As per Gartner, augmented analytics uses machine learning and artificial learning to gather and prepare data, generate business insights, and insight into the use of augmentation in exploring and analyzing data.
The exploration and analysis of data take place using analytics and business intelligence platforms.
It is known that analytics has become a crucial part of business operations via data monetization or data measurement.
Another point to note is that businesses often face challenges while analyzing complex and bulky data sets. This is further complicated because data is constantly growing and is related to the business process.
Data is the key to unlock answers to various questions that derive from a business’s daily activities (operations).
So it becomes essential for business leaders to have a solution in place that can track, identify, understand, and act on the best course of action for the business.
However, modern technological advancements have helped businesses identify, manage, and understand data with the use of
- Natural language processing
This article will help you understand the meaning, use, and benefit of augmented analytics.
Table of Contents
What is Augmented Analytics?
Augmented analytics uses artificial intelligence (AI) and machine learning (ML) technologies to transform the way analytics can be built, consumed, and shared across the business organization.
If explored further, augmented analytics can be used in your data analytics life cycle. This can help everyone involved in the business operations research analyse, understand and act on data.
Not just that, it can enable you to transform, democratize, and automate the use of data for all other users.
Because the augmented analytics approach is designed for automating the analysis process, it is essential to make it usable by the masses, the corporate employees.
Previously these analysis automation features were typically found in specialized data science and machine learning digital products.
But with continuous development at the technological front, it is now available for businesses of every size.
The constant use of visual-based data discovery tools has helped AI and ML capabilities get incorporated directly in analytics and business intelligence platforms.
What are the factors that incorporate augmented analytics?
There are several emerging, established, and still evolving techniques that incorporate augmented analytics. Some of which are:
Data preparation or ETL
The ETL (extract, transfer, load) phase encompasses the algorithms for detecting schemas and joins, data profiling, and data enrichment via automation of data transformation steps.
Analytics enables businesses to run always-on analysis in the background of their operation. There is no longer a need for manual data discovery with this technology, and real-time data changes can help gain timely insights.
Natural language generation and natural language processing
Natural language is now daily fodder for mainstream business intelligence platforms. The technology helps business leaders to generate insightful data descriptions that make it easy to understand complex information. These two technologies, as mentioned earlier, help business leaders analyze and interpret text or voice to generate engaging information.
These insights can be in machine-generated visualization, variance analysis, or calculation creation and get triggered when users post their queries. Machine-generated insights help in the auto-generation of any chart creation, calculation, or analysis.
What are the benefits of Augmented Analytics, and how is it being used in business today?
If you want to deploy effective augmented analytics, you have to take in the best machine intelligence and workforce aspects. This will allow your business to
- get faster insights
- consider data from various unique angles
- increase the operational productivity of your business
- help your employees make quicker and profitable data-driven decisions
Let’s delve a little deeper into the subject here and help you understand the benefits.
Delivering value at a faster pace
When you create the perfect blend of data science and artificial intelligence, you get
- Faster data preparation
- Speedy visualization
- Accelerated insights
- Higher productivity
Manual processes get replaced by intelligent algorithms and start recommending associations between various data sources.
Your employees can drag-and-drop data for auto-generating charts, maps, KPI objectives, and other helpful visualization based on the selected data set.
Uncovering the possibilities with better data insights
With traditional business intelligence tools, users had to carry out most of their daily routines on guesswork.
With advanced AI analytics, intelligent algorithms take the driving seat that provides contextual suggestions for uncovering insights.
They create relationships, correlation, and outliers and smoothen the process of decision-making.
Increasing business-wide data literacy
Businesses create and collect massive amounts of data, and everyone in the organization needs to get valuable data insights.
AI analytics helps promote data literacy by automating data insights generation, providing critical recommendations, and building confidence amongst the users to timely act on the processed data.
The capability of doing a faster search for insights using natural language and visualizing the insights with little effort helps create a data literate workforce.
Frequently asked questions on augmented analytics
Here are some of the most frequently asked questions related to augmented analytics. Here is a classical data value chain of BI and Analytics. Augmented analytics will make massive changes to each step involved in this value chain.
How businesses benefit from augmented analytics?
Augmented analytics helps business owners speed up their data processing and conversion speed using faster natural language and advanced AI analytics. This makes it easier for the user to search and visualize the insights and enhance organizational data literacy.
How augmented analytics work?
With the help of artificial intelligence and machine learning, business leaders can easily automate repetitive tasks of data preparation, pattern recognition, code generation, and visualization.
Augmented analytics provides suggestions for insights by automatically analyzing user behaviors and intent. With the infusion of conversational analytics, users can quickly gain insights by getting faster and accurate answers to their queries with the help of natural language.
What are the best practices in augmented analytics?
Businesses cannot afford to wait on the data lying around in the silos. They need to start using artificial intelligence in data analytics if they want to perfect their data sets. You can choose small, high-value projects that align with your business KPIs.
You must collaborate with co-workers across business functions to promote more transparency and develop trust through insights.
Most importantly, you need to provide your workforce with the appropriate tools and training to succeed with artificial intelligence data analytics.
The future of business intelligence lies within the successful implementation of augmented analytics
Data is now big data with countless devices and users creating more digital records with each passing second. This data is processed and stored in a complex manner with the help of powerful and robust analytical systems and AI assistance.
We hope that this article could shed some light on the importance of augmented analytics in your business.
Suppose you have not implemented augmented analytics or use traditional BI tools. In that case, it is time to go for the upgrade and create powerful insights to help you sail your business towards profitability.