business intelligence

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.

Data Science and Big Data: characteristics, benefits and differences

Data Science and Big Data are interrelated concepts. Both of these concepts are key to using data to drive decision, innovation and value. The active development in the field of data implies the presence of data science and big data analytics. Data Science and Big Data, although related, are different concepts in the field of data analysis.

The focus of Data Science is on the application of statistical and machine learning methods to extract information from data and solve problems. This process includes collecting, cleaning, researching and interpreting data. Big Data refers to large and complex data where the capabilities of traditional data processing methods are not enough.

The key differences between Data Science and Big Data:

  1. Concept and characteristics

Data science is an interdisciplinary field that integrates scientific methods, algorithms, and systems for extracting information from structured and unstructured data. Data is a key source for analysis and decision making. For this, statistical methods and machine learning algorithms are used.

Big Data includes structured (databases), semi-structured (xml) and unstructured (texts and images) data from many different sources. This technology allows for preliminary cleaning and processing, as well as analysis of huge amounts of data in real time.

  1. Scope and methodology

Data Science uses statistical analysis, machine learning, data visualization, and exploratory data analysis to understand data patterns, predict, and find solutions.

Large datasets in Big Data are processed using infrastructure technologies. These include distributed storage and data processing systems. Parallel processing, scalability, etc. provide high-quality control of large volumes and high data transfer rates.

  1. Goals

The goal of Data Science is to represent, extract knowledge and solve complex problems using data.

The goal of Big Data is to efficiently store, process and analyze huge amounts of data.

  1. Usage

Data Science has been widely used in business intelligence to analyze customer behavior, market trends, and sales data. In healthcare, this technology is responsible for analyzing patient data for diagnosis and predicting treatment outcomes. Data Science also helps in clinical decision making and disease outbreak detection. In financial institutions, it helps to detect fraud, simulate risk and make informed investment decisions. The ability to analyze human language makes it possible to use applications such as chatbots, voice assistants, and machine translation.

Big Data enables insights into customer preferences, interests, behaviors, and buying patterns to improve products and inventory management, optimize pricing strategy, increase efficiency, and personalize marketing campaigns. This technology is used to analyze social media data, including user interactions, sentiment analysis, etc.

  1. Benefits

The main advantage of Data Science is the ability to make informed decisions based on the information extracted from the data. This happens with the help of statistical analysis, machine learning methods and data visualization methods. Offers a wide range of applications as well as cost savings through efficient data management.

The main advantage of Big Data is the ability to process and analyze huge amounts of data, as well as gain valuable information and make decisions based on data. Provides a platform for advanced analytics and machine learning applications.

  1. Disadvantages

The use of Data Science requires qualified specialists in the field. Pre-processing and cleaning of data requires significant time and resource costs. Ethical issues can also arise because Data Science deals with sensitive information.

Big Data also requires certain skills and experience in the field. Security and protection issues can be a problem when dealing with sensitive information.

Key benefits of Advanced Analytics

The main purpose of advanced analytics is to improve data analytics, data sharing and business intelligence. To do this, advanced analytics uses machine learning and natural language processing. Using this tool gives companies the ability to make better decisions, provides employees with greater access to analytics and company flexibility.

Key benefits of advanced analytics:

  1. Flexibility of the company

The transformation of a company into a data-driven company is possible with clean and high-quality data. However, the data cleansing process can take a long time. Advanced analytics platforms are able to clean, combine and transform data from different systems in a very short time, and provide data quality reports. This helps accelerate future digital transformation projects;

  1. Expanded access to analytics

With the help of advanced analytics, the system becomes more convenient and comfortable for users, as well as more accessible to more users. Often, only a specific business analyst team uses business data for reporting purposes. Advanced analytics allows to expand this access. For example, the ordering department has access to analytics that previously only the finance and accounting departments had access to. This, in turn, will allow for analysis and increase customer satisfaction according to various criteria;

  1. Making informed decisions

An effective decision can be made based on qualitative data. Many leaders understand this and are moving to a data-driven approach. By doing this, they increase the demand for access to data and analytics. However, other users may also use the data to make decisions in accordance with their position in the company and responsibilities;

  1. Speed up the decision-making process

Advanced analytics eliminates the various manipulations and tasks with data that were previously performed by humans. This significantly saves time and speeds up the process of making an important decision;

  1. Cost reduction

The combination of artificial intelligence and machine learning ensures complete automation of the process. So, tasks such as processing data, cleaning it and joining it into a table to create reports that were previously performed by a person are automated. The rate of obtaining information is much higher – the user receives data in real time. In the long term, the return on investment in 3-5 years will exceed the initial investment by 30-50 times. Advanced analytics contributes to a more dynamic and efficient business development.

