What data types are used in a business analytics project, and how can you analyze them effectively?

??Quantitative Data: Numerical, measurable, countable data like sales figures, ratings, website traffic, and financial metrics. ??Use descriptive statistics (mean, median, mode) to summarize data. ??Apply inferential statistics (regression, hypothesis testing) for deeper insights. ??Visualize with charts (bar, line, scatter plots) to identify trends and patterns. ??Leverage machine learning for predictive analytics and clustering.

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: numerical, measurable data like sales figures, ratings, traffic. Analyze using statistical methods like regression, correlation, and trend analysis. ??Discrete data: finite or countable values, such as orders or product counts. Use bar charts, histograms. ??Continuous data: infinite, uncountable, like revenue or time. Analyze with line graphs, distribution models. ??Qualitative data: descriptive, such as customer feedback or opinions. Analyze using text mining, sentiment analysis, or thematic analysis. ??Ensure data cleaning for accurate results in both types.

Uno de lo primeros pasos a considerar en el análisis de datos ?? es reconocer con el equipo empresarial la pertinencia de la información que permita gestionar, Definir en primera instancia en consenso con las diferentes áreas y niveles organizacionales que datos son necesarios para la toma de decisiones en los diferentes niveles. Qué tipo de datos serán soporte en la gestión para luego encaminarse al desarrollo de BI adecuado que los permita analizar

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.

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.

Qualitative data refers to non-numeric information, such as opinions, descriptions, or categories, used in business analytics. It includes data like customer feedback, survey responses, or interview transcripts. Analyzing qualitative data involves identifying patterns, themes, or sentiments. Methods like content analysis, thematic analysis, or sentiment analysis are often applied. Properly analyzed, qualitative data can provide deep insights into customer behavior and decision-making.

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.

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.

Structured data is the backbone of reliable and scalable analytics. The challenge with disparate sources is not just organizing the data, but transforming it for efficient analysis across the enterprise. Using SQL with structured data enables real-time analytics, turning data into clear visualizations and insights in tools like Power BI. Structured data also supports advanced analytics, including machine learning and AI. By applying dimensional modeling techniques like the star schema, data marts can be created to support specific business functions, facilitating cleaner data for model training. In conclusion, structured data is about creating a foundation that supports everything from routine reporting to cutting-edge AI-driven analytics.

Pra que se tenha uma análise bem feita, é primordial que os dados estejam de fácil compreens?o e de fácil acesso. Temos diversas técnicas para tratamento de dados que possibilitam que a estrutura??o dos dados nos garanta a melhor utiliza??o dos dados como ETL, cria??o de datamarts para utiliza??o dos dados em classifica??es específicas e CTAs por exemplo.

Structured data refers to data that is organized into a fixed format, typically in rows and columns, like in databases or spreadsheets. In a business analytics project, common data types include integers, floats, strings, dates, and categorical data. These types help represent quantitative and qualitative information about the business. Effective analysis of structured data involves using statistical methods, data visualization, and machine learning algorithms to derive insights. Proper data cleaning and transformation are key steps before analysis.

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

Unstructured data refers to information that lacks a predefined format, making it challenging to analyze. Common sources include text documents, social media posts, images, and videos. Effective analysis of unstructured data often involves natural language processing (NLP) for text, image recognition for visuals, and machine learning algorithms. By leveraging these techniques, businesses can extract valuable insights and trends. This analysis can drive better decision-making and enhance customer understanding.

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 n?o estruturados. No ambiente online, toda informa??o, seja ela objetiva ou n?o, é concatenada em dados. A interpreta??o dos emissores de dados, considerando motiva??es 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 vis?o 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.

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.