The world of data analytics is no longer restricted to data scientists, IT, and analysts. Augmented analytics combines the best aspects of ML and human curiosity to assist users get quicker insights, consider data from unique angles, increase productivity and assist users of all skill levels to make smarter decisions based on AI analytics.
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Augmented analytics will push the analytics adoption
1. How The Potential Of
Augmented Analytics
Will Push The Analytics
Adoption By 30 Percent
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2. CEO
The entire exercise of doing business analytics today is
a bit time-consuming, for everyone involved. Likewise,
getting actionable insights into the hands of everyone
is increasingly vital to improving business operations.
This is why you're seeing a new wave of disruption
in data analytics tools with the concept of
augmented analytics gaining momentum.
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WHY
AUGMENTED
ANALYTICS ?
We hear it everywhere that business teams are
hungry for analytics. They crave accurate
forecasts and predictions to allow them to make
more reliable business decisions.
3. Augmented analytics is the use of enabling technologies such
as machine learning and AI to assist with data preparation,
insight generation and insight explanation to augment how
people explore and analyze data in analytics and BI platforms.
It also augments the expert and citizen data scientists by
automating many aspects of data science, machine learning,
and AI model development, management and deployment.
This technology helps in numerous business functions, from
decision-making about business deals to identifying prospects.
What Is Augmented Analytics?
- Gartner
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4. Augmented
Analytics
Can Be
Broken
Down Into
Three Parts:
1. Machine Learning (ML)
2. Natural Language Processing (NLP)
3. Automated Insights
For instance, if you are trying to decide on the best pricing strategy
for any of your services, you can use Machine Learning algorithms
to automatically examine your customer relationship history and
competitors' offers and suggest an appropriate price for a
particular customer.
NLP is a conversational AI technology that authorizes human data
analysts to interact and query the data using natural language --
either in the form of voice or text. These attributes have given rise
to self-service analytics.
Here, the technology draws together NLP and ML so that system
users can get the answers to their questions much faster. For
instance, your sales team could ask, "What are the growth
projections for Q1 2020?" and receive a visualized answer.
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5. How Is It Adding Value
To Business Intelligence?
Augmented analytics helps alleviate an organization's dependence
on manual processes or data scientists by automating the insight
generation process with the assistance of AI and advanced
machine learning algorithms.
The scope of augmentation is extending. Originally intended to assist
analyst personas using self-service, augmentation and, increasingly,
giving rise to a new user category: augmented consumers. This change
has the potential to push Analytics and BI beyond the approximately
30% adoption ceiling that has been in place for many years.
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6. Let's Look At The Top 4
Benefits Of Augmented
Analytics
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7. Immediate Automated
Analysis
1
.
The heavy lifting of manually sifting through vast
volumes of complex data (due to lack of time or skills
constraints) is significantly reduced as the analysis is
automated and can always be set run. If your augmented
tool finds a spike or drops or change, it can also
automate the delivery of that insight, ensuring that users
can then act immediately.
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8. Faster Data
Preparation
2
.
Augmented data preparation brings data together from
disparate sources swiftly. Algorithms can be utilized for
integrations and repetitive transformation for
enrichment recommendations and data quality, and you
can even automate the tagging, profiling, and annotation
of your data before you start the process of data
preparation – resulting in reliable analysis in a fraction of
the time.
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9. Improved Data
Literacy
3
.
With Natural Language assistance in place, providing
automated analysis of results and the explanation of
discoveries - can improve their data literacy. With such
kind of transformations, this can help in fostering a data-
led culture that benefits the organization as a whole for
the long-term.
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10. Conversational
Analytics
4
.
Data Analysts can use Artificial Intelligence and Machine
Learning, along with Data Science for Conversational
Analytics. That means data users of various skill levels
can access the data and obtain insights without being
expert data scientists.
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11. 1. Smart Cities
Augmented
Analytics
Use Cases
Smart Cities worldwide are utilizing Augmented Analytics
to process huge volumes of collated data. As more Smart
Cities adopt and follow this transformational
technology in their city administrations, the city
management practices will genuinely enter the Digital
Age. With these advanced technologies in place, city
planners will simulate a "Smart City," predict future and
better manage their existing resources.
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12. 2. Other Industry
Augmented
Analytics
Use Cases
In numerous industries, top management uses
Augmented analytics to make the data relevant through
sophisticated dashboards to make fast decisions. As most
of the collated data are sensor-driven, smart
technologies such as AR are highly sought after to
manage, sort, collect, and display that data in a capsule
format so that the management can capture the most
critical insights before making their business decisions.
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13. Conclusion
We can say that the world of data analytics is no longer
restricted to data scientists, IT, and analysts. If a
company is going to be successful and productive today,
it must allow its business users to access easy-to-use
tools with sophisticated features and functionality so
that the entire team can work from the same roadmap
and stay on track.
Polestar Solutions helps large and medium organizations to generate actionable insights from
their data with our advanced and augmented analytics solutions.
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