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Data warehouse services are an essential tool for organizations to drive business performance. The data warehouse offers a centralized source for analysis and reporting across the enterprise, making it possible to get answers to difficult questions in real time. However, many data warehouses fail to deliver on their potential because they are not designed with a clear purpose in mind from the start. As a result, many data warehouse implementations fail to deliver their expected value. For this reason, we’ve compiled this list of 10 best practices when implementing a data warehouse so that you can optimize your ROI and see measurable results from your investment.
Establish a Clear Vision and Understanding of Value
Before you start building anything, it’s crucial to understand the value of the data warehouse for your organization. At the most basic level, a data warehouse is a centralized source for storing data that can be easily accessed for reporting purposes. But why does your organization need a data warehouse, and for what purpose? It’s important to understand both the short- and long-term goals for building and utilizing a data warehouse so that you can ensure it’s meeting your needs. Depending on the type of data warehouse you’re building, there could be a number of additional benefits. For example, some data warehouses exist primarily to support machine learning or artificial intelligence (AI) initiatives. On the other hand, a data warehouse that’s designed to support reporting and analytics may be able to provide more immediate value.
Define the Scope and Limitations
As with any project, it’s critical to understand the scope and limitations of your data warehouse implementation. This will not only help you determine how much it will cost to build, but will also help you determine which data is appropriate for storage. For example, you may want to consider which data sources you want to keep in the warehouse and which ones you want to keep in their original location. If you’re implementing an end-to-end data warehouse, you may decide to move certain data sources from one location to another. Or, you may decide to keep certain data sources in their original location, but link them to the data in the data warehouse.
Ensure Data Quality and Steward Actions
One of the biggest challenges with data warehouses is ensuring the quality of the data. In fact, many data warehouses fail because the data is not accurate or current. In order to ensure the data is accurate, you must put processes and controls in place to monitor and correct the data. This means you’ll want to work with your data governance team to develop processes around how and when data is cleansed, how often it is reconciled, and how it is audited. It’s also important to develop processes around how data is managed and what happens when data is deleted. For example, you’ll want to decide whether or not you’re going to retain deleted data in the warehouse. This could help you understand how the data is being used, but it may also be a security risk.
Leverage Machine Learning
If your data warehouse is designed to support AI initiatives, it’s crucial to understand how machine learning works and how you can leverage it with your data warehouse. Essentially, machine learning uses algorithms to explore data and make predictions based on patterns it finds. For example, if your data warehouse has information about past sales and what factors influenced those sales, AI can explore that data and make predictions about future sales based on those patterns. In order to leverage machine learning with your data warehouse, you’ll need to store the data in a way that it can be explored and studied. This means data must be normalized, organized, and structured in a way that it can be easily understood and used by AI. You should also be sure to include the right metadata, like timestamps, to make sure the data is easily accessible.
Utilize Real-time Data Piping
New technologies, like serverless architectures, enable you to streamline data transfer and get real-time data into your data warehouse. This means you no longer have to worry about transferring large amounts of data at once, but can instead work in real-time to capture data as it is created. This is especially useful when it comes to storing data from IoT devices, like sensors and smart meters, because you don’t want to be responsible for transferring massive amounts of data at once. Instead, you can use a streaming architecture to receive the data in real-time and store it in your data warehouse. This gives you the ability to explore trends and make predictions based on current data and the rate at which it is collected.
Carefully Select Which Data to Store in the Warehouse
Even though you want to store as much data in your data warehouse as possible, it’s important to carefully evaluate the data you’re storing. For example, you may initially want to store every single data source in your organization, but you’ll quickly realize that it’s impossible to manage and explore that data. Instead, you’ll want to explore your data and determine which sources are most critical to the business. You can then use that data to build your data warehouse, ensuring that you have the data you need to run your business. You’ll also want to consider how frequently data is updated and how current it is when determining whether or not it needs to be stored in the data warehouse. For example, you may want to store data from your financial system, but you only want to store data from the last month or quarter.
Use the Data to Improve User Experience
Put simply, your data warehouse can be used to improve the user experience across the organization. For example, if your sales team uses a sales pipeline that’s powered by data in the data warehouse, they can get real-time visualizations and updates based on the latest data in the warehouse. This kind of functionality can be extended to other areas, like marketing and customer service, to provide real-time updates and insights. You can also use the data in the data warehouse to help drive loyalty and retention by providing personalized experiences for customers. For example, if your data warehouse has information about each customer’s purchase history, you can use that information to provide targeted promotions and recommendations.
Simply put, a data warehouse is an essential tool for organizations to drive business performance. The data warehouse offers a centralized source for analysis and reporting across the enterprise, making it possible to get answers to difficult questions in real time. However, many data warehouses fail to deliver on their potential because they are not designed with a clear purpose in mind from the start. As a result, many data warehouse implementations fail to deliver their expected value. For this reason, we’ve compiled this list of 10 best practices when implementing a data warehouse so that you can optimize your ROI and see measurable results from your investment.
Author: Muthamilselvan is a Team Lead in Digital Marketing and is passionate about Online Marketing and content syndication. He believes in action rather than words. Have 7 years of hands-on experience working with different organizations, Digital Marketing Agencies, and IT Firms. Helped increase online visibility and sales/leads over the years consistently with extensive and updated knowledge of SEO. Have worked on both Service based and product-oriented websites.