Guide to Data Analytics: Types & Examples

When data stays isolated in systems and spreadsheets, teams lose momentum. Opportunities to spot trends, address risks early, and develop informed strategies slip through the cracks.

Data analytics changes that by helping you uncover what’s really happening behind the numbers and showing where to focus next. With the right approach, analytics becomes more than reporting; it becomes a tool for agile decision-making, enhanced collaboration, and more strategic growth.

Understanding the types of data analytics and the techniques behind it is key to putting this power into practice—giving you and your teams the ability to spot patterns sooner, respond to challenges with greater confidence, and build strategies that lead to measurable, lasting impact.

Understanding data analytics

By using data analytics, you’re able to examine raw information to uncover patterns, trends, and insights that can guide decisions. You move from collecting information to understanding or using data to make sense of what’s happening in your business and why.

While the phrase might sound technical, data analytics isn’t just for data scientists. It can be as simple as reviewing a dashboard to spot changes in customer behavior or comparing month-over-month performance across teams. Whether you're tracking marketing campaigns, monitoring supply chains, or measuring employee engagement, analytics helps connect the dots across your organization.

The data analytics process typically involves collecting and cleaning data, exploring it for trends, and using those findings to guide choices. Data analytics helps teams answer questions like: What happened? Why did it happen? What might happen next? What should we do about it?

When used thoughtfully, data analytics becomes a core part of how teams make informed decisions that align with your business priorities.

Types of data analytics

To get the most value from your data, it helps to understand the different ways you can analyze it. The four main types of data analytics—descriptive, diagnostic, predictive, and prescriptive—each play a distinct role in the decision-making process.

Descriptive analytics

Descriptive analytics focuses on summarizing historical data to show what has already happened. It’s the most basic form of analytics and often the first step in understanding performance. Think of descriptive analytics as a snapshot: revenue by quarter, website traffic over time, or support ticket volume by region. These summaries help teams align on current conditions before digging deeper.

Best for: Measuring performance or tracking metrics

Use cases: 

• A retail chain reviewing last quarter’s sales performance by region to evaluate store-level performance.
• A hospital system analyzing patient volume trends across departments to plan future resourcing.

Diagnostic analytics

Diagnostic analytics digs deeper to uncover the reasons behind past outcomes. It answers the question, “Why did this happen?” by identifying relationships, anomalies, and cause-and-effect patterns. When something changes—good or bad—diagnostic analytics helps explain what led to that result.

Best for: Understanding root causes

Use cases: 

• A logistics company investigating delivery delays by analyzing driver performance and route history.
• A finance team investigating a spike in operational costs during a specific quarter.

Predictive analytics

Predictive analytics uses statistical models and machine learning to identify likely future outcomes. By identifying patterns in historical data, predictive analytics helps teams anticipate what’s likely to happen next—so they can prepare instead of react.

Best for: Forecasting future trends

Use cases: 

• A manufacturing company predicting equipment failure based on sensor data to schedule maintenance before downtime occurs.
• A healthcare provider predicting patient readmission rates based on historical trends.

Prescriptive analytics

Prescriptive analytics takes your analytics practice one step further by recommending actions. Using algorithms, business rules, and real-time simulations, it suggests next steps that align with organizational goals. It’s where insight becomes direction. 

Best for: Optimizing decisions and outcomes

Use cases:

• A supply chain team optimizing delivery routes in response to traffic and weather data.
• An e-commerce company recommending personalized discounts and product bundles based on customer behavior, inventory levels, and seasonal trends.

Each type of data analytics builds on the last, creating a data journey from insight to action.

The benefits of data analytics

Data analytics doesn’t just deliver answers—it changes how people think, collaborate, and solve problems. From the front lines to the leadership team, analytics equips individuals with the perspective and context they need to work more effectively and respond to change with greater awareness. Here’s how it makes an impact:

Turning questions into actionable insights

At its best, analytics helps people ask more relevant questions—and find meaningful answers. Whether it’s exploring why a campaign underperformed or identifying which teams need more support, data provides clarity and direction.

Making everyday work more focused

Analytics reduces noise. Instead of guessing which tasks matter most, teams can see what’s working, what’s not, and where their time will have the greatest impact. It’s not about doing more; it’s about knowing what’s worth doing.

Understanding people, not just numbers

Behind every data point is a person: a customer, a team member, a partner. Analytics helps uncover not just behaviors, but motivations—what people need, how they interact, and where experiences can be improved.

