#BI

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.

Advanced data processing and analysis. The revolution in data science is driven by AI. Traditional analysis methods are not always good for large volumes of data. AI-powered data analytics enables an easy and fast process of processing huge data sets and extracting insights. Companies trying to make data-driven decisions have already realized the value of this capability;
Predictive analytics and forecasting. AI and machine learning are driving deep dives into predictive analytics and forecasting. Using historical data helps to accurately identify future trends, predict customer behavior and market dynamics. So, companies can adapt to an environment of active change;
Personalized customer service. The basis for a personalized customer experience is data science powered by AI. Analyzing customer data in real time allows companies to adopt products/services and marketing campaigns to the individual customer preferences and needs. This approach helps to increase customer satisfaction, and therefore increase the company’s income;
Automated data labeling and cleaning. Simplifying the difficult tasks of labeling and cleaning data is possible with the help of AI and machine learning. Automating this process allows data scientists to focus on other, more strategic tasks;
Natural language processing (NLP) and sentiment analysis. Unstructured text data also contains valuable information that can be extracted using NLP. For example, determine consumer opinions about a product/service by analyzing messages on social networks;
Healthcare and medical diagnostics. Machine learning models are capable of performing accurate analysis of images (X-rays, MRIs, etc.). This allows to identify the disease at the earliest stages and promptly begin treatment;
С A key element in the fraud detection process is AI-powered data science. Analyzing huge data volumes in real time using machine learning algorithms allows to identify suspicious patterns and anomalies;
The development of autonomous vehicles relies on data science and AI. Analyzing data from sensors and navigation cameras collected by such vehicles can improve the safety and reliability of autonomous vehicles;
Climatology and environment. Solving environmental problems is impossible without the intervention of data science and AI. Using machine learning models, it is possible to analyze climate data to forecast weather conditions, assess environmental impacts, etc.;
Accessibility and inclusivity. AI-powered text-to-speech and vice versa technologies provide communication support for people with disabilities. AI-powered language translation has the potential to break down barriers and build mutual understanding.

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

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;

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;

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:

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;
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;
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.

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.

Business analytics for startups

Relevant data is the main supporting element in achieving business success and market domination. Correct work with data and extracting the maximum benefit is provided by a business analytics.

Business analytics is a set of knowledge, technologies and practices that are used to explore data and company performance. Its main goal is to obtain the necessary information for making decisions based on data. Business analytics can be effective not only for large companies that have introduced it into all work processes, but also for startups. Many startups are successfully using business analytics to drive future success. Business analytics goal in this case is to identify useful data sets that will lead to increased productivity, efficiency, and revenue. Also, business analytics provides an opportunity to make an accurate forecast, anticipate future events related to the actions and behavior of consumers, market trends, create more efficient processes, etc. This tool is used to analyze data from various sources: cloud applications, CRM, social networks, etc.

How startups use business analytics to succeed:

Business analytics priority

A common mistake small companies make is to think that they don’t need analytics because of the small data amount. However, business analytics can be useful in the early stages. It is wise to start gathering data and developing an analyst strategy right away. In fact, startups own a lot of data (files, emails, calls, server data, third-party data, social media data, etc.). These data types are often overlooked, but it can greatly influence the decision-making process.

Data culture

Data is thinking. It is a cultural shift from making decisions based on intuition to making decisions based on data. A Harvard Business Review report found that 99% of CEOs are looking to transition to a data-driven culture. Creating a data culture in a company is a key factor in its success.

Right technology choosing

Almost all data is in the cloud, so the analytics platform should also be here. There are many vendors that offer analytics tools in the cloud. However, everyone has different capabilities. It is important to choose an analytical platform that takes into account the latest cloud technologies, which will meet all the requirements and requests of a particular company.

Artificial Intelligence

At the moment, Artificial Intelligence doesn’t require complex manipulations or a specialist team to introduce it into work processes. The latest business analytics platforms can automatically leverage the power of machine learning without the need for coding and high costs. With the help of the tool, text recognition, object detection in photos, sentiment analysis are possible.

