What is Data Analytics? Learn the Role & Impact of Data Analytics in Business

January 25, 2024  

Decision speed and accuracy matter. Data analytics automates the gathering and analysis of data, so that accurate information is available when you need it. 

Simply analyzing data can be done in a spreadsheet. But for modern businesses, data analytics includes the analysis and also includes the engineering and technology behind it

Whether you are building a data product for your customers, or providing better insights to internal audiences, you're constantly navigating through a sea of information, seeking insights that can drive your company forward. What is data analytics, if not your compass in this journey? It's not just about handling vast amounts of data; it’s about extracting meaningful patterns and insights that inform smarter, more effective business strategies. This blog post delves into the realm of business data analytics, exploring its role in shaping business decisions and driving organizational growth.


The goal is to turn data into information, and information into insight.

Carly Fiorina, former CEO Hewlett-Packard

This article is a practical guide for understanding analytics, various strategies of using data, corresponding examples, and getting started as a company in building a data culture and making your data a valuable asset.

Data Analytics, what is it?

What is Data Analytics?

Data analytics is everything involved in turning raw data into insights. It includes the gathering of data from various systems that produce or store data, combining data from those sources into a unified dataset, analyzing the combined data for patterns and trends, and presenting findings to people who need insights or answers.

Interestingly, all of these activities could be done completely manually. In fact this is the most common approach. People all over the world use spreadsheets and files to work with data to get answers to questions.


Information is the oil of the 21st century, and analytics is the combustion engine.

Peter Sonderaard, SVP Gartner Research

This article focuses on the automation of data analytics as a business capability. Data can be gathered and unified automatically. Analysis and calculations can be automated. Metrics and insights can be delivered real-time to audiences, allowing them to view and explore on their own.

What Is the Purpose of Data Analytics?

The most important part about data analytics is it provides an objective view of reality. Combining people’s experience and intuition (qualitative information) with data analytics (quantitative information) creates an integrated decision-making model that results in better decisions and more reliable business outcomes.

Integrated decision making with data

Why Is Data Analytics Important?

Companies that use data analytics for integrated decision-making in their business are shown to grow 10 times faster than those that do not. According to Forrester research, the average year-over-year revenue growth rate for data-driven companies is 30%, compared to 3% growth rate for non-data-driven companies.

What Practical, Modern Data Analytics Looks Like

Upon committing to a data analytics and decision-making strategy, many companies immediately recognize a lack of understanding on how to execute. The key parts of a modern, automated data analytics process look like this:

Basic data analytics platform
  1. Data Sources. These are SaaS tools, internal databases, APIs, or other systems that contain data that is useful for analysis. 
  2. Data Warehouse. All this data is automatically brought together to a centralized location. Typically this is called a data warehouse, which is a database optimized for analytics.
  3. Metrics and Data Model. The “raw” data from all the different Data Sources is combined all together and structured in a way that allows for easy querying across all the sources. From this, metrics and key performance indicators can be defined and automatically calculated.
  4. Data Products are things like reports and dashboards that the intended audiences access directly. The visualizations and explorations happen here.

Typically, these aspects of a data architecture are intuitive. Breaking down your data platform in this way helps create a plan for how to execute the strategy.

Types of Data Analytics

These four generally recognized categories encompass all the more detailed types of data analyses

  • Descriptive Analytics. This foundational technique involves summarizing historical data to identify patterns and trends. It answers the "What has happened?" question. Commonly used for reporting, descriptive analytics helps businesses understand past behaviors and outcomes through metrics like sales performance or customer churn.
  • Diagnostic Analytics. Going beyond mere description, diagnostic analytics explores why events occurred. It uses techniques like data mining, correlations, and drill-downs to investigate the causes of particular outcomes. Diagnostic analytics is essential for understanding the underlying factors behind business successes or challenges.
  • Predictive Analytics. This technique uses historical data to predict future outcomes. Employing statistical models and machine learning algorithms, predictive analytics helps businesses anticipate trends, customer behaviors, and potential risks. It's like a crystal ball, giving insights into what could happen next based on past data.
  • Prescriptive Analytics. The most advanced form of analytics, prescriptive analytics not only predicts outcomes but also suggests actions to achieve desired results. It often involves complex algorithms and machine learning to ensure the prescribed actions will most likely produce the desired results.

