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How to avoid SaaS data fragmentation in your organization


When organizations overuse SaaS apps, they invite chaos. How can something so good turn bad?

Chaos in Software as a Service (SaaS) is as costly and inefficient as any on-premises legacy system. Both hide functionality behind layers of code, becoming monoliths that people can’t understand or alter.

According to International Data Corporation (IDC), the SaaS app category remained the top source of public cloud revenue in 2022. 

While the investment in SaaS apps remains great, so are the risks that come with their overuse. When organizations go overboard with SaaS apps, they invite disorder. 

What is SaaS?

Software as a Service (SaaS) applications are those accessible over the internet. SaaS users subscribe rather than purchase and install the application — logging into these apps online.

SaaS applications function like precision tools and adapt to any business need. Domain experts construct custom GPTs, allowing organizations to rely on these tools without the burden of developing their own solutions.

For example, financial fraud experts build SaaS fraud detection tools and medical experts build SaaS diagnostic tools.

Data Sprawl

How can something so good turn bad?

The composable, distributive nature of a SaaS ecosystem makes it powerful, unlike the single-system architecture of the past, which used to do everything. With SaaS, each app comes with its own cloud infrastructure. Plug-and-play code, which avoids a single system’s massive block of code, is easier to debug, change, and upgrade.

Cloud apps require fewer engineers because non-technical users can make significant functional changes. Reporting and machine learning are also faster. This is due to the use of parallel processing and distributed computing, which splits a single process into two or more tasks. Then multiple machines perform the tasks simultaneously, increasing computational power.

But these benefits degrade when hundreds of SaaS apps run in the same ecosystem. An ecosystem of 100 apps involves 100 different infrastructures.

When a company uses three SaaS apps for payment, scheduling, and client relations, its data is in three places. They’ll need to run three queries over three networks to know if a client has paid for a scheduled appointment. 

This is an example of SaaS sprawl, which results in data and application fragmentation. The proliferation of SaaS apps has led to a mismatch between the responsibilities assigned to engineers and their core competencies.

Related: Support data governance with these effective tools

While the integration and maintenance of these numerous solutions demand engineering expertise, engineers often find themselves tasked with configuring low-code SaaS platforms, rather than leveraging their talents for application development and coding.

This inefficient allocation of resources not only underutilizes their skills but also risks stifling innovation.

To truly harness the value of engineering talent and foster a cohesive SaaS ecosystem, organizations must realign engineers’ responsibilities with their strengths, allowing them to focus on application creation, and unlocking their full creative and technical potential.

What problems does SaaS data fragmentation cause?

Data and functionality are distinct parts of the SaaS ecosystem in which it lives. And when these parts are spread across too many endpoints, problems can occur. 

Data fragmentation raises several issues in:

Network security 

SaaS providers ensure the most advanced security safeguards for their products. However, they can’t guarantee the security of an organization’s networks and data transfers.

Less data transfer minimizes security compromises, and integrating data from multiple sources into fewer apps can dramatically reduce network usage.

Data quality

Many SaaS apps carry the same information in different formats — leading to excessive data variety, duplication, and incompatibilities between applications. Data integration prevents this. It cleans up the data, removes duplicates, and unifies formats — reducing the data set to only what is necessary.

Through this process, data integration creates a central data source for all reporting, automation, and analysis.

Data availability

There are too many queries required to produce data subsets used for AI data feeds, sales reporting, and financial fraud detection. Perhaps more troubling is the unmanageability of the queries. Often, the data processing relies on complex queries that are hard to define and debug.

A single centralized source streamlines this, requiring fewer queries to access all data from all systems.

Related: Data lake vs lakehouse: Why a lakehouse is the better option

Regulatory rules and data governance

Increasing regulations require ongoing auditing on a single data set. Audits on fragmented data take longer for regulators, making the process more cumbersome and less trustworthy. 

Application fragmentation

Application fragmentation adds its own problems. 

Your growing arsenal of SaaS apps will start overlapping and competing. Any employee or team can install a SaaS app. For example, some teams might use Asana to manage their projects, while others use Trello. Still, others might use Asana. As a result, cross-team project management becomes disjointed.

Imagine more than one customer relations management (CRM) app. Fragmented customer data may cause parts of an organization to have conflicting data on the same customer. This inconsistency can lead to flawed customer relations. Also, unnecessary licenses multiply costs. 

Having one app per domain for the whole company reduces confusion and provides a uniform user experience. It also removes redundancy, making the organization more efficient. Plus, with some training on one app, license-sharing can be spread over more people, which could ultimately cut costs associated with having multiple SaaS apps.

Overall, SaaS sprawl and fragmentation cause many problems. They can harm security, threaten data quality and governance, overcomplicate functionality, raise costs, and degrade customer experience.

What does the impact of SaaS fragmentation look like in real life? 

SaaS fragmentation affects all industries, from healthcare and finance to government services. However, these industries experience the impact differently. 

Compliance complexity undermines the heavily regulated industries of healthcare and finance, and high licensing fees overwhelm government budgeting. 

Fragmentation of data in healthcare

Let’s look at three areas of concern. 

1. Medical diagnoses

Analyzing medical knowledge in light of a patient’s symptoms relies on massive amounts of data that can come from many sources — both inside and outside the medical institution. This data can become fragmented among the institution’s cloud-based customer and medical knowledge databases. 

Merging this data into one source enables the machines to access and process the data more quickly and efficiently. It also reduces incomplete, missing, or unclean data because integration processes are built to detect, highlight, and fix such data quality issues. 

