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:
|
Parameters/Choice |
Consider Open Source |
Consider Proprietary |
|
S/W Orientation |
Internal Business Users |
External Customers |
|
Propensity for Change |
High |
Low |
|
Governed by Regulatory Compliance |
No |
Yes |
|
Requirement of External Support |
Low |
High |
|
Intent to extend existing solution |
Yes |
No |
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.
