“In-memory computing will have a long term, disruptive impact by radically changing users’ expectations, application design principles, products’ architecture and vendors’ strategy”
Donald Feinberg,
Gartner VP and Distinguished Analyst
“In-memory
is just a hype spread out by SAP”
Introduction:
Be it the support for the
increase in bets from 12000 per second to 15000 per second for the online
gaming company Bwin.party, the near-real-time insight into product availability
for customers by the retailer Edgenet or be it the response by ConAgra, an $18
billion-a-year consumer package good company to the fluctuating costs of 40,000
raw material, the in-memory analytics made it possible. Before going to the business aspect of
in-memory analytics let’s have a look into what is in-memory analytics and how
it is helping business to grow faster?
In-memory analytics
facilitates querying of data from Random Access Memory (RAM) instead of
physical disk. Data can be loaded from multiple source into the system memory
directly. This helps in faster processing of data and faster business decision. Following figure depicts the general difference between the Traditional Computing and
In-Memory computing.
Types:
There are 5 types of
In-Memory analytics and they are:
Major
Industry Drivers:
The figure bellow shows powerful
drivers push organizations towards In-memory analytics.
The
first and foremost driver for In-memory analytics is Big Data. To handle wide
volume, velocity and variety of data faster computing technology is required
and In-Memory is the need of the time. Real-time analytics is the second and an
important driver. To make faster, accurate business decisions, organizations
are relying on the real-time analytics and In-memory analytics makes it easy. Another
driver for this new technology is the need of scalability and flexibility that
required for the business. In the other hand some factors are there standing as
hindrance for this technology to adopt. Among them lack of knowledge and
development, security challenges are main hinders.
Value Creation:
The four
dimensions of the value created by In-Memory Analytics are:
1. Performance: The time taken for data analysis comes down drastically from hours to seconds. In-Memory Analysis allowed levering the
most recent data for the purpose of analysis and informed decisions.
2. Process innovation: The performance gain gives space for
innovation in various application and it leads to competitive advantage for the
organization.
3. Simplification: Due to reduction in layer the complexity of
data models can be reduced significantly. This simpler architecture reduces the
sources of potential error.
4. Flexibility: During analysis new data source can easily be
plugged in as an additional source of information and this provide flexibility
to the data analysis.
Adaptation Rate:
As
per a survey done by Deloitte (German CIOs) 52% of respondents said that they
are still evaluating the IMA technology, where as 22% CIOs said that they are
on the plot and only 4% said they have adopted this technology. The bellow
graph shows the break down:
Conclusion:
IMA is
the future of computing but requires a clear strategy for all steps from
evaluation to implementation. This includes, but is not limited to, the
identification and evaluation of opportunities for the utilization of IMA, business case development, management of the implementation as well as learning and change management for pilot scheme and roll out.
References:
·
http://timoelliott.com/blog/2013/04/why-in-memory-computing-is-cheaper-and-changes-everything.html