2. Data governance

Regulatory authorities rely on extensive audits and reporting of institutional data. Data fragmentation creates unnecessary challenges for them in viewing this data in a timely and reliable way. Delays in reporting raise suspicions, and too many reports complicate data oversight. 

The EU’s Medical Device Regulation Act (EU MDR) has transformed its industry by requiring easy and fast access to more data to improve data governance. Likewise, the General Data Protection Regulation (GDPR) has strengthened its health and patient data oversight, demanding more data uniformity and privacy. 

A single data source simplifies access and ensures data trustworthiness. Regulations are beginning to require it, making data integration necessary. 

3. Security

Securing data is a priority. The healthcare industry faces increasing threats, with cybersecurity attacks growing by 22%

Regulations emphasize robust security as part of the regulatory framework to protect private data. For example, not acting on breaches promptly can lead to personal data leaks. Data fragmentation inhibits security monitoring and fixes. Data integration goes a long way towards ensuring better data security oversight.

Despite this, data integration can bring new security concerns. One of SaaS’s benefits is that its providers secure the data. With data centralization, however, healthcare institutions must implement industry-standard measures to secure their centralized database. 

For example, the Health Insurance Portability and Accountability Act (HIPAA) requires administrative, technical, and physical safeguards for protecting private data. Institutions must allow only authorized persons to access the data, implement hardware and software processes to record and examine database activities, and ensure data is not improperly altered or destroyed.

HIPAA also requires measures to protect data transmitted over a network.

As you can imagine, this micro-management of compliance can be costly and intricate. To simplify, many institutions have begun moving their integrated data to protected cloud storage or using less costly cloud-based tools like integration platform-as-a-service (iPaaS).

IPaaS guarantees robust security measures that follow standards and relevant industry regulations.

Fragmented data in finance 

The finance industry faces the same regulatory and security challenges as healthcare. For example, banks are legally obligated to implement robust fraud detection measures to comply with financial regulations and data protection laws. 

Fraud detection systems monitor both data and application activities. Many use machine learning (ML) models to uncover behaviors outside expected patterns. These systems must run in real time and cover the whole of an institution’s ecosystem. 

This massive undertaking, both in learning the patterns and detecting the anomalies, is only possible in a fully interconnected ecosystem. Spreading out your data and applications over disconnected channels will leave gaps in your system that fraudulent actors may exploit. 

Data integration can also aid in financial analysis. ML-based AI must sift through vast market and historical data to identify assets and adjust investment strategies in real time. AI can also improve risk assessment by analyzing complex data and automating investment decisions.

In this scenario, you want to encourage the diversity and complexity of information that can come from a SaaS ecosystem. Still, you also want to ensure that your analytics tools are not wasting time or being sidetracked with unnecessary or redundant information.

In sum, SaaS fragmentation adds unneeded noise to these analytic and fraud detection systems. In contrast, a manageable, integrated SaaS ecosystem offers diverse and relevant data within a homogenous structure that allows for real-time, complex monitoring, and processing.

Read also: How a data lakehouse can reshape financial services

Data fragments in government 

Gartner predicts, “By 2025, over 75% of governments will operate more than half of workloads using hyperscale cloud providers.” This increase in SaaS spending is poorly matched with dwindling government budgets. For example, California has a $68 billion deficit, hindering its state and local digital transformations. 

That said, simplified operations and customer service are central to modern government services, including streamlined tax and administrative procedures. SaaS can de-complexify operations with its expert-built precision services.

It can also unclutter the back end by offloading its data to the cloud.

SaaS applications provide a low-cost alternative to a costly single-system architecture, but they must be implemented carefully. By limiting the number of subscriptions and centralizing data, fewer people need to use these SaaS applications directly to view their data.

Like all industries, governments can benefit from cost-effective iPaaS cloud services.

What can organizations do to resolve data fragmentation?

While SaaS fragmentation issues are serious and varied, they are not permanent. Several solutions can help organizations get the best out of their SaaS choices. 

Data integration

Integrating your data will lead to more uniform data, which is easier to access and more secure. It also boosts compliance. Technologies like iPaaS integrate your data on their clouds, and, like all cloud-based services, they cost less than an on-premises solution.

SaaS app consolidation

Consolidating your SaaS apps will result in fewer apps and, therefore, a less fragmented application ecosystem. 

With fewer applications, you reduce licensing fees and lower the usage of the applications you keep because some users only need to see the data; which is now produced by the centralized data warehouse.

App consolidation also results in easier maintenance and more efficiency in the back end. A straightforward application framework provides a more streamlined and uniform user experience.

Restructuring your organization

Structuring your organization to function with fewer apps will lead to simpler workflows, employee training, and better organization-wide collaboration.

Say goodbye to SaaS sprawl and fragmentation 

SaaS gives us the power to simplify things. Simplicity comes from a “less is more” approach to SaaS.

At Lumenalta, we ask our clients:

  • How many apps do you have?
  • Where is your data?
  • How much are you spending on your SaaS ecosystem?

We then review the importance of data integration and application consolidation. Industries and organizations differ; therefore, we meet with executives, strategists, leaders, and users to tailor our services to your objectives. We evaluate your tech, resources, and budget. Then the work begins: together, we plan, design, and build.

Work with us to see how you can cut costs and end the chaos of SaaS sprawl and data fragmentation.

Read next: 5 best practices for effective data product management

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