If you are following the big data market, then one of the most pressing questions on your mind is: “Is there a ‘big data bubble’? And will it burst?” Let’s take a closer look…
The Fuss over Big Data
Big data analytics (BDA) involves large quantities of data and uses predictive algorithms to detect patterns. The resulting information can be integrated into an organization for decision-making processes. However, with such high stakes involved in big data analytics, companies may benefit from considering whether they’re investing in the technology to create value or trying to follow fads and trends that come and go quickly.
This infographic presents how BDA works and points out which sectors might be currently overusing it:
The ‘Big Data Bubble’
With such a high demand for big data analytics and the vast amounts of money, companies spend on it, you might think that there is no bubble in sight. And although some people claim that the technology isn’t overhyped and still has room to grow, others argue otherwise: Big data will be useless if we cannot find enough data scientists and machine learning experts to analyze and give meaning to the gathered information! This is because big data brings with it new challenges: How can we make sense of unstructured information?
What level of automation should we aim for when analyzing large quantities of data? If our organization fails at finding answers to these questions, then all investments in BDA may just go down the drain. Moreover, when investing in BDA, companies should also consider how their competitors are using the technology because the results may vary greatly. Let’s take a look at some of the industries currently benefitting from big data analytics to understand this further:
The Advantages of Big Data Analytics
Some sectors could be overdoing it with big data analytics (BDA) like retail and banking while others make good use of these new methods – here are examples of both situations: Retailers. Although there is no doubt that big data can help retailers improve their customer service by providing personalized offers or ads based on consumers’ preferences, many retailers go overboard with collecting too much information. This overloads their teams with data to analyze and creates “noise” which leads to poor analysis. On the other hand, there are some retailers that don’t have a need for big data analytics because they have the luxury of being able to experiment with new business models and keep track of their performance based on a limited amount of customer data. Banks With its ability to process large amounts of data at a fast speed, big data enables banks to improve fraud detection capabilities by correlating events from different sources. In addition, these emerging technologies enable them to offer convenient services – from mobile check deposits to peer-to-peer payments – while better managing risk.
The Big Data Bubble in Banking
Although it’s true that banking companies spend more money on big data every year, one could argue that they’re doing this because they have to comply with regulations and adapt to changes in the market. Thus, investing heavily in big data analytics can be seen as a way for them to remain competitive or even transform themselves into technology companies: “By 2018, 45% of all financial services Business and IT decision-makers will view themselves as providing analysis and insight as a service.” – International Data Corporation (IDC) Report, Survey Findings: Financial Services Market Transformation Worldwide, 2016 All that said, there’s no doubt that big data has given this industry a lot of attention due to its potential benefits but it could also prove dangerous by causing banks to overinvest money they don’t really need.
The Secret Sauce? AI!
In addition to understanding which industries are benefitting from big data analytics, it’s also interesting to understand the secret sauce that makes big data tick. And in this case, that secret sauce is artificial intelligence (AI)!
“Not all companies are well equipped to take advantage of big data. But when it comes to AI, even small companies could make meaningful progress by simply plugging in existing machine learning tools.” – Tom Eck Data scientists use these tools to build models which are powered by algorithms. The more data available for training purposes, the more accurate and refined these algorithms become! So not only does AI provide a huge advantage over traditional methods but its development is also much faster than one might think because many of these tools are free or open-source.
Conclusion:
Big Data Is a Force to Be Recked With!
We’ve seen that Big Data Hadoop can work well for some industries while others may be overdoing it. In addition, we’ve seen how big data is dependent on AI and how these new technologies are revolutionizing certain industries. And although the hype surrounding AI is not surprising, companies should make decisions based on data from experiments or from consulting experts in this field instead of pouring money into unproven technologies which could cost them their investments. Big Data Analytics (BDA) Industry Report This article was originally published in The Big Data Startup Blog. Follow the link to read the full post: Who’s Benefiting From Big Data Analytics?
