Who Are Using Data Today—and How?
Politicians, public services, the police and national security authorities. The military, researchers, and financiers. Sports clubs, TV channels, production and industrial enterprises. For all these and many others, data is already essential. And in what is increasingly becoming a datafied information society, most people will at some point work with data and use data at one level or another.
Skills in using data are perhaps needed most of all among those who work in connection with critical social infrastructure. But activists, marketers, and even farmers and many others are also now using data-driven methods as well.
More data, better services
There is no doubt that data will only become more and more important in people’s lives and work. A 2021 Norwegian government report stated that “the government wants Norway to exploit the opportunities that lie in data for increased value creation, more new jobs throughout the country, and an efficient public sector” (our translation).
The government’s initiatives include entering into international collaborations across the Nordic region and the EU, and increasing competence in education and industry. But a significant effort is aimed at improving access to data.
The public sector collects, produces, and stores huge amounts of data. Data about where we live and what we work with. But also about weather, geography, and information about traffic and transportation.
What is data used for?
Over time, our society has grown increasingly complex. One could argue from a sociological viewpoint that digitalisation is our way of navigating this complexity: we rely on digital technology to make sense of our intricate world. This is evident in the substantial growth of technologies that can gather and process vast amounts of data.
Even though we might not think so, both individuals and society as a whole are very routine-based. We have many individual and collective habits and behavioural patterns that can be uncovered, among other things by analysing big data using machine learning and other techniques.
Big data: Five characteristics and five challenges
New data is continuously generated and collected. In all possible contexts. Drivers for increased data volumes include the Internet, the Internet of things (IoT), cloud computing and social media.
Big data refers to data volumes that are so large, diverse or complex that we humans alone do not have the capacity to analyse, understand and give them meaning. Instead, we use technologies like machine learning to create order and find patterns in the data.
Below, you can read about five characteristics of big data, and five challenges it creates for managers.
Technology helps us to find the secrets hidden in this big data: We can gain more knowledge about how we can coordinate and organise ourselves better. It can support us in finding solutions to complex challenges related, for example, to the climate and environment.
Someone who is an expert at extracting these insights and creating value from data is called a data scientist.
Data science
Data science is the specific field dedicated to studying and extracting value and insight from data.
Earlier in the chapter, we mentioned that the term “data science” sometimes confuses people. It came about in the 80s and was frequently used as a different term for statisticians and computer scientists.
However, in the digital age, where data is seen as our main resource, the term ’data science’ has come to cover a wider range of skills.
Insight
What does a data scientist do?
Data science combines statistics, programming, data analysis and business insight into a cross-disciplinary field of study.
Fundamentally, data science can be explained as the art of using all (scientific) methods, processes, and tools you have in your toolbox to structure, analyse and make data accessible, with the purpose of achieving a specific goal. Just as the natural sciences use biology, chemistry, physics, and geology to understand nature and natural phenomena.
A data scientist is, therefore, an expert at structuring data, extracting data-driven insights and creating value through for example automations and better workflows.
If you spent a full workday shadowing a data scientist, you’d mainly see them coding and programming. Their goal is to extract, analyse, interpret, and arrange data for business purposes.
Of course, you can use data science skills for private, political and other activities. There are many examples of data being used to optimise training and diet, for instance. If you have a Fantasy Premier League team at the office, you can actually use data science to build a team, and outsmart your colleagues.
But when we talk about data science, we typically mean how it’s used for commercial purposes or in the public sector.
In many industries, you’ll find data on sales, purchases, inventory, finances, customer information, orders, transportation, maintenance, equipment, and supplier details, to name a few examples. Industrial companies also have operational data generated from their equipment, machines, and processes.
To extract insights and create value from these data, several things need to happen.
Firstly, you must know what the goal is and what fundamental problems you are trying to solve. Then you must collect and structure the data. Finding, collecting, cleaning up and organising the relevant data can be very time-consuming and intricate work—typically around 80 percent of the job in a given project, measured in time and effort. You will learn more about all of these things in chapters 3-5.
Finally, once you have prepared the data, the real fun begins, namely exploring the data with machine learning models and other means and look for patterns and insights that can be used to support decision-making: What has happened? Why did it happen? What is going to happen, when will it happen and why? What should we do about what’s happening?
To accomplish this, a data scientist must not only understand the technology but also have a solid understanding of the industry or the company’s domain. This mix of technical and domain knowledge enables them to see how data can be used to make better decisions and streamline operations.
While data science is a specialised field today, it’s becoming increasingly accessible to non-experts.
There’s a steady growth in software that makes data more comprehensible for the general public, without needing years of programming or IT study. This “democratisation” of data is something we’ll delve into when we discuss data literacy later. But before that, let’s examine some of the opportunities and challenges that come with the use of data.