¿Qué tipos de datos se utilizan en un proyecto de análisis empresarial y cómo puede analizarlos de forma eficaz?

In business analytics, you commonly deal with quantitative data (like sales figures), qualitative data (like customer feedback), and time-series data (like monthly revenue trends). Effectively analyzing these involves using statistical methods for quantitative data, text analytics for qualitative data, and trend analysis techniques for time-series data. For example, combining sales data analysis with customer review sentiment can provide comprehensive insights into product performance.

Quantitative data is vital for business analytics across sales, finance, operations, HR, and CRM. It's divided into continuous and discrete types. Continuous data, like revenue or customer lifetime value, can take any value within a range. Analyzing it involves descriptive statistics such as mean and standard deviation. Discrete data, like product counts or customer complaints, consists of distinct numerical values. Effective analysis uses frequency distributions and measures like mode to discern patterns. Statistical tests like chi-square or t-tests compare discrete variables. Quantitative data offers indispensable insights crucial for decision-making, performance evaluation, and strategic planning.

Well, this is something that predicts the future and prevailing situation of a given project, In a business analytics project, you deal with different types of data. There's numeric data, like sales figures, and you analyze them using math and statistics and you use charts and graphs to make sense of them. Time series data involves looking at things over time. Text data involves written stuff like customer reviews. Geographic data is about locations, like where customers are, and you use maps and spatial analysis. To analyze all these data types effectively, you clean and organize the data, look for patterns and trends, and use various tools and techniques to draw insights and make informed business decisions.

We use quantitative data around gross sales, commissions revenue, and rebate payments to analyze trends among customers by lines of business and contract types. This data can be visualized for stakeholders using pivot tables, charts, and graphs to tell the story of our business at a given point in time or across a specified timeframe.

Various data types are used to gather insights and make informed decisions. Here is one common data types and how it can be effectively analyzed: Numerical Data: Types: This includes quantitative data like sales figures, revenue, expenses, etc. Analysis Techniques: - Descriptive statistics: Mean, median, mode, range, and standard deviation. - Time series analysis for trends and seasonality. - Correlation and regression analysis to understand relationships between variables. - Data visualization using charts, graphs, and dashboards.

Qualitative data provides descriptive information that cannot be expressed numerically. It can include customer feedback, survey responses, or narrative data. Analyzing qualitative data involves techniques like thematic coding or sentiment analysis to extract meaningful insights and understand the underlying context or sentiments associated with the data.

Qualitative data is often gathered through methods such as interviews, observations, focus groups, and open-ended survey questions. By analyzing qualitative data, we can uncover unique insights, identify patterns, and generate hypotheses for further investigation. When combined with quantitative data, qualitative data adds richness and context to the overall understanding of a topic or research question.

I consider NLP as an indispensable tool for analyzing and responding to client comments. Through techniques like sentiment analysis, NLP can swiftly categorize feedback as positive, negative, or neutral, offering a clear gauge of client satisfaction. Moreover, NLP's topic modeling capabilities enable companies to identify and address prevalent themes in customer feedback, leading to more targeted and effective business strategies. Additionally, NLP facilitates the development of sophisticated chatbots and automated response systems, which can handle routine inquiries, significantly improving response times and enhancing overall customer experience.

If I may add is the comparison of the outcome with the expected result and decision what to do with the results of the analysis. That is plan of action.

This data type includes non-numerical information like customer opinions, employee feedback, and brand images. Analyzing qualitative data is like interpreting a story. It often involves reading through the data, identifying themes, and understanding underlying meanings. Techniques like content analysis or thematic analysis are useful here.

Business analytics involves both quantitative (numeric) and qualitative (non-numeric) data types. Quantitative data, comprising numbers like transaction volumes or percentages, is analysed through statistical methods like, leveraging tools such as Excel or statistical software. Qualitative data, including customer feedback, demands techniques like sentiment analysis, content categorization, and NLP. Effective analysis requires a blend of statistical methods, data visualization, and specialised tools tailored to each data type for comprehensive insights.

