data literacy

AI and ML impact on Data Science

The foundation of all innovation, decisions and competitiveness is data. Today this is the new «oil» that can bring success. Companies across industries continue to collect vast data amounts, requiring the use of sophisticated tools and techniques to extract valuable information.

Artificial Intelligence and Machine Learning have contributed to the advancement of data science. These technologies help data scientists conduct analysis, make forecasts and identify trends, automate routine tasks, etc. Data science coupled with AI and machine learning is shaping a data-driven future. Consider the impact of AI and machine learning.

Artificial Intelligence for data analytics

Artificial Intelligence is widely used in many applications, including for data analytics. AI is used to analyze large data sets that allows to obtain valuable information, identify trends, make forecasts, etc. Machine learning algorithms provide fast and accurate work with huge data volumes.

It is important to use AI in the process of data analysis because there are several advantages:

AI is supposed to complement the work of data scientists. The main ways to use AI in data analysis are:

Data storage for quick insights

We know that the main and most valuable resource of modern business is data. However, is all data equally valuable?

Data loses its value within a few days – it becomes irrelevant and provides little benefit to the company. The goal of every company is to find a solution that can extract useful information and insights from data before it becomes outdated.

Data quality and algorithm efficiency can greatly complicate the challenge of delivering data in a timely manner. So, it is worth paying attention to the data warehouse. Making the right storage decisions is essential to delivering insights quickly.

At the very beginning of the digital revolution, companies stored data to comply with regulations or evaluate past performance. The purpose of data storage has changed over the past 10 years. Now historical data can help to make forecasts and determine future trends. This has led to the opening of new opportunities in the business decision-making process. At the same time, the volume of generated data has sharply increased, and many technologies have emerged for collecting and analyzing it.

Data storage is often not a priority. Huge amounts of data are still stored on disks. This significantly complicates access to data, increasing financial costs and energy consumption. A quality data storage solution frees up time and shifts the focus from high-cost issues to analytics, optimization, and AI solutions. For example, in pharmaceuticals, the time it takes to obtain information is essential in fighting pandemics, developing new, more effective drugs, and improving existing ones. Therefore, an efficient data storage solution is necessary for the operational process of data acquisition.

One of the common challenges in the data management process is data quality. Often the desire to put all the information into data lakes turned the lake into a «swamp». Data that does not correspond to reality reduces efficiency. Therefore, it is important for companies moving to data-driven management to emphasize how they measure and address quality gaps.

Data infrastructure can affect data quality. Built-in AI-based tools can ensure that information is stored correctly, taking into account requirements, mandatory checks and security measures. Data infrastructure presents enormous opportunity while eliminating the human costs and energy inefficiencies of legacy storage systems.

Data Science for fintech companies

The basis of any business is information. Now, there are a large number of different data analysis methods that are used by companies to automate and achieve maximum efficiency. Financial companies that provide financial services are no exception. They own and process a huge data amount that requires quality management and a high level of protection. Data Science plays a key role in orchestrating these and other processes.

  1. Fraud detection and prevention

A key indicator of a financial organization success is the security of customer funds and data. This is a rather difficult and time-consuming process. It is important to use not only technologies that will help block fraudulent activities, but also technologies that can detect suspicious activity at an early stage and assess the situation. Such technologies need to be regularly monitored and updated.

The huge number of transactions and processes makes it impossible to manually track suspicious activity. However, Data Science is a great tool in this situation. It allows to create an algorithm for self-analysis of certain actions, which will automate the detection process. This technology is capable of self-learning. Processing more data leads to more experience and knowledge. Detecting counterfeit documents, copies of financial transactions and invoices, suspicious activities, and preventing fraud is possible thanks to Data Science.

  1. Risk assessment and management

Machine learning and AI, among other things, help assess financial risks and security. New machine learning models enable more effective risk analysis and management.

Competitors, authorities, investors and other participants may pose certain risks to the business. Any situation must be resolved taking into account an understanding of the risks, potential losses and possible growth points. This process requires the analysis of a large amount of processed and raw data.

