2017 saw huge growth in both cloud and Business Intelligence (BI) analytics. More and more businesses are recognising the opportunity of big data and are realising their potential by analysing that data with BI. But now 2017 is over and it’s time to look to the future. We can expect big data to become the standard operating procedure for businesses and, with it, new and interesting means of analysing that data. In this blog, we take a look at what 2018 looks set to deliver.
Artificial Intelligence (AI) and BI are natural companions. Business intelligence is all about looking back at data, then deconstructing and visualising it to extract valuable intel. AI can be used to make analysing data quicker and easier but is also about sourcing data to make predictions about the future.
It’s already used in many industries to develop modern technology, such as facial recognition or self-driving cars. For businesses analysing data, you can use AI to automate time-consuming and labour-intensive processes. For example, AI can analyse tables, visualise data, and produce reports automatically. But where AI becomes truly valuable is its ability to recognise patterns and use that information to predict future trends. It can also be used for security purposes where the AI recognises patterns of potential threats, such as attempting to overload a server with requests.
Advanced Machine Learning
Machine learning was one of the biggest disruptors of 2017. It is essentially the application of AI that allows computers to understand data and recognise patterns, then making fast, automated conclusions based on that data through detailed analytics. It’s technology that has previously been used to analyse images, video, handwriting, and more. Advanced machine learning is going to take it to the next level, where previously difficult-to-find anomalies can be located and correlated across data. It promises better unsupervised algorithms for finding hidden patterns in data and delivering faster, more accurate outcomes.
The discussion about the future of machine learning has recently expanded to quantum computing. Machine learning will involve solving problems by manipulating and classifying large numbers of vectors in high-dimensional space, but the algorithms we use to solve problems like this take time. Meanwhile, quantum computing looks like it will greatly increase the speed at which Machine Learning will run. Quantum computing essentially allows the smallest form of data – a bit – to exist in more than one state at a time, meaning a quantum bit, or ‘qubit’, can store much more information than the ones and zeros of binary data. It’s expected to increase the number of vectors and dimensions to boost the speed at which machine learning algorithms will run.
Natural language processors
Natural language processors (NLPs) combine AI and Machine Learning with linguistics to allow people to talk to machines in a normal human dialect. One of the biggest consequences of this will be that anyone can use BI and big data, meaning companies will have to start thinking about making their BI space more user-friendly and accessible so that anyone in the company can make better, data-informed decisions.
With the evolution of AI, we can expect NLPs to help BI deliver more insight from requests. When NLPs have learned about semantic relations and inferences, BI can offer direct answers to queries, rather than offering a list of pages to analyse. By making NLP available to the right workflows and allowing it to become second nature, it will empower staff to ask more nuanced questions about their data for answers that lead to better insight and decisions.
Multi-cloud strategies are expected to be incorporated by 70% of enterprises by 2019. Businesses can feel sceptical about being tied to a single location. Although the multi-cloud method does increase overhead costs, it also provides more flexibility as well as the best performance and support. Some businesses don’t appreciate being bound to a specific provider’s setup and execution, and having multiple clouds allows organisations to employ different cloud solutions for different types of project. Perhaps one project requires advanced development and testing environments, while another needs data storage and BI; a multi-cloud strategy allows managers to use whichever provider is best suited to the project. Just keep in mind that additional SaaS providers mean more company expenses as well as IT staff that are required to understand the nuanced differentiation of the different providers’ systems.