Not only professionals but everyone discusses what this means for society, for education, and naturally, what does this mean for business. If the openia.com portal is so popular, we can expect that they will monetize traffic with an ads system like others have done.

ChatGPT revolution in IT

ChatGPT has changed the lives of many professionals, like marketers and programmers. Should they now look for their own creative solutions, or is prompting and copy-pasting more productive? Some IT managers suggest replacing the coding process programmers do in a creation and code review process with the process of prompting and testing results, as it is much more effective than the traditional process. But even modernizing coding with the help of ChatGPT as an assistant, we can leverage efficiency.

ChatGPT revolution in creative industry

In the case of creativity, if you are a piano player, you must have some skills in your head and hands to play a melody, but when you apply ChatGPT, you should just know how to express what you want verbally or by typing. It creates a completely different level of creativity. Everyone can be a graphic designer, poet, or a music composer. We can find examples of hundreds of posts published in mere minutes or content generated automatically. Sometimes monetization of such an effort cannot be easy, but people try. People publish projects of AI-generated profiles on Instagram and other media which have gained millions of followers or subscribers. Does this give any value to society? Even authors call them SEO-content or SEO-purpose projects. ChatGPT revolution. You can see examples of hundreds of posts published in minutes or art effort cannot be monetized easy

ChatGPT in business planning

At least some areas of AI can influence business in the near future:

1. Risk management, investment protection, and monetization, as well as scaling.
2. Business continuation practices, efficiency and effectiveness, and business resilience.
3. Customer service models, customer experience, and value creation or value devastation.
4. Management role both as the people managing and the technology promoters for management. Trend and bandery setters. Mind and machine desclimers.
5. Digitalization and augmentation, extended reality, and secure privacy.

Most probably, all these five takeaways require rethinking principles and budget reengineering. Build resources to be updated on a strategy level and on operational procedures so as to not lose market position. A passive business analyst team will not satisfy this need. CEOs should get clear directions about market trends and areas they need to improve. If analysts use ChatGPT revolution, the explainability problem becomes, that is why analysts should understand business and economy.

Companies will face the problem of determining which talents they must retain as key resources for maintaining competitiveness. The mix of analytical skills, open-minded thinking, and machine utilization looks promising for the market. The most important factors for the company to consider when selecting technology to drive business are:

1. Adaptability to business criteria
2. Ability to suport innovations in key business areas
3. Reduce redundancy
4. Capture value

The deployment of such a solution becomes critical for the business. A term that has emerged is Digital Employee Experience, representing the blend of utilitarian aspects of solutions in use within the company with people’s roles in value creation for business development and human well-being in such circumstances.

Digital Employee Experience (DEX) refers to the overall experience employees have with digital technologies and tools within an organization. It encompasses various aspects of the employee’s interaction with digital systems, applications, and devices throughout their workday. DEX aims to enhance productivity, engagement, and overall satisfaction by providing a positive and effective digital working environment.

Key elements of Digital Employee Experience may include:

1. User-Friendly Interfaces: Ensuring that digital tools and interfaces are intuitive and easy to use, promoting efficiency in completing tasks.
2. Collaboration Tools: Integration of digital platforms that facilitate communication and collaboration among employees, regardless of their physical location.
3. Mobile Accessibility: Support for employees to access digital tools and information from various devices, promoting flexibility and remote work capabilities.
4. Employee Well-being: Incorporating features that support employee well-being, such as tools for stress management, mental health resources, and work-life balance solutions.
5. Training and Development Platforms: Offering digital resources for continuous learning, skill development, and career advancement.
6. Performance Monitoring and Feedback: Implementing digital systems for tracking and assessing employee performance, providing constructive feedback, and facilitating performance reviews.
7. Automation and AI Integration: Leveraging automation and artificial intelligence to streamline processes, reduce manual tasks, and enhance overall efficiency.
8. Data Security and Privacy: Ensuring that digital systems prioritize the security and privacy of employee data and sensitive information.
9. Feedback Mechanisms: Establishing channels for employees to provide feedback on digital tools, allowing organizations to make improvements based on user insights.
10. Personalization: Tailoring digital experiences to individual employee needs, preferences, and roles within the organization.

