Augmented Analytics & Business Intelligence Services are Provided by Tellius
As the next generation of business intelligence, augmented analytics improves the self-serve model in several distinct ways. Using artificial intelligence, it simplifies data prep by automatically sourcing data from various databases and integrated tools. And once the data is in the platform, it allows users to self-serve ad hoc reports on a conversational UI using natural language queries.
Augmented analytics doesn’t
just simplify data analysis on the backend. It also delivers insights and
visualizations via natural language generation (NLG) to make data more accessible
and valuable to the average user. The software also slices and dices the data
in real-time to provide insight into the “why” behind the reported information
— not just the what, who, and when. And, over time, the algorithm develops a
deeper understanding of user intent, which allows it to deliver more targeted
and nuanced answers to complex questions.
However, with augmented analytics tools, you can
automate data preparation and simplify integrating with all of your data
sources — including data warehouses like Amazon Redshift, cloud platforms like
Salesforce, web service tools like Amazon S3, and analytics platforms like
Google Analytics.
When the data (and metadata) has been added to the
pipeline, everything from data cleaning to dataset unification is done for you,
automatically. This makes it possible for your data scientists, data engineers,
and developers to focus on creating new analyses to deepen insights.
Insight discovery is the step in the data analytics
process where the algorithm analyzes the data through the lens of a predefined
model to find answers to questions, such as quarterly revenue or customer
acquisition rates. However, because models traditionally have to be developed
manually by data scientists, insights can be lacking in specificity.
With augmented analytics, insight discovery is both
easier to initiate and more thorough. Queries can be made using natural
language and voice inputs instead of hyper-specific keyword entries, and
machine learning algorithms can dig through all of your data (no matter how
many rows there are) to find detailed, targeted insights to answer your
question.
However, with augmented analytics, time-to-insights
and human effort can both be reduced dramatically. Using natural language
generation, augmented analytics platforms deliver insights in real-time that
can be viewed from an online dashboard. These insights include both the direct
answer to the natural language query and the reasoning for the answer.
This means your decision-makers can consider all relevant factors before moving forward with their decision and can effectively share the knowledge across your organization to achieve better overall outcomes.
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