When scientists describe the beginning of the universe as “the big bang”, it feels like a missed opportunity for a much better name. The same is true for the term big data. “Big” doesn’t quite do it justice. It’s not big at all, it’s inconceivably massive and growing at a rate that is beyond astonishing.
Characterised by volume, variety, velocity, veracity, value, variability and visualisation, the 7 Vs of Big Data will drive a monumental shift in technology in 2023.
To help shape your data strategy for this year and beyond, our experts explore the 23 Big Data trends for 2023.
1. Big data is growing rapidly, with the amount of data being generated expected to reach 175 zettabytes by 2025
What’s a zettabyte? A billion terabytes or a trillion gigabytes. According to Cisco, humanity entered the Zettabyte era around 2016. That milestone slipped by unnoticed by most of us, but it was when the amount of digital data in the world exceeded one zettabyte.
It’s taken the entirety of human history to reach the zettabyte era, and in less than 10 years we’re expecting that number to multiply by 175. That’s the velocity of big data.
2. Cloud computing is a vital supporting technology
Given the size and rate of expansion, cloud computing and big data will become inseparable. The advent of cloud computing has helped to power the data revolution. It gives us the capacity and the scalability to handle huge quantities of data. Cloud computing makes big data accessible and cost-effective. It’s an essential infrastructure supporting the widespread implementation of big data.
3. Big data is driving innovation in organisations of all sizes across diverse industries
By collecting data, putting it to work and leveraging it to its fullest extent we can transform operations in any industry, by making better and more informed decisions.
From manufacturing to transport and even agriculture, big data is driving innovation and empowering business in diverse industries across the world. In the future we can expect the power of big data to improve every business sector regardless of size. We are already seeing smaller companies and even sole traders forming alliances or data cooperatives to bring the power of big data within reach.
4. The Internet of Things (IoT) is a major contributor
This is one of the key drivers of big data. We live in an increasingly connected world, everything from your car to your fridge could one day be connected. It’s possible right now. As more and more devices join the network, the internet of things fuels the growth of big data with real-time data collection and analysis.
The industrial internet of things takes this technology into the engineering arena allowing companies levels of insight that previous generations would only have dreamed of.
5. Big data analytics demands ever more sophisticated artificial intelligence and machine learning
With such large quantities of data, we will see a symbiotic relationship between big data, machine learning and artificial intelligence.
AI and machine learning play a vital role in unlocking insight. These are the technologies that allow us to put big data to work. Through pattern recognition on a monumental scale, we can identify opportunities for streamlining processes or ways to boost sales. This powers predictive modelling which helps us plan for uncertainty in the future, and real-time analysis helps us organise the information and extract the insights we need, exactly when we need it.
As big data grows, we will develop increasingly powerful artificial intelligence and machine learning tools. The growth of one will fuel the other.
6. Big data expands what we consider to be data
Variety is one of the 7 Vs of big data, and the trend we’re seeing is for the acceptable types of data to grow and become more numerous. Traditional data stored in a database consists of information (usually words and numbers), ordered in columns and files. This is considered “structured data” but with big data we must rethink that and start again because it can include a great deal more.
Big data can include various formats from structured through to unstructured data like audio, video and image files, sensor logs and even social media data. In the future we can expect an increasing variety and variability of data, and our infrastructure will have to grow to accommodate it.
7. Big data will see new and improved methods of data storage and processing
What’s the difference between a database, a data warehouse and a data lake? Everyone has heard of a database. It’s a way of storing traditional data in a structured way. We have a products database, or a customer database where we keep all the information relating to those things. It’s designed to be transactional and flexible so you can move data around and get specific details from it. A data warehouse is a special type of database used for reporting and analysis.
With big data we have a much greater variety of formats and that’s the point of a data lake. It’s another way of storing data, one which is designed to cope with unstructured data in any conceivable format.
8. Improved decision-making and increased efficiency
Big data gives us big insights based on enormous datasets. It allows us to spot market trends as they emerge with precise information on customer behaviour and changing market situations often on a global scale. Big data helps companies predict and monitor the effects of their decisions, allowing them to offer better customer experiences based on the cumulative knowledge of many previous interactions and many other data sources.
Big data also helps companies improve the accuracy of their insights because of the larger sample sizes they base their decision on, it promotes better risk management and improved agility.
