By Kevin Ondriezek, Director of Solutions Architecture at Ocient
Exponential data growth has been like a Big Data Godzilla looming on the horizon, threatening to smash conventional tech stacks for the last decade. Yet the last decade has also seen most organizations find creative ways to keep their legacy systems alive — and keep Godzilla at a distance. But as we enter the hyperscale era, Big Data is finally stomping through tech stacks and the runway on legacy tech is coming to an end. Case in point: Ocient’s 2023 Beyond Big Data survey showed two in three companies are now actively looking to switch data warehouse providers, seeking modern solutions fit for hyperscale demands.
But tech stack modernization is not as simple as upgrading to the “new model.” The nascent hyperscale era has seen an explosion of vendors offering solutions that claim to meet the modern enterprise’s hyperscale data processing and analytics needs.
How do you make the right choice? This blog will provide nine key questions to help you evaluate data warehouse solutions — and the vendors behind them.
The connection is now clear: analyze more data –> drive more revenue
Our 2023 Beyond Big Data survey showed virtually all executives agree that the volume, variety, and velocity of data are growing exponentially: 97% of the business leaders surveyed expect their data to grow fast or very fast in the next 1-5 years. What does “fast” mean in numbers? The “global datasphere” (all the data in the world) is growing by around 23% annually, and will go from around 64 zettabytes (that’s one billion terabytes) in 2023 up to 612 zettabytes by 2025, according to IDC. But IDC says the “enterprise datasphere” (all the business data in the world) grew by nearly double that rate in the last several years.
More to the point, business leaders recognize that their ability to harness all that data is critical to revenue growth. That means expanding the volume of data analytics: a full 98% are prioritizing increasing the amount of data analyzed over the next 1-3 years. Increasing data accessibility (the amount of data available for analytics) by just 10% will drive an estimated $65 million increased in net income for the average Fortune 1000 company, according to Forrester.
Businesses also need to accelerate the speed of data analytics: 78% say they see faster data analytics driving revenue growth. The more quickly companies can analyze data, the better they can respond to (or even anticipate) changes in the marketplace.
Legacy data warehouses can’t keep up
This year presented a tipping point in the tech modernization story: For years, companies have been finding ways to keep legacy systems alive, staving off the need for modernization. But the volume of data — and the business value in analyzing it at speed and scale — crossed a threshold in the last 12 months, pushing the majority of tech leaders (67%) in our Beyond Big Data Survey to say they’re actively looking to modernize their data warehouse solutions.
In fact, 1 in 3 companies say they’ve already started a “rip and replace.” And another 55% say they’re planning to start their data warehouse modernization within the next 6-12 months.
The reasons are all variations on the same theme — legacy tech just can’t keep up:
9 Questions for Choosing a Modern Data Warehouse Partner
1. Why are you switching?
You need to carefully define your motivations for switching data warehouse solutions/providers in order to outline your decision-making criteria. The 2023 Beyond Big Data survey showed three common goals behind tech stack modernization:
- Maintaining security and compliance as data volume and needs grow (63%)
- Scaling data management and analysis in a cost-effective manner (49%)
- Streamlining the number of systems and ecosystems under management (48%)
2. What are your performance requirements?
Once you’ve established your high-level objectives, it’s time to get into the specific performance requirements that will define a successful solution for your organization. Requirements should include things like:
- Query response times
- Concurrent user support
- Data ingestion speeds
3. What data storage and querying capabilities do you need?
Among the growing number of hyperscale solution providers, you’ll find vendors coming at the problem from a variety of angles, offering different storage and querying options. There’s no absolute “best” approach, so it’s important to consider how a vendor’s offering aligns with your specific requirements. For example:
- Support for structured and unstructured data
- Real-time analytics capabilities
- Advanced analytics tools like machine learning and artificial intelligence (AI)
4. What is your data migration plan?
There’s a reason most organizations have resisted making a proactive switch: Switching data warehouses is not a plug-and-play endeavor. Data migration can be a complex and time-consuming process, and the efficiency (and reliability) of that data migration is often the make-or-break factor in the success of the modernization initiative. Critically, it’s not just about time and cost; if you lose data (or lose data dimensionality) in the process of migration, you’ll never realize the full long-term potential in your hyperscale analytics.
- You need a well-defined plan for migrating your existing data, ETL (extract, transform, load) processes, and any custom applications integrated with your current data warehouse.
- Your new data warehouse should be able to support the data formats and migration tools you plan to use.
5. Is the new data warehouse scalable?
Hyperscale is not an endpoint; rather, we’re just entering the hyperscale era. Data demands will continue exponential growth, and your new data warehouse needs to be able to seamlessly scale with your organization’s needs and requirements.
- Horizontal scaling: ability to add more servers to expand use cases.
- Vertical scaling: ease of upgrading server capacity to take on more volume or increase velocity of workloads.
6. What are the costs and licensing models?
As with any procurement scenario, it’s critical to investigate and accurately estimate/calculate the total cost of ownership (TCO) for a new data warehouse solution. Getting this full cost assessment is essential not just to evaluating whether a solution fits within your budget, but to estimating time-to-value and long-term ROI.
- Include licensing, hardware, and ongoing operational expenses in your TCO calculation.
- Ensure the solution delivers the full flexibility and scalability of a SaaS solution, with subscription-based, pay-for-what-you-need structure.
7. How will you ensure data security and compliance?
The 2023 Beyond Big Data survey showed that data security is the top concern among IT leaders planning to upgrade their data warehouses. As you evaluate a modern data warehouse solution, consider the following:
- Does the solution address your organization/sector’s unique security and regulatory compliance requirements?
- What type of data encryption is provided?
- How is access control managed?
- Does the solution deliver an automatic digital audit trail of all activities?
8. What support and training options are available?
Like any tech solution, the value or ROI of a hyperscale data warehouse will be limited by real-world adoption/usability in your organization. An incredible tool that no one uses is worthless. Best-in-class data warehouse and analytics solutions are built to accelerate adoption and shorten time-to-value through specific features, including:
- Intuitive interfaces that are quick to learn and easy to use
- Embedded analytics capabilities to democratize advanced analytics (alleviating the time-crunch on data science teams to expand use cases)
- Vendor training and support to facilitate a smooth transition to the new data warehouse.
9. What is the vendor’s track record and reputation?
At the end of the day, the best way to evaluate a technology vendor is also the most timeless: look for real-world proof.
- Research the vendor’s track record and reputation in the industry
- Seek out customer reviews, case studies, testimonials, etc.
- Talk with existing customers about their experience, if possible
- Look for established success and proven ROI — hard numbers.
Ready to jump into the hyperscale era? We can help.
Change is difficult — there’s no sugarcoating that reality. But as more organizations step up to modern data storage, processing, and analytics solutions, the risks associated with maintaining the status quo are growing quickly. Tech modernization is no longer simply a matter of stepping up capacity to meet demand. It’s the make-or-break difference between harnessing predictive insights and deep business intelligence to build competitive advantage and drive growth — or playing catch-up and chasing dwindling market share.
Our Customer Solution and Workload Services has helped dozens of enterprise organizations modernize their tech stacks and step up to a hyperscale data warehouse and analytics solution. Our experts can help you define your key tech modernization objectives, identify your criteria and requirements, and walk through the process of evaluating Ocient against other best-in-class vendors.
We’re here to help. Feel free to get in touch today.