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.

Data impact on marketing campaigns effectiveness

Information on the Internet appears at an exponential rate. In this regard, search engines had to figure out how to solve the problem of content management on the Internet. The solution has been found, and now search engines are transforming content into data that is easy to quantify and analyze. So, the user can receive relevant links for their search queries. Also, users can see snippets that meet their needs without having to click on the link.

Businesses use these opportunities to improve their services. Big Data and content analytics tools based on Artificial Intelligence provide new opportunities for marketing, namely:

  1. Better results understanding

The main purpose of using Big Data in marketing is to understand the target audience. Understanding the characteristics, preferences and behavior of users in the digital space makes it possible to increase the effectiveness of marketing campaigns. This, in turn, allows to convert clicks into sales.

SEO companies use Big Data to get more detailed information about their customer base. AI-based analytics tools allow to provide useful information that can be used to optimize company services. For example, working with relevant words in order to determine the specific intentions and demographics of the user. This allows to create better content, which helps to increase the number of conversions on the site.

  1. Transition to targeted marketing

Modern analytics tools are becoming more and more precise and allow processing a huge amount of data in a short period of time. Along with this, companies are increasingly willing to use targeted marketing. Target marketing removes the need to spend millions to increase reach without the guarantee of an increase in conversion rate. At the same time, special analytics tools use AI and Big Data to identify specific users who are interested in the company’s products or services.

At the moment, companies are often looking for a Big Data expert to help them better understand the data and improve their marketing strategy. This allows to make a targeted offer to users who are already interested in a particular product or service.

  1. Data from social networks

A large amount of data allows for a qualitative analysis of trends and patterns. Social networks contain a huge data amount. Users number of popular social networks (Facebook, Twitter, Instagram etc.) is increasing every day, which makes it possible for businesses to make sales of their products and/or services. AI allows to analyze user behavior and determine which product they are interested in, which platform they searched on, etc. Search information is collected by big data systems through autonomous analysis systems. Such information may be used to target advertisements from companies that offer a similar product.

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.

Big Data use in different business areas

Data value has increased dramatically in recent years. Every business in every field of activity owns data and uses it for the main purpose – to increase revenue. It is no longer possible to imagine a business that makes decisions without relying on data but relies only on intuition. And this is logical. Data reflects a real and predictable picture, which allows to make informed and effective decisions.

Below are the top 10 industries that use big data applications.

  1. E-commerce

One of the reasons for the rapid development of this industry is Big Data. They play a key role in the process of improving the user experience. Advances in technology have made it possible for e-commerce sites to use data in almost every activity (getting recommendations based on a customer’s preferences, showing specific products that match a customer’s past purchases, etc.).

  1. Education

The higher education system is actively using Big Data. This allows to track the entry of each student into the system, find out and analyze the amount of time spent on different pages in the system, analyze students’ progress etc. Also, Big Data allows to evaluate the level of teachers’ efficiency. So. it’s possible to analyze and ensure an effective educational process and interaction between teachers and students.

  1. Media and communications

Users want to consume information in different formats and on different devices. Consumer data mining, analysis and usage makes it possible to understand the patterns of media content use in real time.

  1. Health sector

Data is very actively used in the healthcare industry. This made it possible to solve many problems in this area, as well as improve the economy. Data has made it easier to conduct research, identify the chain of diseases and their spread. Also, historical data and medical information usage has helped discover new medicines.

  1. Gaming

The gaming industry is also an active user of Big Data. They provide an opportunity to increase income by providing information about trends and player preferences. This, in turn, allows to make relevant offers to players.

  1. Financial sector

One of the most common financial problems faced by many companies is fraud. Big Data helps to solve this problem. With its help, financial institutions monitor activity in financial markets, and network analytics allows to identify illegal trading activities in financial markets.