It’s important to note that the concept of Digital Employee Experience continues to evolve as technology advances and organizational priorities shift. For the latest developments and trends, it’s recommended to explore recent publications, industry reports, and expert discussions in the field of digital workplace and employee experience.

Analysts could play an important role in such an environment.

The post Is 2024 ChatGPT revolution? appeared first on Customer Experience Management.

See also Part 1 for beginers: Python for Business Analytics – Customer Experience Management (mietwood.com)

Blog Post 2: Data Handling with Python

Python libraries like Pandas provide powerful data structures and easy-to-use functions for data gatering, cleaning, preprocessing, manipulation, and transformation. These features allow business analysts to efficiently handle missing values, remove outliers, perform aggregations, merge datasets, and more, ensuring the data is in a suitable format for analysis.

The easiest way to get some date is to make a list. Python ofers several types of list. One of it is just an array called in python a list.

a = [1,2,3,4,56,7,78,88,99]

You can save it easaly to file as you can see here. We can save in one line (row) or in many lines. Some preprocessin is already done. Any data you can save and read from file in similar way. But for analyst some other methods are very importatn as reading data from csv, xlsx, from databases and from internet data streems. In this subject Pandas library makes live a lot easier.

Reading Data from CSV Files: Python’s Pandas library offers a simple yet powerful method, read_csv(), to read data from CSV files. Using this function, business analysts can easily import structured tabular data from CSV files into a Pandas DataFrame. The function provides several parameters to customize the import process, such as specifying the delimiter, handling missing values, and defining column names. Once the data is read into a DataFrame, analysts can perform further data manipulation and analysis tasks. Let’s create csv-type file:

column_str = ','.join(column_names)
with open('xx.txt', 'w') as f:
    f.write(column_str+'\n')

with open('xx.txt', 'a') as f:
  for i in range(5):
    a2_str = ','.join([str(random.randint(1,200)) for x in column_names])
    f.writelines(a2_str+'\n')   

Now, let’s read this file with help of Pandas.

pd.read_csv('xx.txt')

Running this code we have got dataframe. What is data frame you can read in the internet but imagine it as a excel type table. You can read data also from other sources.

1. Reading Data from Excel Files: Pandas also provides the read_excel() function to read data from Excel files, making it effortless for business analysts to extract information from spreadsheets. This function allows analysts to specify the sheet name, range of cells, and desired columns. It supports both .xls and .xlsx file formats, providing compatibility with various versions of Excel. The imported data is stored in a DataFrame, enabling analysts to perform data cleaning, transformation, and analysis operations.
2. Reading Data from MS SQL Server Database: Python’s Pandas library incorporates the read_sql() function, which enables business analysts to extract data from MS SQL Server databases directly into a DataFrame. By establishing a connection to the database using third-party libraries like pyodbc or sqlalchemy, analysts can execute an SQL query and retrieve the results into a Pandas DataFrame. This functionality empowers analysts to seamlessly integrate their data analysis workflow with their database systems, allowing for efficient data retrieval and analysis.
3. Reading Data from JSON Files: In addition to CSV and Excel files, Pandas provides the read_json() function to read data from JSON files. This function allows business analysts to import structured or semi-structured data stored in JSON format and convert it into a DataFrame for analysis. The read_json() function supports various JSON file types, including single-line or multi-line JSON, nested JSON structures, and JSON arrays. Analysts can customize the import process by specifying parameters like orient, lines, and precise data locations within the JSON file, providing flexibility in handling different JSON datasets.

By leveraging Pandas’ diverse range of functions, business analysts can effortlessly import data from diverse sources, including CSV files, Excel files, MS SQL Server databases, and JSON files, into Pandas DataFrames. This versatility enables analysts to work seamlessly with different data formats, providing a unified and efficient approach to data handling in Python for business analysis.

Introduction to Data Handling in Python

2. Python Libraries for Data Handling

• Discuss popular Python libraries used for data handling in business analysis, such as NumPy, Pandas, and matplotlib.
• Provide an overview of their key features and functionalities.

