The 4 Types of Data Analysis and their difference
Businesses and organisations that continuously learn and adapt are the most successful. No matter what sector you work in, it’s critical to be aware of the recent past, the current state of affairs, and potential future developments. So how do businesses accomplish that?
Data analytics holds the solution. Most businesses constantly gather data, yet this data is meaningless in its unprocessed state. What you do with the data is what really matters. Data analytics is the act of examining raw data to identify patterns, trends, and insights that might provide valuable information about a certain business domain. Following that, informed judgments based on data are made using these insights.
AI and sophisticated analytics have gained popularity in recent years. There are a lot of blogs out there that discuss the benefits of employing sophisticated analytics in your company.
It is tempting to go right in and try to get advanced analytics straight immediately given the amount of value that they may provide. But these insights cannot be attained without the right foundations. What is the first step to obtaining these insightful data, then?
Advanced analytics success and the use of AI can be ensured by comprehending the analytics development and getting started in the proper spot.
56 percent of respondents claimed data analytics resulted in “faster, more effective decision-making” at their firms, according to MicroStrategy’s The Global State of Enterprise Analytics survey (pdf). Other advantages mentioned include:
- increased productivity and efficiency (64 percent)
- better financial results (51 percent)
- Finding and generating new sources of revenue for products and services (46 percent)
- enhanced client acquisition and retention (46 percent)
- enhanced client experiences (44 percent)
- advantage over rivals (43 percent)
WHO IS IN NEED OF DATA ANALYSIS?
Any business professional who takes judgments must have a solid understanding of data analytics. Data access is easier to come by than ever. You may overlook significant possibilities or warning signs if you design strategies and make decisions without taking the facts into account.
Skills in data analytics can be useful for the following professions:
Skills in data analytics can be useful for the following professions:
Marketers develop marketing plans by using information about customers, market trends, and the results of previous campaigns.
Product managers improve their companies’ goods by analysing market, industry, and user data.
Finance experts predict the financial trajectories of their organisations using historical performance data and market trends.
Human resources and diversity, equality, and inclusion specialists can use information on industry trends and employee perspectives, motivations, and behaviours to make significant organisational changes.
1. Descriptive Analytics
The foundation for all other types of analytics is descriptive analytics, which is the most basic type. It enables you to quickly summarise what occurred or is happening by drawing trends from the raw data.
What happened is answered by descriptive analytics.
Consider the scenario where you are studying the statistics for your business and discover that sales of one of your goods, a video game console, are increasing at a seasonal rate. Here, descriptive analytics can inform you, “Sales of this video game system increase each year in early December, early November, and October.”
Charts, graphs, and maps may clearly and understandably display data patterns, as well as dips and spikes, making data visualization a good choice for expressing descriptive analysis.
Data Aggregation:
Definition: Data aggregation involves combining and summarizing individual data points to form a higher-level overview. This can be done based on specific criteria such as time periods, locations, or categories.
Example: Aggregating daily sales data into monthly or yearly totals to understand overall performance.
Summary Statistics:
Mean: The average of a set of values.
Median: The middle value in a dataset when it is ordered.
Mode: The most frequently occurring value.
Standard Deviation: A measure of the amount of variation or dispersion in a set of values.
Range: The difference between the maximum and minimum values in a dataset.
Percentiles: Divides the data into 100 equal parts, helping understand the relative standing of a particular value.
Purpose: These statistics provide insights into the central tendency, spread, and distribution of the data.
Data Visualization:
Graphs and Charts: Represent data visually to identify patterns, trends, and relationships.
Examples: Bar charts for categorical data, line graphs for trends over time, scatter plots for relationships between two variables, and heat maps for visualizing variations in a matrix.
Purpose: Enhances understanding and communication of complex data patterns.
Data Exploration (EDA):
Techniques: Histograms, box plots, and summary statistics to identify data characteristics.
Outliers and Missing Values: Identification and handling of unusual or missing data points.
Purpose: Gain initial insights, understand the structure, and identify potential issues in the dataset.
Data Profiling:
Data Types: Understanding the types of data (numerical, categorical, etc.).