Complex self-learning Data Science algorithms will be most useful in this case. They evaluate data to analyze risks, allowing companies to create a reliable model for future development.

  1. Client data management

The most valuable resource of any company is data, and its management is too important. It is advisable to process such a large data amount that financial companies own automatically. AI will provide fast and efficient analysis of unstructured data.

  1. Personalization

The income of a modern business depends on accurately guessing customer needs. The better the company guesses the client’s desire, the higher the chance of receiving more income. It works the same way for financial companies. Customers will be much more willing to use the company services that has an offer tailored to them based on their income, needs and situation.

Data Science allows to track user behavior and provides a complete picture. The business is able to make more informed decisions, and the client receives a unique and personalized offer.

  1. Analytics

Data collection and analysis are 2 key processes. Modern technologies make it possible to efficiently process huge data amounts of various types. This allows to track data changes and make changes to prevent risks. For example, analyzing customer data opens new opportunities, allowing to respond to customer interest and build a high-quality marketing campaign.

Work with data and team training

The modern world is driven by data. Every person and company generate a huge data amount every day. And if a person can forget about data, companies can’t miss the possibility to work with data. Modern businesses are active data users in their daily routine. Based on the assigned tasks, various indicators are analyzed, and forecasts are made. This allows companies to move away from a «blind» approach and act based on real and reliable data.

Modern analytics tools are quite easy to use, that allows any company employee to perform a certain set of data functions. However, for better and more complete work with data, training and regular professional development are necessary. Full involvement and commitment of the team can guarantee success and a good overall result.

Company leaders often encounter resistance from employees to learn something new about data. Below are a few recommendations that will help overcome resistance and resolve the main problems when implementing the training and professional development process.

  1. Overcoming resistance

Employee resistance to undergo training is one of the main problems. It’s worth looking at the problem as a marketer. You should make the training so that people want to take it. Proper promotion of training is one of the most important elements of employee engagement. Engaging company leaders who can highlight the value of acquiring data skills will be a powerful selling point. However, it is worth focusing not only on the final result and company benefits, but also on the capabilities of each employee and their career success.

  1. «Simplicity» of data

The main reason for employee resistance is the fear of the new and the perception of working with data as something extremely complex and incomprehensible. First of all, it is necessary to clearly explain why working with data is so important for everyone and for the company in general. In addition, machine learning, Artificial Intelligence and data science create a fear of «unnecessary» employees. Managers should explain that the introduction of these technologies doesn’t imply a reduction in the team, but rather the automation of some functions to optimize, simplify and improve the employee’s work. Good use of data is the key to making effective decisions across one position in the company, and therefore, one team and the entire company.

  1. Understanding data usage

The use of data must be clear to employees. Each employee must understand why he is using the data: what his specific goal is, what the company’s goal is, to improve what business processes, etc. Such transparency and understanding will allow employees to feel more confident and correctly present the data in discussions about the results.

Key benefits of Business Analytics

An indispensable tool for modern business is Business Analytics. Regardless of the field of activity, each company generates huge data amounts. Proper work with it opens many opportunities for business development.

Business Analytics is a tool that uses quantitative methods to extract valuable information (meaning) from the data provided. Based on the information received, the business is able to make informed decisions, take certain actions and conduct a detailed analysis of the situation.

Key benefits of Business Analytics:

Data Science challenges and opportunities

Data science has become a transformational field that has the power to change and drive decision-making processes. Data active growth and technological advances create certain difficulties and challenges. This is also an opportunity to find new solutions. Below are the key challenges and opportunities for data scientists.

Challenges and tasks:

  1. Data quality and complexity

Ensuring data quality and reliability is one of the key challenges of data science. Large data amounts from different sources create a number of problems, such as missing data, inconsistencies, inaccuracies, etc. Cleaning and pre-processing is necessary to obtain accurate and high-quality information. These processes are quite complex and can take a long time.

