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Data Trinity: What you should know about the Data Big Three

Written by Astrid Roest on 20 March 2018 for Professionals

What if we told you that more data has been created in 2014 and 2015 than in the entire previous history of the human race. Could you even imagine that?

We are fast approaching the ‘Data Age’, an entirely new era that will see the rise of everything from humanoid robots to autonomous vehicles and more. Back in 2014, the ‘digital universe’ – the data we create and copy annually – was doubling in size every year. While 2013 saw the creation of an estimated 4.4 ZB (that’s 4.4 trillion gigabytes) worth of data, 16.3ZB will be generated this year and that by 2025, this figure will grow tenfold to 163ZB.

The power that this data will bring is virtually limitless and will change the way we communicate, work and live. Within the global datasphere – the sum of all data created, captured, and replicated on our planet – work can be categorized into three main areas: Data Science, Big Data and Data Analytics.

This complete beginner’s guide aims to unravel the meanings behind many cool sounding job titles and thoroughly demystify this intriguing field.

What are the ‘Big Three’? Understanding the basics

The first step on the path to understanding the global datasphere is to examine its fundamental parts. These are the fields of data science, big data and data analytics. Understanding each of these fields will illuminate the value of the datasphere and highlights its uses.

Data Science

The data science field involves the use of mathematics, statistics and data capture in order to analyze unstructured and structured data. This field gives businesses the chance to gain insights into their businesses practices and identify new trends that could otherwise have eluded them. Data scientists constantly collect, cleanse, prepare and analyze data in order to extract valuable information and insights.

Data science is best thought of as an umbrella term. It includes data analytics as well as machine learning (ML) and data mining. The main purpose of data science is to examine past patterns and try and predict future trends.

Big Data

‘Big Data’ is a term used to describe the flow of data that comes from both outside and within companies. As this data can be both structured and unstructured, handling it poses a significant challenge. Traditional data handling and data mining techniques struggle to analyze big data.

The value of big data is that businesses can combine it and package it for analysis. With the correct analytical approaches, businesses can enhance their decision making and strategic moves.

Many firms are also using big data analytics to bolster their cybersecurity, for example. And Uber even used it to make, wait for it, a restaurant guide!

Data Analytics

Although data analytics is part of data science, its purpose is to answer the questions created by data scientists. Data scientists focused on cyber security may use big data analytics and machine learning to highlight potential threats to a company, for example. If they create a baseline based on statistical data, they can determine whether any activity is out of the ordinary. If data scientists highlight a potential threat, they turn this over to data analysts to determine the source of the problem and how to solve it.

So, what do data analysts do? Ultimately, they generate insights by applying mechanical or algorithmic processes to big data. This involves looking for correlations with sets of data and helping to verify or disprove various hypotheses.

Whereas the role of data science is to flag up problems, the purpose of data analytics is to provide solutions. In cyber security, these solutions could include detection and response services.

Data trinity

Understanding the Big Three: What are they used for?

Let’s make things more hands-on! In defining the three areas of the global datasphere we’ve already touched on some of the uses of Data Science, Big Data and Data Analysts. In this section, we’ll discuss more of what these areas actually entail.

What is Data Science used for?

Capable data scientists can package big data into a form that data analysts can make use of. Here is a brief snapshot of two industries that are using data science to grow and become more efficient.

Fighting fraud and money laundering

The Monetary Authority of Singapore is a good example of how central banks are using data science to address money laundering and financial fraud. They started a Data Analytics Group last year are using data science to gain new insights into how to combat all manner of financial crimes including terrorist funding and identity-fraud based financial crimes.

Netflix & Chill

Like we said previously, a data scientist is expected to forecast the future based on past patterns. Imagine watching Netflix, for example. Data scientists could examine huge volumes of raw data to illuminate the behaviors and patterns of viewers on their platform. This analysis makes that Netflix can recommend relevant movies and tv-shows to individual users. So they not only gain useful insights but also add a lot to the user-experience.

So the next time you find yourself binge watching season after season, you know who to blame!

Extracting business value from data assets

The main purpose of data science is to help businesses increase their efficiency and profitability. Data scientists are increasingly able to spend more time finding value in big data thanks to a plethora of cloud platforms such as Google Cloud. According to Google, its Cloud Platform is able to provide data scientists with a wide range of key technologies and tools such as fast SQL analysis and managed Spark clusters. This helps data scientists analyze data assets faster and identify more value from them.

What is Big Data used for?

Big data has the potential to be useful to virtually every company on the planet, alongside traditional data series. Here are quick glimpses into two areas where big data is already having a profound impact on the ways that we communicate and do business.

The development of edge computing

Many analysts predict that the ongoing evolution of the Internet of Things (IoT) will create such huge volumes of data that cloud computing will become inefficient. In its place, ‘edge computing’ will become a practical necessity.

As a variety of upcoming technologies such as autonomous vehicles, smart devices and AI-powered software will require real-time responses and processing in order to function, edge computing will become the only practical solution.

Gaining economic insights

Many central banks have already begun to embrace big data as a way of gaining deeper economic insights. The Bank of England’s Mark Carney has already created a data council, a data lab and an analytics unit in order to analyze economic statistics. The Bank of Japan (BoJ) began using big data in 2013 in order to help the government understand growth. The BoJ’s analysts have helped to improve the accuracy of its forecasts and predictions.

Making restaurant guides

Wait, what? That’s right! Ever heard of big data dining? Besides using all of their data to optimize routing and pickup spots, Uber used it’s rider data to determine each city’s most popular restaurants. Instead of relying on TripAdvisor or Yelp, why don’t go full data driven when you’re looking to have a nice meal.

What is Data Analytics used for?

While data science is all about asking questions, data analytics involves finding the answers. Data analytics is the sharp end of the sword when it comes to creating solutions. Here is a quick look at two areas that are being redefined by data analytics.

Optimizing online advertisements

While it’s widely known that search engine providers have long made use of data science algorithms in order to improve their search results, the way that this field improves revenue is less well known.

The entire digital marketing spectrum uses data analysis to generate algorithms that help digital ads get a higher click through rate (CTR). This powers revenue increase and helps generate higher returns for advertisers.

The combination of big data and AI is redefining the business model that underpins advertising. Any form of advertisement or recommendation based on a user’s previous search result is based on the analysis of big data. Such is the prevalence of tracking software such as cookies from third-party ad networks that last June Apple introduced its Intelligent Tracking Prevention, (ITP) on macOS and later on iOS. ITP limits the lifespans of tracking tools to just 24 hours and prompted a backlash from many major ad groups.

Helping retailers gain a competitive advantage

Brick and mortar retailers and e-commerce giants are using data analysis to better serve their customers. By analyzing customer transaction data, social media data and the data from loyalty programs and store-branded credit cards, a number of firms are seeing a significant increase in their bottom lines.

What are you waiting for?

The Data Age is in full effect. So if you want to be a part of it, you better act fast. Hopefully you now have a better understanding of what might be in store for you when you start your career as a data scientist, big data specialist or data analyst.

But there’s more! We’re currently listing all the skills you need to have to launch yourself straight into the front line of the Data Age. So keep an eye on our blog and Facebook page and who knows, we might see each other again!

Written by Astrid Roest Talent Development Manager