Thomas Bolt’s Post

What are the main types of Data Analytics? 🔬 Descriptive Analytics: What happened? Descriptive Analytics, a foundational type of Data Analytics, offers insights into past events by analyzing historical data. This type focuses on understanding changes that have occurred over time, identifying patterns, trends, and the strengths and weaknesses of a business. It lays the groundwork for accurate Business Intelligence and decision-making processes. 🩻 Diagnostic Analytics: Why did it happen? Diagnostic Analytics delves into past performance to determine the reasons behind specific outcomes. Data analysts use techniques like drill-down, data discovery, and cause-and-effect analysis to pinpoint the root causes of observed changes. This enables businesses to address operational and strategic issues effectively, enhancing their analysis capabilities. 🧬 Predictive Analytics: What might happen next? Predictive Analytics involves extracting insights from existing data to forecast future trends and outcomes. By applying statistical algorithms and machine learning techniques, data analysts can anticipate future results and provide strategic insights. This proactive approach helps organizations identify opportunities, manage risks, and make informed decisions. 💊 Prescriptive Analytics: What should we do next? Prescriptive Analytics is crucial for data-driven decision-making, recommending actions based on predictions to help decision-makers understand likely outcomes. Utilizing algorithms and machine learning, it enables automated decision-making, offering insights into future events and the reasons behind them. This type enhances the role of data analysts in organizations by contributing to strategic decision-making processes. More information via: Harvard Business School Online (https://lnkd.in/dhN8_fz3) #Data_Science #Descriptive_Analytics #Diagnostic_Analytics #Predictive_Analytics #Prescriptive_Analytics #Business_Intelligence #Data_Analytics

💡💡💡Unlocking Insights: The Power of Data Analysis 💡💡💡 In today's data-driven world, data analysis is a game-changer. But what exactly is data analysis? Data analysis is the process of examining, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making. It involves various techniques and tools to turn raw data into actionable insights. Why is Data Analysis Important? - Informed Decisions: Helps businesses make data-driven decisions, reducing guesswork. - Identify Trends: Reveals patterns and trends that can inform strategic planning. - Improve Efficiency: Optimizes operations by identifying inefficiencies and areas for improvement. - Customer Insights: Enhances understanding of customer behaviour, leading to better products and services. Key Steps in Data Analysis: 1. Data Collection: Gathering relevant data from various sources. 2. Data Cleaning: Removing inconsistencies and errors to ensure accuracy. 3. Data Transformation: Converting data into a suitable format for analysis. 4. Data Modeling: Applying statistical methods and algorithms to analyze data. 5. Data Interpretation: Drawing meaningful conclusions and insights from the analysis. Tools and Techniques: - Statistical Analysis: Using statistics to interpret data. - Machine Learning: Applying algorithms to predict outcomes. - Data Visualization: Presenting data in graphical form to make insights accessible. Embrace data analysis to unlock the full potential of your data and drive success in your organization! #DataAnalysis #BigData #BusinessIntelligence #DataScience #Analytics #DataDriven #MachineLearning #DataVisualization

📊 Understanding the Distinction: Data Analysis vs. Data Analytics 📈 In today's data-driven world, the terms "Data Analysis" and "Data Analytics" are often used interchangeably, but they represent distinct facets of harnessing insights from data. Let's delve into the nuances: 🔍 Data Analysis: At its core, data analysis involves examining raw data with the goal of drawing conclusions. It encompasses tasks like cleaning, transforming, and visualizing data to uncover trends, patterns, and outliers. Data analysis is essential for understanding historical performance and making informed decisions based on past data. 📈 Data Analytics: On the other hand, data analytics is a broader discipline that extends beyond mere analysis. It involves the application of statistical and mathematical techniques, as well as predictive modeling and machine learning algorithms, to derive actionable insights. Data analytics is forward-looking, aiming to predict future trends and behaviors based on historical data. Understanding the differences between data analysis and data analytics is crucial for organizations aiming to leverage data effectively. While data analysis provides valuable hindsight, data analytics empowers businesses to proactively shape their strategies and optimize outcomes. Both disciplines are integral components of a data-driven culture, where informed decision-making and innovation thrive. By recognizing their distinctions and complementary roles, organizations can unlock the full potential of their data assets. #DataAnalysis #DataAnalytics #DataDriven #BusinessInsights #DecisionMaking #AnalyticsStrategy