Anticipating, not just reacting

By spotting patterns and surfacing early signals, analytics helps teams move before problems grow. It shifts decision-making from reactive to responsive—from delayed hindsight to timely foresight.

Building a data-driven culture

When data is visible and easy to explore, it invites more people into the conversation. That shared understanding leads to greater alignment, more trust, and decisions that stand up to scrutiny.

The data analytics process

The journey from raw data to meaningful insight typically follows a set of core stages. At Domo, we think of this as a continuous cycle that helps individuals and teams make decisions with clarity and confidence.

1. Collect. The process starts by bringing together data from across your organization, including sales systems, finance tools, and marketing platforms. At this stage, the data often comes in different formats and levels of quality.
2. Prepare. Once collected, the data is cleaned, organized, and transformed so it’s ready for analysis. This step might include correcting errors, aligning data types, or filtering down to what’s most relevant. With Domo’s Magic ETL, anyone—not just technical teams—can shape and prepare data without needing to code.
3. Analyze. After preparation, people across departments can explore the data using visualizations, filters, comparisons, and AI-powered tools to find trends, gaps, or opportunities.
4. Act. Insight becomes impactful when it leads to action. That might mean adjusting timelines, shifting resources, or setting up alerts when certain metrics change.

This process isn’t a one-time event. As your data grows and priorities shift, the cycle adapts, helping everyone stay aligned and informed.

Data analytics techniques and tools

Modern data analytics isn't one-size-fits-all. Different teams—whether you're deep in the data or presenting to the C-suite—rely on different technologies and methods to explore information, find answers, and take action. Here are a few foundational tools and techniques used across industries:

• Artificial intelligence (AI)
  AI helps automate complex analysis, detect anomalies, and highlight trends. It plays a key role in surfacing insights that might otherwise be missed.
• Machine learning models
  A branch of AI, machine learning uses past data to make predictions and identify patterns. It’s commonly used for forecasting, customer behavior modeling, and operational planning.
• Data mining
  Helps uncover hidden patterns and relationships in large data sets. Often used by data teams to explore new hypotheses or spot trends that haven’t yet surfaced.
• Natural language processing (NLP)
  NLP allows people to ask questions about their data using everyday language. It removes technical barriers and makes it easier for more team members to participate in analytics.
• Cloud computing
  Cloud-based platforms offer flexibility, scalability, and real-time access to data—without the need for local infrastructure. Teams can collaborate from anywhere and work with large, fast-changing data sets.
• Statistical analysis
  A go-to for analysts, this method applies mathematical formulas to data to understand trends, averages, correlations, and outliers. It’s essential for benchmarking performance and identifying shifts over time.
• A/B testing
  A practical method for comparing two versions of a process, campaign, or product feature. It’s especially useful for marketers and product teams looking to validate decisions with real-world results.
• Data visualizations
  Visual tools like charts, graphs, and dashboards simplify complex data for executives and frontline teams alike. No-code and low-code tools make it easy to build visuals that align with specific goals and help teams spot trends, track progress, and communicate findings clearly.

These technologies and methods work together to make data more approachable and actionable across your organization.

Real-world data analytics examples

Data analytics isn’t limited to static reports or reserved for technical teams. It’s embedded into everyday workflows, giving people across roles the tools to explore data, spot patterns, and act quickly. 

Here’s how real people use different types of analytics to solve problems and improve performance. 

Marketing teams adjusting campaign strategy on the fly

Marketers use descriptive analytics to track engagement, conversions, and ad spend across channels. When a campaign underperforms, they turn to diagnostic analytics to dig into regional trends, audience behavior, and messaging variations to find out why. Data analytics helps teams move from awareness to understanding at a faster rate, so you can make in-campaign changes instead of waiting until it’s over.

Sales leaders prioritizing the right deals

Sales managers may start with descriptive analytics to see pipeline status and rep performance. Layered on top, predictive models forecast which deals are most likely to close based on deal size, sales cycle length, and past buyer behavior. These insights help reps focus their efforts while managers allocate resources more effectively. With analytics, sales strategy becomes less reactive and more proactive—grounded in data, not guesswork.

