An Enterprise Data Warehouse, or EDW, is a database or, in some cases, a collection of databases that house an enterprise’s business data in a centralized location. Business data can be collected and stored from many data sources. Usually, a business’s Customer Relationship Management (CRM), Enterprise Resource Planning (ERP), web services, and other processing systems will provide the bulk of the business data within the Enterprise Data Warehouse.
If your business wants to take advantage of predictive analytics and other HiTech advancements to make data-driven decisions to plan your enterprise strategies, you need to use an Enterprise Data Warehouse. In order to make the best strategic decisions, you need to be able to analyze all of your business data.
An Enterprise Data Warehouse gives you the ability to gather all of your enterprise data in one place, ensure that it is formatted correctly for predictive analytics, and use a business intelligence tool to analyze all of your business data at once.
Let’s gain a better understanding of Enterprise Data Warehouses. We’ll look at the architecture behind these data warehouses, the different types of EDWs, and the advantages of using these tools for data storage.
Enterprise Data Warehouse Architecture
There are a few different architectures an Enterprise Data Warehouse can follow. They vary in complexity based on the number of tiers they incorporate. The simplest EDW architecture has one tier, and the most complex data warehouses have three tiers. There are also two-tiered EDWs as well.
One-Tier Architecture
The single-tier data warehouse is the simplest data storage structure. In this model, your reporting tools are directly connected to your Enterprise Data Warehouse. The pros of this model are in the structure’s simplicity. The one-tier data warehouse is simple to set up and implement. However, simplicity can cause issues too.
If your business has a large amount of enterprise data, the one-tier architecture can cause performance issues. Since the reporting tools are directly connected to the data warehouse, whenever a query is made of the business data, the reporting tools have to go through every piece of information in your warehouse to get an answer. Most modern businesses have hundreds if not thousands of gigabytes of data.
Combing through massive datasets like this during every query significantly reduces performance and limits your ability to leverage business intelligence tools to make smarter business decisions. The single-tier data warehouse best serves businesses with small amounts of data, but in today’s digital world, how many businesses does that even apply to?
Two-Tier Architecture
We’ve seen how the one tier data warehouse architecture can cause performance issues. In order to mitigate the issues caused by directly connecting reporting tools to the EDW, the two-tier model introduces a data mart layer between the reporting tools and the data warehouse.
The data mart layer is actually composed of many smaller data marts that are specific clusters of information based on channels. For example, you would likely see a data mart for sales and another for marketing, HR, etc. The advantage to segmenting business data into data marts is better performance.
In a two-tier Enterprise Data Warehouse, when you want to make a query using your reporting tools, you no longer have to go through every single piece of enterprise data in your warehouse to find what you are looking for. If you are looking for sales information, you can use your reporting tools to search the sales data mart specifically.
Three-Tier Architecture
Finally, the three-tier data warehouse architecture offers the most complex yet, most powerful data structure. In the three-tier model, another layer is added between the reporting tools and the data mart. The added layer is OLAP (Online Analytical Processing).
The OLAP data layer consists of data cubes. These cubes are three-dimensional renderings of your business data, and they allow business intelligence tools, like Machine Learning, to perform predictive analysis tasks more thoroughly and efficiently.
The three-tier model is the most complicated to set up and implement. However, if you have a large amount of business data and you rely on predictive analytics to make strategic decisions, this Enterprise Data Warehouse architecture is the best choice for your enterprise.
The Different Types of EDWs
When it comes to data storage, businesses have two real choices:
- Cloud-based storage
- On-site storage
There are pros and cons to each approach. We’ll share some information and insights with you so that you can make the best decision for your business.
Cloud-Based Storage
Cloud apps and services are very popular with enterprises because they scale so easily. There are also smaller investment costs associated with cloud storage. Your business does not have to buy the hardware and software required to store and manage your enterprise data or take the time to set up or implement them. In the event that there is a disaster or damage at your site, cloud data is safe and easily recovered.
The main concerns with cloud-based data warehouse approaches are lack of access in the event you don’t have an Internet connection, data security, and performance latency. Cloud data storage is often very secure, but some businesses don’t like the idea of storing sensitive data where it could be potentially accessed by others.
On-Site Storage
Choosing to bring your Enterprise Data Warehouse on-site means you will not only have to buy your own hardware and software, but you’ll have to implement and maintain it over time. This requires a larger investment, and it hampers your ability to scale your storage resources. Still, with greater responsibility comes greater benefits.
If you have an on-site data warehouse, you get to choose the hardware, software, architecture, and more, network latency will be practically zero, meaning you will have an ultra-fast connection, and it is far easier to ensure and maintain the security of your sensitive business data.
An on-site approach may require more work and financial investment, but if you have a large amount of data that you need to access regularly, it probably makes more sense in the long run.
Advantages of Using an Enterprise Data Warehouse
We’ve taken the time to explore the different approaches to data warehousing, but we haven’t yet explained the benefits and advantages of utilizing an Enterprise Data Warehouse. These are the advantages of using a data warehouse:
- Standardized data
- Single data source
- Complete customer insights
- Empower your teams
- Less data manipulation
- Predictive analytics
Standardized Data
Business data is collected by multiple sources, and thus, it can come in a wide variety of formats. The advantage of using an Enterprise Data Warehouse is that when data is integrated into the warehouse, it is standardized using the extract, transform, load data pipeline. In order to take advantage of predictive analytics and business intelligence tools, you need standardized data.
Single Data Source
Data warehousing gives businesses the ability to pull data from a single source instead of checking multiple different systems and data sources. This is a big advantage to large, distributed organizations who rely on information from other parts of the organization to accomplish business goals. The advantage of having a unified data source when it comes to executing daily business operations is immense.
Complete Customer Insights
Enterprise Data Warehouses allow businesses to use their unified data to get a complete overview of their customer. Understanding the customer journey improves campaign performance, minimizes churn, and eventually drives revenue growth. When you understand your customers and how they interact with your business, you can use this information in app development to create better ways to engage your existing customers and attract new ones.
Empower Your Teams
Data warehouses benefit all business operations, not just the marketing and sales teams. For example, if you have a mobile app development team, they can use data into customer insights to design a better Customer Experience for your app. Enterprise data can be very valuable and empower your teams to make smarter, more strategic business decisions.
Less Data Manipulation
Strategic decision-making requires the most accurate possible data. When data is being pulled from multiple sources, it is easier for data to be manipulated inadvertently or be outdated. Using an Enterprise Data Warehouse ensures that accidental data manipulation is kept to a minimum and all data is up-to-date. Since the warehouse it is a single data source, any errors can be noticed and corrected by other members of the organization.
Predictive Analytics
You could use business intelligence tools without an Enterprise Data Warehouse, but it would be like making a decision without having all of the facts in front of you. The great advantage of a data warehouse is that it compiles all of your enterprise data in one place. This allows the predictive analytics of business intelligence tools to be more accurate and provide better strategic insights. When it comes to analysis, the more information you have, the better.
Final Thoughts
In the modern business world, comprehensive business data and predictive analytics are necessary to stay competitive. Using an Enterprise Data Warehouse can give you better insights into all facets of your business, help you make better strategic decisions, and ultimately, drive revenue growth.
If you’re unsure how to get started, talk to an enterprise app development partner. You don’t have to go it alone. Use the industry experience and technical expertise a development partner can offer you to further your business and achieve your goals.
