What is Data Science and how is it Different from Big Data and Data Analytics?

Big Data Data Analytics Data Science

Humanity has often divided and categorized history according to the most prominent feature of a given period. With the advent of digital computers, we entered the digital age. In the early ’90s, during the mass accessibility of the world wide web came the age of the internet. Today we have just passed the precipice on another age: the age of Big Data.

Big Data is defined as data sets that are so large in volume and diverse in complexity. This data is impossible to interpret meaningfully through traditional statistical and computational methods. Most of this data is generated by 4.4 billion internet users around the globe. According to the IDC, all of us are producing 44 zettabytes (that is a billion gigabytes) per day in 2016 and it will increase to about 463 zettabytes by 2025.

US companies have an average of 100 terabytes, or 100,000 gigabytes of data stored


What started out as a buzzword to define a large amount of data we are producing, it transformed into something that can be processed and analyzed. This process leads to gaining useful insights, which in turn are made into solutions that help businesses to improve their business.

Data Science: Transforming Data

Data Science is a catch-all term for everything related to data gathering, storage, preparation, and analysis. It is the the result of combining different techniques derived from diverse fields such as Mathematics and Statistics, Computer Science, Programming, and Information Technology.

Data Science springs forth from several different areas of specialization, depending on the processes they handle. Data generation is often done on the software level, with data collected using applications created by Software Developers. This data is transported via pipelines. In these pipelines, Data Engineers clean, store and reformat raw data into something that can be processed by the next step: Data Analytics.

Data Analytics: Deriving Insights and Value

After being processed, data is now ready for analysis. Data Analysts derive workable information from this data which in turn is used by people in Business Intelligence to make improvements to the business process or to the software. Modern methods of Data Analytics make use of AI algorithms to perform statistical analysis. Through inferential statistics, data analysts derive conclusions derived from inference.

Data Analytics is divided according to degrees of maturity: descriptive, predictive, and prescriptive. Descriptive analytics is data analytics in its simplest form. It is used to diagnose the properties and relationships between variables found in data sets. The next level of analysis, predictive analytics, deals with forecasting events based on data trends. This allows a business to adapt to change in advance. The last and most thorough data analysis is prescriptive analytics. This level of analysis not only gives out predictions, but it also helps determine the best course of action to reach an intended goal.

Applying Data Science to Businesses

According to Mike Shane, President of FilAm Software Technology: "you almost cannot interact with something or someone today without being affected by Data Science." With every browse, search, purchase, post, tweet, among many other activities, people who do Data Science either collect data or present you with relevant choices or information from analyzing data. It is hard to imagine modern society without Data Science.

Americans use 4,416,720 gigabyte of internet data every minute


Here is how Data Science helps specific types of businesses or industries:

Search Engines and E-Commerce: Easily the sector with the biggest share of use of Data Analytics, Data Science provides algorithms to handle search functions and return queries with the best possible answers. Data science also helps determine effective ad and product placements; decisions that translate to actual sales.

Media and Publications: Data Science offers a more in-depth look at the habits and preferences of their consumers, fine-tuning their content to better suit the market. Through proper selection of content for mass publication, analyzing data helps media companies get new subscribers, keep existing customers, and expand their market shares significantly.

Healthcare Industry: Collecting and analyzing data from patients and clients leads to improvements in medical treatments, pharmaceutical research, and public health. Data Science can also help insurance providers deal with fraudulent claims and help give better service offerings to those who need it the most.

A third of healthcare organizations in the US have made use of Big Data. Another half plan to use Data Science in the next 2 years


These are but some of the many industries that have gained many benefits from the successful use of Big Data through Data Science and Data Analytics and methodologies. Some of these companies dedicated large amounts of resources to their Data Science teams. These resources are allocated to hiring, training and paying people to work on a permanent basis within their companies. This often costs a lot and most companies are hesitant to fully commit to having internal Data Science teams. Thankfully, there’s an outsourcing option available that can provide the same benefits of having an internal Data Science team. Even the biggest companies have utilized one form of Data Science outsourcing or another. But not all third-party data service providers can be trusted to deliver useful results. The problem lies in selecting an effective partner to handle Data Science that will translate into business value.

Choosing an Effective Data Science Partner

When business innovators realized that there’s a lot of lucrative opportunities with Data Science as a marketable service, data companies have started rising up left and right. There are lots of companies available nowadays and choosing the right one can be difficult at the very least. Here are some of the skills and characteristics any business owner should look for when selecting a long-term Data Science partner or an expert service provider for a limited-time project:

Industry Certified Expertise: It is not enough to choose a company with lots of people who have post-graduate degrees. If a company has earned certifications and/or recognitions, such as the Microsoft Partner Network, for competencies related to Data Science from organizations of repute, then these companies have industry acknowledged skills and expertise to actually do what they claim they can do.

The Right Balance Between Business and Technical Skills: Any business owner would want to work with companies that understand their client’s business process, as well as have the actual skill to improve upon them. Look for companies who have mastered balancing between business intelligence, the ability to comprehend who and what they’re working with and how they could provide value, and technical capability, the knowledge of the various tools and techniques to successfully perform Data Science.

Experience is still the best indicator: Does the Data Science service provider have upwards of a hundred different projects completed? Have they worked with industry leaders or clients that are like your own? Did their partnerships, existing or otherwise, resulting in a harmonious and productive relationship? If the answer is yes to all these questions then there’s a large chance you have found what you are looking for. Data Science remains as the younger brother to other service sectors in the tech industry. This means there are a lot of fairly new service providers out there who are eager for gaining clients but often fail to deliver meaningful value. Look for companies that have significant statistics when it comes to experience in Data Science.

The Data Services outsourcing market is poised to grow more than 30% in the next 5 years


According to experts from Ecuiti, there are more factors to consider when selecting a business partner. Every case and situation have different requirements. We have only listed points that should be a significant component of your selection process. Do you have anything else you like to add to the list above, or any stories to tell about your own experience with Data Science? You should leave a comment below or contact us here. Don’t forget to subscribe for more information about Data Science.

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