By analyzing Big data ( structured or unstructured data arrays of large volume), we can find common properties, matching categories, similar behavior, and so on. The article will discuss how big data analysis affects business processes and what needs to be done to get maximum profit via implementing big data technologies into your operation.
AI and Statistical methods to work with Big Data:
Table of Contents
- Machine learning and neural networks;
- Predictive analytics and big data;
- Simulation modeling;
- Statistical analysis;
- Data mining and data storage
Along with these widely used methods, we can identify new trends for 2022 and focus on them in more detail.
Today it is impossible to imagine life without digital technologies. Every second person on the planet uses a smartphone. Whatever financial transaction we make, big data accompanies us everywhere. Due to the pandemic, humanity has become more likely to make online purchases, visit stores and museums online, and search for necessities via browsers. And these are thousands of gigabytes of data stored to be analyzed. Therefore, the data array is an integral part of our lives, although it is impossible to touch them physically. And those business entrepreneurs and companies who are the fastest in introducing innovative solutions for data storage and application will capture the market and gain the consumers’ loyalty. After all, the client loves personal offers and safe and fast services.
Technological solutions based on big data
• Machine learning and neural networks
It is worth noting that a supercomputer is not a human, although some models can process hundreds of thousands of gigabytes of data in seconds, i.e. machines count very well, they cannot think like humans. For example, the machine will not understand that “Makita 5 mAh Screwdriver”, “Makita Screwdriver”, “Screwdriver 10” are the same tool.
For Artificial Intelligence to think like a human, it is necessary to generate an artificial brain. In this case, a network of neurons is created that can analyze massive arrays of data. At the beginning of the analysis, they receive input data, “pass” through their neurons, and give the final result. This is how deep neural networks work.
• Predictive analytics and big data
Predictive analytics helps to predict possible future events based on past data. For example, based on the number of sales over the past 5 years, you can assume and set a KPI for the following year.
Mathematical models are built based on the embedded data, allowing steps to exclude the rupture of partnerships or warn about it in advance.
• Simulation modeling
Simulation modeling looks like predictive analysis, only here we take not real data, but assumed ones. For example, if you are going to change the cost of a product, then you should pay attention to how the demand will change. In living conditions, it is dangerous to do this, you can be left without clients. It remains to build a mathematical model in which to lay down various parameters – price, number of units of goods, number of sellers and visitors, etc. You can choose the most successful innovations and implement them in business based on the math modeling with minor risks by changing these data. But, as the model does not exactly repeat the real-life conditions, some deviations from the plan still exist.
• Statistical analysis
In business, you can’t do without statistical analysis. To successfully apply statistical analysis, calculate the trends, and set the forecasts, it is necessary to know the numbers. For example, you need to find out how many customers are satisfied with the company. One of the problems can be a small sample of the respondents. An even bigger problem is that setting the analysis for multiparametric constantly changing data demands very complicated algorithms. It turns out that the model is unreliable. In this case, we have to switch to ML technologies or stay with average values.
• Data mining and clustering
Mining means collecting the data for future analysis. And clustering is to grouping them based on the common usage patterns. Thanks to the systematization of data, we get the opportunity:
- Classify, i.e. distribute by known parameters;
- Divide objects into many sub-objects – for example, the study of consumer demand;
- Associate – analyze data samples that are repeated. For example, a certain purchasing power;
Data mining is used wherever it is necessary to extract figures for analysis of any trends or patterns. For example, we can assess risks, allocate a market segment, and anticipate demand for a particular product.
Upcoming trends in analytics and data for 2022
Due to the rapid market changes and consumer behavior development, entrepreneurs have to think about making decisions in the face of constantly changing data.
The main difficulty is that most indicators cannot be structured. Therefore, many data sources, such as videos and photos, are not suitable for direct analysis. Such data centers are subsequently called “swamps”, from where it is difficult to obtain the necessary data and conduct a qualitative analysis.
Citizen science is the concept of conducting scientific research with the involvement of a wide range of volunteers. This direction is still developing, but there is no doubt that it will only progress rapidly in the coming years, thanks to trends towards optimization and cost reduction for the deployment of prototypes and flexible organization of managed decision-making systems. For example, the reduction of program code for applications and websites.
Currently, the market is dominated by large arrays of data – big data. Still, as mentioned above, it is very difficult to structure row data and to organize the proper storage of ever changing dynamically updating sets. Respectively, a lot of time and financial costs are spent to make it properly done. The technologies updates and the automation of row data storage and labeling can be assigned to the ML software developers.
Data analytics is gradually becoming a business necessity. Sometimes data should be processed and analyzed “on the spot”, rather than moving them to a central repository. This method is cheaper and faster. Therefore, cloud storage and processing solutions are gaining momentum.
Data lakes and warehouses is an architecture that provides visibility of data, the ability to move and copy them, as well as access them in hybrid cloud storage. Near-real-time analytics allows you to monitor data location in the clouds and storage. This helps make sure they get to the right place at the right time. Data factories will become more popular and provide data-centric management rather than storage.
Instead of storing all medical images on a single NAS server, it will be possible to use analytics and user reviews to segment them. For example, copy them to provide access to MO tools for clinical research. Or move the most important data to immutable cloud storage to protect them from ransomware.
To maximize profits, organizations sometimes use the principle of three rules:
- Collect all the data in one place;
- Effectively manage what is;
- Apply the potential of data.
However, an important task is how to manage unstructured data and thereby reduce risks and costs, while increasing productivity.
It remains to implement multi-cloud strategies that would combine several clouds created for different goals and objectives. When managing data, transferring it from one place to another is irrational. The reason is the expensive cost. The solution to this problem can be a server located in the data center, which would be directly connected to cloud providers.
Data arrays are a little-studied area because conditions are constantly changing, but at the same time, they are considered one of the most promising areas in the world. Thanks to the automation of business processes based on artificial intelligence, we can observe drastic changes in our lives. A small smartphone becomes more powerful than a laptop.
In the next 10 years, we are unlikely to see significant changes related to the transformation of big data, but there is already a tendency to search for new directions for extracting information. For example, obtaining data by vector similarity indexes and searches using vector representations of data, not by keywords or properties.
The modern world teaches people to use electronic gadgets, social networks, the Internet. Accordingly, there are traces of what a person did, what kind of action he/she performed. And those companies that will be able to analyze these data and build customer-based solutions will be on the wave of success.