Data analytics involves examining and interpreting large sets of data to uncover trends, patterns, and actionable insights that help businesses make smarter decisions. By collecting, cleaning, and analyzing data from various sources, companies can better understand their operations, customers, and market trends to drive growth and innovation.
In this guide:
- What is data analytics?
- Why is data analytics important?
- Types of data analytics
- What are the key steps in the data analytics process?
- Techniques and methods for data analytics
- Essential tools for carrying out data analytics
- Get started with data analytics
What is data analytics?
Data analytics is the process of examining large amounts of quantitative and qualitative data. It’s useful for companies looking to understand and predict business trends for better planning and decision-making.
The goal of data analytics is to pull out important business insights from the various information collected about customers. This process involves data collection, data cleaning and preprocessing, exploratory data analysis, data visualization, and predictive modeling. By analyzing data from multiple sources — such as CRM systems, user engagement dashboards, and feedback forms — companies gain a deeper understanding of their operations, customers, and market trends.
For example, a B2B SaaS company might use data analytics to identify which features of their software are most frequently used by enterprise customers, enabling them to prioritize development efforts and improve customer retention.
Data analysts extract raw data, organize it, and then analyze it. Once their analysis is complete, they transform those large datasets into understandable insights that stakeholders can act on.
With advancements in technology, data analytics is now more accessible and scalable, allowing businesses to make data-driven decisions at every level of the organization.
Why is data analytics important?
Data analytics is essential for modern businesses, enabling informed, data-driven decisions that foster growth and competitiveness. Organizations leveraging data are 23 times more likely to acquire customers, six times more likely to retain them, and 19 times more likely to achieve profitability. By analyzing historical data and trends, businesses can refine strategic planning, enhance forecasting, and quickly respond to market changes.
One key advantage is operational efficiency. Data analytics identifies bottlenecks, streamlines processes, and reallocates resources, leading to significant cost savings and improved ROI. A reported 69% of executives credit data analytics with better decision-making, and 54% have achieved measurable cost reductions. Additionally, analytics plays a critical role in detecting waste and fraud, safeguarding financial health.
The applications of data analytics span industries. For example:
- In retail, large corporations like Walmart leverage data mining to analyze purchasing trends, offering personalized recommendations that increase customer satisfaction.
- In healthcare, analytics predicts patient readmissions and optimizes hospital resources.
- In finance, it supports risk assessment and fraud detection by identifying transaction anomalies.
The ability to harness data effectively is not just an advantage — it’s a necessity. Organizations that invest in robust data analytics capabilities are better equipped to make confident, evidence-based decisions that drive sustainable growth and long-term success.
Types of data analysis.
Data analytics can be broken into four categories: descriptive, predictive, prescriptive, and diagnostic.
Descriptive analytics.
Descriptive analytics examines the past and describes what happened by looking at datasets for patterns and trends. This is the core of most businesses’ analytics.
Example: A retail company analyzes its sales data from the past year to identify seasonal trends. For instance, sales of winter clothing consistently spike in November and December, while demand for swimsuits peaks in June and July. These patterns inform inventory planning and marketing strategies.
Predictive analytics.
Predictive analytics is used to make predictions and educated forecasts using historical data. It extrapolates trends into the future to understand possible outcomes.
Example: A subscription-based streaming service forecasts customer churn by analyzing viewing habits, subscription duration, and customer feedback to identify patterns associated with cancellations.
Prescriptive analytics.
Prescriptive analytics builds on predictive analytics by helping you understand why future outcomes might happen. It uses data from sources like statistics, machine learning, and data mining to reveal the best course of action.
Example: A logistics company optimizes delivery routes using historical data on traffic patterns, weather conditions, and delivery times. Predictive analytics forecasts delays, while prescriptive analytics suggests the most efficient routes.
Diagnostic analytics.
Diagnostic analytics investigates the past to explain why things happened the way they did. It goes beyond descriptive analytics to determine causation.
