5 reasons your legacy data warehouse won’t cut it
Global Head of Product and Solutions Marketing, Data Analytics, Google Cloud
As we engage with enterprises across the globe, one thing is becoming clear: Today’s businesses are solving complex business problems that are data-intensive. But often, their data platform infrastructure is holding them back. Data platform architectures that were designed in the 1990s are not ready to solve business problems for 2020. We don’t have to tell you about the explosive data growth that’s going on for businesses around the world. If you’re managing data infrastructure today, you already know plenty about that data growth. Ever faster and larger data streams, global business needs, and tech-savvy users are all putting the pressure on IT teams to move faster, with more agility.
Despite all these changes, it is often the legacy, traditional data warehouse where most of the data analytics tasks take place, and they’re underprepared for those demands. When we talk to people working in IT today, we hear a lot about the constraints that come with operating legacy technology while trying to build a modern data strategy. Those legacy data warehouses likely aren’t cutting it anymore. Here’s why—and here’s what you can do about it.
1. Business agility is hard to achieve with legacy tools.
Business agility is the main goal as organizations move toward completely digital operations. Think of online banking, or retailers staying ahead of always-on e-commerce needs in a competitive environment. All these great, cutting-edge innovations reflect cultural and technical change, where flexibility is essential. A business has to be able to manage and analyze data quickly to understand how to better serve customers, and allow its internal teams to do their best work with the best data available.
We hear that lots of data warehouses running today are operating at 95% or 100%, maxing out what they can provide to the business. Whether it’s on-premises or an existing data warehouse infrastructure moved wholesale to cloud, those warehouses aren’t keeping up with all the data requests users have. Managing and preventing these issues can take up a lot of IT time, and the problems often compound over time. Hitting capacity limits slows down users and ties up database administrators too.
From a data infrastructure perspective, separating the compute and storage layers is essential to achieve business agility. When a data warehouse can handle your scalability needs and self-manage performance, that’s when you can really start being proactive.
2. Legacy data warehouses require a disproportionate degree of management.
Most of the reports and queries your business runs are probably time-sensitive, and that sense of urgency is only increasing as users and teams see the possibilities of data analytics. In our engagements with customers, we often observe that they are spending a majority of the time on systems engineering, so that only about 15% of the time is spent analyzing data. That’s a lot of time spent on maintenance work. Because legacy infrastructure is complex, we often hear that businesses continue to invest in hiring people to manage those outdated systems, even though they’re not advancing data strategy or agility.
To cut time on managing a data warehouse, it helps to automate the system engineering work away from the analytics work, like BigQuery enables. Once those functions are separated, the analytics work can take center stage and let users become less dependent on administrators. BigQuery also helps remove the user access issues that are common with legacy data warehouses. Once that happens, users can focus on building reports, exploring datasets, and sharing trusted results easily.
3. Legacy data warehouse costs make it harder to invest in strategy.
Like other on-prem systems, data warehouses adhere to the old-school model of paying for technology, with the associated hardware and licensing costs and ongoing systems engineering. This kind of inefficient architecture drives more inefficiency. When the business is moving toward becoming data-driven, they’ll continue to ask your team for more data. But responding to those needs means you’ll run out of money pretty quickly.
Cloud offers much more cost flexibility, meaning you’re not paying for, or managing, the entire underlying infrastructure stack. Of course, it’s possible to simply port an inefficient legacy architecture into the public cloud. To avoid that, we like to talk about total cost of ownership (TCO) for data warehouses, because it captures the full picture of how legacy technology costs and business agility aren’t matching up. Moving to BigQuery isn’t just moving to cloud—it’s moving to a new cost model, where you’re cutting out that underlying infrastructure and systems engineering. You can get more detail on cloud data warehouse TCO comparisons from ESG.
4. A legacy data warehouse can’t flexibly meet business needs.
While overnight data operations used to be the norm, the global opportunities for businesses mean that a data warehouse now has to load streaming and batch data while also supporting simultaneous queries. Hardware is the main constraint for legacy systems as they struggle to keep up.
Moving your existing architecture into the cloud usually means moving your existing issues into the cloud, and we hear from businesses that doing so still doesn’t allow for real-time streaming. That’s a key component for data analysts and users. Using a platform like BigQuery means you’re essentially moving your computational capabilities into the data warehouse itself, so it scales as more and more users are accessing analytics. Unlimited compute is a pretty good way to help your business become digital. Instead of playing catch-up with user requests, you can focus on developing new features. Cloud brings added security, too, with cloud data warehouses able to do things like automatically replicate, restore and back up data, and offer ways to classify and redact sensitive data.
5. Legacy data warehouses lack built-in, mature predictive analytics solutions.
Legacy data warehouses are usually struggling to keep up with daily data needs, like providing reports to departments like finance or sales. It can be hard to imagine having the time and resources to start doing predictive analytics when provisioning and compute limits are holding your teams back.
We hear from customers that many of them are tasked with simplifying infrastructure and adding modern capabilities like AI, ML and self-service analytics for business users. The best stories about digital transformation are those where the technology changes and business or cultural changes happen at the same time. One customer told us that because BigQuery uses a familiar SQL interface, they were actually able to shift the work of data analytics away from a small, overworked group of data scientists into the hands of many more workers. Doing so also eliminated a lot of the siloed data lakes that had sprung up as data scientists extracted data one project at a time into various repositories to train ML models.
These large-scale computational possibilities save time and overhead, but also let businesses explore new avenues of growth. AI and ML are already changing the face of industries like retail, where predictive analytics can provide forecasting and other tasks to help the business make better decisions. BigQuery lets you take on sophisticated machine learning tasks without moving data or using a third-party tool.
We designed BigQuery so that our engineers deploy the resources needed for you to scale. It means your focus can change entirely toward meeting the needs the business has put forth, and bringing a lot more flexibility. BigQuery is fully serverless and runs on underlying Google infrastructure, so it integrates with our ecosystem of data and analytics partner tools. This architecture means you’re continually getting the most up-to-date software stack—analytics that scale, real-time insights, and cutting-edge functionality that includes geospatial and machine learning right from the SQL interface.
Streamline your path to data warehouse modernization with BigQuery by learning about Google Cloud’s proven migration methodology and get started with your data warehouse by applying for our migration offer.