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Augmented Analytics Elucidated

In the current epoch of digital life, where automation comes as an imperative stage particularly when it comes with Business Intelligence. Business Analytics is all about automation of data preparation and advanced analytics tasks. Augmented Analytics guarantees the hope for Data Scientists. In Augmented Analytics, Machine Learning (ML) algorithms and Natural Language Processing (NLP) techniques in tandem achieve automated Data Preparation and Business Intelligence.

This newfangled technology comes up with some sophisticated tools to the everyday business users, so they can quickly gather, collate, prepare, and analyze data to extract on-demand intelligence whenever they need it during their daily work. This neoteric approach to Business Analytics can easily transform any business operation, small, medium, or large without additional investment on skilled manpower or data center setups. The cornerstone is to get Advanced Analytics to everyone’s desktop.

Augmented Analytics: The Future of Data Science

One of the top marketing company Gartner published a report on Critical Capabilities for BI and Analytics Platforms report, where it stated

“By the year 2021, the number of users of modern BI and analytics platforms that are differentiated by smart data discovery capabilities will grow at twice the rate of those that are not, and will render twice the business value.”

In the report, Gartner characterizes Augmented Analytics as:

“An approach that automates insights making use of machine learning and natural-language generation, marks the next wave of disruption in the data and analytics market.”

Why you should care about Augmented Analytics? Here is the answer!

Before you know about ‘Augmented Analytics’ completely, you should know how it solves the problem. That is, we must understand why generating acumens from data remains a huge challenge for almost all businesses.

At this time, everyone agrees that data analytics, like vitamins, is good for a business, and has the potential to drastically increase traction and revenue. However, the problem is that data analytics is not exactly the easiest thing to bring off in the real world.

One needs to understand that, data is not like a vitamin that you can just pop in your mouth and be done with it.

In order to go from raw data to insights, you need to go through many technical steps, including

1) Gather the data from multiple sources.

2) Clean the data so it is ready for analysis.

3) Conduct the analysis properly.

4) Generate insights, and finally

5) Communicate those insights with the organization and transform them into action plans.

Is Augmented Reality going to be the next trend in Digital Disruption?

Human thinking is often disquieted by personal biases, preconceived knowledge, and emotional judgments, which Augmented Analytics can accurately balance with completely data-centric, unbiased outcomes.

So how mature is Augmented Analytics outright?

However not mature but growing at a tremendous rate for a couple of years..!

All Augmented Analytics Algorithms work on three main stages:

It includes:

• Data Preparation and Discovery.
• Signal Detection.
• Actionable Insight Generation.

Stage 1: Data Preparation and Discovery

Right off the bat, this stage brings forth crucial players such as IBM Watson Analytics, Tableau Insights, and Qlik Sense.

Here, Augmented Analytics algorithms cater a great complement to existing data scientists or analysts but do not have the ability to completely substitute them.

Furthermore, the algorithm’s primary objective is to automate boring data preparation tasks such as data cleaning, data labeling, and data collection.

It may be able to discover some correlations and anomalies in the data, but most of these detections are noise, and data scientists still need to parse out real signals manually.

Stage 2: Signal Detection

At this stage, the analytics algorithm can detect true signals in a company’s data with extreme reliability. However, it is unable to connect these inventions with business situations or business actions.

Many companies are likely to reach this stage within a couple of years.

Stage 3: Actionable Insight Generation

The final stage is where the augmented analytics engine can directly interface with executives in the company with little or no need for input from a business analyst or data scientist.

A large knowledge base of former business cases will be developed to help the augmented analytics systems connect trends in the company’s data to the larger context of the business. It can then deliver concrete action steps based on these insights.

More crucially, the system will be able to keep track of the implementation of these actions and provide additional insights into what the company can do better next time to maximize its operational effectiveness.

Here, the augmented analytics engine is not only an alternative for business analysts but can also do a lot of things current analysts cannot perform.

This stage is definitely a significant leap compared with the other previous stages. It is believed that many businesses will start to reach this stage in 5–10 years.

The Visible Benefits of Augmented Analytics in the Forerunning Industry

Some fundamental benefits of Augmented Analytics include:

• Given the Data Preparation and Smart Data Discovery capabilities of AR, data experts can now pay attention to more strategic business goals and methods to achieve those goals.
• Many Citizen Data Scientists will now be encouraged to take ownership of their own decisions.
• Advanced tools ensures positive outcomes from routine Analytics exercises in enterprises.
• The solutions in the Augmented Analytics world will not only be more precise but clearly measurable and repeatable in many areas of businesses.
• The automated capabilities of these new Data Management technologies will naturally enhance user adoption and data literacy.

Benefits for the Business Users include:

• Augmented Analytics supports for day-to-day business decisions.
• Augmented Analytics bring forth insight, perspective, and analysis.
• Augmented Analytics results in quick hypothesis and prototyping.
• Augmented Analytics enables improved agility for business development.
• Timely and accurate decision-making is done by Augmented Analytics.
• The emergence of power users and data popularity is by Augmented Analytics.
• Transformation to citizen data scientists is paved way by Augmented Analytics.

On the other hand, benefits for Data Scientists include:

• Augmented Analytics helps in reduction in day-to-day requests.
• Augmented Analytics supports the ability to focus on strategic projects.
• Augmented Analytics focus on projects that require 100% accuracy.
• Augmented Analytics provides the ability to achieve mature modeling goals.

How Augmented Analytics Prepares Your Small Business for the Future?

• Augmented analytics craves out busywork.
• Augmented analytics assist you to ask questions faster.
• Augmented analytics conjectures the right path.

Final Thoughts

Augmented Analytics are going to rule the world in the forthcoming years. It is good to know about them in advance!

Hope you would have enjoyed!

Stay Tuned!