Enterprise Analytics

Enterprise analytics uses data and predictive modeling to help organizations make business decisions

What is enterprise analytics?

Enterprise analytics is the practice of leveraging data from various sources and systems, both external and internal, to gain insights that improve customer engagement, sales and marketing strategy, product direction, operational risk, and other key business areas.


The goal is to optimize critical business functions by identifying successes, uncovering areas needing attention, and laying out strategies and recommendations for what to do next.


Enterprise analytics is not a single technology or platform. It typically involves many parts and requires the right techniques, approaches, and technologies to get it right. The process constantly evolves as you gain insights and implement optimizations, so your solutions must be flexible enough to change and grow with the business.

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Why is enterprise analytics important?

By adopting enterprise analytics, organizations gain awareness and insights driven by real-time and historical data, helping them run their businesses more efficiently and effectively.


Enterprise analytics provides value for all aspects of the business because it encompasses data from many sources, including customer support systems, CRM, ERP, content repositories, HR systems, and financial systems. When augmented with external sources such as social media, news, weather, and online docs, analysis becomes even more contextual and insightful.


The adage “you can’t fix what you don’t know is broken” is especially true in a large enterprise. The sheer size of the organization and its many layers contribute to information silos that stifle productivity and hinder effective customer engagement. When data access, data sharing, and general insight are all lacking, inefficient processes and poor practices often go unnoticed and are never addressed.


Enterprise analytics seeks to remove these barriers to improvement by uncovering areas that need attention and providing a prescriptive path to correct them.

Enterprise analytics use cases

Enterprise analytics can help optimize nearly every part of the business. Here are just a few examples of how it works:

Customer engagement

When engaging customers, it’s critical to provide a personal experience that makes them feel respected and important. Failure to do so risks dissatisfaction and churn. By connecting data from various customer-facing systems and providing real-time insight from the combined information, an organization can deliver the personalized experience customers expect.


For example, consider a cable customer who has used multiple avenues to communicate an issue with their provider. First they use online chat. Then they send an email to their local office. Then they call a support center. If the customer has to start each conversation from scratch because the different channels are unaware of their previous communications, then the customer is likely to start looking for a new cable provider with better customer service.


Enterprise analytics ensures that the data between systems running the website chat, field offices, and regional call centers is shared in real time so that agents at each channel know of previous interactions and greet the customer with knowledge of their issue and recommendations for solving it. The difference between each experience is the difference between a lost customer and a loyal one.

Operations optimization

Business operations such as finance, production, supply chain, inventory, and asset management are inherently complex because they all involve many systems and a wide variety of data. Each operation can benefit from insight into the effectiveness of their processes, especially when augmented with prescriptive recommendations on improving them.


For example, in supply chain management, timely information on potential disruptions is paramount to maintaining tight timeframes and minimizing costs. Enterprise analytics helps by monitoring key systems in the chain such as shipping ledgers, carrier manifests, port availability, drayage fees, and wait times, and by providing recommendations on the best, most cost-effective supply logistics.

Fraud detection

Financial systems are constantly at risk of fraud, which, if left unchecked, not only costs the organization money but also threatens its financial integrity.


Enterprise analytics can assess all financial systems and transactions across the organization in fine-grained detail, using advanced algorithms to detect evidence of potential fraud and stopping it before it can cause harm.

Benefits of enterprise analytics

Enterprise analytics can have an overwhelmingly positive impact on organizations. Benefits include:

Better decision-making

You make better choices when your information comes faster, has added context, and is augmented with recommendations.


Increased productivity

Optimizing processes and tasks helps you produce higher-quality work in less time.


Lower costs

You increase cost-effectiveness by identifying and eliminating needless, wasteful processes and tasks.


Loyal customers

You can satisfy and retain customers by enabling a more personalized experience and helping produce better products and services.


Challenges of enterprise analytics

Because it involves large volumes of high-velocity data from disparate systems, enterprise analytics is inherently complex and involves numerous steps. Successful implementations, therefore, require careful planning and a rock-solid strategy. It’s critical to carefully map out a strategic direction and detailed plans for every step.


Another challenge is dealing with various types of data from the source systems. There’s structured data from legacy systems, semi-structured data from modern systems, and unstructured data, such as natural language text, from media systems. To properly unite and analyze these different data types, you must first contend with the differences. The JSON data format is ideally suited for this because it can accommodate various types of data.


Also, note that users of mobile and edge applications produce a vast volume of data at the edge. This means you must consider technical options for capturing, synchronizing, and analyzing data at the edge as well as in the cloud.

Enterprise analytics techniques

Enterprise analytics typically employs three distinct types of processing:

  • Descriptive analytics – The analysis of key performance indicators (KPIs) using historic and current data to track business performance over time
  • Predictive analytics – The evaluation of trends in data applied to make predictions about likely future outcomes
  • Prescriptive analytics – Recommendations on best actions under specific business conditions


You can use these analytics techniques in combination to achieve the desired benefits.


For example, descriptive analytics can uncover underperforming customer engagement efforts. Predictive analytics can assess a customer’s likelihood to respond to a given offer to improve engagement. Prescriptive analytics can recommend the ideal demographics to present such offers to.

Couchbase Capella and enterprise analytics

Couchbase Capella™ is a cloud-native, distributed NoSQL document Database-as-a-Service (DBaaS) that combines multiple database models into a single technology. Capabilities include:


  • Processing of key-value data in memory for hyper-fast responsiveness
  • Distributed storage of JSON document data for flexibility and resilience
  • Mobile and edge computing support
  • SQL query support
  • Full-text search
  • Eventing


Capella fits into the enterprise analytics landscape because it provides the scale to handle massive amounts of data, the speed to enable real-time insight, and the flexibility to process a wide variety of types and formats of data thanks to its JSON-based storage.


Capella also offers the Capella columnar service, an analytics capability (currently in private preview) that includes a columnar store for ultra-fast processing of queries on large volumes of data, extensive integration with the Capella DBaaS, and capabilities for ingesting data from other databases such as MongoDB™, DynamoDB, and Amazon S3 in real time. The Capella columnar service allows for real-time analysis of operational data without the need for ETL.


Because of its scale, flexibility, diverse features, JSON storage, and other capabilities, Capella is a great option for data storage and processing within your enterprise analytics implementation.


By adopting enterprise analytics, organizations can reduce costs, improve business performance, better engage customers, and compete more effectively through data-driven insights and proactive recommendations.

See how Domino’s uses Couchbase for real-time analytics to improve customer marketing.

Sign up for the Capella columnar service private preview.


Try out Couchbase Capella for free.


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