Being Data Driven

10 Questions to Help Define a Successful Data Warehouse Strategy

Written by An Bui|May 10, 2018

When defining your data warehouse strategy—or moving from on-premise to cloud—you’ve got lots of options. But your data warehouse is only as good as the strategy you use to put it in place. Bottom line: You simply can’t tackle today’s data management challenges—including big data analytics—without a clear plan.

When looking at your choices, take a minute to ask yourself these 10 questions.

Ask Yourself This . . .

1.      Can the data warehouse handle diverse data sources with structured, semi-structured, and unstructured data?

It’s no big surprise that we’re facing exponential growth in unstructured data (email, video, audio, multimedia, etc.) and that 93% of the digital universe will be unstructured by 2022. Smart data warehouses need to be able to easily gather and analyze all types of data, from diverse data sources. And they need to not only collect and convert that data, but turn it into valuable insights that can enable business decisions.

2.      Can it easily manage massive—and rapidly increasing—data volumes?

This is an incredibly important question—especially when you consider that the world’s data is doubling every two years, with 50-fold growth from 2010 to 2020. Look for a data warehouse that can not only handle the velocity of data growth but also do this without compromising speed, usability, cost, and performance.

3.      Can you gather data from any source? Does it deliver out-of-the-box integration with widely used data-source platforms and business intelligence tools?

Ideally, you want a data warehouse that lets you quickly and easily consolidate the data from your databases, cloud services, and applications into a single data management platform—without the hassle of coding. Look for a platform that includes out-of-the-box integrations with all the leading data source applications (think Salesforce, Google, Marketo, Tableau, Amazon, and more). The solution should also provide SDKs in many of the most common programming languages, so that you can easily tailor the data warehouse to your needs and connect to any data source.

4.      Will it streamline data management, eliminating time-consuming coding?

One of the biggest challenges of running a data warehouse in any fast-paced environment is to continuously manage capacity and performance as schemas and workloads rapidly evolve. With a data warehouse solution that automates manual tasks, your IT staff can focus on deriving insights from data instead of maintaining databases. Smart data warehouse management can save you a lot of time that was previously spent on maintenance and trial-and-error tuning.

5.      How does it automate data collection, scaling, and modeling?

Look for a data warehouse solution that provides automated end-to-end data management—from initial data collection to analysis and reporting. Ideally, it should automatically aggregate data as it streams in (regardless of source) and let you to analyze everything almost immediately without data configuration, schema, or modeling. The data warehouse should be able to automatically scale to support any increase in data, workload, and concurrent users and applications without the need for data movement, data marts, or data copies. With an architecture that automatically handles infrastructure, optimization, and availability, you can focus on putting your data to best use.

6.      Can I believe the claims that it will quickly get us to business insights, moving from raw data to data analysis and value?

It makes sense to ask for real-world examples—or better yet a demo or free trial—to see how the data warehouse can take your raw data and convert it into actionable insights. Dashboards and visualization tools are crucial here, so make sure to actually see what you’ll be able to do with them.

7.      Do we need advanced tools for data analytics, such as machine learning and natural language processing (NLP)?

Being able to accurately analyze increasing amounts of unstructured data is one of the significant benefits of tools like machine learning and NLP. A data warehouse that utilizes machine learning and NLP can more easily model and streamline the data journey from source to analysis. This enables sophisticated analytics and drastically shortens the time from data to value.

8.      Does it make sense to use cloud services for cost savings, flexibility, and agility? Or should we stay on-premise or hybrid?

A cost-effective data warehouse should be able to scale compute capacity to match demand, and then quickly and easily scale back when usage decreases. The cloud can help solve this problem, but only if the underlying architecture of the warehouse supports it. Most businesses today are gradually moving to the cloud, and with good reason.

9.      If we’re not ready to go all in, can we have usage-based pricing—to pay as we grow?

Yes. Many cloud data warehouse solutions offer flexible pricing to meet your precise usage needs. Ask about specific plans. Usage-based pricing for compute and storage means that you only pay for the amount of data you store and the amount of processing performed. That means no big upfront costs, overprovisioning, or idle clusters.

10.     With a cloud data warehouse, when does the payoff come? Where is the ROI?

The simple answer is that there are much better economics with a flexible, pay-per-use model that offers financial clarity and predictability. Work with your data warehouse vendor to do a simple calculation of the cost savings you can achieve through better use of IT resources and cloud vs. on-premise storage.

Find Out More

When you’re ready to define your data warehouse strategy, talk with us. Panoply is a cloud data warehouse built for analytics professionals by analytics professionals. We’ve created a smart data warehouse that automates the collection, modeling, and scaling of any data.

To see Panoply in action, take advantage of a free trial:

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