9. Big data can be used for predictive analytics, allowing businesses to anticipate future trends and events
Big data provides companies with a wealth of information helping perform predictive analytics. That includes historical data in all its wide variety of forms, machine learning algorithms and statistical models. By incorporating big data into predictive analytics, organisations can stay ahead of the curve by making better and more informed decisions which will lead to improved efficiency and competitiveness.
10. We can use big data for sentiment analysis, allowing businesses to understand customer opinions and preferences
One of the big problems with artificial intelligence performing customer interactions is the issue of sentiment analysis. It can be difficult for humans, let alone computers, to identify sentiment and convey things like tone of voice, especially in writing. Machines cannot feel or experience sentiment or emotions in the same way that humans can, and this can lead to break downs in communication.
Big data is an important tool in understanding and improving this situation. It seems unlikely that a machine will ever get a consistently perfect sentiment analysis, but with the vast numbers of interactions recorded along with images, facial recognition and voice recordings, that reality is getting ever closer.
11. Big data security is a major concern
Data is a valuable resource. It has become a commodity that is bought and sold. As we invest in big data, the issue of security will become ever more important.
In recent years we have seen a number of high-profile security breaches. The current challenges come from a multitude of directions, ranging from physical security through to authentication or authorisation issues, lack of transport encryption and insecure user interfaces. As we invest in cloud computing, data sharing and increased connectivity between software, hardware and people, including different organisations along the value chain, data security will continue to be an pressing issue.
Manufacturing companies will need ever more sophisticated systems to mitigate the risks associated with data security. That will include ever greater encryption to protect data either in storage or in transit. We can expect improved access controls to become the norm with multifactor authentication to verify users’ identities.
Big data also brings its very own security tools specifically designed for the purpose which provide additional security features to protect data from unauthorised access, modification and theft.
12. The use of big data can lead to ethical questions
Sooner or later, most conversations about data turn to ethical matters. In Europe and the UK we’ve had the GDPR (General Data Protection Regulations) in force since 2018, and the USA has similar legislation on federal and state levels.
Those regulations tell companies how they can collect and use data. As big data grows they become increasingly important. Issues of privacy, consent, accuracy and data cleansing are perennial, but there is more to ethical data use than simply complying with the law.
We need to be asking deeper questions about the balance between public, private and professional life. The ethics of big data is a huge topic, and one that needs to be taken very seriously indeed. Manufacturers, like all organisations, collect data about individuals accessing and using their products and services.
We must ensure that individuals are aware of how their data is being used and that they have given explicit consent. Big data can reveal insights that were previously unknown, but the analysis can also lead to biased outcomes. Engineers must be careful to avoid implicit bias when designing and analysing models and ensure that their systems are fair and unbiased.
13. The demand for big data professionals, such as data scientists and engineers, is on the rise
The growth of big data is making big changes to the labour market. Data is useless if you don’t have the skilled staff to analyse it. That’s why we are seeing a sharp rise in demand for data and computer science skills.
There are a whole range of new and developing roles based on big data. We’ve got data scientists, data engineers, data analysts, visualisation specialists, business intelligence engineers, machine learning and AI scientists, and that’s just the current situation. In the short-term future we can expect demand for these and similar roles to grow at an exponential rate. In the long-term, expect big data to completely change the employment market and the skills we need and value. Data science is a vital part of modern manufacturing, and this will be sure to increase in the future.
14. Big data can be used for fraud detection and prevention
This is a really exciting use of big data and one that will have an immediate positive impact. Manufacturers can use big data to improve the ways they analyse data and find patterns of behaviour to identify unusual activity and flag up potential fraud cases.
Fraud can come from internal sources as well as external ones. As technology companies collect and store ever increasing amounts of data, the threat of fraud increases. Hackers are often searching for ways into our systems, and sadly there are also cases of employees using company information for fraudulent purposes like false reporting and data tampering.
We will certainly see companies increasingly putting big data to work to improve their systems and processes to prevent fraud and improve security.
15. The use of big data for personalised marketing and advertising
Imagine a world where product or service providers contact you with such a well targeted and well-timed offering that it’s a relief to check your mail because you know it will contain the solutions you need.
Big data has already started to empower manufacturing organisations to identify user behaviours, trends and preferences, and then offer targeted products that closely match customer requirements.