Analytics are not just charts

Many users view business analytics only in the form of charts, graphs, and reports. However, modern business analytics platforms are much more than just visualization. It includes the entire data pipeline. Some platforms combine data collection and transformation with analytics, visualization, and machine learning. Such a complex solution is more efficient in the process of working with data.

All data collection

It is possible to get a complete picture in the case of analyzing all the data. The analytics platform must accept and process all data, including semi-structured and unstructured sources (NoSQL, files, emails, audio, video, social media etc.). This approach will help to make informed and effective data-driven decisions.

Infrastructure

It is important to consider the cost of the entire company infrastructure (database, data warehouse, servers, etc.). These elements can increase quite quickly, which in the end can be more expensive than the price of the platform. Cloud or serverless analytics platforms can start services and functions only when needed. This allows to save money by consolidating data warehouse into a single repository.

Business analytics features usage

There is no need to invest large sums at once. It makes sense to start with data collection and automation, and as a company grows, add visualization and advanced features.

Feedback

It is advisable to appoint a specialist in each company department who will provide feedback on data and analytics using. Each user should be able to use the data and conduct further analysis to complete their tasks and make decisions.

Self-service analytics

Self-service analytics provides users with tools and capabilities to solve various issues and quickly find the necessary information.

Data and analytics for business development

The determining factor in achieving success or failure in business is increasingly data and the effectiveness of interaction with them. Now, information can be obtained from many different sources using available technologies to extract it. Today we are witnessing a shift from an «intuitive» decision-making model to a business model where all decisions are made based on reliable data. Such a wave will cover all industries in 2023 and beyond. This approach adds more confidence in the correctness of the decisions and actions taken.

Data and analytics world is very dynamic, where new technologies are constantly emerging for faster and more accurate access to information. New trends encourage consideration of ways to use them in business and in society.

The most important data and analytics trends for business development in 2023 are:

Data democratization

Expanding the ability to work with data of all team members is one of the important trends of this year. Data democratization refers to providing improved access to data for each team member in order to fulfill duties and tasks. In addition to access to data, it is necessary to organize a learning process to acquire the necessary knowledge and skills. This will help to use data qualitatively, draw the right conclusions and identify new ideas and opportunities. In turn, this implies new forms of augmented work (apps, tools, and devices delivering intelligent ideas to each employee for better results).

Understanding customers, developing a quality product and service, optimizing internal operations, reducing costs are all possible with the help of data. More and more companies are realizing this. However, the ability to work with data and make decisions based on it must be available to all departments of the company and all staff (technical and non-technical).

An example is the use of natural language processing (NLP) tools by lawyers to scan document pages. Also, the use of hand-held terminals by sales assistants to access real-time purchase history, allowing them to better recommend products and make additional sales. A study by the McKinsey Institute found that companies that provide enhanced data access to their employees claim the positive impact of analytics on revenue.

Artificial Intelligence

Artificial Intelligence will have the greatest impact on the life of business and life in general in the future. Its main objectives will be to improve the accuracy of forecasts, reduce the time required for daily and repetitive work (data collection, cleaning, etc.), providing more opportunities for users to act based on data, regardless of roles and technical knowledge.

AI aims to make the process of analyzing data and extracting valuable information faster and more understandable using software algorithms. Today, the principles of machine learning and AI technologies are most often used in business. These include NLP, that allows computers to understand human language and communicate with us, computer vision to understand and process visual information using cameras, and generative AI to create texts, images, sounds and videos.

Cloud and Data-as-a-Service (DaaS)

The work of Data-as-a-Service technology is implemented with the help of cloud. Companies have access to data sources collected and processed by third parties through cloud services. Payment for such services occurs upon the use of services or by subscription. As a result, companies don’t need to build their own costly data collection and storage systems for many types of applications. In addition, DaaS providers offer analytics tools as a service.

