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4 Simple Steps to Maximize Value from SaaS App Data

Find out how to make your cloud app data available for re-use in whatever apps and systems your users want.

• By Joe Gaska
• April 30, 2021

Just about every business today relies on cloud, or SaaS, applications. From CRM and ERP to e-commerce and marketing automation apps, they're where business takes place and decisions get made. So perhaps it's not surprising that 97% of organizations use SaaS applications according to Gartner, or that business users are eager to leverage SaaS data in their own systems for analytics, machine learning, and AI training data sets, customer support, or product development.

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This explains why companies increasingly target their SaaS application data for ingestion and integration into their overall DataOps ecosystems. However, doing so in an efficient and effective way can be challenging and costly, particularly if you use APIs to access data stored in the vendor's app. Fortunately there are concrete ways to make it much easier and cost-effective.

Getting Started

Before you can extract value from your data, you need to figure out which downstream users want to consume which data. With a hyperconverged app such as Salesforce, there are customer support, marketing, fulfillment, business intelligence, IT, product teams, and others who want to use that data in their organizations. To get the right data to the right users, create a data consumption map across your organization. Then, add frequency requirements for each data consumer type. This indicates how granular they want their data to be.

For instance, system administrators might need to capture snapshots of data for disaster recovery at 60-minute intervals while fulfillment teams want to ingest subsets of the same data into their supply chain system every 15 minutes. Product development teams may want a near real-time stream of historical data from which they can run their apps.

The SaaS Data Value Maturity Curve

Now that you know what data to capture, for whom, and how often, you can begin incorporating it into your data ecosystem for downstream consumption. For most companies, this is a graduated, four-step process.

Step 1: Backup your data

To ensure the data your company needs is readily available, you need full control over it. More organizations are doing this by adopting a data lake strategy: backing up data directly from their SaaS app into their AWS, Azure, or GCP data lake. (Note: Unbeknownst to many people, although SaaS vendors store your data in their apps, most either don't back it up at all or provide only rudimentary capabilities.)

It's much more efficient to run analytics using data in your own data lake than it is to use APIs to query any cloud app. Think of it as plugging your data consumers into a watering hole that has a regularly refreshed, full copy of the data instead of making them line up at the API spigot. Of course, remember to capture all the data your users may need to tap into as frequently as they need it.

Step 2: Archive your data

Organizations turn to app cost and performance. As the data generated and stored in your SaaS app grows, app performance degrades. To combat this, you have to buy more storage. This works well until you hit the new capacity limit again and need to buy more.

To break out of this never-ending cycle and preserve app performance, enterprises can archive that data into their own cloud data lake environments. This costs significantly less than what SaaS vendors charge for storage. You can expose that archived data wherever it's needed for further processing without having to suffer the pain of having the data actually live in your SaaS app's storage. Talk about having your cake and eating it, too!

Step 3: Observe and navigate through data changes

This stage is where you start to derive new insights from your data. To understand what your SaaS app data is telling you, you must be able to observe and navigate changes that have occurred over time. Because you're capturing all your historical data in your own cloud infrastructure, you can go back to any point in time to see trends and compare and contrast differences. You can also observe the data for corruption and other issues by monitoring anomalous deletions or overwrites and inserts.

Step 4: Re-use your data

Here's where the big pay-off comes for DataOps and business acceleration. In this step, you make your cloud app data available for re-use in whatever apps and systems your users want. Because users aren't tapping the data that resides in your SaaS apps, you don't have to worry about hitting API limits and degrading app performance. Instead, the native cloud app data residing in your data lake is streamed directly into other systems such as your data warehouse or ERP app; used to train AI algorithms; or added to analytics dashboards and reports. You can aggregate data from a variety of sources using tokenization to get a 360 degree profile of the entity. By analyzing that picture over time, you'll get new insights into patterns and opportunities, and be able to take new actions to propel your business.

A Final Word

The best news is this path to SaaS data value doesn't have to be a long-winded, heavy lift. There are tools that make it simple to progress from backup to reuse as quickly as you'd like, without burdening your IT teams or busting your budget.