications of Big Data:

1.) Retail

When trying to remain in the retail business and staying competitive, the important key is understanding and serving the customer better. This would require proper analysis of all sources of disparate data dealt with by companies daily which include customer transaction data, weblogs, loyalty program data, social media and store-branded credit data.

2.) Communications

Telecommunication service providers have priorities of retaining customers, gaining new ones, and expanding the current customer bases. In order to do this, the act of combining and analyzing tons of customer and machine-generated data created on a daily basis.

3.) Financial services

Big service providing firms such as retail banks, credit card companies, insurance firms, private wealth management advisories, institutional investment banks and venture funds make use of big data for their financial services. The major challenge experienced by all of them is the large amounts of multi-structured data embedded in multiple disparate systems and can only be taken care of by big data. Big data is used in various ways such as fraud analytics, customer analytics, operational analytics and compliance analytics.

Applications of Data Analytics:

1.) Management of energy

Data analytics for energy management is used by most firms which include energy optimization, smart-grid energy, building automation in utility companies, and energy distribution. Its use here is focused on monitoring and controlling of dispatch crews, network devices and managing of service outages. Utilities get the enablement to integrate millions of data points within their network performance and allows engineers utilize analytics for monitoring their networks.

2.) Healthcare

The major challenge hospitals having cost pressures that tighten is treating many patients effectively, and having the intention of improving the quality of care. Machine and instrument data is used increasingly for tracking and optimizing treatment, use of equipment in the hospitals and patient flow. It is predicted that there would be a gain of 1% in efficiency which could result to yielding over $63 billion in health care savings globally.

3.) Gaming

The advantage analytics plays in gaming include collection of data in order to optimize and spend within across games. The companies manufacturing these games get a good insight into likes, dislikes and the relationships of the users.

4.) Travel

Data analytics helps in optimizing buying experience via social media and weblog/mobile data analysis. Customers’ preferences and desires can be gotten; the correlation of the current sales to subsequent browsing would raise conversions that are more of browse-to-buy in nature through customized offers and packages which would help products to get sold up.

Data Science vs Big Data vs Data Analytics Skills Required to be a Professional

If you want to be a Data Scientist, you need the following:

1. Degree: 46% have a Ph.D. while 88% have a Master’s Degree
2. Working with unstructured data: A data scientist must be able to work with unstructured which the most important irrespective of where it comes from like audio, social media or video feeds.
3. Hadoop platform: This is not a major requirement but a good knowledge of it is preferred. Also, if you have some experience in Pig or Hive, this will give you an edge.
4. Python coding: Python is known to be the most common coding language used in data science with others such as Perl, Java, C/C++, etc.
5. Very deep knowledge of R and /or SAS; R is preferable in Data Science. 
6. SQL database/coding: Although Hadoop and NoSQL are major parts of Data Science, knowing how to write and execute complex queries in SQL is also preferable.

If you plan to be a professional in Big Data, then you should consider the following:

1. Mathematics and statistical skills: This is very necessary for all areas of data which includes big data, data science, and data analytics. After all, this is where it all begins.
2. Analytical skills: This is the ability to make meaning out of the tons of data you get. Analytic abilities help you determine the most relevant data needed to solve a problem on ground.
3. Computer science: Computers are the engines that power every data strategy. Programmers use them for coming up with algorithms for processing data into insights.
4. Creativity: You need to be able to put new methods together for gathering, interpreting and analyzing data.
5. Business skills: You will need to have a very good understanding of various business objectives needed with the processes in the background which pushes the business to grow along with its profit.

For becoming a Data Analyst, you need:

Statistical skills and mathematics: Inferential and descriptive statistics along with experimental designs are compulsory if you intend to be a data analyst.

Programming skills: As an aspiring data analyst, you need to have a very good knowledge of programming languages such as Python and R because they are very important.

Data Intuition: To be a data analyst, you need to think and reason like a data analyst.

Data wrangling skills: You need to map out and convert raw data into another format that will make it more convenient for consumption.

Other skills required are Machine learning skills, Data Visualization, and Communication skills.

Data Science vs Big Data vs Data Analytics Salaries

Although these three fields are within the same domain, their salaries actually vary due to various factors. We shall take a look at the salaries each professional earns yearly.

According to Indeed.com, the average salary earned by the data scientist is $123,000 per year while Glassdoor states it to be $113,436 a year. For the Big Data professional, Glassdoor claims it to be $62,066 per year while that of the data analyst is $60,476 per year.

Data Science vs Big Data vs Data Analytics Economic Importance

Data is the major backbone for almost every activity carried out nowadays, whether it is research, education, technology, healthcare, retail and many other industries. It is general knowledge that businesses have moved from being just focused on their products to being data-focused. The smallest piece of information is very important to companies which make it very important for deriving as much information as possible. This has resulted in the increase in the need for experts who can bring in significants insights that can be used for various purposes.

These experts in question are data scientists, big data professionals, and data analyst; they seem to be similar in specialty because they work on and with data to offer information for business and other purposes.

Earlier mentioned the areas where they are applied showing that they are very important in our economy.

With the slash in price of IT hardware along with the Cloud adoption by the whole world, the IT industry is certainly experiencing a new revolution in data. We all know that data has become very important in the 21st century to human existence with more than 90% of the data created in the past few years. Nowadays, we have various means of storing, innovating and manipulating data which is very important to everyone.

The increase in data creation has led to many industries taking advantage of the benefits of accessing massive tons of data that being produced today.

The knowledge of analyzing data by experts in the field like data scientists, big data professionals and data analysts with the right tools and techniques spearheads this revolution we are experiencing. Companies use this data for driving service and product innovation than ever before which make them excel.

Whether it is all about Data Science vs Data Analytics or Data Science vs Big Data, we know that each of these areas of specialty is very important to companies today in today’s world. So, if you are an IT expert with the plan of taking your career in data analytics to the next level, then you should consider any of these fields. Also, companies can also consider employing the services of these professionals because they help out in so many areas of insight and decision making.