Back in its early days, Big Data was nowhere as big as we now know it. There was a time where only massive companies could afford the technology behind it. But times have changed, and big data has evolved incredibly fast, making it possible for small businesses to benefit from it as well.
Nowadays, with a little help from artificial intelligence, the internet of things, and the cloud, big data has emerged from being a popular IT trend to becoming part of the way companies do business. If, in 2015, big data adoption was sitting somewhere at 17%, in 2018, it reached 59%, and the numbers are only getting larger. But before diving into the big data trends that emerged at the beginning of 2019 and are set to continue gaining more and more interest in the second part of the year, a brief history of how big data came to be the backbone of business that it is now is necessary.
What Is Big Data?
Big Data describes the large volume of structured and unstructured data that overwhelms companies on a daily basis. The massive amount of information can then be analyzed to obtain insight that can improve decisions and strategies regarding the company’s next business moves. A perfect example is strategic initiative management software which allows companies to predict whether their initiatives will finish on time and within budget. Businesses can also identify if these initiatives are affecting the targeted KPIs and provide the insight needed to cut underperforming projects. This empowers companies to solve issues and drive performance.
The concept of big data is not as new as some may think, as the idea of collecting and storing huge amounts of information for the purpose of analyzing it, later on has been around for decades. However, the term “big data” as it is now known, is relatively new. In the early 2000s, big data was defined as “the three main Vs.”:
- Volume: companies gather data from all sorts of sources, some of it still having unknown value, such as social media, while others give exact information, such as business transactions. While in the past, storing this massive amount of data was quite an issue, as technology advanced, it became easier to do so.
- Velocity: data is generated quickly, and some of the information has to be dealt with almost immediately, as products need to adjust and operate in real-time and require constant evaluation and adaptation.
- Variety: data comes from various sources and can be both structured and unstructured. If in the past, the only sources analyzed by applications were spreadsheets and databases, nowadays, the information comes in all sorts of forms, from emails to photos, PDFs, and audio.
Despite the name, the importance of big data does not revolve around its amount but, more precisely, how it is handled. Data can be used to find solutions for cost and time reductions, generate custom software development and make smart business decisions. And when the information is combined with powerful analytic tools, it can make an important difference in a company’s strategic moves. And with new technologies emerging, big data is sure set to continuously change the way business is done.
Internet of Things Data Streamed for Machine Learning
The efforts to use the Internet of Things (IoT) to combine Machine Learning and Streaming Analytics will continue throughout 2019 as well. The main goal is to offer more adjustable and correct responses to all sorts of situations, especially when communicating with humans.
Machine Learning, as we know it now, uses a specific amount of stored data with the purpose of training in a controlled environment. In the new model, developers aim to stream data from the IoT to provide real-time Machine Learning in a much less controlled environment. But this process requires much more complex algorithms, as Machine Learning can train the systems to offer a more accurate outcome prediction.
Taking, for example, the automotive industry big data continues to reshape. While big data was used previously for the purpose of improving diver experiences, safety, and reducing carbon emissions, now it can be used to lay the foundation of autonomous cars. IoT eases the way data is shared and received, offering the possibility to make cars more intelligent and eventually helping with the development of fully autonomous vehicles. As technology advances, vehicles will be able to collect data regarding maps, routes, obstacles that may appear on the road, engine status, and tire pressures. IoT will allow vehicles to share valuable information with each other, to help with the passengers’ safety.
Artificial Intelligence Platforms
In terms of recent developments, three AI trends are worth keeping an eye on in 2019:
- AI Assistants: Alexa and Siri have been around for quite some time now, with nearly 50 million US adults having a smart speaker in their homes, but things are not stopping here. Virtual assistants are able to perform quite a large number of actions, and they’re only going to become more advanced, as they continue to be integrated with IoT devices.
- AI-Powered Search: People are often using their virtual assistants to run online searches for them. If they don’t provide accurate results, it can reflect negatively on both the virtual assistant and the search engine. One of the most common reasons for inaccurate search results is the fact that people often speak differently than they type. The results can be much more precise by adapting search engines to longer spoken phrases and different accents.
- Chatbots: Social media platforms and e-commerce websites have already included bots that have customer service capabilities and can help users with their particular needs. This will only continue to grow throughout 2019 as more and more new businesses that create bots for companies are emerging.
The term “cloud” is no longer big news for anybody who has and utilizes a smartphone. But there is a new trend emerging in means of Cloud Architecture, which is Hybrid Clouds. To put it simply, hybrid clouds aim to combine an organization’s private Cloud, which stores secure data on-premises, with the possibility to rent a public Cloud, which can be used to store high-volume projects that don’t require much security. This allows businesses to benefit from the advantages of both private and public clouds.
One of the biggest benefits of hybrid clouds is cost-effectiveness. The costs for increasing capacity to an on-premises Cloud, which can sometimes mean building entirely new data centers, can be extremely high. By renting a public Cloud, where a company can store non-sensitive data, such as email advertisements, backups, and archived data, the organizational costs can lower significantly. This way, companies can use pay-as-you-go services and eliminate the need for big financial investments.
Flexibility is another benefit of the hybrid cloud, enabling organizations to move resources from the private to the public cloud and back. When it comes to development and testing, the hybrid cloud allows developers to spin up new VMs and apps without the intervention of IT operations, as well as extend portions of the applications into the cloud, to better handle peak processing requirements. The cloud also provides various other services, such as business intelligence, analytics, and the Internet of Things, allowing companies to benefit from them, without having to build them separately.
Studies have shown that around 2.5 exabytes of data are created daily, and the numbers only continue to grow. The solution to storing and analyzing such a vast amount of data seems to be quantum computing. This will not only provide means for data to be stored but can also facilitate the analyzing speed, as the computer will not need to examine the entire data set to search for the necessary data. And considering how fast technology is evolving, the era of quantum computing may not be that far. A 72-qubit processor was created only last year, while in 2017, a computer with even a quarter of that power was considered a distant dream. Considering that a 300-qubit computer would be more powerful than all computers in the world connected together, the future of big data processing and analyzing seems pretty bright and quite close.
By combining quantum computing with AI, data scientists can analyze patterns that may now be less obvious to search for. This can lead to improvements in various industries, such as the medical sector, where researchers are already using AI to expedite cancer research. This could be done much more effectively with quantum computing.
While technology continues to advance, big data will continue to play an even more important role in the way companies conduct their business in order to meet customer demands and keep up with their competition. The ultimate goal that spreads throughout this year’s trends is to create smarter solutions to streamline business processes while keeping them both time and cost-effective.