Operations teams managing disruptions in real time

Operations leaders use descriptive analytics to monitor fulfillment rates and delivery times across facilities. When something falls outside the norm, diagnostic analytics helps pinpoint the issue, whether it’s a supplier delay or a staffing shortage. From there, prescriptive analytics comes into play through automated alerts and built-in workflows that recommend adjustments or reroute logistics. In Domo, these insights are not just informative—they’re actionable.

HR teams improving retention and engagement

HR professionals use descriptive analytics to visualize turnover trends and engagement scores across departments. When one team shows higher attrition, diagnostic analytics helps uncover the cause—perhaps tied to onboarding experiences or manager feedback. Armed with those insights, HR can work with team leads to address gaps and improve employee experience, no SQL required.

Finance teams planning for what’s next

Finance leaders use predictive analytics to model potential outcomes based on changes in revenue, expenses, or market conditions. These forecasts help teams evaluate best- and worst-case scenarios, while prescriptive analytics offers strategic options—such as delaying hires or adjusting vendor spend. With Domo’s real-time data connections, finance teams can simulate, assess, and act all in one platform.

Executives aligning teams around shared goals

Executives rely on descriptive analytics to get a unified view of company-wide KPIs—from sales and marketing to customer support. But alignment happens when leaders can also use prescriptive analytics to guide the next move, like reallocating resources or adjusting goals based on emerging trends. By integrating cross-functional data in one place, teams can move with clarity and consistency.

Challenges in building a data analytics practice

A strong data analytics practice can transform how people make decisions and drive progress. But the road to building one isn’t without obstacles. Along the way, teams often encounter challenges that go beyond just choosing the right tools—they involve people, processes, and priorities.

Collecting the right data—not just more data

Many teams struggle with data overload. It’s not just about gathering information; it’s about identifying which data is meaningful. Without a clear data strategy, teams can find themselves drowning in numbers without gaining real insights.

Managing data storage and accessibility

As data sets grow, so do storage needs. Storing data is only part of the challenge. It also needs to be easy for the right people to access and use. Systems that are too complex or fragmented can slow down analysis and make collaboration harder.

Integrating data from different systems

Data rarely lives in just one place. One of the biggest challenges is bringing together data from different sources—CRM platforms, marketing tools, financial systems—into a unified view. Without seamless integration, teams risk working with incomplete or outdated information, leading to siloed decision-making.

Bridging the skills gap

Not everyone is a data scientist—and they shouldn’t have to be. One major challenge is enabling people with different skill levels to explore, understand, and act on data without relying entirely on specialists. No-code tools like Domo’s drag-and-drop ETL help close the skills gap, but adoption also depends on cultural support.

Turning insights into everyday action

Data analytics doesn’t create value unless it leads to action. Even with strong dashboards and reports, teams can struggle to connect insights to workflows. Embedding data into decision-making processes—and using alerts, recommendations, and real-time updates—helps make insights part of the daily rhythm.

Balancing cost and value

Building a scalable analytics practice requires investment, not just in technology but in training, support, and change management. Teams must weigh short-term costs against long-term gains, ensuring analytics drives measurable impact, not just more reports.

Data analytics best practices to follow

Building a successful analytics practice is not just about having the right tools—it’s about how you approach the work. To get the most value from your data analytics efforts, focus on these core best practices:

1. Start with a clear data strategy
   Before diving into dashboards, define the problems you’re trying to solve. Let your most important questions—not just available data—shape your analytics strategy.
2. Prioritize data quality early
   Clean, consistent, and reliable data lays the foundation for meaningful insights. Investing time in validation and governance at the start saves far more time downstream. 
3. Connect and unify your data sources
   Powerful analytics happens when data from across platforms—CRM, finance, operations, marketing—is integrated into a single, accessible view. Unified data removes silos and helps teams work from the same truth.
4. Make data accessible to everyone
   No-code or low-code tools to empower people across roles and skill levels to explore and act on data. But remember, access is only useful if your culture encourages data exploration and shared learning.
5. Tie analytics to action
   Insights alone aren’t the finish line. The best analytics practices rely on recommendations, real-time alerts, and tie workflows directly into decision-making so data moves teams forward, not just sideways.
6. Build for continuous growth
   Analytics needs can change as your needs evolve. Choose flexible, scalable tools that make it easy to add new data sources, adapt models, and expand insights without starting over.

Domo makes it easy to turn these best practices into action. From unifying your data to enabling self-service analytics at scale, Domo helps you move from insight to impact—all in one platform. 

Watch a demo to see how Domo can support your analytics journey.