What are the best data analysis techniques for driving business decisions in BI?

To do this type of analysis you need to ask What has already happened? The most common examples of descriptive analysis are company business reports where raw data are provided in a historical review like: 1. Data Queries 2. Reports 3. Descriptive Statistics 4. Data Visualization 5. Data Dashboard and more

Effective data analysis is crucial for driving informed business decisions in Business Intelligence (BI). Here are some key data analysis techniques commonly used in BI to derive actionable insights: Descriptive Analytics: Diagnostic Analytics: Predictive Analytics: Prescriptive Analytics: Segmentation Analysis: Time Series Analysis: Correlation and Regression Analysis: A/B Testing: Text Mining and Sentiment Analysis: Geospatial Analysis: Data Visualization: Data Mining: Selecting the appropriate combination of these techniques depends on the specific business questions and objectives. Combining multiple techniques often provides a comprehensive view of the data, enabling better decision-making in various business scenarios.

"Descriptive analysis lays the foundation for understanding 'what happened.' It transforms raw data into meaningful summaries, painting a clear picture of historical trends and patterns." "Diving into the data archives with descriptive analysis! Unveiling the story behind the numbers, one trend at a time. Because understanding the past is the compass that guides future decisions. #DescriptiveAnalysis #DataStorytelling"

Minha experiência destaca a eficácia da análise descritiva em oferecer uma compreens?o abrangente dos dados, identificando padr?es e tendências cruciais para embasar decis?es estratégicas.

What it does: Descriptive analysis is like telling a story about what happened in the past using numbers. It helps us figure out the usual or common things in our data by looking at averages, how often something happens, and drawing pictures (charts) to make it easier to understand. Best for: When we want to know what's going on right now, find patterns, and tell others the important stuff we found in the data. Example: Imagine we're looking at sales data. Descriptive analysis helps us see which products sold a lot and which didn't by giving us a clear picture of the sales trends. It's like looking at a graph that shows which products are the superstars and which ones need a bit more love.

Diagnostic analysis:- Examines "why" things happen, using techniques like correlations and data mining. Best for: Finding out the main reasons something is happening, figuring out why trends are going a certain way, and discovering connections that aren't obvious. Example: Imagine If lots of people are quitting a service. Diagnostic analysis helps us figure out why by looking at different clues in the data. It's like asking questions and finding out if there's a specific reason, like maybe they don't like a new feature or the prices went up. It's like solving the mystery of why people are leaving.

"Diagnostic analysis goes beyond the 'what' to uncover the 'why.' It dissects data anomalies and deviations, providing insights into the root causes behind observed trends." "Playing detective with data! Diagnostic analysis helps us unravel the mysteries behind trends. It's not just about the symptoms; it's about diagnosing the data ailments for precise decision-making. #DiagnosticAnalysis #DataDetective"

Em situa??es desafiadoras, a análise diagnóstica revelou-se fundamental. Identificar causas raiz de problemas através de uma abordagem técnica aprofundada permitiu a implementa??o de solu??es direcionadas e eficientes.

To do this type of analysis you need to ask Why did this happen? Some techniques that we could use in Diagnostic Analysis: 1. Data Discovery 2. Data Mining 3. Correlations

In my experience, knowing the “why” is the most important of the whole analysis process It’s good to know “what” happened which is the descriptive analysis, it is more important to know why because they would help create measures or business decisions to ensure it doesn’t happen again (in a case where the incident has a negative impact on the business)or optimize the process in the case where it’s vice versa.

"Predictive analysis is the crystal ball of BI. By leveraging statistical algorithms and machine learning, it forecasts future trends, enabling businesses to proactively prepare for what's to come." "Peering into the future with predictive analysis! Turning data into a fortune-teller, helping businesses anticipate trends before they unfold. Because foresight is the ultimate superpower in decision-making. #PredictiveAnalysis #DataForesight"

To do this type of analysis you need to ask What is going to happen in the feature? Some techniques use developed models with past data to predict the future or apply impact on one variable to another variable. Popular techniques are 1. Linear regression 2. Time series analysis and forecasting 3. Data mining and so on

Predictive analysis is like looking at the past to guess what might happen in the future. Techniques like regression and machine learning use historical data to forecast future outcomes. Best for: Planning, seeing what could go well or not so well, and using resources (like money or time) in the smartest way. Example: Imagine you run a store. Predictive analysis could help you figure out how much of a certain product to keep in stock. By looking at how much people bought in the past, it can predict how much they might buy in the future. This way, you won't have too much or too little on your shelves. It's like having a crystal ball for your business!

