INTELLIGENT BRANDS // Software for Business
Companies should embrace
open source software
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For organisations to remain agile and responsive, it’s a business
imperative to embrace open source software but meeting
regulatory requirements remains a challenge, says SAS Country
Manager, South Africa, Akesh Lalla.
W
e’re increasingly seeing analytical
models being developed in
open source and it’s easy to
understand why. If as an organisation you’re
hoping to perform as an agile company,
developing in open source is attractive, not
least because an open source environment
supports rapid and agile development of
projects and models. At SAS we’ve embraced open source and
developed an open platform that supports
and accelerates the entire end-to-end
analytics life cycle, from data to discovery
through to deployment in production and
then managing and governing those models.
Evolving companies’ analytics platforms
and operationalising analytics is where the
challenge lies.
The skills we see emerging from universities
into our industry and professional
environment, are supporting this trend, with
a marked upturn in graduates with the skills
for developing in open source software,
using widely available programmes like
Python and R. The hybrid approach of combining open
source with proprietary software can deliver
the best of both worlds, because proprietary
software like SAS’s product offering can
address the challenges of taking a project
into production and moving to scale for
enterprise-wide use.
It’s rapidly becoming a tired cliché, but
one thing remains true – data is the new
oil. Open source provides an open space to
tackle new challenges, to explore the data
and see what answers it contains. New
projects can enable significant successes
with fast deployment, and importantly,
it also supports the ‘failing fast’ strategy
without incurring significant development
and infrastructure costs. We also see
tremendous enthusiasm for open source
software in companies where innovation is
a top priority. Governance is key, particularly in South
Africa, where the regulatory environment is
The problem many companies encounter
is when the open source model needs to
be taken into production and scaled. While
companies are doing a lot of development
in open source playing with concepts and
products, putting it into production still
presents a challenge.
www.intelligentcio.com
Akesh Lalla, SAS Country Manager, South
AfricaObsidian Systems
becoming tighter and more challenging. It’s
important to be able to see right through
your entire data lineage, to know how the
data has changed and how AI inside the
model has impacted the results and outputs.
When the regulator asks those questions,
organisations must be in a position to
answer – regulators expect companies to
provide the appropriate levels of traceability
and auditability. This is particularly true of
organisations operating in the financial
sector, where regulatory requirements are
extremely demanding.
One of SAS core principles is digital
guardianship, i.e. providing mechanisms
to meet both expectations of the people
entrusting you with their data and the
internal organisational moral compasses for
protecting agreed upon use of the data.
Crucially, controls are also necessary to
provide trust in the data. Businesses have
to trust that the results of models are
accurate and that those models will continue
to perform into the future. Transparency,
governance and security are all essential
components and they become even more
critical when organisations scale their efforts.
I believe that the hybrid approach
of combining both open source and
proprietary software is the most effective
way to combine rapid development with
deployment at scale and reliability in model
management. Robust end-to-end data
science platforms benefit any company
working with data at scale. It’s imperative
to work with products that allow data
scientists to write code and build models
in any analytic programming language of
their choice, but deploy and run them in a
controlled, enterprise-grade environment
with full end-to-end traceability. n
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