Data Integrity: Assessing the accuracy and consistency of data.
Completeness: Identifying missing values or incomplete records.
Anomalies: Detecting irregularities or outliers.
Purpose: Assess the overall quality and reliability of the dataset.
Segmentation:
Definition: Dividing data into meaningful groups or segments based on specific attributes or characteristics.
Example: Segmenting customers based on demographics, buying behavior, or preferences.
Purpose: Identifying different patterns and behaviors within the dataset for targeted analysis or decision-making.
Time Series Analysis:
Definition: Analysis of data collected over time to understand patterns, seasonality, and trends.
Techniques: Moving averages, trend analysis, and forecasting.
Purpose: Uncover temporal patterns and make predictions based on historical data.
In summary, descriptive analytics involves various techniques to organize, summarize, and visually represent data, providing valuable insights into its characteristics and patterns. These techniques serve as a foundation for more advanced analytics and decision-making processes.
2. Diagnostic Analytics
The following logical question, “Why did this happen?” is answered by diagnostic analytics.
This sort of analysis goes a step further by comparing current trends or movements, finding relationships between variables, and, when possible, establishing causal linkages.
Using the previous example, you might look at the demographics of video game console users and discover that they range in age from eight to 18 years old. The average age of the patrons, however, is between 35 and 55. Data from customer surveys that have been analysed show that buying a video game console as a present for kids is one of the main reasons people do so. The increase in sales throughout the fall and early winter may be attributed to the gift-giving holidays.
Using diagnostic analytics to identify
Root Cause Analysis:
Definition: Root cause analysis involves identifying the fundamental factors or reasons that contributed to a specific event or outcome. It aims to go beyond surface-level observations to uncover the underlying causes.
Techniques: Correlation analysis, regression analysis, and dependency analysis help in understanding relationships between variables and identifying potential causes.
Process: Investigating patterns and trends in the data to determine the root causes, which may involve exploring historical data, conducting interviews, and using various analytical tools.
Comparative Analysis:
Definition: Comparative analysis involves comparing different datasets or segments to identify variations, differences, and similarities in performance. It helps in understanding the relative performance of different entities.
Techniques: Comparative analysis may involve metrics such as percentages, ratios, and indices to quantify and compare performance. Visualization techniques like bar charts and heat maps are often used for easy comparison.
Purpose: To identify patterns, anomalies, and trends by comparing different dimensions, such as regions, time periods, customer segments, or product lines.
Hypothesis Testing:
Definition: Hypothesis testing is a statistical method used to validate or reject potential explanations (hypotheses) for observed outcomes. It involves formulating hypotheses, collecting data, and performing statistical tests to assess the significance of relationships.
Process: Formulating a null hypothesis and an alternative hypothesis, collecting data, conducting statistical tests (e.g., t-tests, chi-square tests), and interpreting the results to make informed decisions about the hypotheses.
Purpose: To provide evidence for or against proposed explanations and make data-driven decisions.
Drill-Down Analysis:
Definition: Drill-down analysis involves examining data at a detailed, granular level by exploring specific dimensions or attributes. It allows for a deeper understanding of the data and helps uncover insights that may be hidden when looking at higher levels of aggregation.
Techniques: Pivot tables, detailed reports, and interactive dashboards are often used to facilitate drill-down analysis. Users can navigate through layers of data to explore specific details.
Purpose: To identify specific trends, outliers, or patterns that may not be evident at a higher level of aggregation.
Cohort Analysis:
Definition: Cohort analysis involves studying groups of individuals or entities that share common characteristics or experiences. It helps in understanding how different cohorts behave over time and how they may differ from one another.
Techniques: Time-based segmentation is a common technique in cohort analysis. The behavior of specific cohorts is tracked over time to observe changes and trends.
Purpose: To gain insights into customer behavior, product performance, or employee performance by analyzing groups that share similar characteristics or experiences.
Data Mining and Machine Learning:
Definition: Data mining and machine learning involve using advanced algorithms to discover patterns, correlations, and predictive relationships in large datasets.
