Why is Data Science … Big?
In this article, we look at what data science is, and what is driving its growth and value to businesses and organisations worldwide.
Data science uses multiple disciplines, scientific methods, and processes (e.g. domain expertise, programming skills, data engineering, data preparation, data mining, predictive analytics, machine learning, data visualisation, and knowledge of mathematics and statistics, and more) as well as algorithms and systems to extract knowledge and insights from structured and unstructured data. Data science also apples knowledge and actionable insights from data so that the insights gained can add value and create actionable plans for companies and other organisations.
Vast Amounts of Data Generated and Collected
We now live in a data-driven society with more data being generated than ever before, with most of the data generated in only the last few years. It has been estimated that more than 2.5 quintillion bytes of data are generated every day. The IDC predicts that by 2025, the total (and constantly growing) amount of digital data created worldwide be 163 zettabytes. Data science and the skills of data scientists have enabled companies to use this data to find new opportunities, make better “data-driven decisions”, and turn the insights from the data into added value and competitive advantage.
Drivers of Data Generation and Collection
The key drivers of data generation and collection include:
– The growth of the world’s internet population. For example, just before the pandemic in 2020 (the pandemic has boosted Internet growth further) the internet had reached 59 percent of the world’s population (i.e 4.57 billion people with web access), a 3 percent increase from the previous year (DOMO), with 4.2 billion active on mobile and 3.81 billion using social media (social media companies are the biggest collectors of personal data).
– The growth of artificial intelligence (AI) and AI becoming more accessible to (and affordable for) businesses. AI enables vast amounts of data to be analysed and insights to be found much more quickly and efficiently than ever before. Data scientists and their use of technologies and tools, such as AI, have enabled businesses to tackle and get value from their ‘big data’ (i.e. vast amounts of data they’ve collected) that’s proven too much of a challenge to tackle before.
– The growth of technical innovations like 5G wireless technology, making data collection and application easier and enabling further growth of the Internet of Things (IoT) e.g. wearables, sensors, monitors, and scanners to collect information on a single network, thereby providing more data for data scientists to work with. In 2020 it was estimated that the number of IoT devices was thought to be anywhere between 30 and 50 billion worldwide which could generate more than 4 zettabytes of data in one year.
– The continuing rise of mobile technology has meant the growth of apps, most of which collect data.
– An accessible international marketplace due to the rise of the Internet and communications technology growth.
The Value of Data Scientists
Given that we are in a data-driven society, data science is now at the forefront of what some have called the fourth industrial revolution. This is the reason why, as far back as 2012, the Harvard Business Review suggested that being a data scientist is the “sexiest job of the 21st century”.
The value of Data Scientists to companies and organisations is based on the fact that they can use their understanding of multiple scientific and technical disciplines to:
– Analyse data sets to produce actionable plans which, because they are based upon real-world data (i.e. data-driven) can be more successful.
– Use programming, machine learning, risk analysis, and research skills, to help make data comprehensible for everyone else on a team / present key data in a way that others can understand. This enables the value of other team members to be unlocked as they can make more informed and directed decisions and suggestions that help create value-adding and cost-reducing solutions and opportunities.
– Improve business processes to make operations and marketing more efficient and effective.
– Improve marketing by using data insights to increase data-driven personalisation and help businesses to take advantage of (and navigate) important patterns in business trends.
– Ask the right questions and identify data sources and their value, both of which are vital platforms on which to build business decisions.
– Help to set global data security standards.
Data Science and AI
Although artificial intelligence is a tool that can help to power data science operations, data science is not totally dependent on AI. A data scientist uses their skills to make decisions about extract value from data, but they also need machine learning algorithms to help with and to speed up that process.
Examples of how data scientists have can positively impact industries include:
– Saving lives and improving processes and outcomes in the healthcare industry (30 percent of the world’s warehoused data is from the medical arena) e.g. developing AI-powered diagnosis models for cardiologists.
– Using data to innovate and improve safety and performance in the transport industry e.g., feeding into the development of autonomous vehicles (cars and aircraft).
– Using data analytics software to help with supply chain management e.g., FoodService Co. using a data-driven dashboard to save labour-hours and inventory reconciliation.
What Does This Mean For Your Business?
In our data-driven society, the data collected by businesses can hold insights that can be a source of value creation, reduced costs, innovation, and competitive advantage. Data scientists have the skills to unlock that value by using multiple disciplines and tools to spot patterns and trends that feed into the improvement of products and services, operations, and marketing. These insights can be transformative, and this explains why data science is a growing field that has become so valuable in all industries over such a short space of time.