At Infosys Cards and Payments, we help our clients harness the power of technology-led innovation across the entire payments ecosystem encompassing payment networks, merchant services, stored value, FI payment services, and payment aggregators. Our thought leadership and a design thinking approach helps us co-create solutions with our clients to address their business problems.

« Tokenization as a service | Main | Card Numbers - Are they really a necessity? »

Open Source Data Analytics Software for Payment Processing Companies- Is it viable?


Payment processing industry is a highly sophisticated industry that employs best in class IT solutions for supporting various business processes.  Enabled by advancements in technology, the industry is seeing innovation in terms of new products offerings like Mobile Payments, Digital Wallets etc. This industry also needs to comply with several regulations like PCI-DSS, SOX, local country regulations etc.

Paradoxically, this industry suffers from the lack of sophisticated approaches and platforms to analyze business data to help make better business decisions.

Does advocating the use of open source tools for data analysis in a technologically mature and highly regulated industry make sense?

We have heard of the oft repeated disadvantages of open source software in terms of steep learning curves, compatibility issues with existing infrastructure and lack of dedicated support juxtaposed with the advantages like near zero licensing fees, continuous and real improvement due to quick-turnaround for bug fixes and patches, lack of dependency on the company that originally created the software and availability of source code to the IT team.

There are several Open source data analytics tools, visualization programs and languages like R, Talend, JasperSoft, Pentaho, SpangoBI etc. available in the market. Many of these softwares are used extensively by companies in various industries. At the same time, several Data Analytics software offered by traditional vendors are also available in the market.

The one thing to note is that for data analytics, 'one size fits all' logic doesn't apply and every organization has a different philosophy and approach towards the data analytics function. Moreover data analytics function can be a potential source of competitive advantage and hence it is unlikely that it can be fulfilled by a COTS product.

Moreover the analytics function is internal facing and thus likely to be immune from any external regulations making it a good option for open source software.

Analytical solutions usually need to evolve over a course of time so that they can adapt to the changing needs of the organization. In such a scenario, it makes sense to adopt an Open Source tool which comes along with the source code making it easier for IT teams to tweak, modify and extend the solution to make it truly beneficial to end users.

The Go-No Go Decision wrt. Open Source Data Analysis software can be summarized in the following matrix:


Consider Open Source

Consider Proprietary

S/W Orientation

Internal Business Users

External Customers

Propensity for Change



Governed by Regulatory Compliance



Requirement of External Support



Intent to extend existing solution




One good approach to utilizing open software is to choose a 'subscription based' version of the open source software. Such an option comes equipped with essential security features, dedicated user support, bug fixing, consulting, training services etc. making it compelling offering with respect to a proprietary competing software.

Post a comment

(If you haven't left a comment here before, you may need to be approved by the site owner before your comment will appear. Until then, it won't appear on the entry. Thanks for waiting.)

Please key in the two words you see in the box to validate your identity as an authentic user and reduce spam.