Real time data

When working with data to find new solutions and insights, it is critical to understand the current situation. Outdated data (yesterday, last week, etc.) is of no use in this case. Only real-time data is a valuable source of information for business.

Working with such data involves a more complex data infrastructure and analytics, which increases costs accordingly. However, being able to act «here and now» based on data (analysis of data on site visits, determining the best offers and promotions for each client, tracking transactions, and much more) is a strong advantage. Facebook analyzes hundreds of gigabytes of data per second for a variety of use cases, including displaying ads, preventing fake news, and more. Real-time video analysis is performed in a South African national park to detect poachers.

Regulation and data management

Many governments are taking data security into account. Laws are being passed to regulate usage of personal and other data types. Now, there are such data protection regulations: GDPR (Europe), PIPEDA (Canada), PIPL (China). Gartner predicts that 65% of the world’s population will be covered by GDPR-like regulations in the near future.

This will affect every company, regardless of its location. Their internal processes for processing and storing data will need to be documented in a certain way. It also means that companies will have to be tested to see if they know and understand what information they have, what data they collect and store, and for what purpose it is used. Of course, this can become additional work. However, it will be an advantage in the long run. When entrusting their data to the company, it is important for customers to be sure of their security. In turn, companies can use customer data to improve their products or services, as well as develop new ones based on customer needs.

Effectiveness of BI usage in education

Business Intelligence (BI) can be defined as a set of processes with a purpose to transform raw data into useful insights. The sphere of education is quite passive in interacting with BI, explaining this by the lack of relevance. However, classical approaches to learning aren’t already so effective. There is a need for a data-driven solution. A quick and effective data dive can reveal useful information that can maximize learning efficiency. Certain BI tools and processes can be helpful in enrolling students, optimizing class schedules, improving student achievement, and more.

Collecting data, analyzing it and transforming it into useful insights are the main tasks of Business Intelligence. With the help of this tool, operational flows are optimized, and existing models are improved. Besides Artificial Intelligence, BI is one of the main tools that can improve the field of education.

The use of data warehouses in the education system

Educational institutions and systems hold a huge amount of relevant data (various academic records, grade tables, test scores, overall grades, etc.). Such data is useful for the development of a new and more efficient model, as well as for the development of data-driven decision-making culture. But incorrect storage of this data can lead to insufficient or unprofessional use. In most cases, key education stakeholders don’t understand the meaning of the data they hold and what it can be used for. This can be explained by unorganized and decentralized data collection. For convenient and efficient data storage, it is recommended to use a data warehouse. This will allow organizations to obtain suitable storage media, which will increase the speed and efficiency of processing complex datasets. As a result, enrollment records, exam results and other information will be available to users in seconds. However, it is always worth remembering the issue of security and data protection in order to avoid unauthorized use of them.

Data Transparency and Reporting in Education

For high-quality work and new ideas identification, in addition to the raw data, it is necessary to introduce the principles of analysis. It is easy enough to communicate the importance of information and the usefulness of a particular idea with a visually understandable and attractive form. Using data warehouses involves collecting all the necessary data in one place. This improves Business Intelligence processes efficiency and allows to create accurate dashboards. With the help of such dashboards, it is easy to get information about certain education systems (schools, universities, colleges, etc.). As a result, this will allow to analyze the performance of students in these institutions and improve it.

Data driven decision making

Organizing Business Intelligence processes using quality data will help allocate resources (time, labor etc.) more efficiently. Data analysis can reveal which teachers were able to improve student achievement in a particular subject. Also, educational institution administration gets the opportunity to make more informed decisions based on data. Such processes, albeit slightly, improve academic performance.

To improve data visibility and get a clear picture of the current situation, it is necessary to use data warehouse concept. This allows to centralize the collected data in one place for more convenient and efficient analysis.

