A Guide to Enterprise Analytics

Enterprise analytics is a business practice in which you use data-driven insights to make informed decisions with greater accuracy and speed and at scale. This practice includes identifying, collecting, processing, analyzing, and visualizing relevant internal and external data sets. Enterprise analytics can help you better understand the customer experience, increase operational efficiency, and grow your business.

There are four main types of enterprise analytics: descriptive, diagnostic, predictive, and prescriptive. These types of analytics range from the simplest (descriptive analytics) to the most complex (prescriptive analytics).

Descriptive analytics is the simplest type of analysis and is focused on the current state of the business or key performance indicators (KPIs). Here, you typically use data mining (to sort data sets and identify trends and patterns), querying, reporting, and data visualization tools to understand the outcomes of decisions that were already made.

Diagnostic analytics, sometimes called root cause analysis, uses techniques such as drill-down, data discovery, data mining, and more to tease out why something occurred and uncover causality.

Predictive analytics uses advanced statistical techniques including machine learning algorithms and deep learning to devise or “predict” likely outcomes. More sophisticated than diagnostic analytics, it considers current trends and anticipates future outcomes.

Prescriptive analytics is the most advanced type of analytics. It looks at past performance and enables you to create completely new scenarios—one after the other—extrapolating potential outcomes and proposing future actions.

Enterprise analytics helps you make better informed decisions with greater accuracy, speed, and scale across your entire organization. It can drive business growth—enabling you to experience these five benefits.

Analyze historical and real-time data to make predictions and forecast demand with greater accuracy. Democratize insights with easy-to-understand visualizations without relying on technical analysts to collect, analyze, and explore the data.

Create new buyer experiences or products to fill unmet customer needs based on emerging business trends. Discover trends and patterns and get more insight into the customer journey. Personalize customer experiences using up-to-date data.

Identify and assess risks accurately and quickly using a single source of truth. Collect and analyze large data sets, share meaningful visualizations with stakeholders, and align with decision-makers to rapidly pivot and mitigate risks.

Identify process inefficiencies in your company, remove bottlenecks, and save costs using data insights. Use a single source of truth and current data to inform operational changes and improve efficiency across your business.

Measure employee productivity and engagement with personalized and contextual data insights. Implement programs based on data-driven insights, track improvements, and record changes in employee productivity and engagement.

There are several key challenges facing your business that can prevent it from using enterprise analytics effectively. These challenges impact everyone—from executive leaders and product managers to salespeople and IT analysts. As a result, it’s critical to address these challenges from the get-go to ensure everyone can better understand the customer experience, increase operational efficiency, and ultimately grow your business.

The amount of data in organizations today is immense—but without a plan to collect, clean, and analyze that data, it is meaningless. Data quality, data management, and integration remain challenges without a continuous process of collecting, cleansing, analyzing, and interpreting your data to drive informed decision making and improve business performance.

Businesses collect an enormous quantity of personal identifiable data, and your customers rely on you to protect it, ensuring data privacy and security. Your enterprise analytics and data strategy must maintain that trust, from how data is collected and cleaned to how it’s stored and analyzed. Ensuring you use trusted enterprise analytics tools and techniques is critical.

To use analytics effectively across your business, you need to ensure that people have the requisite data literacy skills. Non-analysts or business users in particular may not know how to access and use data in their day-to-day work. You can democratize data-driven insights by developing a plan to audit and assess current staff and incentivize internal training programs that build data literacy.

Imagine you can access data-driven insights to help you identify trends, opportunities, and make market shifts faster. With enterprise analytics, you can do that by exploring charts, graphs, and data visualizations using real-time data. An enterprise analytics platform can provide a single source of truth that helps your entire organization better understand the customer journey, uncover patterns and trends, and innovate faster.

You don’t need to imagine any of this—it’s already happening. At Tableau, we’ve worked with companies in several key industries that use enterprise analytics to better understand customer expectations, forecast demand, improve their operations, and drive growth. Here are several industries we’ve helped customers build, grow, and succeed:

Enterprise analytics has improved customer experiences by helping financial service companies track customer satisfaction, use secure, trusted data to deliver improved customer service, and enable customers to reach their goals with personalized wealth and asset management.

