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Augmented Analytics: Accelerating
Data Insights Into Actions
By Apurva Pathak / Senior Executive - Marketing / 27 March, 2019
Companies in banking and securities, healthcare, manufacturing,
insurance, transportation and trade generate massive amounts of
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data. Yet deriving meaning from the data remains a challenge.
Data Analytics plays a crucial role in unlocking the hidden
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potential of this information, serving as a guiding light for
Cloud & Inf rast ruct ure
informed decision-making across various industries.
Massive amounts of data, generated across industries like
Dat a and AI
banking, healthcare, and manufacturing, flow from diverse
sources.
Through
a
process
of
standardization
and
Indust ry Expert ise
transformation, this data becomes ready for injection into the
Analytics workflow. Here, powerful Big Data Analytics techniques
are used to sift through the vast information, uncovering valuable
patterns. These patterns are then transformed into actionable
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insights, often through the power of Data Visualization, providing
the foundation for data-driven business decisions.
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Ongoing Challenges In Analytics
The journey of going from data to insights requires a team
consisting of data scientists, machine learning engineers, domain
Uncovering Stage-Wise Analytics
Maturity for Business
experts and analysts. They collaborate to make sense of this data
– which requires a significant investment of time and resources.
Adding to that, data collection and pre-processing tasks like
cleaning data, labeling data infamously account for 60-80% of the
time of the Data Scientist. The analysis is a time-consuming
manual task of selecting features, finding parameters to discover
patterns. Insights to be inferred from these patterns are for
decision-making, and business users require familiarization with
sophisticated Visualization and Business Intelligence tools like
dashboards and reports.
Augmented analytics addresses these challenges by automating
the analytics workflow to deliver unbiased decisions and impartial
contextual awareness. It will transform users’ interaction with
data – from how they consume it to how they act upon it.
Augmented Analytics: The Road Ahead
Gartner defines Augmented analytics as
“A next-generation data and analytics paradigm that uses machine
learning to automate data preparation, insight discovery and
insight sharing for a broad range of business users, operational
workers and citizen data scientists.”
Augmented analytics will impact multiple stakeholders in the
existing workflow. Automating data preparation will help the data
scientists to spend more time on sophisticated tasks and
decrease efforts spent manually on cleaning data. Data scientists,
engineers and customers can obtain insights at a much faster
rate and with lesser bias compared to time-intensive manual
methods.
Augmented analytics will also lead to the rise of Citizen Data
Scientists – a person who creates or generates models that use
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predictive or prescriptive analytics, but whose primary job function
is outside the field of statistics and analytics. This empowers
them to interact with simple to moderately sophisticated analytic
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and
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AI
applications that without augmented insight discovery would
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andto
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advanced
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engineering
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are
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Insights
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innovators.
and allows interaction using natural-language. It is achieved by
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focusing on the four important areas:
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1. Augmented data preparation using
machine-learning
for
collating, cleansing and enrichment of data.
2. Advanced
analytics
automates
feature
selection,
hyperparameter tuning in modeling, pushing people in the
workforce without the specialized skill set to handle advanced
analytics.
By Prashant Puri | Senior Technical Lead
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3. Augmented data discovery generates the most
relevant
statistical insights. It recommends the best way to display data,
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enabling business people and data scientists to understand
and visualize clusters, correlations, forecasts and trends
without building models or write algorithms.
4. Integrating
Natural-language
query
and
conversational
analytics with data discovery can drive analytics adoption by
allowing business users to interact with data using natural
language. This can be carried out even on mobile devices to
query the most pertinent actionable insights by interacting with
digital assistants.
Fig 2: Traditional Vs Augmented Analytics Workflow
Tools To Drive Augmented Analytics
Augmented analytics tools are in their nascent stages. Many
cloud providers are working towards providing advanced analytics
with Auto ML solutions that aim to enable any developer to use ML
for building high-quality models with minimal expertise.
1. Google Cloud Platform’s Cloud AutoML: It enables developers to
build state-of-the-art models by interacting with simple user
interface within minutes. It also helps with tedious jobs of
labeling data. AWS’s ML services provide fully managed
automated model building, training, model tuning and deploying
capabilities.
2. Azure’s Automated ML: It currently supports regression and
classification problems.
3. Auto-sklearn AutoML: Open source modules like sklearn have
auto-sklearn
toolkit
for
algorithm
selection
and
hyperparameter tuning by leveraging Bayesian optimization,
meta-learning and ensemble construction.
4. H2O AutoML: There are platforms like H2O that make it easy for
the non-experts to perform experiments with machine learning
using its AutoML functionality that automatically runs through
all the algorithms and their hyperparameters to compare and
build the best model.
These tools will reduce time-consuming exploration, identification
of false insights, efforts spent on creating and building models,
thereby helping data scientist to focus on more meaningful tasks.
Visualization Of Patterns Through BI Tools
The patterns generated through these models can be visualized
by various BI tools, one of which include Qlik that plots autogenerated charts, forecasting important features and important
correlations from data. Qlik bridges the gap between visual
discovery and conversational analytics by interfacing through a
Bot (CrunchBot) enabling users to query using Qlik Sense UI,
Slack, Skype, Salesforce Chat and Microsoft Teams. It also
powers interactions by voice to services like Amazon Alexa.
Analytics Game Changer
Adoption of these augmented analytics tools will minimize timeconsuming exploration tasks, help in identifying biases and
reducing errors. Advanced analytics will propel the transition
towards data-centric business without completely relying on data
science professionals. Automation of the data to insights using
augmented analytics will enable the enterprise workforce towards
an accelerated decision-making process.
Reference
[1] Gartner.com
[2] Azure.microsoft.com
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Embrace
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T
he high-tech
is
digital
D.R.E.A.M
a complete
treasure
possibilities
world
to decisive
trove
cyber
is converging
of
to
resiliency
insights
outcomes
an agile
into
that
with
cycle
dynamic
and
security
bring
ofclient
digital
a
Infrastructure
ecosystems
solution
competitive
assessment,
success
development
- explore
edge.
of
practices,
products,
our
Withproven
- aexplore
engineering
roadmap
tools,
impact
how
andto
capability,
everything
industry
inmanage
driving this
and
connects
Your cloud journey isn't the same as your peers.
under our
leverage
governance.
through
connections,
services
data,
robust
Twhere
he
weand
outcome
technology
leverage
ideas
solutions
and
is
AIa
DNA,
and
collaboration
proactive
across
advanced
product
multiple
threat
engineering
analytics
are
sectors
drivingUnderstanding where you are and implementing relevant
footprint,
where
identification
and
new
business
every
and
byte
and
functions
opportunities.
dependable
brings
remediation
value
- tailored
partner
Over
asposture
the
to
three
network.
business
specific
besides
decades,
needs.
evolves.
cloud strategy or migration, or modernization, we help
compliance
Xoriant
has developed
adherence.a strong technology partner
you capitalize
Know
More and thrive on the cloud.
ecosystem that includes world-leading technology
Know More
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innovators.
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