Downstream Data Team’s Perspective on SaaS Application Integration Challenges: What You Need to Know!
The rise of Software as a Service (SaaS) has transformed the way businesses operate in almost every market. With its ability to manage marketing data, customer data, order data, master data, etc., SaaS solutions promise the hassle-free capability to run business operations. But what about providing easy access to the application data to gain new insights and opportunities to grow business?
Data is the lifeblood of business and it needs to flow around.
As a downstream data team, you might have encountered several challenges regarding data integration or data egress with SaaS applications. We understand the importance of these tools in streamlining business processes. Still, there are certain barriers that we must overcome in terms of easy access to the data generated in these SaaS applications.
This blog post will explore the challenges of SaaS solutions in modern business practices from a data team’s perspective. We’ve got you covered, whether it’s data integration, data egress, data reliability or other issues.
The Ups and Downs of SaaS Solutions
SaaS solutions have several advantages over traditional business models. One of the primary benefits of SaaS is its cost-effectiveness compared to traditional software models. This is because SaaS business models eliminate the need for companies to purchase and manage physical software and hardware infrastructures, as everything is kept digital.
In this post, we do not question the nitty-gritty of SaaS benefits but rather the capabilities surrounding easy access to the underlying data in SaaS applications.
There is always a trade-off between complexity and flexibility.
Many organizations face significant challenges due to the unused and growing data that SaaS or similar applications generate. Vendors may unintentionally or intentionally establish certain limitations around data egress capabilities, preventing companies from leveraging their data’s power. As a result, most organizations cannot use the valuable information generated by these applications, which can impede their growth and success.
API or Data Stream as an option for Data Integration
The very concept of “SaaS” implies that there are APIs that facilitate data exchange between a SaaS solution and other applications. However, many SaaS solutions assume their APIs will manage all connectivity requests and customer data integration needs. While it is advisable to put SaaS APIs at the center of a SaaS integration strategy, it is not always easy or even possible to follow the “Here’s my Application Programming Interface, use it as you need” approach. As a SaaS vendor, you must ensure your documentation is up-to-date and complete. Your API should cater to consumer data integration needs (Batch, real-time, and, most importantly, operational support). Additionally, you should consider whether your customers have enough development resources to study your API endpoints and build integrations with them at scale for their various needs.
But it’s not that simple because the analytical data needs are more defined by the four V’s of data, which are: 1. Volume, 2. Velocity, 3. Variety, and 4. Veracity. These cannot be possibly supported via. an API or an Streaming integration without proper understanding of the data requirements of an organization.
- Velocity: It describes the speed at which data can be processed. Based on SaaS solution capabilities, data usually arrives in batches or is streamed continuously.
- Volume: It describes how the SaaS solution caters to the varying needs of data team requirements from a volume perspective (Incremental, Full, etc.)
- Variety: Organisations and applications generate and process data according to their specific needs, ensuring a wide variety of data on the planet.
- Veracity: Veracity can be defined as conformity to facts or accuracy, and it refers to the element of accuracy that data possesses.
Let’s examine some of these SaaS integration challenges:
Challenges with SaaS integrations from a Data Accessibility standpoint
Missing Query Interface: Certain Software as a Service (SaaS) applications do not offer a query interface to users, making data extraction or browsing challenging. This often requires exporting data to an analytical platform to analyze it there. Often, the applications team might not have the required skill set.
Missing Bulk Data Export: Sometimes, the data team may need to extract or export historical data spanning a few years or more. However, they might encounter a scenario where the available integration options are limited to a Data Stream or API in the SaaS application. In such situations, replaying historical events or pulling data in small chunks might not be practical. It can be tedious and time-consuming, requiring considerable effort and resources. Volume and velocity handling are important factors in data team requirements.
APIs enable SaaS, but huge business logic complexity lurks underneath: In certain scenarios, retrieving data from multiple objects involves using distinct APIs. Each API has its own set of rules for filtering, searching, and invoking data. Consequently, data teams may have to invest significant time and effort in navigating through multiple APIs, understanding their unique requirements, and extracting the needed data. With the added complexity, veracity comes into question.
Challenges with SaaS integrations from a Data Operations and Reliability Perspective
Data Reconciliation: Data reconciliation is a crucial process that involves cross-checking multiple data sets to spot and fix inconsistencies, discrepancies, and inaccuracies, thereby ensuring data accuracy and consistency across various business operations. It is often observed that SaaS solutions do not offer support for the reconciliation needs of the data team ( with their data integration capabilities like streaming or API). However, having an end-of-day or batch reconciliation process in place helps the data team maintain the reliability and trustworthiness of their data.
Backfilling Missing Data: Backfilling missing data refers to filling gaps or missing values in a dataset with appropriate information. This is often necessary to ensure the dataset is complete and can be used effectively for analysis, reporting, or other purposes. Sometimes, the data team may need to execute a historical data pull or backfill of missing data from the source systems due to operational incidents. However, SaaS integration options might not be equipped with the capabilities to backfill data requirements, which can complicate the operational aspects of the data team.
Schema Evolution: Schema evolution is critical to business as it evolves with ever-changing business needs. Both data producers and consumers must take full responsibility for managing schema evolution. It is concerning that SaaS solutions lack a standardized method of defining data contracts to handle schema changes over time effectively.
SaaS integration options are important in the success of a cloud-based journey. Combining data from various sources with SaaS data is crucial, allowing for informed decision-making and timely actions. In contrast, the inability to integrate data can be frustrating and negatively impact growth. It is imperative to clearly understand the integration capabilities of any new SaaS solution being introduced to your system. To achieve this, working closely with the data team and comprehending the SaaS data integration aspects(new SaaS solutions onboarding)tailored to the organization's data needs is essential.
I hope you found it helpful! Thanks for reading!
https://medium.com/@DataEnthusiast
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Authors
Himanshu Gaurav — www.linkedin.com/in/himanshugaurav21
Bala Vignesh S — www.linkedin.com/in/bala-vignesh-s-31101b29