3. Data Cleaning and Preprocessing

• Explain the importance of data cleaning and preprocessing for accurate analysis.
• Illustrate how Python can be used to clean and preprocess data, including techniques like handling missing values, removing outliers, and standardizing data.

4. Data Visualization

• Discuss the significance of data visualization in business analysis and decision-making.
• Demonstrate how Python libraries like matplotlib and seaborn can be utilized to create informative and visually appealing graphs and charts.

5. Exploratory Data Analysis (EDA)

• Explain the concept of EDA and its role in understanding the data.
• Show examples of using Python to perform EDA, including techniques like descriptive statistics, frequency analysis, and correlation analysis.

6. Data Manipulation and Transformation

• Explain various data manipulation and transformation techniques using Python libraries like Pandas.
• Provide examples of tasks such as filtering, sorting, aggregating, and merging data.

7. Statistical Analysis

• Discuss the significance of statistical analysis in business decision-making.
• Highlight the statistical functions and capabilities offered by Python libraries like NumPy and SciPy.

8. Data Modeling and Predictive Analytics

• Explain the use of Python for data modeling and predictive analytics in business analysis.
• Provide an overview of Python libraries like scikit-learn for tasks such as regression, classification, and clustering.

9. Conclusion

• Summarize the key points discussed in the blog post.
• Emphasize the importance of Python in data handling for business analysis and encourage further exploration and learning.

Remember to organize your content in a logical and coherent manner and provide code examples, screenshots, or diagrams whenever possible to enhance understanding.

Introduction:

• Highlight the importance of data handling in business analytics.
• Introduce libraries for data analysis: Pandas and NumPy.

1. Introduction to Pandas:

• Overview of Pandas data structures: Series and DataFrame.
• Reading and writing data with Pandas.

2. Data Cleaning and Preprocessing:

• Dealing with missing values using Pandas.
• Removing duplicates and outliers.

3. Exploratory Data Analysis (EDA):

• Basic statistical analysis using Pandas.
• Creating visualizations with Matplotlib and Seaborn.

4. Introduction to NumPy:

• Basics of NumPy arrays.
• Performing mathematical operations on arrays.

5. Data Manipulation with Pandas:

• Filtering and selecting data.
• Grouping and aggregating data.

6. Case Study: Analyzing Business Data:

• Applying the learned concepts to a real-world business dataset.
• Drawing insights and making basic business recommendations.

Conclusion:

• Emphasis on the practical application in business analytics.
• Encouragement to explore more advanced data analysis techniques.

The post Data Handling for Analysis with Python appeared first on Customer Experience Management.

1. The Evolving Landscape: The business environment is in a perpetual state of flux, marked by technological advancements, market shifts, and evolving consumer behaviors. Business Analysts act as interpreters of change, translating complex business requirements into tangible solutions. Their role is pivotal in ensuring that organizations not only adapt but thrive in the face of uncertainty.

2. Analytical Acumen in Demand: For high university students, embarking on a career in Business Analysis offers an opportunity to harness and apply analytical skills. In an era driven by data, BAs are the architects of informed decision-making. The ability to dissect and analyze data is a coveted skill that holds the promise of a rewarding and intellectually stimulating career.

You can also find public lecture presentation here to be rad and downloaded: https://mietwood.com/career-path-of-a-business-analyst-in-business-and-ict

Bridge Between Business and Technology

3. Bridge Between Business and Technology: One of the distinctive features of the Business Analyst role is its position at the intersection of business and technology as analyasis of customer feedback. For those considering a career transition, this presents an avenue to bridge the gap between domains. BAs act as facilitators, ensuring that technological solutions align seamlessly with business objectives.

4. Problem-Solving Prowess: Business Analysts are, at their core, problem solvers. They thrive on identifying challenges, proposing innovative solutions, and optimizing processes. This innate problem-solving prowess makes the profession appealing for individuals seeking intellectually engaging roles that contribute tangibly to organizational success.

5. Communication as a Core Competency: Effective communication is the cornerstone of successful business analysis. BAs serve as mediators between stakeholders, ensuring that everyone speaks the same language. For university students and seasoned professionals alike, honing communication skills is an invaluable asset that transcends industry boundaries.