Predictive analysis: (WHAT WILL HAPPEN IN FUTURE?) This will help us to forecast future outcomes or behaviors to take proactive actions. The objective of predictive analysis is not limited to precise outcome prediction; it also encompasses risk mitigation, opportunity identification, operational streamlining, and competitive advantage through proactive decision-making for enterprises.

Utilizando técnicas de análise preditiva, pude antecipar tendências futuras e comportamentos do mercado. Essa abordagem informada é essencial para proje??es precisas e tomadas de decis?es preventivas.

To do this type of analysis you need to ask What will be your suggestion to fulfill the requirements? Some techniques are 1. Model optimization 2. Simulation 3. Decision Analysis and more

A análise prescritiva, em minha prática, tem sido uma ferramenta valiosa para recomendar a??es específicas com base em cenários previstos, orientando estrategicamente as decis?es de negócios para otimizar resultados.

Prescriptive analysis not only tells you what might happen but also Goes beyond prediction to recommend the best course of action, often using optimization algorithms. Best for: When you want to make decisions based on data, like figuring out how to make the most money or spend the least. It's great for making things run smoother without needing you to figure it all out. Example: Imagine you run an online store. Prescriptive analysis could tell you not just what products might sell well, but also exactly what discounts or promotions to offer to each customer for the best results. It's like having a personal shopping assistant for each customer.

Prescriptive Analysis: Which action should be taken to achieve the goal or target? By analyzing past decisions and events, organizations can estimate the likelihood of different out comes. It assists firms in making well-informed decisions that result in better outcomes and increased performance by taking into account a variety of elements, restrictions, and possible outcomes.

A análise exploratória proporcionou insights iniciais vitais. Minha experiência destaca a importancia de técnicas como visualiza??es interativas na identifica??o rápida de padr?es emergentes e oportunidades.

Exploratory analysis helps us find interesting things in data without having a specific idea in mind. Best for: Figuring out new stuff, finding connections we didn't know about, and seeing where things could be better. Example: Look at pictures and graphs of how customers act to find out new things about what they buy and when.

"Exploratory analysis is the journey into the unknown. It involves open-ended investigation, uncovering new patterns and insights that might have been overlooked, making it a crucial step in the data exploration process." "Embarking on a data expedition with exploratory analysis! Charting new territories, discovering hidden gems within the data landscape. Because sometimes, the most valuable insights are found off the beaten path. #ExploratoryAnalysis #DataAdventure"

Em contextos de valida??o de hipóteses, a análise confirmatória mostrou-se crucial. Utilizando métodos estatísticos avan?ados, validei conclus?es, fortalecendo a confiabilidade das decis?es baseadas em dados.

Um tipo de análise extremamente útil é aquela que te alerta quando algo acontecer, e te guia o que precisas fazer. Como por ex: uma tabela com informa??es de clientes, e uma coluna contendo a data limite para fazer x atividade com esse cliente. Aplicando regras de cores e notifica??es, o time responsável será alertado quando a data estiver se aproximando, facilitando seu dia a dia.

I agree with Tim Timchur that different report users will respond differently to different visualizations. I would add that it is important to understand the makeup of the business decision making audience. It is likely there will be a wide range of statistical understanding - so the visualizations must serve the intake abilities of all of the decision makers. Arranging the visualizations from simple to complex to more complex informs the decision making team in a way that allows the team to make better decisions -- the whole point of driving business decisions.

Remember that different report users will respond differently to different visualisations. Sometimes I like to present data in a combination of text, tables and graphs, as some people find it easier to understand what you're trying to say, if you present it on a way that resonates with them.

While I was learning, I also found one more analysis is Cognitive. These analytics help us to learn what might happen if circumstances change and determine how we might handle these situations.