5 weaknesses of legacy data warehouses

Today’s businesses are solving complex business problems that are data-intensive. Unfortunately, their data platform infrastructure is holding them back. Those legacy data warehouses likely aren’t cutting it anymore. Here’s why and what you can do about it.  

1. Business agility is hard to achieve with legacy tools

Business agility is the main goal as organisations 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 analyse data quickly to understand how to better serve customers and allow its internal teams to do their best work with the best data available.

A lot of data warehouses running today are operating at full load, 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 slow 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 warehouse requires 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. Businesses are spending a majority of the time on systems engineering so that only about 15% of the time is spent analysing 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legacy infrastructure is complex, we often hear that businesses continue to invest in hiring people to manage those outdated systems, even though they’re not advancing data strategy or agility.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, as BigQuery enables. Once those functions are separated, the analytics work can take centre 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. 

The 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. Moving to BigQuery isn’t just moving to the cloud. It’s moving to a new cost model, where you’re cutting out that underlying infrastructure and systems engineering. 

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 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 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.

Since BigQuery uses a familiar SQL interface, businesses 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 like Singapore Press Holdings (SPH) 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.