Traditionally data was stored in structured relational databases. SQL queries are the best way to extract the data you require. MS Excel can also be a powerful tool to clean and transform the data once it is collected. It also allows you to do some basic visualization using pivot tables and charts. Modern tools such as Tableau, PowerBI and Alteryx can also be used

One common example of structured data is a customer database, where each customer's information is stored in separate rows with different attributes like name, age, address, and purchase history. Other examples include financial records, inventory databases, or any dataset that is well-organized and can be easily classified into categories. Structured data is often used in various industries and domains, such as business analytics, finance, healthcare, and e-commerce. Its structured nature allows for efficient data management, reporting, and the application of statistical analysis techniques. By leveraging structured data, organizations can gain valuable insights, make informed decisions, and improve their overall operations.

Structured data is highly organized and formatted, often residing in relational databases. Examples include spreadsheets or tables where each piece of information has a predefined format. Analyzing structured data involves querying databases and utilizing SQL or other languages to extract, transform, and analyze data efficiently.

Very important to understand, that data stored in structured way, dont have to be used as structured output. By using json packages and build in functions, most of sql languages and DBs, are able to translate this data to unstructured output, and even make it accessible as rest api service. Data is accessible to the developer as structured data with the traditional sql indexes, partitions and others benefits like comfortability and visualization, and delivered as unstructured data by the common way today to pass data - json rest api service.

Como rule of thumb para qualquer projeto de análise é preciso uma coisa: adaptabilidade. A melhor maneira de aplicar uma abordagem multifacetada é quando lidamos com dados não estruturados. No ambiente online, toda informação, seja ela objetiva ou não, é concatenada em dados. A interpretação dos emissores de dados, considerando motivações e expectativas em seus contextos, é essencial. A análise das estratégias dos concorrentes confere vantagem competitiva, permitindo um planejamento estratégico abrangente. Feedbacks negativos viram oportunidades, transformando críticas em conteúdos. Uma visão abrangente do mercado é essencial, oferecendo insights para estratégias eficazes e sustentáveis.

Examples of unstructured data include customer reviews, social media feeds, call center transcripts, and even sensor data from Internet of Things (IoT) devices. Analyzing unstructured data can provide organizations with valuable insights into customer sentiment, market trends, brand perception, and other important factors that may not be apparent through structured data analysis alone. With advancements in technology, organizations are finding innovative ways to process and analyze unstructured data to gain a competitive edge and drive meaningful insights.

Unstructured data, on the other hand, lacks a predefined structure and includes sources like text documents, images, or social media posts. Analyzing unstructured data requires advanced techniques such as natural language processing for textual data or image recognition for visual content. These methods help derive valuable insights from the unstructured and often voluminous data.

This includes data that's not in a predefined format, like emails, social media posts, or video content. Analyzing unstructured data is more challenging because it requires tools that can interpret complex and varied forms of data. Techniques like NLP, sentiment analysis, and data mining are often used. Tools like IBM Watson and other AI based platforms can be effective in handling unstructured data.

Unstructured data in business analytics comprises text, images, videos, social media posts, and more. To analyse effectively, employ natural language processing (NLP) for text mining, sentiment analysis, and topic modelling. AI-driven or machine learning algorithms can classify, cluster, or generate insights from unstructured data, allowing businesses to derive valuable insights from diverse sources.

Por mi parte trabajo con datos estructurados haciendo diagnóstico profundo de indicadores y procesos con inteligencia artificial. Analizo todas los factores que determinan los KPI para poner de manifiesto problemas y oportunidades ocultas

📊 In Business Intelligence we can use quantitative and qualitative data, which can be structured or unstructured. This data classification can come from sources internal to the business or from external sources. Having a dictionary of organizational data is very useful for the ETL process and modeling of related tables that support dashboards and visualizations.

One of the data types that may not be widely recognized but proves highly impactful in the realm of sports analytics is spatial data. Analysis of spatial data involves mapping, spatial clustering, interpolation, and geographic information systems (GIS) techniques to understand spatial relationships and patterns. Overall, spatial data plays a crucial role in enhancing performance, strategy, planning, and fan engagement in the world of sports, contributing to a more data-driven and informed approach to sports management and analysis.

When analyzing these data types, it’s crucial to maintain data integrity and quality. Ensure that your data sources are reliable and that your analysis methods are appropriate for the type of data you're working with. Also, consider the ethical implications of data analysis, particularly when dealing with sensitive or personal information. And remember, effective communication of your findings is as important as the analysis itself.

Here I want to Talking about Tools for Data Analytics and Data Visualisation. Tool like MS Excel and Power BI.; Using Very Basic tool of Microsoft Office we can deliver a Better Results using over own intelligence.