  1. Scalability and infrastructure

Increasing the size and complexity of datasets creates a major scalability issue. To efficiently process big data, specialists need a reliable infrastructure and powerful computing resources (implementation of scalable algorithms and development of a system that allows processing huge data amounts).

  1. Shortage of qualified specialists

Now, there is a gap between the supply and demand of data scientists. Demand greatly exceeds supply, which creates a shortage of personnel. The feature of data science is the combination of skills in mathematics, machine learning and knowledge in the subject area. Finding a specialist who has all the necessary skills and knowledge can be a challenge.

  1. Ethics and confidentiality

The popularity and some dependence on data raises ethical issues that are related to privacy, security and responsible use of data. Data scientists need to know, understand and comply with all data protection regulations, requirements and laws. It is important to strike a balance between the use of data and respect for confidentiality, which can also become a certain complexity in the workflow of a specialist.


  1. Decision making and business understanding

Data science opens new business opportunities, namely making decisions based on data and gaining valuable insights. Data is a powerful tool that can optimize all processes, identify opportunities for expansion and growth, improve customer experience, maintain a leading position in the market and be flexible in the face of its dynamic changes.

  1. Predictive analytics and machine learning

Predictive analytics and machine learning open new possibilities and enable organizations to anticipate trends, identify patterns, and make accurate predictions. The scope of predictive analytics and machine learning is huge, from predicting customer behavior to optimizing supply chains.

  1. Automation and efficiency

A key element in automating tasks and improving efficiency is data science. Automation allows to reduce manual work, thereby increasing productivity. The efficiency of the organization, the way and quality of work with data, as well as decision-making based on data is possible with the help of automated data pipelines, intelligent decision support systems based on AI.

  1. Interdisciplinary interaction

Data science is successfully developing, including through interdisciplinary interaction. It brings together experience from various fields (computer science, mathematics, social sciences and business). This allows data scientists to solve complex problems and find innovative ways and methods to solve problems.

Unstructured data to achieve business goals

Modern companies own a large data amount. Approximately 80% of them are unstructured data. Now the volume of unstructured data is increasing dramatically at a rate of 55 – 65% per year. Such data can be very useful in achieving business goals. It is worth paying attention to tools for studying unstructured data, that will allow to get additional information and use it in the decision-making process.

Main purposes of using unstructured data

  1. Product development

With the help of unstructured data, companies can study and analyze consumer sentiment, support calls, social networks, etc. This approach will allow companies to find ways to improve their services and/or products, which in turn will lead to improved performance indicators;

  1. Sales and marketing

The main purpose of using unstructured data in marketing is to determine the buying trend, brand perception, and evaluate customer sentiment. Analysis of messages in social networks, discussions on forums and other sites will help to evaluate the effectiveness of sales and marketing. Also, unstructured data is useful for algorithms that are used in CRM platforms. Predictive analytics provides insights that companies can use to anticipate customer needs. In this case, the company gets the opportunity to offer a certain product exactly at the time when the client needs it;

  1. Customer service

Companies often process customer requests using automated chatbots. They support customer service managers and direct their questions to the appropriate staff who help resolve the issue. This information can be analyzed to determine customer sentiment. Also, it allows to identify non-working and inefficient functions. This information is used when developing a new product or improving an old one.

3 steps to using unstructured data for BI:

  1. Determine the specific purpose of using unstructured data – based on the needs of the company, it becomes clear what kind of data needs to be collected;
  2. Optimization of data sources – first of all, it is necessary to create a common data model in order to ensure the reliability and quality of data, regardless of the source;
  3. Plan and maintenance of data processing programs – it is important to work with providers who specialize in high-performance, high-quality data integration applications and resources.