Types of Data Analytics Tools and Products

If you thought finding the right talent and staffing a team for a new business function was difficult, welcome to the world of finding the tools and products to support your data operations. 

The data analytics industry has gone through a renaissance in the last few years. Many new product categories and products now exist. This ecosystem and general architecture design is loosely referred to as the “modern data stack.” It was originally identified by the venture capital firm Andreessen Horowitz in Emerging Architectures for Modern Data Infrastructure.

Without a solid plan, this can all get overwhelming and is the primary reason for the lack of progress in data analytics initiatives. In fact, an entire team of people publishes an annual landscape, and it is indeed overwhelming

MAD landscape

Below is a summary of the primary categories, ranked in order of importance. Getting the first three right will be a solid foundation for anything you want to do in the future.

  1. Warehouse. this is a database designed for the types of queries typical of data analytics work. Typically, these are “cloud-native” meaning they were built from the ground up to leverage cloud features like performance, scaling, and cost tracking. Examples of data warehouses include Snowflake, BigQuery, Databricks, Clickhouse, and Redshift. In the end, regardless of the internal complexity and varying designs, they all provide a standard SQL query interface. So you can consider them just databases, and most tools–and people!–that can support SQL will be able to work well in a data warehouse.
  2. Replication. This involves extracting data out of operational data stores, SaaS products, databases, APIs, and other data sources–and then pushing all that data into a central location (typically the data warehouse). In the “good old days,” data replication was mixed with data transformations and calculations, creating a big, hard-to-maintain mess. In the modern data stack, these two major responsibilities (replication and transformation) are separated, with dedicated tools to support each. Examples of specialist products that perform data replication are Meltano, Fivetran, Portable, Rivery, Integrate, and Airbyte.
  3. Transformation. This includes the programming code that combines data from the various data sources, calculates metrics, and reshapes the data to make it useful data analytics. Examples of products that perform transformations are dbt Labs, Coalesce, and Talend.
  4. Business Intelligence, Reporting, and Visualization. This refers to what your audiences and stakeholders use to interact with data. Historically, these were static reports, often delivered as PDF documents. Now, interactive dashboards are also very common for curated self-service experiences. A technology called notebooks are used for technical data exploration as well as presenting walkthroughs of deep data analyses. Leading products include Sigma Computing, Hex, Astrato, Mode, Tableau, and Luzmo. (there are way too many to list here. See Datateer's product evaluation matrix for a full list).
  5. Orchestration. An orchestration tool coordinates all of the moving parts of a data platform. Orchestration tools handle scheduling, managing dependencies, and reporting out when any of the moving parts break. Examples include Prefect, Dagster, and Airflow.
  6. Observability & Testing. Data is technically complex. Quickly, all the moving parts and all the data flows overwhelm any human’s ability to keep everything in their head. Data observability and testing tools keep an eye on all the data flowing through the warehouse and raise issues when data quality or technical health are not meeting service level agreements. Leading products include Decube, Metaplane, Qualytics, re_data, and Monte Carlo.
  7. Governance. These products help manage who can access data, which datasets they are allowed to access, and what they are allowed to do with it. Data governance is an important way to keep data secure. Often, the data governance product doubles as a data catalog (although there are even more specialized vendors that only do data cataloging). Examples of vendors that provide governance and cataloging include Select Star, Castor, Decube, and Alation
  8. Reverse ETL. These products are similar to replication products, but they work in reverse. The value they provide is getting your data analytics back into the operational systems where people do their day-to-day work. A common example is a customer 360 effort where the data warehouse is used to build a full picture of all customer interactions across the entire company. That full picture can be pushed into the CRM system, providing information that sales and account management teams can use in their normal workflow. Examples include Census and Hightouch.

Common Data Analytics Skill Specialties

In a constantly evolving world of data, certain core skills stand out as valuable. When building a team, these skills will ensure it has the foundation it needs to make an impact on the business with data analytics. These skills are in order of importance of making the most foundational impact to a business.

