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A data warehouse is a data management system used primarily for business intelligence (BI) and analytics. Data warehouses store large amounts of historical data from a wide range of sources and make it available for queries and analysis. These systems are capable of storing large amounts of unstructured data, unlike traditional relational databases, making them ideal for big data projects and real-time data processing. The value of the data in a warehouse grows over time, as the historical record of customer, product, and business process metrics can be analyzed to identify trends and behaviors.
This article looks at 10 common enterprise use cases for data warehouses.
Data Warehouses for Tactical Reporting
Data warehouses are great for storing data for reporting purposes. Because they’re optimized for high-performance queries, they’re perfect for ad-hoc or on-demand operations and performance reporting. Data warehouses are often used to consolidate data from multiple source systems, providing a holistic, global view of how particular factors are interacting with different areas.
Because of their speed and built-in performance optimization, they’re ideal for grabbing information on the go or for urgent matters. They provide answers almost instantly instead of making you wait for hours or days to generate reports the traditional way. The reports are also more accurate, as they include information from across the organization rather than piecemeal, which can lead to silos or outdated information.
Data Warehouses for Big Data Integration
It’s estimated that about 80 percent of data generated by enterprises is unstructured—think emails, PDF documents, social media posts, and multimedia files. Unstructured data is notoriously difficult to house and use effectively, and most solutions are not comprehensive enough to integrate all of your organization’s sources of unstructured data effectively, which means you’ll either miss important insights or have subpar results when compared to what you could achieve with an enterprise-grade data warehouse.
Using a data warehouse, the flow of data is more trustworthy because it has been verified at least once by multiple parties through on-demand data queries. It will also let you automate big data analysis, which gives analysts more time to focus on deep dives into specific problems rather than trying to wrangle disparate tools and solutions together. By gathering both structured and unstructured data from multiple sources across your organization and storing it in a data warehouse, you can create a more holistic view of your business’s data for processing and analysis.
Data Warehouses for Natural Language Processing (NLP)
Many organizations are looking to improve customer service through natural language processing (NLP), which allows for quick analysis and provides opportunities for growth in the support, sales, and marketing departments.
A data warehouse can store the massive amounts of structured and unstructured data submitted by customers and clients, which can then be analyzed using NLP models. Adequate analysis of this data leads to a real-time response by organization employees or bots, such as live chat assistance or responses based on past interactions with customers.
This kind of data mining is difficult without a stable data storage system like a data warehouse. It’s important to collect all information about your customers—including email, telephone calls, and social media posts—so it can be properly categorized and filed according to what products or services they use most often. This is essential for constructing profiles about each specific client which make up their unique digital identities, where all related information is stored within one instance.
Data Warehouses for Auditing and Compliance
Auditing and compliance checks are both labor-intensive tasks. Auditors need to look over spreadsheets of data, while compliance officers need to read through legal documents—tedious exercises that make keeping up with regulator demands difficult.
Data warehouses store electronic copies of important documents, saving time and money and reducing the rate of error and enabling more accurate analysis of the results. A good data warehouse will also have a structured storage format, so all relevant records can be retrieved instantly. This makes auditing faster and easier, while also making compliance easier because companies can quickly prove they’re in line with current regulations.
Learn more about compliance regulations for data storage systems.
Data Warehouses for Data-Mining Analytics
Companies like Netflix base many business decisions on data-mining analytics, including which content is most popular, what promotional strategies work best, and which marketing campaigns resonate with subscribers. The data-mining analytics process stores massive amounts of data in a centralized location for easy analysis. Data warehouses are well-suited to data mining analytics, as they can store and make available the data necessary for insights as well as intellectual property and competitive intelligence.
Data Warehouses to Address Data Quality Issues
It’s important to promptly address errors and missed updates to avoid resulting in corrupt data or generating isolated silos, which can cause accuracy problems in analytics. One of data warehousing’s biggest benefits is that it enables business intelligence teams to act on errors in their databases.
Instead of manually correcting each error as it pops up, these tasks can be automated using extract, transform, load (ETL) tools like Informatica or Talend. For example, you could use SQL Server Integration Services (SSIS) to compare customer records with shipping records, and if a problem occurs—for instance, if one person receives multiple shipments from different addresses—you could fix it by adjusting an existing master record or creating a new one.
