Data is the next big thing and key to success for all businesses. There are several new technologies that are being used to make sense of the tons of data that are being generated every minute.
Machine Learning, Data Analytics and Artificial Intelligence are three technological pillars used at the forefront of data analysis. In this blog, we will specifically focus on the role of Machine Learning in Data Analytics at present and in the future.
So, let’s dive straight in.
The Ways of Machine Learning Usage in Data Analytics?
ML in Data Analytics refers to the usage of advanced algorithms and statistics that allow systems to automatically learn from data.
Despite traditional analytics tools, relying on preset rules and are manually guided by a human, ML algorithms provide autonomous data processing and analysis with additional capabilities of making predictions based on smart patterns identification.
To its beneficial sides we refer the fact that ML algorithms are capable of adapting and improving over time as they analyze more data. Moreover, the essence of ML implies usage of a large data amounts for permanent learning, and this ability makes it a powerful tool for businesses wanting to use data for smart insights, predictions, categorization, classification and business decision-making.
Definition of Advanced Analytics
Advanced Analytics refers to a bunch of sophisticated techniques which are meant to analyze data to find hidden patterns and get actionable insights.
Advanced Analytics makes use of Artificial Intelligence and Machine Learning to go beyond understanding historical patterns to give a deeper understanding of consumer behavior.
It makes use of methodologies like predictive modeling and data mining, unlike traditional analytics, which emphasizes historical data. It means that the technology can not only be based on previous data reports but also on real-time data.
As a result, businesses can forecast future trends and enhance decision-making processes.
Use Cases of Machine Learning for Data Analytics
Machine Learning is already being used in Data Analytics at present. It will become more accurate in the future. Let’s have a look at some of the real world use cases at present.
Supply Chain Optimization
Data Analytics with Machine Learning can be used for supply chain optimization in retail and logistics. AI algorithms can predict demand patterns based on consumer behavior.
They can also identify seasonal trends, and inventory levels required, which ensure products are available as per demand. As a result, excess inventory is avoided, and warehouse operations are optimized.
Furthermore, the combination of ML and Data Analytics is useful in dealing with customers’ and partners’ requests. Usage of AI in procurement helps companies to identify the essence and intention of the request, classify it and transfer to the qualified specialist.
This not only cuts down costs but also improves customer satisfaction. For example, Amazon uses ML to increase its operational excellence.
Fraud Detection
There has been a spurt in fraud cases especially in banking and financial services. With the increasing number of transactions and financial instruments, it becomes tough to manually keep a check on such fraudulent activities.
AI & ML technology can be used to identify fraudulent activities and prevent fraud in advance. Banks & other financial institutions are using these technologies to analyze huge data sets in real time to minimize risk of fraud.
As a result, billions are saved from potential losses by using historical data and detecting anomalies. For example, JP Morgan Chase uses AI & ML to monitor a large number of transactions to identify fraud.
Medical Research
Google’s DeepMind is a wonderful example of the use of AI & ML in medical research and healthcare. In 2020, DeepMind’s AlphaFold model was capable of predicting protein folding which was a fundamental biological problem.
This innovation was very important as it can lead to drug discovery by understanding the structure of proteins. Use of AI & ML reduces search timelines by automating predictions and solving healthcare challenges quickly and efficiently.
Predictive Analytics
A huge majority of organizations agree that data analytics is vital for their businesses. Machine Learning for predictive analytics is capable of enabling companies to extract value from the data generated to increase revenue and cut costs.
Analytic solutions based on Machine Learning can operate in real-time, which provides a new dimension to Business Intelligence. This provides information to employees to improve performance hour-by-hour.
Machine Learning in Marketing Analytics can study, learn and make predictions and recommendations. In other words, ML application in predictive analytics allows companies and teams to dramatically enhance their decision-making and optimization processes. It happens thanks to the fact that ML-based tools can analyze the current data and provide recommendations on when the team should shift the focus and what exactly can be done to prevent the incident. Over time, these tools become smarter and more accurate.
For example, one of the cases of ML usage for predictive analytics relates to agriculture and healthcare, when AI can help in predicting the possibility of diseases according to health and life history data.
Manufacturing
Advanced Data Analytics plays an important role in process optimization and predictive maintenance. It analyzes machine data to enable manufacturers to identify patterns and flag potential threats.
This helps a great deal in reducing downtime and preventing costly breakdowns. Advanced Analytics can optimize the manufacturing process by analyzing Big Data from multiple sources like supply chain, inventory and workforce.
Manufacturers can fine-tune their operations, reduce waste, and increase productivity.
Personalization
Advanced Data Analytics can personalize experiences for customers by analyzing their behavior and preferences. As a result, customers have a high degree of satisfaction and loyalty as businesses customize their offerings.
Machine Learning techniques for Data Analysis can identify customers who are thinking about purchasing from competitors. This allows businesses to implement retention strategies and reduce customer churn.
FinTech
Advanced analytics based on ML technologies can provide sufficient aid for the banking and finance industry.
The prior task that can be solved is to define the level of client’s capacity to pay the loan with the following decision on providing it. Banks and financial organizations achieve this through thorough AI-based analysis of customer’s credit history, incomes data, their occupations, address and much more. Though it can be done by human specialists, usage of ML-based technologies dramatically reduces the time spent on it, allowing them to shift their focus on strategic tasks.
Challenges for the Future in Data Analytics
The future of Data Analytics has immense potential, there are some challenges that need to be addressed to ensure continuous growth and effectiveness. We have a brief look at each of them.
Data Security Concerns
As Data Analytics will be used extensively in the future, the volume of sensitive data being processed is only going to increase. This will increase threats of data breaches and cyber thefts.
Industries, especially those of Finance and healthcare are particularly vulnerable to these threats. As a result, encryption and other data protection protocols will have to be implemented.
Ethical Issues
The widespread use of AI & data-driven solutions create ethical issues like biases in algorithms and lack of transparency. Businesses need to ensure ethical use of AI by adopting ethical frameworks.
Ethical issues related to Big Data can be addressed by making use of diverse data sets and inclusive teams. This not only protects users but also increases consumers confidence in use of AI and ML in Data Analytics.
Lack of Skilled Workforce
Although Data Analytics with Machine Learning has been around for some time now, there is not enough skilled workforce in this sector. As technology is still evolving and complex in nature, manpower is not enough to meet the demands of all businesses in various domains.
However, with time, there will be enough skilled Data Analysts, who can make use of Machine Learning, Computer Vision, AI, Predictive modeling for data-driven decision making.
Wrapping Up…
We have just seen the tip of the iceberg as far as Data Analytics and other related technologies are concerned. There will be extensive use of Machine Learning in Business Analytics in the near future.
Data Science is now being offered as a full-time course in Universities across the world. With an increasing number of professionals in this sector, the gap in skilled workforce will be filled. Machine Learning and Data Science will play a major role in advancing Data Analytics in the future.
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