Many organizations have been putting off data warehouse modernization. So how do you know when it’s time to make the move?
In today’s challenging global economy, the business case for more timely analytics has become more urgent than ever. That’s prompting business and technology decision makers to assess whether their existing data warehouse platforms have all the qualities they need — such as performance, scalability, deployment flexibility, and low cost of ownership — to grow and excel.
Many businesses still rely on earlier, increasingly outdated, generations of data warehouse technologies that were not architected for the speed and volatility of today’s fast-changing markets.
In fact, 59% of organizations surveyed last year for Ocient’s Beyond Big Data Report report were looking to replace their traditional, monolithic data warehouses. The primary reason: those legacy systems were not comprehensive or flexible enough.
For many businesses it’s not a question of if they will modernize their data warehouses, but when. It’s not unusual for organizations to delay data warehouse modernization for a variety of reasons, including lack of budget, technical complexity, or conflicting priorities among their data-engineering teams. However, lack of action can result in “technical debt” — and the competitive disadvantages associated with relying on legacy systems that are past their prime.
How do you know when the time is right for this game-changing strategic step?
We have identified five telltale signs that your data warehouse may no longer be keeping up with the demands of today’s competitive business environment. If any of the following issues look familiar, it may be time to upgrade to a modern data warehouse that can deliver performance at scale — in the cloud or on premises — for a wide range of use cases.
Sign #1: Your data workloads are changing
Businesses are generating new and increasingly complex data types, such as arrays, tuples, matrices, lines, polygons, and geospatial data. In addition to this, machine-generated data is pouring in from Internet of Things devices and industrial environments.
If your data warehouse does not provide native support for this such multimodal data — or is incapable of doing so at petabyte scale — that’s a red flag that your system is not well suited to the data sets and workloads that are a growing priority for many organizations.
Each new data type represents an opportunity for actionable insights. Geospatial data, for example, could potentially be used to create personalized customer experiences or to develop products and services for the Internet of Things.
Built-in support for multimodal data has the added advantage of simplifying the enterprise tech stack by alleviating the need for special-purpose databases. Thus, the business case for a new data warehouse gets stronger with every new data type to be managed and analyzed.
Sign #2: Your data warehouse is no longer cost-effective
If your data warehouse has become a money pit, it’s time for a change. That’s true whether your company continues to invest in an on-premises legacy system or has moved to the cloud and lost control of spending due to consumption pricing. Ironically, such cost inefficiencies can actually force an organization to curtail data sharing, resulting in potential blind spots rather than business insights.
The good news is that industry experts forecast that IT spending in 2023 will increase by up to 6%. However, as TechCrunch points out, “CIOs will be smarter with allocation.”
When it comes to data warehousing, that means tech leaders will be looking to support a substantial increase in data volume, users, and workloads while staying within a tight budget.
They will be reluctant to sink funding into inefficient legacy systems that do not offer the latest features and deployment options, such as availability on multiple clouds, sub-second data ingestion, or the ability to build streamlined data pipelines with integrated ETL tools.
At the same time, CIOs and CTOs are becoming increasingly cautious about the consumption-based pricing of some cloud data warehouses. The consumption model may seem attractive early in your adoption but can unexpectedly exceed your budget as more users gain access to the cloud services, driving up queries and workloads.
Fortunately, it is possible to build and deploy a modern data warehouse that supports an exponential increase in data and users while keeping costs within budget. For example, some products are priced by number of CPU cores, not resources consumed, allowing for transparent and predictable costs whether on premises or in the cloud. The type of hardware used may also drive higher performance and thus lower costs.
Sign #3: The platform lacks technical innovation
Does your data warehouse offer the latest data-analysis capabilities and features? Large-scale joins, full-table scans, high-scale SQL optimization, support for semistructured data, and intradatabase machine learning represent the state-of-the-art in data warehousing.
Without these leading-edge technologies, it’s harder to create your own data-driven products and services. The lack of technical innovation inhibits business innovation.
If your organization is still dependent on a legacy data warehouse — typically monolithic systems that require considerable hands-on administration — you may be missing out on opportunities made possible by next-generation architectures, such as high-speed data ingestion and continuous analysis.
Sign #4: You’re not getting the support you need
One of the most exciting trends in data management is the automation of processes and platforms that previously required manual resources. Self-managed cloud services, auto-scaling serverless technologies, and storage optimization are some of the ways businesses are now able to reduce the pressure on IT teams, allowing them to focus on strategy and solutions.
However, advances in automation do not eliminate the need for technical support in implementation or during operation. Responsive support from the data warehouse provider is more important than ever as customers move quickly from planning to production.
Is your current data warehouse vendor providing this kind of high-touch experience? If not, that may be yet another indicator of the need to find one that does.
Sign #5: Deployment options are limited
Can your data warehouse run anywhere? Legacy data warehouses built for corporate data centers lack deployment flexibility. That means your business lacks flexibility, too.
At the same time, cloud-only data warehouses are limited in other ways. Some businesses may need to run their data warehouse on premises for reasons related to data governance or security. For them, a cloud-only solution isn’t viable.
There is a better way to go forward. Data warehouses with a modern, hyperscale architecture that can be deployed in the cloud or on premises offer the best of both worlds. They combine cloud benefits, such as resource elasticity and API integration, with the on-premises advantages of compliance and a high degree of control.
Deployment versatility also allows for improved cost management. Businesses can deploy the data warehouse in their choice of hyperscale clouds, helping to minimize the possibility of cloud lock-in. Alternatively, they may choose to run the data warehouse in their own data center, potentially at a lower cost of ownership.
The Time to Upgrade is Fast Approaching
With the amount of data being captured and stored doubling every 1.2 years, business and technology leaders recognize the need to modernize their data warehouses.
The most sought-after improvements cited in Ocient’s Beyond Big Data report were improved speed and performance (57%), flexibility and agility (55%), and improved access and integration (54%).
All of which is further evidence that the aging data warehouses of a generation ago can no longer keep up with the requirements of always-on, data-driven businesses. That’s likely to factor into investment decisions as CIOs determine how to get the most analytics bang from their data bucks.
There is a point of diminishing returns when it comes to continued investment in legacy data warehouses. For a growing number of organizations, that point is rapidly approaching — or is already here.