Advanced Analytics for deep data understanding

Advanced analytics is an automated process of extracting useful information from a large data amount. For this, machine learning and Artificial Intelligence are mainly used, which makes it possible to improve traditional data analysis methods and find new ways to develop and use data analysis tools. The use of advanced analytics helps to automate many analytics functions (creation, analysis, building a data model, etc.), as well as simplifying the process of interacting with data and obtaining useful information.

The main goal of advanced analytics is to gain a deeper understanding of the results and create more accurate forecasts. Behind every right decision is data, more precisely their analysis and clear understanding. Using this tool, it is possible to identify problems, new opportunities in time, analyze changes in performance indicators. This, in turn, allows to achieve goals and KPIs faster.

Real time information

Advanced analytics solutions allow to get the necessary information in minutes. The use of machine learning and Artificial Intelligence models allows to quickly process millions of data lines and create detailed analytical reports. Thanks to accessible and understandable data visualization, users get a complete picture of the current situation. Information is provided in real time, allowing to benefit and make quick decisions in critical situations.

Detailed analysis

Access to a huge amount of data and variables, which is possible with the use of advanced analytics, allows for deep data analysis. This allows to get more accurate information, explore certain points, identify the causes of the current situation and create more accurate forecasts.

Data democratization

The main data democratization goal is to provide users with enhanced access to the data they need to perform their duties. Any employee can access the data in real time, work with it and create customized views for analysis. Data democratization will allow users to work independently with data, which contributes to increased data literacy.

Making effective decisions

Quite often there are critical situations where it is necessary to make a decision quickly. In this case, there is no way to wait a long time for the necessary information. Advanced analytics solves this problem by processing billions of records and providing accurate information to the user. Also, the user receives an answer to the questions «why» for each result, which allows to make an informed decision.

Business Intelligence for Education

The main Business Intelligence (BI) idea is to transform raw data into actionable insights. Data has become an integral part of any activity field. Its collection, processing and analysis is a key element in the process of successful companies and organizations development. Data can be useful not only for commercial organizations. For example, a healthcare facility uses data to make a more accurate diagnosis and treatment. The education sphere is quite difficult to perceive a data-driven approach, adhering to classical approaches. However, proper data use helps to identify useful information that can maximize the effectiveness of learning processes. For example, BI tools can optimize school admission process, timetable classes, increase student achievement level etc.

Business Intelligence is a set of methods and tools for collecting and analyzing raw data as well as transforming it into useful insights. The main task is to optimize operational processes and improve existing models. BI and Artificial Intelligence are the main technologies that aim to improve the educational process. BI allows to access data and generate reports. This, in turn, enables users to explore the data and extract information about past trends.

Educational institutions store a huge data amount. For example, scorecards, test scores, overall scores and so on are raw data that can be useful in the process of a new model development. This, in turn, allows to develop a culture of data-driven decision making. However, often educational institutions don’t understand what data they store and how it can be used effectively. School enrollment data, exam records and scores, and other data can be used to improve curriculum and identify trends.

Such a large amount of raw data requires the right approach to manage it. For data storage, it is advisable to use a data warehouse, which allows for the rapid and efficient processing of complex data sets. However, it is always worth keeping in mind security measures to prevent data leaks and fraudulent use.

Data is valuable if you work with it effectively. Just a large data amount doesn’t provide any benefit. Therefore, it is important to ensure the correct process of data collecting, storing, processing and analyzing. Compiling study reports and presenting the resulting information in an understandable visual form is able to attract the management attention. This allows to make adjustments to existing models or develop new ones to provide a more effective learning process.

The use of BI tools helps to use the available resources (time, labor force) more efficiently. This contributes to the improvement of academic performance. For example, data analysis to determine the performance of teachers and students in specific subjects, etc. School administrators can also make data-driven decisions using BI tools.

To improve data visibility and visualization, it is important to use the concept of data warehouses. They allow to centralize the collected data in one place and develop a model in order to obtain more accurate information. Another important element in the process of working with data is visualization. It is easy to present and perceive information in a simple and understandable way. This facilitates approval of ideas and effective decision making based on a clear and understandable data presentation.

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