There is no precise definition for the word BIG DATA. But it is not difficult to identify once you see it. It refers to something having a large volume of data that is continuously growing. Data Analytics consists of pulling out the useful data information from the data by forming all possible relations among multiple data. This makes big data appear even bigger. The size of big data is too much for a human data analyst.
Machine learning algorithms in data analytics help manage big data better. Imagine a situation where you have to gather enormous amounts of data, which is clumsy and a time-taking process. You go deeper to receive insightful information by correlating the data, crunching numbers, and comprehending the data's patterns. The data thus acquired helps businesses make fast decisions.
But first, you must understand how machine learning is used. Machine Learning trains the machines by nourishing them datasets and creating algorithms that enable robots to troubleshoot and decision-making. Machine learning tools enhance over time as they have the skills to learn from previous experience i.e., data models.
Features of Big Data
Following are the attributes of big data:
The term "Big data" itself suggests the amount of volume hidden. Information is gathered from a vast number of sources, online and offline. The more the size of good quality data, the better the study. At times, recording and controlling this giant size of data becomes challenging.
Speed here means the pace at which the data gets produced. It deals with determining how quickly the data gets produced from multiple sources through the real world, online and offline. The influx of data is massive for big companies.
Data is available in many forms, such as images, videos, texts, emails, online sources, and offline document records. Data in multiple formats accounts for the diversity of data.
Significance of Big Data Analytics
Big data tools help look for solutions to the issues like time-saving, cost-reducting, and reducing the risk in decision-making. Businesses can gain a lot by leveraging machine learning and big data tools collectively.
Risk management & computing possible risk factors
Ascertaining causes of failure in business norms and removing the causes in the future.
Providing offers at regular intervals to the customers based on their purchases.
Spotting any fraudulent activity by cross-checking the data.
Benefits of Machines Learning in Governance
Machine Learning (ML) has assisted in handling big data for governance as well. The objectives of big data analytics differ in governance than that of businesses. For example, the main aim of governance is sustainable development, maximum outreach among voters, protection of fundamental rights, the scrutiny of voter’s attitude and behavior, policy-making, and much more.
While there are limited decision-makers in businesses, there are quite a lot of government. Different ministers rule different states. Also, gathering and sharing of information from several states is a supreme task for governance.
A few examples of ML in Governance
The government has leverage big data analytics to frame policies in some events from its reliable foresight. For instance, “Open Government Data Platform” is a platform developed using ML algorithms, commenced by the US Government. These computations helped share and gather data from federal and provincial governments. The aim is to frame better policies for industries, research, and academics. The civilians should also benefit as it can address significant national challenges such as economy, healthcare, job creation, terrorism, etc.
Now let’s discuss why AI machine learning is essential in Big Data Analytics.
1. Predicting future trends
ML in big data assists in interconnecting machines with massive databases, making them learn new things on their own. Evaluating big data using ML computations helps businesses predict future trends in the market.
For instance, if an AC manufacturing company can study the demand for AC in the following year by blending big data and ML computations, it can forecast future sales. An appropriate data model for this will commence predicting the weather conditions, competition, and want for the product in the market.
2. Better Workforce
ML computations in big data analytics have helped optimize the nature of the workforce. However, there is one concern that the laborers will lose their jobs because these AI machines will do most of the work. However, it is not entirely true. Machines lack emotions and a human touch. Therefore, there will always be demand for humans at work. The human can evaluate the market conditions in different parts of the world. In contrast, machines can perform as per the algorithms only.
3. Improved Solutions for ML Companies
AI and ML are continually advancing. However, we still lack good ML consulting companies, as solutions are intricate. With big data and ML, the software consulting companies can develop a better solution in a specified time frame. This will increase the AI market giving rise to market adoption.
4. Worldwide Diversification of ML
The price is speculated to decline with the progress in new techs and a rise in production rate. This will lead to the worldwide adoption of AI machines. Because of the differences in religion, language, culture, and political affiliations, AI machines have to be trained in various ways. Therefore, by ML and big data, we can reach the market worldwide without hurting human emotions.
5. Big Data in Healthcare
There is an enormous amount of data in the healthcare sector. Big data and machine learning enables us to identify the patterns of diseases. This will help recognize diseases at early stages. Moreover, it will help create new medicines. It also helps manage the information of an individual relating to his previous medical reports, lab reports, and diseases.
Data is changing our lifestyle. The effect of big data cannot get overlooked. It is impacting our lives in many ways. The volume of information is rising every day, and we have to manage more data than at present. Therefore, ML algorithms are helping organizations to manage big data.