Continuous Learning Opportunities

6. Continuous Learning Opportunities: The business landscape evolves, and so must its analysts. Business Analysis provides a career path that demands continuous learning. This characteristic is particularly enticing for university graduates eager to embark on a journey of professional development or for experienced individuals seeking to remain relevant in their fields.

7. Versatility in Industries: Business Analysts are not confined to a specific industry. From finance to healthcare, IT to manufacturing, the skills of a BA are universally applicable. This versatility offers a broad spectrum of opportunities for those entering the workforce or contemplating a career transition.

8. Impactful Decision-Making: For individuals driven by the desire to influence organizational strategy and decision-making, Business Analysis provides a direct avenue. BAs contribute to shaping the trajectory of businesses by providing insights grounded in data-driven analyses.

In conclusion, the allure of the Business Analyst profession lies in its adaptability, analytical depth, and transformative impact on organizations. Whether you’re a high university student embarking on a professional journey or an experienced professional seeking a new trajectory, the role of a Business Analyst beckons with the promise of intellectual stimulation, continuous learning, and a direct hand in shaping the future of business.

Attractiveness of the Business Analyst Profession

The attractiveness of the Business Analyst profession resonate with the key themes and qualities highlighted in the text. Here are some area that can illustrate the positopn atractiveness.

1. Collaborative Work Environments:
   • Show images of professionals collaborating in a modern office setting. This can convey the teamwork and communication aspects of the Business Analyst role.
2. Data Analytics and Visualization:
   • Feature visuals of data analytics tools, charts, and graphs to represent the analytical nature of the profession. This can highlight the importance of data-driven decision-making.
3. Professional Development:
   • Illustrate a person engaged in a learning environment, attending workshops, or participating in online courses. This conveys the continuous learning aspect of the profession.
4. Impactful Decision-Making:
   • Depict scenes of professionals in boardrooms, discussions, or presenting findings to executives. This illustrates the influential role Business Analysts play in shaping organizational decisions.
5. Intellectual Stimulation:
   • Use images that convey intellectual engagement, such as people reading, discussing ideas, or working on complex problem-solving tasks. This reflects the intellectually stimulating nature of the profession.

Landscape of organizational needs

In the dynamic landscape of organizational needs, the role of a business analyst has become pivotal, particularly for entities dealing with extensive operational data in transactional systems. The study conducted by Mieczysław Pawłowski and Dorota Walentek sheds light on the evolving attractiveness and market requirements for business analysts, focusing on a sample from Poland.

More from authors on google scholar and research gate profiles

The authors aim to classify job offers for analyst positions based on job titles and requirements, utilizing a combination of expert classification and machine learning algorithms. Their findings reveal a nuanced set of keywords associated with business and financial analysts, financial controllers, data scientists, system and security analysts. The study also uncovers the intricate relationship between job titles, responsibilities, and how these titles contribute to professional prestige.

While the research is limited to a specific data sample, its practical implications extend to managers seeking multidimensional knowledge. Furthermore, the study’s social impact lies in providing a comprehensive description of an analyst position, aiding diverse groups such as students in aligning their skills with industry demands.

The novelty

The novelty of this research lies in its innovative approach, utilizing both expert judgment and machine learning techniques. By categorizing job positions based on text summaries prepared through machine learning, the study bridges the gap between theoretical frameworks and current market realities.

In the wake of the COVID-19 pandemic and escalating cyber threats, there has been a surge in demand for specialists in data analysis, agile analysis, and digital initiatives. The study emphasizes the evolving nature of the business analyst role in response to these shifts in organizational structures and industry demands.

As the introduction lays the groundwork for the significance of business analysts in contemporary workplaces, the subsequent sections will delve into the methodology employed and the intriguing results that provide a deeper understanding of the market requirements and position attractiveness for business analysts in Poland.

Methodology Overview: Unveiling Business Analyst Dynamics in Poland

Pawłowski and Walentek’s methodology centers on decoding the evolving landscape of business analysts in Poland. Leveraging data from the prominent portal “Pracuj.pl,” they gathered 3,467 job offers (1.3% of the IT and Finance categories) through web scraping. The dataset underwent rigorous cleaning, feature extraction, and translation to English.