  • Data Engineering (especially with SQL). SQL is essential for working with databases. It's used for querying and managing data, which is a fundamental part of any data analyst's role.
  • Data Visualization and Reporting. Making data easy to understand is crucial. This skill involves creating clear charts, graphs, and dashboards. Tools like Tableau or Power BI are often used for this purpose.
  • Communication and Storytelling with Data. Being able to clearly convey findings and translate complex data into engaging stories is key. This skill bridges the gap between technical analysis and practical business application.
  • Programming Proficiency (Python or R). Python and R are important programming languages in data analytics. They allow for deeper and more flexible analysis than standard tools.
  • Business Acumen and Industry Knowledge. Deep knowledge of the specific industry or sector is valuable. It allows for more nuanced data interpretation and provides actionable recommendations.
  • Machine Learning and AI Literacy. Understanding machine learning and AI is becoming more important. This includes knowing how algorithms work and how to use them for predictive analysis.

How is Data Analytics Used in Business?

Data analytics gives businesses visibility into operations, and–more importantly–helps ensure consistent, predictable business outcomes. This is a mirror image of what business leaders strive to achieve already–optimize performance and efficiency, maximize profit, satisfy customers, and make better strategic decisions.

Adding data analytics to these efforts gives businesses an edge.

The best way to get inspired is often through the examples of others. Here are several examples of companies using data analytics integrated into their overall business strategies.

Increased Revenue & Customer-Facing Analytics

  • Intercom - Intercom is known for making customer service better. They also have a tool that helps companies see how well their customer service is working. This includes metrics like the number of completed conversations per employee, which helps teams become more efficient and effective. By integrating these customer-facing analytics, Intercom enhanced user engagement and retention, providing a competitive advantage
  • Spotify - The music streaming service uses customer data to create personalized experiences, like the annual Spotify Wrapped feature. This gives users access to personal analytics such as minutes played, top songs, and favorite podcasts. This not only engages users but also promotes the brand and attracts new customers, providing a competitive edge for Spotify. 
  • Equinix Metal - By combining customer data and behavioral data from their CRM, customer support, and product usage, Datateer’s customer Equinix Metal was able to identify key factors of churn timing and upsell readiness. Using this information, their account management teams are now prepared to have the right conversation, with the right customer, at the right time. Churn has since dropped by over 10%, and upsells have increased by 15%. 

Efficient Operations & Better Decision Making

  • Strava - Strava lives and dies by its product engagement in a competitive fitness tracking market. Several dashboards allow them to analyze product usage and customer behavior. This has a direct impact on product feature prioritization and the customer experience. With these business data analytics, Strava is able to improve customer experience and positively impact key performance indicators around their customers. 
  • Alliant - By creating a solid business data analytics infrastructure in the cloud, Alliant was able to realize a 150% improvement in their SLAs. This was the result of a focused effort to design data analytics to deliver the information employees needed to make better day-to-day decisions. 
  • A luxury hotel and resort - This business captures customer interactions to create a more complete, 360-degree view of their guests. They were able to identify that over 70% of upgrades to spa packages involved couples and combine that with the channels and messages most effective to get customers to upgrade. Further, they combined that with an analysis of when spa services were less busy. Analyses like these drive their decision-making about when to target, whom to target, and how to target them to smooth demand and offer the right package at the right time to delight customers.


Data analytics is crucial for businesses today. It involves not just analyzing large amounts of data, but also understanding the technology and engineering behind it. This process is key for making quick and accurate business decisions.

The article covers essential aspects of data analytics, such as data sources, storage, and how businesses can use this data to create reports and dashboards. Real-world cases from companies like Intercom, Spotify, and Strava demonstrate the practical benefits of data analytics, showing its impact on customer engagement and operational efficiency. The effective use of data analytics is vital for business growth and advancement–and is within reach of any company!

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Adam Roderick

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About the Author

Adam's tech career spans startups to global firms, touching finance, tourism, e-commerce, and consulting. Spotting a way to give small- and medium-sized companies an advantage with data, he founded Datateer to democratize analytics. He values relentless progress, simplifying complexity, and putting people first.

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