Over the last decade, the world has changed in many ways but perhaps none of these changes has been more impactful than the sheer amount of global data captured and the influence it has over business and people's day-to-day lives.
Big data combines structured, unstructured, and semi-structured data gathered by organisations that are then processed for information using machine learning, predictive modeling, and other advanced AI analytics applications. But what does the future hold for data analytics and big data?
The sheer volume of data collected during the last few years has increased exponentially. An increase in internet users worldwide in part led by roughly 5.5 billion unique mobile users has meant that mountains of data are collected every single day. The average household has 13 connected devices and enterprise data is increasing by 40% each year. In 2013 the global data sphere amounted to 4.4 zettabytes but by 2025 that figure is expected to grow to 175 zettabytes, about forty times as much in just 12 years.
Users of social media platforms such as Facebook, Instagram, and Twitter now stand at close to 4.5 billion, a number that continues to grow year on year and it’s expected that by 2025 6 billion people, or about 75% of the population of Planet Earth will be interacting with online data every day and having one data interaction every 18 seconds on average. This increase will mean that social media listening will become an even more vital part of a business’s data strategy going forward with companies able to use sets of big data collected by social media output to gain insight into what people are commenting and suggesting about a product or business, allowing them to identify issues and successes as well as audiences for targeted advertising and marketing campaigns with more accuracy than ever before.
The way data is stored has already begun to change, with a seismic shift to cloud storage ongoing. Big Data specifically is usually stored in a data lake which is a storage repository that can hold massive amounts of raw data in its original format until it is needed for analysis. Unlike regional databases that only contain structured data, data lakes can support many forms of data, structured, unstructured, and semi-structured, and are based on Hadoop clusters, NoSQL databases, and cloud-based storage services
Within 5 years half of the world’s data will be stored in these cloud-based storage services by companies like Google, Microsoft, and Amazon. Previously, companies like these had to rely mostly on increasing the size, capacity, and capability of their physical data centres. These won’t disappear altogether, however, lots of companies, for various reasons, can't store sensitive information in the cloud but as the technology around cybersecurity advances this is very much likely to change and in the not-too-distant future, the majority of big data will be stored in the cloud.
Data Security & Privacy
With big data volume ever-growing new problems and challenges surrounding security and privacy arise daily, data protection laws need regular adjustment to keep up with technological advancements and security software faces huge challenges from equally progressive criminal enterprises exploiting it with cyberattacks and data leaks. With the introduction of GDPR in the EU there are now risks of severe financial repercussions for non-compliant businesses who flout the laws regarding data protection and this is likely to further increase the more data is captured.
With dodgy data usage exemplified by the likes of the Cambridge Analytica scandal coming to light, the users themselves are now becoming far more aware of what their data is worth and more importantly, how a breach of it can affect them, this, in turn, will have far-reaching consequences in the future. Businesses who are more transparent about how their user data is used are more likely to become the norm with users far more likely to be drawn to organisations that offer transparency to the user and a level of control over what they allow a company to capture with business reputation being at stake if users aren’t informed of what data exactly is being collected and what it is used for.
Companies such as Genera8 are empowering users with the ability to either gain financially from the data companies collect on them or allow users to opt-out of data collection altogether with far more ease. Industries will have to accommodate for this change of culture and companies will likely soon be forced to pay more for big data analytics, as compensation of the user will become more commonplace. The more users learn about how and when their data is collected the more incentive they will need to part with it.
Real-time data that typically comes in the form of streaming through technologies such as the Internet of Things and event-driven applications is known as 'Fast Data'. Fast data is data in motion, streaming into applications from an almost infinite amount of endpoints. Authorisation systems, mobile devices, financial transactions, logs, retail systems, stock tick feeds, and more contribute to an endless stream of rapid data collection and analysis.
The data collected can be analysed to make quick business decisions in a matter of milliseconds, meaning data analytics is becoming instantaneous and automated. According to Forrester, 26% of companies are now using fast data for most of, if not all, of their applications and 41% of businesses are looking to use streaming analytics in the next 12 months. As a result of using fast data, organisations expect to be able to make better-informed decisions using these insights and 39% of businesses expect the use of fast data to result in improved data quality and consistency, increased revenue, and reduced operational costs. With that said, it’s clear to see why many agree that the future of big data is unimaginably fast and the need for human intervention may soon become superfluous.