Big data will allow manufacturers to provide far more personalised products or services based on improved insights and predictive analysis. With big data, manufacturers can tailor their marketing strategies across every aspect of the marketing mix, from product development through to channels of distribution and beyond
16. Better supply chain management and logistics
Logistics and supply chain management is an area already well known for using large quantities of data. There are the ERP systems and information coming from a wide range of sources like business forecasts, transport and traffic data, vehicle diagnostics and even things like weather reports which can affect the supply and movement of resources.
Using big data will amplify and enhance this trend which can be used to identify risks, avoid disruption and improve decision making.
17. Big data will lead to more accurate weather forecasting and natural disaster prediction
Modern manufacturing and metrology equipment is so sensitive that slight changes in the weather, like atmospheric pressure, can have a negative impact on product quality.
Predicting the weather and other natural phenomena has always been important, but as we face climate change and growing frequency of natural disasters, the need to understand global weather systems has become an imperative. It’s more than a matter of product quality and calibration, for many people it’s life and death.
Scientists have been collecting climate data for hundreds of years, but now with modern connectivity the amount of data being collected has increased by an order of magnitude. The analytical tools of big data are giving us extra capabilities to turn it into useful insights leading to better, faster and more accurate predictions.
18. Big data can be used for network security and intrusion detection
Holding large quantities of data makes companies an attractive target for cybercrime. Modern hackers are more creative than ever. However, big data analytics can also be a power tool in the fight against online crime. It gives us the ability to identify threats or possible areas of weakness by looking at patterns in the data and heading off an attack before it happens.
In fact, we can go one step further than that, with artificial intelligence and machine learning we can increasingly identify hackers by their malicious activity in real-time.
19. The use of big data will lead to improved health and safety provision at work
Health and safety is a major concern for manufacturing companies, but even here big data is having a positive impact.
Big data enables companies to assess risks and take mitigating action with far greater accuracy. Organisations can employ predictive analytics looking at historical data to make judgements about the likelihood of accidents in the future. Big data empowers manufacturers real time monitoring and analysis to identify equipment failure and other safety issues. They can take action sooner and prevent injuries. With big data, manufacturers can collect and analyse more data, faster than ever before. The impacts for health and safety in the workplace are enormous.
20. Big data will help manufacturing companies achieve their sustainability goals
Big data is already playing a massive role in helping the world achieve sustainability. This is especially true for manufacturing companies. Big data is excellent at helping find new ways to improve efficiency.
In terms of energy consumption, big data helps us track historical usage and in real time giving insights into how and where savings can be made. We can look at waste management strategies with the insights provided by enormous data sets and identify where problems exist, and where improvements might be made.
Other areas where big data helps achieve sustainability are in wider facilities management, resources conservation and reducing transport emissions.
21. Big data can be used for predictive maintenance in manufacturing and other industries
Machine downtime costs money. Predictive maintenance has long been used to keep machines in top condition, identifying and fixing problems before they occur.
Big data can take this to the next level by making your predictive maintenance more accurate and reliable. This will reduce downtime and extend the useful life of equipment even further. Sometimes, we can err on the side of caution with predictive maintenance, which sometimes results in unnecessary maintenance.
The accuracy of your predictions is a limiting factor. Big data helps you make better predictions.
22. Improved energy usage, efficiency and resource management
Efficient use of resources is an imperative that cannot be ignored. In the UK, energy prices have gone up by more than 50% in some cases over the last 12 months. Globally, energy prices are extremely volatile because they are so susceptible to external influences like civil unrest and weather phenomena all of which are difficult to predict.
Big data helps us smooth the peaks and troughs and optimise consumption by leveraging data from a diverse of inputs, and aggregating systems that supply energy supply with those that demand it. Big data will help us with better predications and by automating where possible, detecting ways to prevent loss or failure, and identifying patterns in demand.
As we exchange fossil fuels for renewables, now is the time to put the best practices in place and optimise our energy infrastructure.
23. Big data will improve city planning and urban development
Big data is even finding its way into urban town planning. It allows us to better assess planning applications and proposed developments for the impacts they will have. We have historical data going back hundreds of years, and now there is real-time information constantly being collected which allows developers and town planners to create better cities where all the resources and amenities are available on demand.
Big data allows us to take a step back and see a wider picture about how we life and the way it is changing. We can identify patterns that would otherwise have been very difficult to spot. We can look at the movements of entire populations and the demands of whole industries to perfectly predict what needs to be built or manufactured to improve our experience in line with sustainability goals. Big data allows better decisions that benefit everyone.
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