Unlocking the Power of Data Analytics: From Raw Data to Actionable Insights Data analytics is more than just crunching numbers—it's about deriving meaningful insights that drive decision-making and innovation. Here's a high-level overview of the data analysis process: 1. Data Collection: Gathering data from various sources. 2. Data Cleaning: Ensuring data quality by handling missing values, outliers, and inconsistencies. 3. Data Analysis: Applying statistical methods and algorithms to uncover patterns and trends. 4. Data Visualization: Presenting data through graphs, charts, and dashboards for easy understanding. 5. Reporting: Sharing findings in a comprehensible format for stakeholders. Understanding each step can significantly improve the outcomes of your data projects. For a deeper dive, check out my latest Medium article where I explore each stage in detail and share practical tips from my tech journey. https://lnkd.in/dt4wEKqh #DataAnalytics #DataScience #TechJourney #CareerDevelopment #DataDriven"

Probing further into Data Analytics 🔍✨ What is Data Analytics? 🤔 Data analysis is the systematic process of examining, transforming, and extracting insights from data to support informed decision-making, problem-solving, and business strategy. It involves using statistical, quantitative, and qualitative methods to identify trends, patterns, relationships, and correlations within data 📊🔢. Types of Data Analysis: Several types of data analytics are categorized based on their purpose, scope, and complexity. They include: 1. Descriptive Analytics: Examines historical data to understand what happened 📜. a) Reports on past performance 📉. b) Identifies trends and patterns 📊. c) Answers the question, What happened? 2. Diagnostic Analytics: Analyzes data to understand why something happened 🔍. a) Identifies causes and correlations 🛠️. b) Uses statistical models and data mining 📊🔬. c) Answers the question, Why did it happen? 3. Predictive Analytics: Forecasts future events or trends 🔮. a) Uses machine learning and statistical models to predict probabilities and outcomes 🤖📈. b) Answers the question, What might happen? 4. Prescriptive Analytics: Provides recommendations for future actions 🧭. a) Uses optimization techniques and simulations ⚙️. b) Evaluates potential outcomes and answers the question, What should we do? I am incredibly grateful to Greater Works Institute of Technology - GWIT and my tutor Osborn Edudzi Worlasi for this rich knowledge on Data Analytics! 🙏 #DataAnalytics #LearningJourney #DataDriven #DescriptiveAnalytics #DiagnosticAnalytics #PredictiveAnalytics #PrescriptiveAnalytics #TechSkills #DecisionMaking #BusinessStrategy #MachineLearning #CareerGrowth #Opportunities Greater Works Institute of Technology - GWIT

💡 Data Wrangling vs. Data Cleaning: Are You Doing Both? 💡 In the world of data analytics, the terms data cleaning and data wrangling often overlap, but they serve distinct purposes. Let’s break it down: 🛠️ What is Data Cleaning? ✔️ Focus: Removing inaccuracies and inconsistencies in the data. ✔️ Goal: Ensure data integrity by handling missing values, duplicates, or incorrect entries. ✔️ Example Use Case: Fixing typos in customer names, standardizing date formats, and removing duplicate rows. 💡 Think of data cleaning as the foundation—your data is only as good as its accuracy! 🔄 What is Data Wrangling? ✔️ Focus: Transforming raw data into a structured, usable format. ✔️ Goal: Prepare data for analysis by reshaping, aggregating, or merging datasets. ✔️ Example Use Case: Converting raw transaction data into monthly sales summaries, pivoting datasets, or combining multiple sources into one cohesive dataset. 💡 Data wrangling is where the magic happens—turning cleaned data into actionable insights! 🚀 Key Takeaway: Data Cleaning ensures accuracy. Data Wrangling ensures usability. Both are essential steps for successful data analysis, and mastering them can save hours of effort while boosting the quality of your insights. 💬 How do you approach data cleaning and wrangling in your workflow? Share your thoughts below! #DataCleaning #DataWrangling #DataAnalytics #DataPreparation #BusinessIntelligence #DataScience #TechTips #Efficiency