It has also empowered these businesses to drive operational efficiencies by consolidating disconnected data sources into a data warehouse or lakehouse to create a single source of truth, which helps everyone in the company better mitigate risks and make smarter decisions faster. By removing data silos, moving from manual to automated data cleaning and preparation processes, and scaling governed enterprise analytics, financial service companies have accelerated and simplified digital transformation, making decisions more impactful, reliable, and cost-effective.

Retail and consumer goods businesses are using enterprise analytics to improve customer experiences, better manage product availability, and enhance their demand forecasting.

For example, retailers are using data and analytics to create highly personalized customer experiences, both online and in person. By seeing what customers have bought in the past, they can recommend new products and create new lifecycle programs to increase customer loyalty and repeat sales. For customer concerns, managing customer data in a trusted, unified platform helps retail and consumer goods companies improve the quality and delivery of their customer service. They’re also able to implement chatbots and AI agent technology to effectively address customer issues and questions.

In addition, companies using relevant and personalized data are well-positioned to track product availability and ensure on-time delivery. And by analyzing historical inventory data, retailers and consumer goods businesses can better identify patterns of customer behavior and forecast demand to plan accordingly.

An enterprise analytics platform centralizes how you collect, clean, and analyze data—and makes data accessible, lowering the barrier to entry using analytics tools that even a novice in data analytics can use. Whether you are performing descriptive, diagnostic, predictive, and/or prescriptive analytics, you need a platform that can transform complex business data into visually appealing charts and graphs—that deliver insights to more people faster—when, where, and how you want to access it.

Below, we’ve identified four issues to factor into your decision making when choosing an analytics platform and several key features that address each of these considerations.

Enterprise analytics must enable access to fresh data from all of your trusted sources, regardless of where it’s housed (on-premises, in the cloud, a hybrid deployment). It needs to streamline your data cleaning or data preparation, transformation, and loading processes. Look for a platform that can quickly gather data of different formats, simplify transforming and analyzing it, and ultimately present or visualize it as relevant insights so you can make better informed decisions.

• Integration with existing IT infrastructure
• Secure semantic layer
• Strong ecosystem of APIs and connectors

A key benefit of using a flexible, technology-agnostic approach is having the ability to collect, clean, and analyze data regardless of your existing IT infrastructure. By choosing a platform-independent solution such as Tableau Enterprise you can run your data analytics from anywhere.

A secure semantic layer provides rich metadata for better data. With an at-a-glance visual representation of your data and behind-the-scenes automation, this semantic layer automatically enriches analytics data with business context and meaning—helping you discover and understand relevant data.

Finally, connectors and APIs give you out-of-the-box development tools for better investments. This enables you to create scalable, secure data integrations with prebuilt connectors and APIs to any system, including Salesforce Clouds, mobile, streaming, and hyperscale systems.

To increase data accessibility across your business, you need an enterprise analytics platform that is easy to use for non-technical people—business users—and technical teams. Your platform must deliver consumer-friendly data visualizations or reports that are simple to understand. This will enable your people to drill down, ask questions, and gain greater insight into their respective spheres of influence.

• Data catalog
• Accelerators

Use a detailed data catalog like Tableau Catalog to provide a complete picture of your data and show how it is connected to analytics in the Tableau environment, increasing the trust and discoverability for both IT and business users. In addition, Tableau Accelerators deliver prebuilt dashboards designed to help you get a jump start on data analysis.

Data security and compliance are critical issues. Your enterprise analytics platform must do more than comply with regulatory requirements that impact how you collect and use data. People need to trust your data security, and that includes controlling who can access and explore the data visualizations.

• Trusted data modeling layer
• Secure data governance
• Data privacy and governance

Tableau Data Management helps deliver trusted, self-service analytics by scaling data automation and operationalization throughout the entire data and analytics lifecycle. It makes secure, trusted data available where you need it while ensuring visibility and control. As a result, you achieve agile governance for better investments.

To accommodate future business growth, look for an enterprise analytics platform with the flexibility to support your deployment model—whether it’s on-premises, in the cloud, or a combination of the two. By building and scaling data architecture with reusable models and analytics, you can connect to all data sources, including future increases in how you collect, process, and analyze data, without additional infrastructure investments.