Data warehousing makes such fixes possible because it lets companies track and update data in large volumes over time, so errors don’t pile up and go unnoticed. And once a data warehouse is set up, IT departments can add functionality with minimal effort—no need to reinvent data systems when regulations change or when new uses arise for their data. By taking advantage of built-in data management features when necessary, IT professionals also spend less time trying to patch together ad hoc solutions.
Data Warehouses for RTDW Processing
Real-time data warehousing, or RTDW, refers to the instantaneous processing of all enterprise data for analysis as soon as it enters an organization’s information system. This effectively reduces or eliminates costly and time-consuming post-processing long data backlogs. Here are the major benefits of RTDW that help enterprises derive better business results:
- Instant decision-making support to line of business users and customer service personnel
- More accurate predictions and forecasts
- Better data governance and security with fewer updates and reconciliations
- Improved data quality through real-time validation, quality assurance, and error checking—data is continuously cleaned, updated, and validated
- Streamlined operations, which can help identify inefficiencies and improve process optimization
- Reduced costs through predictive analytics and automated diagnostic reporting
- Reduced manual processing errors through early detection and resolution
- Increased operational efficiency with advanced high-speed data retrieval
- Improved customer service and satisfaction through real-time responses to customer behavior and patterns
- Risk mitigation through faster issue-responses
- Reduced capital expenditure through efficient resource usage
- Augmented business agility and resiliency through reduced dependence on manual processing
Logistics and manufacturing are two industries where real-time data warehousing can have a big impact on operations. For example, a manufacturer may want to know about a faulty component as soon as it is installed to initiate a recall or initiate preventive measures, or logistics providers could analyze shipment data to better prepare for demand spikes and optimize routes.
Data Warehousing for Big Data Analysis
Organizations dealing with large volumes of data—internet-based businesses that process millions of credit card transactions every month, for example—need to manage all that information. Data Warehouses are specifically designed to deal with massive amounts of data quickly and reliably, which makes them an essential tool for analysis purposes.
Traditional data processing systems like relational databases simply can’t cope with such quantities of data. They also lack necessary features such as security and database indexing, which significantly increases latency times during both writing and reading operations.
Also read: Top Big Data Tools & Software 2021
Data Warehouses for Data-Driven Decision-Making
Data warehouse solutions make it possible to make critical business decisions based on new insights from your company’s historical data. You can then use your new knowledge to inform big-picture plans, such as where to focus marketing efforts or what products and services to develop.
For example, consider the University of St. Andrews, where student administrators relied heavily on data warehouse and reporting systems to generate insights into student data. Keeping data on more than 10,000 students created numerous problems with the school’s legacy systems. It implemented a hybrid architecture and approach to its system, allowing staff to analyze student data on-demand and implement the flexibility for future upgrades and developments.
Data Warehouses for Business Intelligence
For true end-to-end system visibility, enterprises need a BI platform that can act as a hub for all of their structured and unstructured data. An online transaction processing (OLTP) data warehouse is great for storing transactional data at high volumes, but not optimized for business intelligence—depending on how the OLTP and BI systems are designed, they may not even integrate. Alternatively, online analytical processing (OLAP) systems are optimized for fast data processing and analysis, enabling businesses to promptly and easily pull insights from large amounts of data, identifying patterns and trends in order to inform business decisions.
An OLAP data warehouse will provide better access to important information in real time and help simplify complex data queries by consolidating critical data in one place. If you already have an operational data store in place but want to go further with your big data strategy, then building out a scalable business intelligence platform is key to moving forward with information discovery efforts across an enterprise.
Bottom Line: Data Warehouses for Enterprises
Data warehousing allows businesses to understand past data performance to develop effective plans and provides historical information they can refer back to later when making important business decisions. Data warehouses are designed to store massive amounts of structured and unstructured data for analysis and business intelligence, providing a holistic and historical record and serving as the enterprise’s “single source of truth.” A successful data warehouse strategy helps businesses understand exactly where they stand today and set measurable benchmarks that can drive long-term growth.
Read next: Enterprise Data Storage Compliance Guide
