Defining The Data Warehouse: Practical Applications, Real-World Use-Cases, And Best Practices

We’re really beginning to experience another industrial revolution. That is, we’re actively entering into the ‘Age of Data.’ As you look at your own life, business, and world around you - you’ll quickly notice that so much of it is now connected in some way. And, soon, our society will become persistently connected as we spread connectivity even further across the globe.

A recent report from IDC indicates these key trends around data:

  • The evolution of data from business background to life-critical. Once siloed, remote, inaccessible, and mostly underutilized, data has become essential to our society and our individual lives. In fact, IDC estimates that by 2025, nearly 20% of the data in the global datasphere will be critical to our daily lives and nearly 10% of that will be hypercritical.
  • Embedded systems and the Internet of Things (IoT). As standalone analog devices give way to connected digital devices, the latter will generate vast amounts of data that will, in turn, allow us the chance to refine and improve our systems and processes in previously unimagined ways. Big Data and metadata (data about data) will eventually touch nearly every aspect of our lives—with profound consequences. By 2025, an average connected person anywhere in the world will interact with connected devices nearly 4,800 times per day—basically one interaction every 18 seconds.
  • Mobile and real-time data. Increasingly, data will need to be instantly available whenever and wherever anyone needs it. Industries around the world are undergoing "digital transformation" motivated by these requirements. By 2025, more than a quarter of data created in the global datasphere will be real time in nature, and real-time IoT data will make up more than 95% of this.

That being said, it’s important to understand how you can gather, quantify, and actually analyze this information. Coupled with solutions around data analytics and big data processing, data warehousing allows you to take valuable information to an entirely new level. From there, powerful data warehouse solutions help you create data visualization to make better decisions around your business and the market.

But, we’re getting a bit ahead of ourselves. Let’s define data warehousing, look at some use-cases, and discuss a few best practices.

  • What is a data warehouse? At a very high level, a data warehouse is a system that pulls together data from many different sources within an organization for reporting and analysis. From there, the reports created from complex queries within a data warehouse are used to improve business efficiency, make better decisions, and even introduce competitive advantages. It’s important to note that a data warehouse is definitely different than a traditional database. Sure, data warehouses and databases are both relational data systems, but they were definitely built to serve different purposes. A data warehouse is built to store large quantities of historical data and enable fast, complex queries across all the data, typically using Online Analytical Processing (OLAP). A database was built to store current transactions and enable fast access to specific transactions for ongoing business processes, known as Online Transaction Processing (OLTP).

So, data warehousing allows you to aggregate data, from various sources. This data, typically structured, can come from Online Transaction Processing (OLTP) data such as invoices and financial transactions, Enterprise Resource Planning (ERP) data, and Customer Relationship Management (CRM) data. Finally, data warehousing focuses on data relevant for business analysis, organizes and optimizes it to enable efficient analysis.

  • How are data warehouses used?  Unlike databases and other systems which simply ‘store’ data, data warehousing takes an entirely different approach. Let me give you a few examples and uses. Data warehouses normally use a denormalized data structure, which uses fewer tables because it groups data and doesn’t exclude data redundancies. Denormalization offers better performance when reading data for analytical purposes. On that note, data warehouses are used for business analysis, data and market analytics, and business reporting. Data warehouses typically store historical data by integrating copies of transaction data from disparate sources. Data warehouses can also use real-time data feeds for reports that use the most current, integrated information.

Here’s the other cool part when it comes to use-cases, the structure of data warehouses makes analytical queries much simpler to perform. No advanced knowledge of database applications is required. Analytics in data warehouses is dynamic, meaning it takes into account data that changes over time.

Finally, the cloud. While a traditional data warehouse implementation can sometimes be a very expensive project, SaaS solutions are taking data warehousing to a new level. New cloud-based tools allow enterprises to setup a data warehouse in days, with no upfront investment, and with much greater scalability, storage and query performance.

  • A few data warehousing best practices. First of all, working with data can be a complicated and stressful process. However, that certainly doesn’t need to be the case. Working with great partners can help you establish a baseline around your own data requirements so that you can design a data warehouse that works for you. That being said, when you look at a data warehouse, know that, traditionally. data warehouses were built using a three-tier architecture:
    • Bottom tier—database server used to extract data from multiple sources
    • Middle Tier—OLAP server, which transforms data to enable analysis and complex queries
    • Top Tier—tools used for high-level data analysis, querying, reporting, and data mining

So, when creating your own data warehousing architecture, follow these three tiers to help identify data points, how you'll analyse them, and what the visualization will look like.

From there, data warehouses are usually structured using one of the following models:

  • Virtual data warehouse—a set of separate databases, which can be queried together, forming one virtual data warehouse.
  • Data mart—small data warehouses set up for business-line specific reporting and analysis. An organization's data marts together comprise the organization's data warehouse.
  • Enterprise data warehouse (EDW)—a large data warehouse holding aggregated data that spans the entire organization.
  • Cloud-based data warehouse—imagine everything you need from a data warehouse, but hosted in the cloud. Cloud-based data warehouse architectures can typically perform complex analytical queries much faster because they are massively parallel processing (MPP).

As you take this all in, remember the one big point I made earlier in the blog. You don’t need to do this all alone. Good partners can help you establish a date baseline and really understand the type of data warehouse architecture you require. From there, you really begin to unleash the power of data as you analyze vast amounts of information and help visualize it for your business.

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