Importance of data cleansing

Data cleansing is the process of detecting, correcting, or removing corrupted or inaccurate records from record sets, tables, databases, in order to identify incomplete, incorrect, inaccurate, irrelevant data parts.

Research conducted by the Harvard Business Review found that the cost of inaccurate data is $3.1 trillion. About 80% of Forbes data scientists working time is spent collecting, cleaning and preparing data. And only 20% of their time is spent directly on data analysis. Companies generate massive amounts of data every day. And this in turn adds to the cost of bad data.

Not all companies use data warehouses and master data management systems. This approach eliminates the ability to ensure data accuracy. This increases the risk of making the wrong decision based on incorrect data. However, more and more owners understand the value of quality data and the high cost of correcting errors with it. So, this increases their interest in implementing solutions for continuous data cleansing.

Quite often, data scientists and analysts have tight deadlines for completing their tasks. Time chasing keeps them from focusing on data quality. The entry of low-quality data into the system has a strong impact on all operations (for example, market research and its opportunities, analytics, planning and forecasting, efficiency calculation, customer support, etc.). Also, poor quality data can cause the system to overflow. This, in turn, will entail the inability to search for the necessary information in the database, assess the market, demand and other important operations.

A common cause of underachieving sales and revenue targets is the use of incorrect and outdated data. Data is a key component of successful work of analysts and business in general. Redundant tasks and manual data validation are time consuming and reduce productivity.

Customers are the main source of any business. The only thing that every client wants is to receive a product or service that fully meets their needs and expectations. Business analytics is aimed at processing customer data and identifying their needs, behavior analysis, etc. to increase customers’ loyalty. However, it is very difficult to achieve this by using false customer information. As a result, this leads to the opposite effect – a decrease in customer loyalty and satisfaction.

To prevent this situation, it is rational to implement one of data cleaning solutions. This is a necessary step for running a successful business activity given the generation of huge data amounts.

So, 3 components of successful work with data:

high quality data
effective integration
continuous data cleaning

Business Intelligence and Data Management

The continuous data growth that currently exists in the digital world makes business intelligence tools more sophisticated. BI includes various applications, tools, and processes that are used to analyze and present data. It improves decision making process. The long-run objective of using BI can be different: to increase revenue, increase operational efficiency, improve customer satisfaction, and so on. There are different scenarios for using BI depending on data type and business goals. This approach will help to achieve and maintain a competitive advantage in a dynamic market development. Regardless of a company size and its field of activity, the main BI goal is to provide complete and reliable data for making informed and effective decisions in order to improve overall performance.

Data Management is a subset of BI and includes many different applications. Data management includes the processes of collecting data, ensuring its accuracy, integrating different data types, and managing data based on business objectives. Efficient data management is a key component to the success of any BI solution.

The conceptual origin of BI occurred in the 1950s. Now this tool is widely used not only by large corporations. Now, there are many startups that are also BI users. BI solution providers quickly understood this and quickly modified their software to better satisfy the consumer.

It is worth noting that the BI professional tool, however, is no longer too difficult to use. The advent of the cloud and SaaS has transformed Business Intelligence into more accessible software. Also, thanks to the development of SaaS and cloud technologies, BI tools and data management have become more practical and accessible to end users.

Data management has a significant advantage for BI. The development, modification and sophistication of BI tools depends on the continuous growth of data flow in today’s digital world. This makes it easy to model and work with data, which in turn gives the organization a competitive edge.

Benefits of using unstructured data

With corporate data growth the volume of unstructured data is growing in parallel. Their volume is increasing annually at a rate of 55 to 65%. By ignoring such data companies don’t receive certain knowledge and don’t have a possibility to use it for analytics. This automatically doesn’t allow them to use all the possibilities. However, it is very important to know how to properly use unstructured data to achieve business goals.

