A data warehouse is designed to integrate information from multiple internal and external sources into one centralized system that can then be used to facilitate business decisions by analyzing the data contained within it. These systems are capable of storing large amounts of unstructured data, which traditional relational databases are not well-suited to handle, which makes them ideal for big data projects.
The data warehouse is a perfect place to centralize and store information about your company’s customers, products, and business process metrics. It can be used to identify trends and learn what drives success in various areas of your organization. Some examples include:
- Identifying profitable segments for marketing campaigns
- Finding out which product lines are seeing growth or decline
- Understanding customer patterns such as when they prefer to shop, how much they buy at a time, and what kind of payment methods they prefer.
Also read: Data Lake vs Data Warehouse: What is the Difference?
Data Warehouses and Digital Transformation
A data warehouse is designed to withstand large volumes of information, allowing it to remain efficient over time. As your business grows, your data storage needs will too, which is why a good data warehouse should grow right along with you. When it comes to data warehousing, getting started is easy, but making sure you do it right can be challenging. Here are 10 use cases that will help ensure that your data warehouse offers maximum value in today’s constantly changing environment.
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 reporting. Data warehouses are often used to consolidate data from multiple source systems to get a global view of data or see how particular factors are affecting different areas. They’re also ideal when you need to get information fast. Because of their speed and performance optimization, you can get answers almost instantly instead of waiting hours or days for reports to generate.
Big data integration
If you can’t rely on other solutions for big data integration, then a data warehouse might be a good option. The problem with other solutions is that they may not be accurate, timely, or even comprehensive enough to integrate all your sources of data effectively. This means you’ll either miss important data or have subpar results when compared to what you could achieve with an enterprise-grade solution like a data warehouse. A data warehouse will provide consistent data that you can trust because it has been verified at least once by multiple parties. 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.
Natural language processing (NLP)
Many organizations are looking to improve their customer service through NLP, which allows for quick analysis of data, but also provides opportunities for growth in support, sales, and marketing. A data warehouse can be used to store huge amounts of structured and unstructured data that can be analyzed using NLP platforms. Analysis of these insights 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 customer, whether it’s sent through email, telephone calls, social media posts, etc., so they 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.
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 — both tedious exercises that make it difficult for firms to keep up with regulators’ demands. Data warehouses store electronic copies of important documents, saving everyone time and money by reducing errors and enabling more accurate analysis of results. A good data warehouse will 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.
Also read: 7 Essential Compliance Regulations for Data Storage Systems
Data-mining analytics
Companies like Netflix base many of their business decisions on data-mining analytics, like which movies and shows are most popular, what promotional strategies work best, and which marketing messages resonate with subscribers. Data-mining analytics is a process that stores massive amounts of data in a centralized location for easy analysis. If you’re an entrepreneur who wants to build a business around data mining analytics, it may be worth considering setting up your data warehouse to store all that valuable information. It can be valuable not only for insight into your company’s operations, but also valuable for intellectual property, as well as a great source of competitive intelligence.
Address data quality issues
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, users can automate these tasks using ETL (extract, transform, load) tools like Informatica or Talend. For example, they can use SSIS (SQL Server Integration Services) to compare customer records with shipping records. If a problem occurs—for instance, if one person receives multiple shipments from different addresses—they can fix it easily 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. In other words, they needn’t reinvent their 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.
Real-time data warehousing
Real-time data warehousing refers to the instant processing of all enterprise data for analysis as soon as it enters an organization’s information system, thereby reducing or eliminating costly and time-consuming post-processing. Real-time data warehousing has 10 major benefits that help enterprises derive better business results.
- Minimizes operational overhead due to manual data extraction
- Provides instant decision-making support to line of business users and support personnel
- Improves governance and security of data due to real-time data integration and consolidation with fewer updates and reconciliations
- Facilitates improved data quality through real-time validation, quality assurance, and error checking
- Reduce costs by providing predictive analytics capabilities and automated diagnostic reporting
- Eliminates manual processing errors through early detection and resolution
- Increases operational efficiency with advanced high-speed data retrieval
- Increases customer satisfaction due to faster response time to business queries
- Reduces capital expenditure by utilizing hardware and software resources efficiently, and by enhancing competitiveness through flexibility
- Augments business agility and improves business resiliency through reduced dependence on manual processing.
In terms of industry, 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 so that they can initiate a recall or initiate preventive measures immediately. In another scenario, real-time data warehousing could enable logistics providers to analyze shipment data to better prepare for demand spikes and to optimize routes, thereby improving supply chain visibility and significantly reducing customer delivery time.
Data warehouse for big data analysis
Organizations dealing with large volumes of data, for example, Internet-based businesses that process millions of credit card transactions every month, need to implement data warehouses to be able to manage all that information. These data storage facilities 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, on top of that, 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
Enable data-driven decisions
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, Walmart used its data warehouses to test different methods for managing inventory in its U.S. and Canadian stores and discovered that demand for certain products was far greater in Canada than in the U.S. As a result, Walmart opened up new locations in Canada by 2010—and closed 154 locations in the U.S. by 2016.
For small businesses without access to expensive data-warehousing technology, determining similar market insights is still possible if you’re willing to put some time into research; surveys are often helpful for getting valuable information on consumer preferences and purchasing habits.
High-performance business intelligence platform
While an OLTP data warehouse is great for storing transactional data at high volumes, it’s not optimized for business intelligence. Depending on how your OLTP and BI systems are designed, they may not even be integrated. For true end-to-end system visibility, you need a business intelligence platform that can act as a hub for all of your organization’s structured and unstructured data. Not only will it provide better access to important information in real time, but it will also 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.
Why Should Businesses Care About Data Warehouses?
Data warehousing allows businesses to understand past data performance to develop effective plans; it provides companies with historical information they can refer back to later when making important business decisions. 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: Gaining a Competitive Edge Using BI and Data Management
