What Is Machine Learning? And How Does Data Analytics Facilitate It?
Data holds the answers to many advanced queries and questions for business, science, and other fields. Through it, we can recognize patterns, determine outcomes, and act accordingly. Today, by using machine learning, we can dive deeper than ever before and perform data analysis that would otherwise be impossible.
Let’s discuss machine learning and how data analytics facilitates it to help you better understand this technology.
But first, let’s ask a fundamental question upon which we will build our understanding.
What Is Data Analytics?
Data analytics is the process of cleaning, inspecting, modeling, and transforming raw data to find valuable information and conclusions, enhancing the decision-making process. In simple words, it takes raw data and helps extract valuable insights or information from it.
This process requires data analysts and data scientists to intervene for a logical result. Generally, professionals need to create dashboards for businesses for accurate and meaningful data analysis.
The insights help businesses make informed decisions on their future moves. The dashboards comprise visualizations and tables that show trends, outliers, and other data analysis markers. Technical teams still perform the bulk of the analysis, and the dashboard software is there to help facilitate the results of core queries regarding the business or the data itself.
This process is time-consuming, labor-intense, and cannot fully test every scenario, which can be frustrating. Often, data analysts need to access data multiple times to conclude a hypothesis. There is also a lot of guesswork and presumptions involved in data mining, which may further dilute the result.
What Is Machine Learning?
Machine learning is a branch of artificial intelligence (AI) based on the idea that systems can learn from large data sets. They can identify patterns, derive meaning, and make decisions accordingly without much human intervention.
The idea is that a machine learning algorithm can improve its decision-making through experience gained from automatic data learning. There are many ways to use machine learning, which we will discuss ahead.
A simple example of conventional machine learning would be how Netflix uses your viewing habits to learn patterns and automatically suggest movies and shows through filtering.
There is no human intervention involved in this process, and everything is done by the algorithm of Netflix’s machine learning recommender system. It uses statistical and predictive analysis to find patterns and automatically give recommendations.
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Difference Between Data Analytics and Machine Learning
As you may have guessed, a key difference between data analytics and machine learning is how they use data. Data analytics uses any amount of data to generate actionable insights for companies and answer specific queries, whereas machine learning uses it to create algorithms and train them with the provided data.
These algorithms can then function independently with little to no human intervention, allowing automation of decision-making tasks.
How Does Data Analytics Facilitate Machine Learning?
Data analytics can facilitate machine learning for faster, deeper, and more conclusive insights and results. It takes the best of both processes to automate the entire data analysis.
Machine learning algorithms compute all the data combinations with a greater understanding of important boundaries. Since this is a machine, there is no human bias or presumptions; however, the best part is that there are no time constraints, making the process much faster.
It can test out hypotheses to answer key queries and questions regarding a business or data set. It can run every possible scenario in an incredibly shorter time. Data analytics makes finding accurate and predictive solutions to precise queries possible via machine learning possible.
Here are examples of data analytics and how it facilitates machine learning.
Data analytics can provide a company with a comprehensive dashboard that monitors its performance in the market through visual interpretations of sales data. It cleans and transforms the data to extract the correct information.
Add machine learning, and the company can use their data analysis to model future predictions based on the current data analysis. It does this by predicting a value or category for each input record.
This is commonly known as predictive analytics, and machine learning uses statistical algorithms to play out all scenarios and accurately represent how the company may perform in the next quarter.
Other ways to use machine learning with data analytics include techniques like “elasticity,” a machine learning method that allows you to determine which factor resulted in an outcome. This can be very helpful in cases where multiple factors of your data analytics are changing simultaneously.
Elasticity provides the cause of the effect, whereas data analytics alone cannot determine such a thing with much accuracy. Companies and businesses often use elasticity to determine whether their marketing caused a boost in sales or some other factor.
Similarly, a “clustering” method allows machine learning algorithms to identify commonalities between your data analytics, even when they are not entirely obvious. This method allows quick and easy identification of common markers or points in the data sets.
Companies often use clustering for accurate segmentation and personalization to better understand customer behavior and market their products and services to relevant audiences. This is also a way to enhance the customer experience of
Many other techniques allow machine learning to use data analytics and determine outcomes and the underlying drivers from the data. It gives businesses opportunities to capitalize on their data and make accurate predictions and informed decisions to grow.
Initiatives for the Future
Machine learning aided by data analytics can help give faster, deeper, and more conclusive insights into your data analysis. It saves time and resources while giving you accurate outcomes that are non-biased and determined by playing out every possible scenario.
Improvements in AI and machine learning are empowering businesses and organizations to learn more from their data every day. Machine learning also helps automate complex tasks where decision-making relies upon data-driven results.
Applications like recommendations on Amazon and Netflix, fraud detection, precisely targeted marketing, and even modern marvels like self-driving cars are all made possible by machine learning facilitated by data analytics.