Unstructured data benefits:

Product development. With the help of unstructured data it is possible to study users’ moods and needs, analyze requests that come to the support service or social networks. This approach will improve company service or product;
Sales and marketing. In this case, unstructured data is used to identify shopping trends and brand perception. The advantage of such data is the ability to assess consumer sentiment. Studying social media posts, forum discussions, support calls, and more can help increase sales and marketing strategy effectiveness. Unstructured data usage by CRM algorithms allows to conduct predictive analytics and know in advance consumers’ desires. So, employees of the sales department will be able to offer the necessary product or service to the consumer in time;
Customer service. Automated chatbots allow to direct customer requests to the right people to resolve the issue quickly. Then the analysis of these issues is carried out, as mentioned above. This allows not only to know consumers’ moods and wishes, but also to identify effective and inefficient features of a product or service. This, in turn, will allow to improve the product or service.

Using unstructured data for BI involves 3 main steps:

Determine the purpose of using unstructured data. It is necessary to clearly understand what problems need to be closed with the help of external data and how exactly it will be used;
Optimize data sources. To create a set of valid data it is necessary to create a common data model. Since unstructured data is drawn from different sources and in different formats, it is possible to ensure data consistency and reliability by quality data flows creating;
Create a plan and upgrade data processing programs. It is worth partnering with providers of high performance and high-quality data integration applications and resources. The key issue is an internal interface and methods definition for connecting data sources.

Top ways to improve customer service with BI

The greatest value of any business is customers and their loyalty. It is possible to gain customer loyalty through a high customer service. This is what is remembered and what makes the client come back repeatedly. The recent stress caused by the pandemic has forced organizations to drastically change their policies, move all business online and learn how to maintain a level of customer service in these conditions. Supporting customers during difficult times leads to continued customer retention and loyalty in the long run. At the moment, the market is very dynamic, influenced by various external factors and changes. Under such conditions, customer-centric companies that can make decisions based on data will have a clear advantage. This is where Business Intelligence can help.

BI is an indispensable tool in the decision-making process and effective business activities. It allows to combine multiple data sources, collect and analyze data to solve problems. The main tasks that BI covers are providing, analyzing and understanding the current business situation, identifying patterns, trends, changes and the ability to quickly respond to them.

Top ways to improve customer service with BI:

A single reliable data source creation. Typically, companies get data from different data sources (ERP, CRM, website, social networks, etc.). Having several sources, it is quite difficult to understand what is happening and where. BI combines all data sources into a single dashboard and provides the user with complete information about the company’s interaction with customers in one place;
Real time data receiving. Previously, data analytics could take hours, and in some cases even days. Decisions that were made on already «old» data are ineffective. This, in turn, had a negative impact on company competitiveness. BI tools allow real-time data analysis by streaming data from different sources to the dashboard. Users have the ability to quickly analyze, draw conclusions and quickly respond to customer behavior;
Making informed decisions. BI allows to use truthful data in the decision-making process that eliminates any inaccuracies and guesswork. The dashboard shows what channels are the most successful in terms of customers conversion. This allows to adapt strategy and make changes to maximize ROI. For example, it is possible to determine PPC campaigns and social media posts effectiveness and if necessary, redirect the budget;
Clients in modern conditions assume the ability to contact the company in any convenient way and at any time to quickly solve their problems. Long correspondence in the chat, then the need to call back and transfer the request to another specialist without transferring information from the client can have a very negative impact on him. According to research, 89% of customers are annoyed by the need to repeat their problem to each new specialist. Omnichannel personalization can increase revenue by 5% to 15%. BI provides insights across all channels to get a complete view of the customer journey;
Decreased customer churn. It is important to analyze and determine the rate at which a customer stops using a product or service. Customer churn has a direct impact on a company’s success and profitability. This is also due to the expensive replacement or return of customers. With the help of a BI dashboard, it is possible to identify and eliminate problem areas, take appropriate measures to avoid losing customers. For example, it is important for a help desk to identify tickets that require more time to resolve a problem. This makes it possible to find out the cause, respond correctly, involve more specialists and resources, and solve a problem promptly.