Techniques: Clustering, classification, regression, and association rule mining are common techniques in data mining and machine learning. These algorithms can uncover hidden insights and automate the discovery process.
Purpose: To extract valuable knowledge from data, predict future outcomes, and uncover complex relationships that may not be apparent through traditional analysis.
In summary, diagnostic analytics involves a set of techniques that go beyond describing data to understand why certain events or outcomes occurred. These techniques help organizations uncover insights, identify causation, and make informed decisions based on a deeper understanding of the data.
3. Predictive Analytics
In order to predict future trends or events or to provide a response to the question “What might happen in the future,” predictive analytics is utilised.
You can accurately estimate what the future may hold for your firm by examining historical data along with current industry trends.
For instance, knowing that, over the previous ten years, sales of video game consoles have peaked in October, November, and the first few weeks of December each year gives you enough information to forecast that the same trend will continue in 2016. This is a logical prediction, supported by upward trends in the video game industry as a whole.
Making forecasts about the future might assist your company in developing plans based on probable outcomes.
Here are some key aspects of predictive analytics:
- Data Preparation: Preparing and cleansing the data to ensure its quality and suitability for analysis. This includes handling missing values, outliers, and transforming the data into a format suitable for modeling.
- Feature Selection and Engineering: Identifying the most relevant features or variables that are likely to have predictive power. This involves selecting informative variables and creating new derived features that may enhance the prediction models.
- Model Development: Building statistical or machine learning models based on the historical data. Common techniques used in predictive analytics include regression analysis, decision trees, random forests, support vector machines, neural networks, and ensemble methods.
- Training and Evaluation: Training the predictive models using a portion of the historical data and evaluating their performance on another portion of the data. This is done to assess how well the models generalize to unseen data and to select the best-performing model.
- Prediction and Forecasting: Applying the trained models to new or future data to generate predictions or forecasts. These predictions can be in the form of numerical values, classifications, or probabilities.
- Model Validation: Validating the predictive models by comparing the predicted outcomes with the actual outcomes observed in real-world scenarios. This helps assess the accuracy and reliability of the models.
4. Prescriptive Analytics
Prescriptive analytics finally provides a response to the query, “What should we do next?”
Prescriptive analytics recommends actionable takeaways after considering all potential aspects in a circumstance. Making decisions based on data can be extremely helpful when using this kind of analytics.
To complete the video game illustration: What should your team decide to do in light of the anticipated seasonality trend brought on by the holiday gift-giving season? Perhaps you decide to do an A/B test with two adverts, one geared toward customers and the other towards the product’s end-users (children) (their parents). The results of that experiment can help determine how best to further capitalise on the seasonal rise and its purported cause. Or, perhaps you decide to step up your marketing initiatives in September with messages centred around the holidays to try to prolong the boost.
Here are some key aspects of prescriptive analytics:
- Optimization Modeling: Developing mathematical or computational models that represent the problem domain and capture the relationships between variables, constraints, and objectives. These models aim to find the best solution that maximizes performance or minimizes costs based on predefined criteria.
- Constraint Analysis: Identifying and incorporating constraints and limitations into the optimization models. These constraints could be related to resource availability, capacity limits, regulatory requirements, or business rules that need to be adhered to.
- Scenario Analysis: Conducting “what-if” analysis by simulating different scenarios and evaluating their potential outcomes. This helps assess the impact of different decisions or actions on the desired outcomes and facilitates decision-making under various conditions.
- Decision Support Systems: Developing decision support tools or systems that can assist decision-makers in evaluating alternative courses of action and selecting the optimal solution. These systems often provide interactive interfaces and visualizations to aid in the decision-making process.
- Recommendation Engines: Implementing recommendation systems that utilize advanced algorithms to suggest the best actions or options based on user preferences, historical data, and real-time information. These engines are commonly used in personalized marketing, content recommendation, and product recommendation.
- Continuous Learning and Adaptation: Prescriptive analytics systems can be designed to continuously learn from new data, feedback, and outcomes. This allows for the refinement and adaptation of models and recommendations over time, leading to improved decision-making and performance optimization.
