4 Reasons Why Legacy Data Warehouses Are Not Enough

Legacy Data Warehouse

It’s understandable why legacy systems were used decades ago. With the slower pace and the amount of data, traditional on-premises systems like legacy data warehouses were able to handle what organizations needed. Those same legacy systems built in the 1990s are simply not able to handle the modern complexities of businesses in 2022. Yeah 

Furthermore, e-commerce and the rise of social media have changed the way consumers buy, research, and even interact with companies. In the end, organizations are gathering more data than ever before from multiple sources. 

Today’s modern data challenges are alleviated by modern tools and solutions. With the influx of data that businesses gather each day, legacy infrastructure is more of a curse than a blessing. Data warehouses are where most data analytics tasks take place. Meaning that traditional data warehouses are likely not able to keep up with the data needs of businesses today. Gartner’s research found that 57% of data and analytics leaders are investing in data warehouses. It’s time for organizations to understand the true value of data warehouse modernization.

Here are five reasons why legacy data warehouses are not enough in today’s modern marketplace.

1. Costs and Maintenance

As enterprises try to expand their usage of the legacy data warehouses and data volume grows, they are facing a tremendous challenge with high costs. Coupled with the fact that on-premises legacy data warehouses require server machines, storage disks, network accessories, and engineers who will manage and configure everything, organizations are looking at a hefty price tag. Not to mention license renewals and upgrades!

Modern data warehouses today are typically found on the cloud. The cloud offers much more cost flexibility, meaning you’re not paying for, or managing, the entire underlying infrastructure stack. 

In fact, Google Cloud’s serverless modern data warehouse, BigQuery, analyst firm ESG’s research found that there was a 52% higher Total Cost of Ownership (TCO)  for legacy on-prem data warehouses than BigQuery. 

With businesses becoming increasingly data-driven, legacy data warehouses and the cost of upkeep and maintenance aren’t worth it in comparison to modern data warehouses on the cloud.

2. Business Agility

Given today’s economic implications brought about by the Covid-19 pandemic, organizations have had to meet new standards and become more agile to suit the needs of consumers. The rapid influx of data from multiple sources in addition to the shift in adopting modern technologies to remain competitive requires a business to be flexible and agile in their ability to manage and analyze data quickly.  

Simply put, to match the pace of these new challenges, organizations need data storage options that can scale and grow with the business. Whether it’s on-premises or an existing data warehouse infrastructure moved wholesale to the cloud, those warehouses can become overwhelmed and slow with large data sets. 

With digital transformations accelerating across industries, modernizing a legacy data warehouse is a piece of the puzzle that can facilitate growth towards other modernization efforts. In fact, most cloud data platforms like Google Cloud are built with data warehouses at the heart of their efforts in assisting organizations to foster a culture of data. Those organizations recognize the importance of agility and being able to pivot as needed with the changing marketplace, leading to successful business ventures.

In fact, a recent Deloitte survey showed that “Organizations that reported having the strongest cultural orientation to data-driven insights and decision-making are twice as likely to have reported exceeding business goals in the past 12 months.”  

3. Built-In Predictive Analytics

Legacy data warehouses are struggling with daily data needs, so it can be hard to imagine having the time and resources to focus on predictive analytics projects. Especially when computing limitations are holding teams back.

Artificial intelligence (AI) and machine learning (ML) initiatives are already changing the landscape of industries like retail and healthcare. Predictive analytics can provide forecasting and other tasks to help the business make better decisions. 

Gartner predicts that by 2025, AI will be the top category driving infrastructure decisions, due to the maturation of the AI market, resulting in a tenfold growth in compute requirements. The utilization of AI and ML initiatives has become almost a requirement rather than a luxury in order to remain competitive and data-driven. 

With modern data warehouses such as Google Cloud’s BigQuery offering built-in predictive analytics functions, organizations can take on sophisticated ML tasks without moving data or using a third-party tool.

4. Data in Real-Time

Fresh, current data is what lends itself to accurate data analytics. Most organizations run reports and queries that are time-sensitive and require a sense of urgency to make quick decisions.  

For instance, a retail business in 2022 might want to generate supply-chain, customer, or pricing insights in real-time to possibly drive sales figures during a specific holiday season. With a modern data warehouse, these capabilities are attainable. 

IDC claims that by 2025, more than 25% of data created in the global datasphere will be real-time in nature. 

Legacy data warehouses struggle with effectively loading data, which is hindering data access and delaying realizing data-driven insights. By the time the data is able to be queried, it’s too late to make decisions based on the insights. Industry leaders rely on real-time data, which is not something that traditional data warehouses are able to provide. 

It’s Time to Modernize Your Legacy Data Warehouse

In today’s world, data IT teams are feeling the pressure to assist their teams to capitalize on the vast quantities of data gathered. As companies become more insights-driven, those struggling to realize the potential of a data warehouse are feeling the effects. 

Not only is it costly and time-consuming to maintain a legacy data warehouse, but the insights gathered are often not presented in real-time, leading to missed opportunities. Additionally, with AI and ML empowering predictive analytics and automation to free up resources, it’s hard to imagine business objectives without them. 

It is easier said than done to modernize a legacy data warehouse. Yet, those who are seeking a digital transformation don’t need to go at it alone. A serverless and fully-managed modern data warehouse like Google Cloud’s BigQuery is dynamic and there are experts who can help accelerate your journey while mitigating risk and reducing costs.

Pandera’s cloud specialists have you covered. From planning, implementation, to optimization, we work with organizations to strategically plan, execute, and manage solutions to help you leverage the advanced capabilities of modern cloud technologies.

Learn more about what Pandera can do for your data warehouse today!

Learn more about our Data Warehouse Modernization services.