𝗤𝘂𝗶𝗰𝗸 𝗪𝗶𝗻𝘀 𝗶𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀: 𝟯 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 𝘁𝗼 𝗠𝗮𝗸𝗲 𝗮𝗻 𝗜𝗺𝗺𝗲𝗱𝗶𝗮𝘁𝗲 𝗜𝗺𝗽𝗮𝗰𝘁 Perhaps the one thing most people should know about data analytics is that to drive value, it doesn't necessarily have to be large datasets or complicated models. In reality, many impactful insights actually drive value from small, actionable improvements. Here are three quick wins any organization can start using today to unlock hidden value in their data. - 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝗮𝗻𝗱 𝗙𝗶𝘅 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗜𝘀𝘀𝘂𝗲𝘀 Poor data quality will almost always be in the top list of major things standing in the way of effective analysis. Audit for the more simplistic, common issues such as duplicates, missing values, or out-of-date entries. Simple cleaning steps like these can greatly impact insights and ensure that the decisions their stakeholders make based on the data are valid. - 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝘃𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 It is, however, important first to take a closer look at what has already happened before diving deep into predictive modeling. Descriptive analytics-such as tracking trends in monthly revenue, customer churn rates, or product performance-are a very strong fundamental understanding of the business. These insights provide a reality check and set the roadmap for further analysis. - 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝗦𝗶𝗺𝗽𝗹𝗲 𝗥𝗲𝗽𝗼𝗿𝘁𝘀 Too many teams waste hours every week pulling the same reports. Automate regular tasks to free up resources to higher-impact analysis. Using tools like Power BI, Tableau, or even just Excel, reporting is easier since stakeholders need to be informed quickly and accurately. And the best? These approaches don't require big budgets and technical transformations. They are easy steps that, practiced every day, create big, financially impactful results. Have you tried any of these quick wins in your analytics work? Let me know in the comments, or feel free to share your go-to data hacks! #DataAnalytics #QuickWins #BusinessIntelligence #DataDriven

Did you know that Data Analysts typically spend 70-80% of their time cleaning and preparing data before diving into the exciting part—analyzing it? Data cleaning, though meticulous, is a crucial step in the analytics process. Raw data is often messy, filled with missing values, duplicates, and errors that can skew outcomes if not addressed. Analysts ensure accuracy, completeness, and reliability by organizing and scrubbing data. After data cleaning, analysts can unleash their creativity by applying statistical models, crafting visualizations, and extracting insights that drive informed business decisions. The next time you admire a polished report or a stunning data visualization, remember the significant effort put into data preparation for every data-driven choice! While not always glamorous, data cleaning forms the bedrock of any successful analysis. 🧹💡 #DataAnalysis #DataPreparation #DataQuality #BigData #Analytics #DataScience #DataDrivenDecisions

The Power of Running Analytics on Incomplete Data – Better Than Guessing In 2022, Sentier published a thought leadership article that started with the admittedly overly simplified statement of “Good data equals good analytics and bad data equals bad analytics.” No one wants to work with bad data, but what qualifies as good data, or more importantly, good enough data? In the world of data-driven decision-making, there's a common misconception that data needs to be pristine and complete before it can be useful. However, waiting for perfect data can lead to missed opportunities for financial growth and strategic planning. Instead, running analytics on imperfect data is often a far better approach than relying on gut feelings or guesswork. Incomplete data occurs more often than we would like. Some examples of data limitations include: • Limited Quantity – New situations such as brand launch or COVID • Sharing Limitations – Data unavailable from copromote teams or other departments such as Medical Affairs • Limited Reporting – Not all payers may report identified sales data or privacy restrictions block a subset of data Despite these data gaps, the show must go on. Stakeholders still want data-driven answers to critical business questions to make the best decisions for the company. Therefore, we must maximize the utility and insights derived from the available data. Some of the effective approaches for overcoming incomplete data include: • Data Augmentation: Using domain knowledge or interpolation, supplement with similar data sources to create proxy data • Synthetic Data Generation: Use techniques like bootstrapping or generative adversarial networks (GANs) to create synthetic data that mimics the properties of the original dataset • Iterative Analytics: As we work to gather additional data, perform analysis iteratively, refining models and methods as more data becomes available. Each iteration will improve the accuracy and relevance of the insights. • Focus on Key Metrics or Segmentations: Focus on analyzing the subset of data that is complete by using techniques like pairwise deletion where only the incomplete cases are excluded from key analyses rather than the entire dataset. Determine if other segmentations are likely to respond in a similar manner.   Waiting for perfect data is a luxury that most businesses can't afford. Incomplete data can still point you in the right direction and reduce the risk of significant missteps. Decisions based on partial data are more grounded than those based purely on intuition. Remember, the goal is not to achieve perfection but to make the best possible decisions based upon the data at hand. #sentier #dataanalytics #dataengineer