Data analysis is nothing but the practice of extracting useful insights from business data that can be used to help a business in any way. This includes identifying customer patterns and minor market trends that could otherwise have been harder to spot manually.
As Geoffrey Moore, Author of Crossing the Chasm & Inside the Tornado once said:
“Without big data analytics, companies are blind and deaf, wandering out onto the Web like deer on a freeway.”
With that said, where does machine learning come in? How does it play a role while doing data analysis? Well, here’s how we can put this simply; Machine learning is the process where machines mimic human behavior based on certain training scenarios.
Data analysis is not an automated process and only has a significant level of human intervention. However, the fusion of it with machine learning is what provides it this capability. As a result, data analysis becomes a complete package, consisting of automated analysis techniques and predictive qualities.
The benefit of using machine learning for data analysis is that large-scale automation can be done, and the predictive nature of machine learning could be utilized in enhancing data analysis techniques, eventually making data analysis a lot more fruitful for businesses.
However, this is just the basic idea. Further in the article, we will dive deep into the specifics and see why so many people are after the use of machine learning in the world of data analysis. Let’s start!
10 Benefits of Machine Learning for Data Analysis
I’ll be explaining each benefit of machine learning in data analysis thoroughly and with examples where required, so you get a thorough idea of the whole concept and the idea behind it. Let’s see.
No matter how good your data analysis pipeline is, if it contains any number of fraudulent entries or inconsistencies, it will never deliver you the results you’re looking for. In fact, this reduces the performance to a great extent.
Here, machine learning plays a vital role. It’s been the pioneer of fraud detection and recently, there has been a lot of advancement in the said area. Currently, there are a lot of industries employing machine learning to detect fraud. Once fraudulent transactions or data is detected, they can take appropriate steps to take care of it and even avoid it in the future.
Data analysis tells a lot about customer behaviors and their trends, but it’s really machine learning that can predict unseen scenarios to increase customer acquisition. Once you successfully extract customer trends, machine learning helps you use those trends in your new goals, say, marketing plans.
This might not seem very significant right now, but since capturing more customers is amongst the most primary data analysis goals, this application of machine learning is of great essence.
Spotting trends is the basic goal when it comes to data analysis. However, to fulfill this deed, it doesn’t employ any predictive algorithms. Data is studied to very fine levels and any possible trends are extracted. However, machine learning has a different approach to it.
Based upon previous trends and the situations in which they have occurred before, machine learning tries to find new trends in similar places. Not only is this very fast than the brute-force approach of data analysis, but it’s also a lot less computationally expensive.
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Even though there are very advanced data analysis tools that require minimal human intervention, machine learning is the core power behind them, fueling them with all this automation.
If it weren’t for machine learning, data analysis would still require manual managing, processing, and almost complete human overlooking of the processes. This would increase the costs by a lot since the need for human resources would require a sharp rise.
However, there’s still room for more automation in the area and machine learning is the sole player in the field. By using more efficient and accurate algorithms, data analysis could be made more independent.
Just like we talked about fraud detection first, machine learning could also help data analysis identify certain threats based on relevant data. These threats could range from the simple threat of a competitor to a potential brand identity theft.
From a business perspective, foreseeing possible threats in the future is a pretty big deal, and no business would want to miss out on such an opportunity. Moreover, as machine learning continues to progress, data analysis enjoys the perks of advancements as well.
Various unsupervised ML algorithms are being used to detect anomalies; these anomalies are what the threats look like in the practical world. For example, if you see ten sheep present in a garden. But in reality, there are nine sheep and a dog amidst them; this could be a possible threat. ML would identify the dog as an anomaly and notify you so you can take appropriate action.
Learning from Similar Situations
We simply cannot discuss the benefits of machine learning without mentioning the learning nature. Certainly, it’s the basis of the whole idea that machine learning revolves around. But how do we exactly use this from the perspective of data analysis?
There can be various scenarios in data analysis, especially in the same company, where some similar scenarios are being repeated repeatedly. While data analysis alone would ignore this, machine learning would make no mistake in spotting these repeated occurrences of an event and would take significant leverage from them.
What happens is that if similar situations arise in the future, the ‘learned’ behavior helps to deal with the problem in the most efficient way possible, all thanks to ML.
For example, if a machine learning model has collected data from similar sources a few times, it would recognize how the data variables are related. In the future, there will be lesser integration problems after collecting the data from sources based on similar variables.
Machine learning algorithms are not only great in predicting unforeseen events based on the training data, but they’re also quite efficient in continuous learning, even after they have learned a particular behavior.
This essentially means that the learning process never stops, and it keeps getting better and better with time. Needless to say, it’s technically impossible to achieve 100% accuracy but what we can do is approach perfection; this is what ML does.
In essence, this means that the data analysis pipeline, when infused with machine learning, keeps improving its precision with every iteration. The data being studied, gathered, analyzed, each process keeps on getting more fool-proof and credible, all courtesy of machine learning.
Unlimited Data Analysis
If businesses couple data analysis with machine learning, there could be a lot to achieve. Computers could work 24/7, and they don’t require any breaks or leaves, unlike us human beings. Once the process of data analysis, such as sending out messages or processing responses, is automated, there is no reason to stop.
Once machine learning algorithms are fully trained and equipped with enough computational resources, there are endless bounds. Businesses will achieve a lot in a very short period. However, one-time investment costs may be a bit high, but they will certainly pay-off in the long run.
Enhancing Customer Experience
Data analysis is the real power behind enhancing customer experience by regularly asking for their feedback and using it to make the processes smoother. However, there is always machine learning being used at the backend to make this possible.
Machine Learning helps to bridge the gap between the customers and the business. If it weren’t for ML, it would become challenging for businesses to employ enough personnel that could regularly take customer feedback into account, that too, in a regular fashion.
Machine learning helps build more personalized relationships with customers by learning their behaviors separately, eventually leading to capturing more customer data and achieving customer loyalty.
Data analysis employs different techniques on raw data, and once the calculations are done, the results can be studied and applied. In contrast, what machine learning does is that it provides real-time results to the analysts so they can make instantaneous decisions.
For example, machine learning could help a supermarket automatically design discount deals based on the customer demand and peak busy time. Even though data analysis is being used at the backend to help make this possible, data analysis alone could not have done this.
Data analysis is an essential part of Data Science and deals with collecting and processing raw data to extract useful insights from it. While there are many tools available that make this process convenient, the need for resources and people is still there.
Luckily, machine learning is something that couples with data analysis quite perfectly and makes things a lot better. From automating data analysis processes to improving them by leveraging the mimicking skills of ML, there’s a lot to gain from the relationship between these two.
Throughout the article, we have discussed why machine learning is beneficial for data analysis and some concrete points on how it can be used to make it a lot better.