  1. Manufacturing and natural resources

Big Data in this area is used for predictive modeling in order to make better decisions.

  1. Insurance

Big Data allows to provide customers with transparent product information. Insurance companies have the ability to predict customer behavior by analyzing data from social networks, GPS-enabled devices, video recordings from CCTV cameras, etc. Also, Big Data can increase customer loyalty.

  1. Human resource management

Big Data allows to explore information on certain parameters. Thus, recruiters have the opportunity to study candidates’ profiles and resumes, analyze information and select the most suitable specialists for a specific position.

  1. Energy

Smart meters collect data almost every 15 minutes. Such detailed data allow for analysis of utility consumption. This, in turn, makes it possible to improve customer feedback and control over the use of services.

Data Science for business

Data science is an interdisciplinary industry, which is aimed at deep data (structured and unstructured) study and understanding using scientific methods, processes and systems. Data science is a continuation and development of certain spheres of data analysis, namely statistics, classification, clustering, machine learning, data production and forecast analytics. However, this science differs from its predecessors using advanced technologies and tools for collecting, processing and analyzing data on a scale that have not been previously available. In addition, it is focused on solving practical problems, for example, how to improve business processes, forecasting and optimization of different aspects of people’s life. Data science is an important tool for the development of different industries, including business, science, technology, medicine, sociology and much more.

Data science is developing very quickly. Modern companies should pick up new trends to maintain a leading position. Below 5 main trends that cost commercial leaders attention:

  1. Real time insights

Systems that are able to determine the best action and provide recommendations on the basis of analytics, retain their importance. These systems, using AI and machine learning approaches, evaluate factors that affect consumer behavior. This, in turn, makes it possible to optimize the style and conditions of relations with users, determine the best option for a particular client in real time;

  1. Processing a natural language (NLP)

NLP is a branch of computer science and machine learning that studies technologies for understanding and processing the human language by computers and other devices. This industry explores methods for transforming text data (emails, documents, etc.) on structured data that can be analyzed and used for decision-making. NLP develops rapidly and is already actively used in many areas. In the near future, the trend in the spread of this technology will increase;

  1. Development low/no code tools

Such tools allow users to create programs, websites, mobile applications and other digital products without writing the code. This makes it possible to quickly develop and run projects without requiring deep programming knowledge. The simplicity of using these tools makes them quite popular. And this trend will only increase. Their use will allow users to quickly receive relevant information, effectively adapt to competition and market dynamics, and increase efficiency in general;

  1. Convergence

The key technologies of the modern digital world are AI, cloud computing, the Internet of things (IOT) and ultrafast networks (5G). Their nutrition source is data. All these technologies are the latest developments in the field of data science, which with joint use, is much more effective than separately;

  1. Industrialization of machine learning

Industrialization of machine learning becomes obvious. This is automation and unification of the process of using machine learning models, which allows enterprises to receive information in a timely manner to support business solutions, ensure successful activities and reduce risks.

The main characteristics of modern analytics and BI platforms

Selecting and obtaining software for data analysis and BI can be an extensive and intricate process that demands careful consideration and time. Now, various analytical platforms are available from numerous providers, making it essential to take several factors into account when choosing a provider, including budget, the cost of specific data analysis tools, and their functionality, which should satisfy the company’s needs and objectives. Additionally, it is vital to comprehend the direct user of the tool before making a purchase.

When searching for and selecting analytics and BI platforms, the main characteristics to take into account are as follows:

  1. Automated Insights: modern analytics platforms must incorporate automated learning methods to inform end users and help them understand key indicators;
  2. Data Preparation: a high-quality tool should generate analytical models based on user data, including metrics, groups, categories, structures, and data from multiple sources;
  3. Data Visualization: effective visualization is essential for clear data display. Users should have the flexibility to approach visualization creatively and easily understand information, draw conclusions, and make informed decisions;
  4. Manageability: an analytical platform should include data verification functions and enable monitoring and control of information exchange and promotion process;
  5. Ease of Use: Modern analytical and BI platforms should be user-friendly for users of all levels, operate smoothly and clearly, and not require specialized knowledge.
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