2017: The attack of the data silos in an enterprise near you.
The data in the enterprises are getting more and more
humongous data in size and these sizes are making the data silos more and more
complex . for thwarting this data attacks now companies are taking agile steps
to keep these complex data problems at bay by investing in advanced cloud-based
platforms services. let’s see the
Gartner report statistics of the enterprise’s investment in the cloud platforms
The growth of the public cloud and Saas, Paas, and Iaas. is
no news in 2017. Per a recent Gartner report (http://www.gartner.com/newsroom/id/3616417)
“As enterprise application buyers are moving toward a cloud-first intellect, we
estimate that more than 50 percent of new 2017 large-enterprise North American
application adoptions will be comprised of SaaS, Paas and Iaas like cloud-based
solutions. Midmarket and small organizations are even further on the adoption
trajectory. By 2019, more than 30 percent of the 100 largest merchants’ new
systems priority will have changed from cloud-first to cloud-only.”
As enterprises adopt a pantheon of SaaS apps, so raises the
risk of having isolated lake of data each trapped under its own SaaS service
and creating yet another data silo. The inherent plug-and-play convenience of
SaaS applications often means that these systems are built to handle an upright
business use case without producing sufficient leisure to integrate into the
existing enterprise application estate. Often, the low cost of service and fast
time to deployment makes it extraordinarily convenient to get these
applications up and running for every use case and for every department. All of
a sudden information on a business entity, such as a customer, is into a dozen
diverse and disconnected places.
Now, data integration across marts has been a problem since
past unknown. Organizations have long seen the value in aggregating data from
multiple systems and channels into an individual, holistic, real-time
representation of a business entity or domain. However, for many organizations,
successfully achieving a single view has been elusive. Technology has certainly
been a limitation – for example, the hard, tabular data model imposed by
traditional relational databases hinders the schema flexibility necessary to
accommodate the various data sets held in source systems. But constraints
extend beyond just the technology to include the business processes needed to
deliver and maintain a single view.
The single view is relevant to any industry and domain as it
addresses the generic problem of managing disconnected and duplicate data. Specifically,
a single view solution does the following:
• Gathers and organizes data from various, disconnected
• Sums information into a patterned format and joint
• Gives holistic pictures of connected applications or
services, across any digital channel;
• Helps as a base for analytics – for instance, customer
cross-sell, upsell, and churn risk.
From scoping to building to operationalization, a successful
single view project is founded on a structured approach to solution delivery.
Identify a repeatable, 10-step methodology and toolchain that can move an
enterprise from its current state of siloed data into a real-time single view
that improves business visibility.
The timescale for each step is highly project-dependent,
governed by such factors as:
• The number of data sources to merge;
• The number of consuming systems to modify;
• The complexity of access patterns querying the single
. Present-time view of specific data. Users are utilizing
the freshest report of the data, rather than expecting for updates to propagate
from the root systems to the single view.
• Controlled application complexity. Read and write
operations no longer require to stay segregated between different systems. Of
course, it is necessary to then execute a change data acquisition process that
accelerates writes against the single view back to the source databases.
However, in a well-crafted system, the mechanism need only be performed once
for all applications, rather than read/write segregation copied in the
• Enhanced application readiness. With conventional
relational databases operating the source systems, it can get weeks or months
worth of developer and DBA effort to update schemas to establish new
application functionality. fealti’s adaptable data model with a dynamic schema
secures the addition of new fields a runtime operation, allowing the
organizations to evolve applications more swiftly.
With all associated data for our business object merged into
a single view, it is possible to run advanced analytics against it. For
example, we can kick-start to analyze customer or client behavior to better
recognize cross-sell and upsell possibilities, or danger of churn or fraud.
Analytics plus machine learning must be able to operate across vast swathes of
data collected in the single view. Common data warehouse technologies are
inefficient to economically store and treat these data amounts at scale.
Hadoop-based platforms are incapable to serve the models generated from this
analysis or conduct ad-hoc investigative queries with the lower latency
demanded by actual-time operational systems.
ENTERPRISE are using techniques like :
Customer 360-degree view
Business metric tracking and understanding the why
Product A/B testing
Big data techniques can be used to gather and process risk
data in order to 1) satisfy risk reporting requirements, 2) measure financial
performance against risk tolerance, and 3) slice and dice financial reports.
The Fealti Converged Data Platform package benefits risk assessment executives
as they can produce an on-demand historical analysis of risk data as well as
gain real-time alerts when limitations are exceeded.
New Products and Services for Consumer Credit Card Holders
Making new products and services open to consumer
cardholders is a continuing action for banks. Enhanced marketing crusades and
ads by effective targeting are needed in order to deliver services to customers
and improve revenue for banks. The Fealti’s Converged Data Platform is
practiced to provide new products plus services to consumers in actual time at
a leading credit card organization. Advanced machine learning including
statistical techniques are exercised over data that is collected in a highly
available Hadoop cluster. it gives the credit card company the capacity to use
machine learning techniques for varied purposes, including fraud detection and
Next Best Offer – Banks can use predictive analytics on a
combination of data to create a series of targeted offers for customers, and
make these offers available in real time at the next point of customer
Individual e-folio of each patient record toward health
management and planning
The healthcare enterprise remains to be under scrutiny and
stress to reduce costs while advancing the quality of care. While the
enterprise is growing more data-driven, the data landscape stretches to grow,
getting it more complex for healthcare organizations to drive insight and go
Unorganized data estimates for 80% of the data that
healthcare businesses rely on and that data is increasing exponentially.
Gaining access to this unregulated data – ranging from data produced by medical
devices, practitioner notes, laboratory results, and imaging records to
clinical data, genomics data, and sentiment data is precious for determining
the precise treatment plans for enhancing patient care, and accelerating
With easy access to all data sources to provide a single
view of the patient, healthcare organizations can:
• Identify at-risk
individuals for health care conditions, such as congestive heart failure or
diabetes, and recommend proven treatment plans
• Monitor patients
in real time and alert care providers the moment there is a change in a
provider scorecards to drive improvements and ensure consistent patient care
• Reduce fraud and
abuse with strict policies and procedures for safeguarding healthcare data.
Patient demographic payment patterns for insurance risk
Genetic and lifestyle data to forecast patient health risks
Hospital cost modeling
With an increasing competition and ever more demanding
customers, manufacturing is never easy.
While factory level automation has significantly improved
all areas of processing for manufacturing companies, it has also created a
staggering amount of data.
Though the entity is
most often a customer, the benefits of a single view in enhancing business
visibility and operational intelligence go far beyond understanding customers.
A single view can apply equally to other business contexts, such as products,
supply chains, Manufacturing, industrial machinery, Aviation, R&D, weather forecasting,
local communities, cities, financial asset classes, and many more.
For those interested in comparing various analyst views of
the fast-growing pubic cloud market, still at its early growth stages in 2017,
can refer to the Forbes ‘Roundup of cloud computing forecasts
The key takeaway for the purpose of our current conversation
is that SaaS in 2016 already jumped to double-digit share of the WW IT spend
and by 2020 will be nearing adulthood with a high teen share of the total WW
Software spend (??).
These all attacks can be thwarted by our next gen armoury,
With all fealti’s agile and robust techniques we collect, transforms,
Visualize, Analyse and provide valuable insight into data which helps to
optimize performance, lead time, product quality, and lower production cost.
Obtain the greatest productivity amidst minimum investment