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I can still remember my first time working with a data warehouse. The year was 2013, and I was working on my first post-school data engagement with an IT software organization. We were going to build a sales-centric enterprise data warehouse that would extract, cleanse and integrate a variety of data into a single, large repository, transforming the way the business managed their sales lifecycle. This enabled their team to leverage data in all facets of the sales lifecycle and optimize their ability to close deals.

I was fascinated by this concept, and its value proposition was clear and powerful. I was hooked at that point and have subsequently spent the majority of my career working on data engineering, data warehousing, and business intelligence solutions.

Shortly after this initial engagement, I began learning about cloud data warehouses. Today, organizations use tools like Azure Synapse and Snowflake to manage massive volumes of data every day. But it took a while to get to this point. Here’s a look at how data warehousing solutions have evolved, and what to consider as you modernize your approach to data storage.

Why Data Warehouse Modernization Is So Important

For many years, regardless of the industry, company size, or the BI platform, data warehouse structure was essentially the same. At the core, there would be a separate relational database to house the data, typically leveraging dimensional design schemas. A nightly data integration process would be developed to extract data from the line of business applications to load the data. These two components would make up the backend of the data warehouse and take the most time and effort to implement. 

On the front end, there would be any number of business intelligence tools to give users direct access to slice and dice the data. This solution supported the operational and management reporting with respect to “what happened” types of business questions.

This was the typical data warehouse for many years, and it has served us well. However, new trends are causing it to break in several different ways, including but not limited to data growth, fast query expectations from users, non-relational/unstructured data and cloud-sourced data. Organizations are unable to meet the growing need to integrate and analyze a wide variety of data being generated from social, mobile and sensor data. Seventy-seven percent say that data intelligence is a major challenge. More importantly, these data warehouses struggle to answer the forward looking predictive questions necessary to run the business at the required levels of granularity or in a timely manner.

However, modern solutions, like cloud data warehouses can be designed to handle these new trends.

The Modern Data Warehouse Structure: What to Consider 

Data warehouse modernization can have a different meaning depending on the organization’s level of Business Intelligence (BI) maturity. Modernization is relative to the organization’s current capabilities and needs. Some organizations today are still struggling with basic reporting and often export data into Excel to organize, filter and analyze their data. Because Excel offers benefits in reporting, some organizations often fail to see the value of investing in BI. Others have very mature data warehouse capabilities with multiple data platforms, advanced reporting tools and sophisticated power users.

From Kenway’s experience, many organizations are expected to upgrade their data warehouses and some of their analytical tools over the next several years. This may require a multi-platform environment to handle both the traditional data warehouse reporting needs and to handle big data analytics. It also may require a transition to a cloud data warehouse solution. 

When thinking about modernizing your existing data warehouse, start by evaluating your existing reporting capabilities and revisit the original business drivers and assumptions. Start by asking the following questions to determine if you have a need to modernize your data warehouse:

• Does your environment quickly handle diverse data sources and a variety of subject areas?
• Can your organization handle excessive volumes of data (social, sensor, transaction, operational and analytical?)
• Do you support a multi-platform architecture to maximize scalability and performance?
• Have you leveraged new capabilities like data virtualization (cloud services) in addition to data integration?
• Are you using structures such as data lakes and NoSQL databases, or are you running only relational data mart/data warehouse structures?

Cloud Data Warehouses and Other Modern Storage Solutions 

Cloud data warehouses are now widely touted as the future of data warehousing. They enable organizations to keep up with ever-expanding amounts of data. Data professionals say that data volumes grow by 63% every month at their companies. Many organizations are already short on IT talent, and managing on-premise solutions becomes unwieldy when data volumes are growing that rapidly. With a cloud data warehouse, you can rely on a third-party to maintain the hardware and system updates for your database needs and allocate IT resources to other, business-critical tasks. 

Along with a cloud data warehouse, there are other new tools, techniques and data platforms available today that can be used to achieve data warehouse modernization:

• In-Database Analytics and Massive Parallel Processing (MPP) can allow organizations to query very large data volumes that traditional relational databases can’t handle well.
• Adding unstructured metrics to your data warehouse allows you to leverage data such as social, mobile, and emails to identify new consumer insights.
• Leveraging the Internet of Things (IOT) by integrating sensor-generated data into your manufacturing, supply chain or operations models helps provide real-time analytics.
• Implementing a data lake can store vast amounts of raw data that can be used to feed the traditional data warehouse, as well as support a “Sand Box” for data exploration.
• Extending your data warehouse to allow for Data Federation allows users to access data that is not stored in your actual data warehouse environment.

Modernize Your Database

In conclusion, traditional data warehouses were never designed to handle the volume, variety and velocity of today’s data centric applications. Therefore, many organizations will need a more modern data warehouse platform to address many emerging business and technology requirements.

Are you interested in learning more about how Kenway can help you modernize your organization’s data warehouse? Kenway’s experts can help. Connect with us today at info@kenwayconsulting.com for a consultation. 

Cloud Data Warehouse FAQs

What is a cloud data warehouse?

A cloud data warehouse is a cloud platform acting as a centralized data store and serving data for analytical use-cases. Cloud data warehouses sit adjacent to a broad toolbox of public cloud data services and enables integration and use of these services to deliver applied data use-cases.

What is the difference between a cloud warehouse and a data warehouse?

Whereas traditional data warehouses require organizations to deploy and maintain on-premise hardware and software, cloud warehouses don’t require any physical hardware. 

How does cloud data warehousing work?

With cloud data warehouses, third-party vendors manage all hardware and software updates. Data is stored in the cloud, and can be accessed from anywhere. When an organization needs to increase its storage capacity, it can simply upgrade its account with the vendor — there’s no need to add more on-premise hardware.

Is AWS a data warehouse?

AWS provides a wide variety of managed services, including data warehousing solutions.