Big data favors many companies today. It is the reason why companies are able to bring innovation and efficiency to their products and services. The logic is companies try to store, process, and extract value from the available data. However, it is a challenging job to generate value from those huge volumes of data. This is where the role of machine learning steps in.
Data is a godsend for machine learning systems. The more data a system receives, the more it learns to be efficient for businesses. Therefore, it makes sense to use machine learning for big data analytics, which proves to be a logical next step for companies as it supports maximizing the potential of big data adoption.
But how does this happen?
Application of machine learning in big data
Machine learning provides efficient and automated tools for data gathering, assimilation, and analysis. It also supports cloud computing by ingesting agility into processing and integrates a large amount of data, no matter the source. Machine learning is applicable to every element of the big data operation, including:
- Data analysis
- Data segmentation
- Simulation
Also, the fusion of both the main terms—machine learning and big data—is a never-ending loop. Not for all, but the algorithms created for certain purposes are monitored and perfected as the information keeps moving in and out of the system.
The three elements listed above create a bigger picture of big data, which is further categorized and simplified to an understandable format. Therefore, machine learning is compatible with all the elements of big data.
Ways of machine learning for big data analysis
The first line of the article speaks well about the role of big data today. And the combination of machine learning with big data shows the best results for many companies, including automobiles. It is one industry that integrates statistical models to data and renders best-in-class automation in the vehicles that will meet user expectations.
We have talked enough about the fusion of the two terms. Now, it is time to see how this combination works well. Below are some top instances that show how machine learning can be put to analyze big data:
Sensing customer behavior
Machine learning helps businesses learn about customer behavior and create a solid framework for customers. User modeling is a dedicated function for this cause wherein data is mined to understand the user’s mind and then make a business decision. The top social media platform, including Facebook, Twitter, and Google, relies on user modeling systems to learn about their customers and make the best decisions.
Implements time series analysis
A normal machine learning dataset is a collection of observations.
Observations = data, here.
When the new data is predicted and the actual outcome is unknown until some future date, it refers to time series analysis, In simple words, it is an array of data predicted together. It is a great tool for analyzing and aggregating data, making it easier for managers to make decisions for the future.
Predicts trends
Machine learning algorithms use big data to recognize the upcoming trends and forecast them to businesses. Using interconnected computers, a machine learning network can engage in daily self-learning and enhance analytical skills.
It not just analyzes current data but also uses past experiences to shape the future. For example, an air conditioner brand can take the help of machine learning to predict the demand for air conditioners and accordingly plan the production.
Helps conduct market research and segmentation
For any business to earn revenue, it needs to understand the market and target audience primarily. Every enterprise needs to understand the audience to become successful.
Machine learning can help in this matter as it uses supervised and unsupervised algorithms to accurately understand consumer patterns and behavior. Majorly, the use of machine learning is seen in the case of the media and entertainment industry as it uses machine learning to understand the likes and dislikes of their audiences, and therefore, extend them the right content.
Offers customization and personalization
These days’ businesses have to shift their focus from generalized goods to customized goods. Every user has different needs, and therefore, looks for something which suits his/her individual needs.
In such cases, it has become important for businesses to render personalized services in order to remain competitive in the market. So, a business implements the power of machine learning to understand customer behavior and create goods on that basis. Netflix takes the help of a machine learning-based recommender system to suggest the right content to the viewers.
However, these functions are possible only if we meet some pre-requisites.
Pre-requisites for accurate machine learning results
Apart from an accurate and well-built algorithm, some more pre-requisites are also important. It would be best if you had clean data, scalable tools, and a clear idea of what you want to achieve.
- First and foremost is data accuracy. It is essential for the data to be sanitized and in the correct format to help reach the quality and completeness of the datasets. Any mislabeled, missing, or irrelevant data can impact the accuracy of the algorithm.
- For better results, the derived computer can be substituted for the real data you generated. An ideal algorithm to solve a specific problem needs a specific type of data to refer to. Derived data is not able to do that perfectly, so it is recommended to use the real data.
Conclusion
Switching to machine learning is a step-by-step process and cannot be simply integrated. Though it may sound clichéd, a step-by-step approach works best for any such transition. The businesses need to build an AI (artificial intelligence) and ML-based strategy in sync with their business goal. They should then focus on generating quality data, which is the key to realizing the full potential of machine learning tools.
Once the company gets a hold of the right people and the right data, machine learning becomes a lot easier. And implementing machine learning is certainly a technological enhancement I would suggest for every business.
