It's no longer enough to just collect and analyze data. Today, organizations must be equipped with the intelligence to make strategic decisions.
Enterprise analytics is a powerful tool that helps business leaders access, organize, and use all forms of enterprise-wide data—usually from multiple sources—to gain the insights they need for data-driven decision making. This cutting-edge technology provides new opportunities for organizations to quickly process large data sets and uncover valuable trends.
Understanding what enterprise analytics is, how it works, and its benefits can help you stay ahead of the competition. Keep reading to find those answers and see how enterprise analytics could benefit your business.
What is enterprise analytics?
Enterprise analytics is the practice of using data to gain insights into business operations and make informed decisions. It involves collecting, processing, analyzing, and visualizing data from various sources to uncover patterns and trends that allow users to make better decisions.
The influx of available data and the need for increased agility has made enterprise analytics an essential component of modern business operations. Enterprise analytics aims to identify patterns, trends, and correlations within these data sets to inform decision-making and optimize performance. Today, this data is vital to ensuring businesses can stay ahead of the competition.
The benefits of enterprise analytics
Improved decision making
Enterprise analytics provides organizations with real-time insights into their operations, enabling them to make data-driven decisions. This approach eliminates guesswork and improves decision-making processes, leading to better outcomes.
Increased efficiency and productivity
By analyzing data, organizations can identify inefficiencies and bottlenecks in their operations and processes. They can then take steps to eliminate these inefficiencies, increasing productivity and efficiency.
Better resource allocation
Enterprise analytics enables organizations to optimize their resource allocation by identifying areas where resources are under and over-utilized. This approach ensures that your business is making the most out of the allocated resources, eliminating waste and repetition
Enhanced customer experience
Customer analytics, including feedback and behavior, gives organizations insights into their customers' needs, preferences, and can even help predict future behavior. Businesses can use these insights to improve the customer experience—increasing customer satisfaction and loyalty.
Enterprise analytics industry use case examples
Example 1: Retail industry
In the retail industry, enterprise retail analytics can be used to analyze customer behavior, optimize inventory management, and improve marketing campaigns. For example, a retailer can analyze sales data from an enterprise data management system to identify which products are selling well and which are not. They can then adjust their inventory levels to ensure that popular products are always available—avoiding rising stockout costs. Additionally, retailers can use data analytics tools to analyze customer behavior, such as browsing and purchase history, to tailor their marketing campaigns and promotions towards specific customer segments.
Bill Schmarzo, Customer Advocate for Data Management Innovation at Dell Technologies, applies enterprise analytics using the concept of nanoeconomics. Hear him explain this idea and how he uses data to uncover customer intent by watching the video below:
Example 2: Healthcare industry
In the healthcare industry, enterprise healthcare analytics can be used to improve patient outcomes, reduce costs, and optimize resource allocation. For example, a hospital can use data analytics to identify patterns in patient data and predict which patients are at risk of developing complications. They can then take proactive measures to prevent these complications, leading to better patient outcomes and reduced healthcare costs. Additionally, healthcare providers can use healthcare analytics to optimize resource allocation, such as staff scheduling and equipment usage, to improve efficiency and reduce costs.
Wellthy is a technology company that helps people navigate the logistic and administrative tasks associated with caregiving. By using self-service analytics, they’ve empowered anyone on their care team to answer questions with data. This access to enterprise analytics allows Wellthy to support more customers and provide a higher level of care—with less demand on their data team.
Example 3: Financial services industry
In the financial services industry, enterprise financial analytics can be used to identify fraud, manage risk, and improve sales forecasts. For example, a bank can use data analytics to identify fraudulent transactions and prevent them from occurring in the future. Additionally, financial organizations can use data analytics to manage risk by analyzing market trends and predicting potential risks.
Finally, financial organizations can use data analytics to improve the customer experience by tailoring their products and services to specific customer segments based on their financial behavior and preferences. That is the case for Northmill, a Nordic based neobank focused on helping people improve their financial life. By using analytics to identify precisely where customers dropped out during the onboarding process, they’ve been able to create initiatives that improved conversion rates by 30 percent.
Key features to look for in an enterprise analytics platform
When it comes to choosing an enterprise analytics platform, it can be overwhelming to sift through the various options and features available. However, there are a few key features that businesses should prioritize to ensure they are selecting the best platform for their needs.
Data integration and management
The ability to integrate and manage data from various sources is crucial for any enterprise analytics platform. This includes support for different types of data sources such as databases, spreadsheets, and cloud-based applications. Additionally, the platform should offer data processing and transformation capabilities, allowing users to clean and transform data into a usable format. Finally, data quality and data governance features are important to ensure data reliability and consistency.
Self-service analysis and reporting capabilities
As with any data project, the purpose of enterprise analytics is to improve decision making and deliver business value. This value is exponentially increased when every kind of user, including business and nontechnical users, can engage directly with data to ask and answer their own questions. That’s why it’s essential to look for a platform that has robust self-service specifically for business users, not just data teams. Look for platforms that go beyond simply drag and drop interfaces to offer interactive data visualization with unlimited drilling, true natural language processing so users can chat with their data, and a direct connection to your cloud data platform so you only conduct real-time analysis on the freshest data possible. Companies like CVS, Disney, and Verizon are seeing 289% return on investments when implementing self-service analytics platforms like ThoughtSpot.
Security and compliance
Ensuring the security and compliance of your data is a top priority for any business. Look for enterprise analytics platforms that offer access controls to ensure that only authorized users can access sensitive data. Data encryption is also important for protecting data at rest and in transit. Finally, it's important to choose a platform that is compliant with relevant regulations such as GDPR or HIPAA.
Scalability and flexibility
As businesses grow, their analytics needs will inevitably change. Therefore, it's important to choose a platform that can scale alongside your business. Look for platforms that can handle large datasets and offer support for both cloud-based and on-premise deployment. Integration with other tools and platforms is also important for ensuring flexibility and ease of use.
The future of enterprise analytics
The future of enterprise analytics will be transformed by the increasing adoption of machine learning and artificial intelligence (AI) technologies. These technologies will enable enterprises to analyze vast amounts of data more quickly and accurately, providing valuable insights that can inform decision-making and improve business outcomes. By automating data analysis, enterprises will be able to make faster and more informed decisions, leading to increased efficiency, productivity, and profitability.
Machine learning and AI will also enable enterprises to personalize their offerings and experiences, improving customer satisfaction and loyalty. By analyzing customer data to identify patterns and preferences, enterprises can tailor their products and services to better meet customer needs. Additionally, machine learning and AI will help enterprises predict future trends and outcomes so they can make proactive decisions and allocate resources more effectively. Overall, machine learning and AI will revolutionize the field of enterprise analytics, enabling organizations to stay ahead of the competition and succeed in an increasingly data-driven world.
Make enterprise analytics the backbone of your strategy
With enterprise analytics you can unlock powerful insights that ensure your organization remains agile in a rapidly shifting market. To get started on your enterprise analytics journey, consider some key features such as self-service, real-time performance capabilities, and AI-Powered Analytics. Additionally, it’s important to make sure the analytics platform of your choice is able to seamlessly integrate with your current system. ThoughtSpot is an advanced cloud analytics solution that offers these features and more—all in one platform. With simple deployment and setup, you can immediately access powerful insights and empower your entire organization to join in on the value that enterprise analytics provides. So take action today, and sign up for a free trial to see how ThoughtSpot can help revolutionize your analysis process while improving your bottom line!