Example: An e-commerce company experiences a sudden drop in website sales. By analyzing data, they discover the decline coincided with a website update that unintentionally slowed page load times. Customer behavior analysis reveals increased cart abandonment rates during this period.
Diagnostic analytics helps businesses replicate positive outcomes and avoid negative ones by identifying the root cause of past events.
What are the key steps in the data analytics process?
The data analytics process involves several key steps:
Collect the data.
Start by auditing which data you need for the analysis. This could include customer information, UX interviews, or website impressions. Look for the data in your CRM system, previous marketing campaigns, or analytics software. Collecting data should be viewed as a constantly evolving and dynamic cycle happening in real time.
Store the data.
Once collected, data needs to be stored securely. Often, this requires transformation into a usable format through ETL (extract, transform, and load) or ELT (extract, load, and transform) processes. Raw data is cleaned, formatted, and organized in storage systems like data lakes.
Clean the data.
Check for inconsistencies, duplicates, or missing information. Properly cleaned data ensures accurate and reliable results.
Analyze the data.
Use software tools to process and interpret the data, uncovering valuable insights that can guide decision-making.
Techniques and methods for data analytics.
Data analysts unlock the potential of data with several different methods, allowing them to process data and extract valuable information. Below are some of the most popular techniques used today.
Regression analysis.
Regression analysis estimates and models the relationships between sets of variables. One example is examining the number of Facebook friends an author has and the number of hardcovers her last book sold to predict a different variable, such as the success of her forthcoming trilogy.
Factor analysis.
Also known as dimension reduction, factor analysis condenses the data of many variables into a “super-variable.” Take, for example, your health. A doctor might measure your height, weight, and blood pressure. They’ll then take all those factors and condense it into one all-encompassing review of your health. Factor analysis helps data analysts uncover variables that drive choices and behavior.
Cohort analysis.
A cohort is a group of people who share a characteristic. Cohort analysis involves segmenting customer data into smaller groups of people, or cohorts. This helps businesses watch trends and patterns that are specific to those people. Getting to the root of how your customers think can be priceless for your business.
Monte Carlo simulations.
Monte Carlo simulations, developed after World War II, are a powerful method for decision-making under uncertainty. Named after Monaco’s casino district for its reliance on probabilities and randomness, this technique involves running numerous simulations to evaluate a range of potential outcomes. Instead of relying on a single average value, data analysts input varying values for uncertain factors, such as market conditions or customer behavior, to better understand possible scenarios and their likelihoods. This approach provides a more comprehensive view of risks and opportunities, helping organizations make informed decisions even in complex situations.
Time series analysis.
This technique helps data analysts make accurate forecasts for the future by measuring the same variable at different periods. For example, consider the amount of school supplies sold at certain times. A time series analysis would likely reveal a steep drop-off in June and July thanks to the summer vacation.
Data analytics tools.
Managing data has evolved far beyond spreadsheets and static graphs, thanks to modern tools and software. Data analysts now use programming languages to handle databases, visualization platforms to share insights through dynamic dashboards, and data mining techniques to uncover patterns in large datasets. These advances turn raw data into actionable insights, empowering smarter decision-making.
Get started with data analytics.
Data analytics explains how your business has performed and why, helping predict future outcomes.
To get started:
- Identify key business challenges, such as customer retention or operational inefficiencies.
- Collect relevant data from available sources like CRM systems or sales platforms, ensuring it’s clean and accurate.
- Analyze this data to uncover patterns and insights that will guide decision-making and strategy, helping you drive growth and improve performance.
Adobe can help.
Adobe Analytics lets you mix, match, and analyze data from any point in the customer journey. With in-depth analysis, comprehensive reporting, and predictive intelligence, you get the insightful foundation you need to build better customer experiences. Adobe Analytics uses AI to deliver predictive insights based on the full scope of your data.
Watch an overview video or request a demo to learn how Adobe Analytics can help your business.
