Data plays a key role in innovation in every industry, including Machine Learning vs Big Data. It helps us understand customer behavior and trends, improve business, make better decisions, track inventory, and monitor competitors.
Data refers to machine-readable information in computing and business. Due to large amounts of user-generated data, also known as “big data,” traditional data management technology is incapable of storing and managing it. As such, big data is complex and comes in different forms, such as structured, unstructured, and semi-structured.
Because regular data warehouses aren’t capable of processing and analyzing big data, platforms such as Spark, Hadoop, NoSQL databases, have emerged to help enable businesses to collect and set up data ponds as repositories. However, simply collecting and managing data directories isn’t enough to gain business value, and conventional data analytics don’t tap into all the benefits of big data.
This is where Machine Learning (ML) comes in. Able to spot patterns and manage large amounts of data, Machine Learning takes data analytics to the next level, allowing organizations to extract more value from their data.
As you plan your career, it is important to understand both the differences between big data and machine learning and where they converge.
Defining Big Data
Big Data is information or statistics acquired by large ventures and organizations. What qualifies as “big data” however, varies depending on the skills and tools of those analyzing it. Additionally, due to its magnitude, it is difficult to compute big data manually, and data analysts and scientists tend to categorize it into “columns” based on type and source.
Similarly, data analysts use big data to extract information systematically, identify trends, patterns, and human behavior to make decisions. In order to make good decisions, one has to not only make the best guess about what is going on, but also the best estimate of what will happen in the future. We do this all the time when we predict what other people will do in certain situations, often by identifying repeated behavior patterns.
Likewise, data with many columns offer greater statistical power but is prone to false discovery rates. To boot, expanding capabilities also make big data a moving target. In other words, raw data is constantly being produced, expanding the volume of big data, thus making it harder to make concrete predictions as a result.
Furthermore, the availability of user-generated data has also grown exponentially with the use of smartphones, Internet of Things (IoT) devices, software logs, cameras, microphones, radio-frequency identification (RFID), and wireless sensor networks. International Data Group Inc. (IDC) predicted that the global data volume would grow exponentially from 4.4 zettabytes to 44 zettabytes between 2013 and 2020. IDC also predicts that by 2025, there will be 163 zettabytes of data.
Defining Machine Learning
A subset of Artificial Intelligence, ML extracts knowledge from data and improves and learns from experience without intervention. In other words, through algorithms and training, ML models process data and deliver predictions. Additionally, many applications also use ML, from medicine and e-mail filtering to speech recognition and Computer Vision (CV).
Now, Artificial Intelligence and Machine Learning are often used interchangeably but are not the same. To read more about the subsets of Machine Learning and how it differs from Artificial Intelligence, read our blog: AI vs. ML – Difference Between Artificial Intelligence and Machine Learning.
How are Machine Learning and Big Data Related?
Machine Learning vs Big Data aren’t competing concepts. What’s more, they are not mutually exclusive either. In fact, their combination provides impressive results. On one hand, data analysts feed ML algorithms big data, and the algorithm analyses its potential value. On the other, ML tools use such data-driven algorithms and statistical models to put together data sets. The ML model then draws inferences from identified patterns and makes predictions based on these patterns.
Comprising ample amounts of raw data, big data correspondingly gives ML systems plenty of materials to derive insights from. In like manner, effective big data management also consequently improves Machine Learning as large quantities of high-quality, relevant data make ML models successful. At the same time, data scientists who create these ML models simultaneously provide a way to manage big data.
A good example is Netflix’s ML algorithms that understand individual viewing preferences to provide recommendations. Similarly, Google also uses Machine Learning to provide personalized experiences, not only for its search function but also for predictive text in Gmail. Google Maps too, uses ML to give users the best directions.
How are Machine Learning and Big Data Different?
The primary focus of data science is data visualization and better presentation. Machine Learning, on the other hand, focuses on learning algorithms and from real-time experience. Thus, for data science, data is the main focus, and for Machine Learning, learning is the main focus. And this is where the difference lies. Given below are key differences between ML and Big Data:
|Big Data deals with the extraction and analysis of information from huge volumes of data
|ML, on the other hand, deals with estimations on future results by using input data and algorithms
|Big Data is classified into three types: Structured, Unstructured, and Semi-Structured
|On the contrary, ML algorithms are classified into four types: Supervised Learning, Unsupervised Learning, Semi-supervised Learning, and Reinforcement Learning
|Data Analysts are the ones who primarily deal with Big Data
|On the flip side, Data Scientists and ML Engineers are the ones who deal with Machine Learning
|Big Data pulls from raw data to look for patterns to help in decision-making
|Oppositely, ML pulls from the training data to make effective predictions
|Extracting relevant features from big datasets is difficult, even with the latest data handling tools because of the complexity of the data volume
|Recognizing relevant features is comparatively easier with ML models as they work with limited dimensional data
|Because of the large volume of multidimensional data, big data analysis requires human validation
|Algorithms do not require human intervention
|Big data is helpful for stock analysis, market analysis, etc.
|Helpful for virtual assistance, product recommendations, e-mail spam filtering, etc.
|The scope of big data is not only limited to handling large volumes of data, as it can also optimize data storage in a structured format, enabling easier analysis
|The scope of Machine Learning, on the other hand, aims to improve the quality of predictive analysis for faster decision making, enabling cognitive analysis and improved medical services
|Examples of Big Data tools include Apache Hadoop, MongoDB.
|Examples of ML tools include Numpy, Pandas, Scikit Learn, TensorFlow, Keras.
Which should you choose?
When it comes to Machine Learning vs Big Data, both go hand-in-hand. Hence, familiarity with both is ideal. Comparatively, both fields offer competitive job opportunities and are in high demand. Moreover, professionals in both fields also enjoy similar remuneration packages. Thus, if you have skills in both areas, you will be an essential asset.
To summarize, choosing Machine Learning vs Big Data depends on your interests. Basically, user-generated data is growing at a fast pace and will continue to grow as time goes on. As a result, the need for data scientists, ML engineers, and other data management and analytics professionals will also increase as more companies opt into big data, Machine Learning, and data visualization tools. Conversely, companies that don’t combine big data and Machine Learning will be left behind.
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