An Introduction To Big Data Analytics
Data is everywhere, growing at a rapid rate and changing the way that businesses operate. This data encompasses the vast volumes and types of information companies can collect and process using increasingly high-tech systems. Artificial Intelligence and the Internet of Things are driving data complexity and creating new forms and sources of data. Big data can be high volume, high velocity and high variety and comes from everything from sensors to devices and social media, on top of which much of it is generated in real time. By the end of 2020, it is predicted that the big data volume will reach 44 trillion gigabytes. However, data is only useful and a business advantage if it is analysed and converted into actionable insights.
What Is Big Data Analytics?
The notion of big data has been around for a long time since the realisation of the increasing volumes of data being produced back in the mid-1990s. Since then the concept of big data has grown to involve not only volume but velocity and variety. Modern data types don’t fit well in traditional data warehouses nor can they always manage the volume of processing demands. Technology has adapted to be able to process the data with advanced analytics using complex applications such as predictive models, statistical algorithms and what-if analysis.
Big data analytics is the practice of scrutinising large and varied data sets using advanced analytic techniques to provide information that can help drive business decisions. Data can be internal or external, structured or unstructured and can come from almost anywhere. This is where analytics come into play. The use of maths and statistics helps derive meaning from the noise of data that companies receive. Different analytical techniques can tell us what has happened, what could happen and, most importantly to businesses, can advise on what should be done.
The combination of the sheer volume of data available and the power of technologies to analyse the data has the potential to provide a great competitive advantage for organisations. Big data analytics allows companies to uncover hidden patterns, unknown correlations and market trends and to use this information to make better and faster business decisions.
Why Is Big Data Analytics Important?
It’s been predicted that the big-data market will be worth €46.34 billion this year making it a rapidly growing industry that provides some serious business benefits. The right information can inform new revenue streams, improve marketing efforts, enhance customer service and boost operational efficiency.
Big data analytics applications allow companies to delve into previously untapped data that is too vast or unstructured to be analysed using standard programs. Not only can a greater variety of data be used but technology has made the process fast and efficient. Businesses that harness big data analytics are able to identify insights for immediate decisions. Having the capacity to be agile in this way gives companies a competitive advantage that wasn’t previously possible.
A great example of big data analytics in action is the way that Netflix markets to its customers. The company has over 137 million subscribers across the world and is able to collect huge amounts of data and this is what has allowed it to gain such a huge competitive advantage. Past search and watch data is used to suggest what subscribers should watch next. This is such a big tool that the recommendations data influences about 80% of streamed content.
Big Data Analytics Technology And Tools
Big data analytics harnesses the power of a variety of technologies in order to deliver its results. There isn’t one single technology that can provide everything, instead the different types work together to deliver the greatest value to businesses. The principal types of big data analytics include the following:
- Data management – systems which allow organisations to establish processes, build standards for data quality and create management programs.
- Data mining – technology which enable organisations to uncover patterns in large volumes of data.
- Hadoop – an open source processing framework that can store and process large volumes of data.
- In-memory analytics – analysing data from system memory allows organisations to derive insights immediately.
- Predictive analytics – this technology uses historical data, algorithms and machine-learning techniques to predict the possibility of future outcomes.
- Text mining – machine learning technology sifts through text-based content to reveal insights.
What Are The Challenges Of Big Data Analytics?
Big data analytics can be a challenge for organisations who lack the necessary internal skills. The high cost of external data scientists and data engineers can be a daunting prospect for those who are new to the technology. However, the growth of artificial intelligence and machine learning technologies has created more user-friendly software that is easier to use without the requirement for specialists.
Another risk with the volume and variety of data being collected is its quality, management and governance. In addition data silos can result when different platforms and stores are used. Integration of new software can be a challenging prospect for IT teams trying to create the right mix within a cohesive data architecture. To counter this, it is predicted that Chief Data Officers (CDOs) will move from their existing defensive position within organisations. This year it is estimated that more than 50% of CDOs will report directly to CEOs, up from 34% in 2016 and 40% in 2017.
What Is The Future Of Big Data Analytics?
The advancement of technologies such as Artificial Intelligence and the Internet of Things will only continue to increase the volume, sources and varieties of data that organisations receive. This will create exciting changes in big data. Cognitive technologies will allow systems to not only capture but to comprehend what data means. Machines will be able to learn faster than ever and to analyse bigger and more complex data sets with speed and precision. Prescriptive analytics will be combined more efficiently with descriptive analytics to allow businesses to make smarter decisions.
All businesses have the opportunity to harness the power of big data analytics. In fact, it will change the way we all live our lives as people, processes and products become increasingly streamlined. Machine learning will become faster and smarter as we find the future being predicted with increasing accuracy. Companies will be able to use big data analytics to make better decisions, improve customer relations and reduce costs.