What’s More Important: Volumes of Data or Better Algorithms?
It is a well-known fact that the world of big data holds enormous untapped potential. However, what happens when this data is just too big and exceeds the existing processing capabilities? The speed and efficiency of analysing the data may be hindered. Therefore, the key challenge today is how to harness this tremendous amount of data and produce meaningful analysis and insights as quickly as possible.
Today, many companies in the technology scene and beyond are turning to Machine Learning for the purpose of data interpretation. One of the fundamental reasons why we use big data is to extract some valuable and actionable business insights that could help make better strategic decisions. And that’s exactly what machine learning helps to achieve!
Big Data Is Getting Bigger by The Minute
“Every minute we send 204 million emails, generate 1.8 million Facebook likes, send 278 thousand Tweets, and up-load 200,000 photos to Facebook.” – Qmee
The trend of “big data growth” presents enormous challenges, but also incredible business opportunities. According to CSC, a company that specialises in providing technology for business solutions, by 2020, more than 30% data will be residing and running on a cloud network. Consumers are tapping into increasing amounts of data on a daily basis and are creating more than 70 percent of all the data in the digital universe.
Fortunately, many enterprises currently have access to a plethora of digital solutions to harness this vast amount of data, and many are turning to machine learning to understand it.
Big Data Unleashes Business Opportunity
Big players such as eBay, Uber, Twitter, and Amazon, are connecting the data dots – not just collecting consumer data. They search for patterns within their data sets to uncover valuable customer insights and utilise these to align customers with relevant content or experiences.
The infamous digital business, eBay works with enormous volumes of data just about every second. However, the company believes the strategic factor that will contribute to its future success will involve implementing machine learning into various processes. The company’s core competitive advantage resides in the lightning speed rate with which it can apply data into a personalised experience.
The increasing amount of data and real-time intuitive analysis promises better experience of engagement between brands and consumers. But, it depends on how rapidly the organisation can keep up and analyse the inflow of data (called streaming data).
How to Turn Big Data Into Insight?
In today’s data-driven environment, organisations are inclined to gain useful insights from big data. But, the major stumbling block to cracking the value from this data is not only tackling the volume, but analysing and acting on it. Data is valuable only when the enterprise can use it to extract insights. These days, more and more companies understand this, and this game changer is called Machine Learning.
Machine Learning Improves Big Data Analytics
Gathering big data is one thing, but interpreting structured and unstructured data is another challenge in itself. As data is becoming increasingly unstructured, traditional analytics tools are no longer suitable for capturing the entire value of big data according to machine learning software company, SkyTree.
Unlike traditional analytics tools, machine learning enables organisations to extract deep insights such as forecasting patterns and trends, driving recommendations based on actions, predicting future behaviour, just to name a few.
Machine Learning is the only means by which the enterprise can make use of all its data – Dataversity
According to NgData solutions, machine learning is already widely used for fraud detection, product or service endorsements, preventing customer churn, and customer segmentation. Powered by algorithms, machine learning helps to discover and interpret insights from new data feeds without real time coding or human interference.
Machine Learning Helps Businesses Optimise Big Data Analytics
Tech experts indicate that the future of big data may become way too big to manage manually. So, to form reasonable interpretations from the sheer volumes of big data, machine learning offers data-driven decision modules that would support the process.
To remain competitive in the future, companies need to embrace machine learning and as a result benefit from the rapid analysis of real-time data. Machine learning is ideal for tackling the opportunities concealed in big data.
The newly discoverable insights obtained via machine learning solutions are presenting businesses with tremendous potential to make better decisions, faster. It’s already clear today that those businesses and industries that realise the value of interpreting big data in real-time by using machine learning are more likely to remain competitive in the long run.
So, is your company ready to put your big data to work and extract valuable business insights?