Clustering job titles using Latent Dirichlet Allocation (LDA) allowed for the identification of unique titles and positions. Analysis of job requirements and benefits verified the alignment with established categories. FLAML, a Python library, optimized the classification model for efficiency.

The study’s culmination involved constructing detailed job personas for each category. By amalgamating insights from clustering, requirements, and benefits analyses, the research provides a nuanced understanding of business analysts, aligning with current market demands and future projections. This methodological journey integrates web scraping, machine learning, and thorough analysis, offering a comprehensive exploration of the multifaceted role of business analysts in Poland.

Decoding Job Title Dynamics

Position Levels: Analysis of position levels reveals a predominant demand for regular specialists, constituting 57%. Senior and junior specialists follow closely at 18% each, collectively making up 35% of total demand. Managerial roles, trainees, and assistants constitute the remaining 9%.

Job Title Statistics: The dataset, pre-processed to eliminate redundancies and lemmatized, resulted in 1,837 unique job titles. Most titles (77%) consist of three or four words. The top 10 positions in demand include “financial analyst,” “business analyst,” “financial controller,” and “data scientist.”

Bigram Analysis: Bigram analysis identifies word collocations in job titles. Notable collocations involve “analyst” with “business,” “system,” and “financial.” The term “analyst” links to diverse areas like “risk,” “credit,” “IT,” and “support,” reflecting the multifaceted nature of the role.

Wording Structure in Job Titles: Topic modeling, employing Mallet, categorized job titles into eight main categories. Clear distinctions emerged, such as “business analyst” emphasizing market and data, “financial analyst” focusing on credit and risk, and “data scientist” highlighting expertise in data-related domains.

Conclusion: The analysis underscores the complexity of analyst positions, demanding individuals to delve deeply into specific business areas. Job titles reflect a broad range of responsibilities, necessitating continuous self-education. The study emphasizes the need for a company’s organizational culture to support ongoing learning, especially in roles where individuals may be pioneering in uncharted territories.

Job Categories and Requirements Overview

Job Categorization: The study employed a combination of expert methodology and machine learning to categorize job titles based on topic modeling. After selecting keywords, a classification model was run to ensure consistency. The resulting seven categories were: Business Analyst, Data Scientist, Financial Analyst, Financial Controller, Security Analyst, System Analyst, and Other.

Keyword Profiles: Each category was associated with a set of keywords representing the skills and responsibilities expected. Notably, some keywords were common across categories, such as “analyst” and “specialist.” While these words didn’t differentiate profiles, they reflected the inherent overlap in certain job roles.

Job Offers Distribution: The analysis revealed that the most sought-after positions were Business Analyst (31%), Financial Analyst (27%), and Data Scientist (16%). Financial Controller constituted 8%, while Security Analyst and System Analyst made up 2% and 7%, respectively. Approximately 10% of jobs remained uncategorized.

Consistency Check: The consistency between job titles and descriptions was evaluated. For instance, Business Analyst had 862 consistent offers, Data Scientist had 185, and Financial Analyst had 422. However, some discrepancies were noted, such as 324 Data Scientist offers classified under Business Analyst.

Job Requirements: Job descriptions were detailed, with the most frequent wordings ranging from 20 to 58 words. A bigram analysis depicted responsibilities spanning analytical support, project participation, business intelligence, budgeting, decision support, and continuous process improvement.

Technical Requirements: Technical skills expected from candidates included SQL, Python, SAP, UML, Power BI, AWS, Docker, and more.

Conclusion: The study not only categorized job titles but also scrutinized the alignment between titles and actual job descriptions. While certain discrepancies existed, the analysis provided valuable insights into the dynamics of job categories and the evolving demands in the analytical sector.

Keep reading: https://managementpapers.polsl.pl/wp-content/uploads/2023/09/177-Paw%C5%82owski-Walentek.pdf

Remember to choose high-quality, professional-looking CV that align with the tone of your aspiration.

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