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How the Augmented Analytics tools leverage Business Intelligence

Arpatech Aug 06, 2019
How the Augmented analytics tools leverage business intelligence

Augmented analytics is one of the recent developments for business intelligence tools. We are living in a digital age. Not only data, but big data: datasets have become so enormous, complex, and fast-moving that traditional BI solutions simply cannot handle them. Either they fail to get the data, to handle the data, to prepare the data, or simply to understand the data… but we must handle it! Data is everywhere and more of it is being produced all the time.

If your organization is going to thrive, it requires uncovering the insights hidden in your data. Digging through this data is hard, but with the correct tools, it can be done. But how will you identify the solution to your changing data needs? And what does all this have to do with augmented analytics?

Here we are going to answer what is augmented analytics and can it add value to the business.

What are Augmented Analytics?

Augmented analytics solutions have gained traction in the business intelligence world. Augmented analytics is intended in practical terms to facilitate more development and help create more revenue.

 According to Gartner:

“Augmented Analytics Is the Future of Data and Analytics”

“Augmented analytics uses machine learning/ artificial intelligence (ML/AI) techniques to automate data preparation, insight discovery, and sharing. It also automates data science and ML model development, management and deployment.”

In simpler terms, everything related to the process of analytics and business intelligence will be changed by artificial intelligence analytics by simplifying or eliminating some steps and thoroughly changing and improving the others.

How is Augmented Analytics Different than a Typical Business Intelligence Model?

If you compare augmented analytics with a usual business model, data analysts reach data through testing their hypotheses and theories while operating on a principle of knowledge. Although, we cannot doubt the knowledge of data analysts, but their viewpoints will always limit people in some capacity. It is quite difficult to show a thorough, unbiased and entirely correct conclusion without being aware of every factor that might influence the results. This implicates a lot of business are potentially working with limited views of their data landscapes, leaving money on the table.

But if we talk about augmented analytics, it invokes the power of machine learning to process way faster than humans can.  As machine learning has a very little human interference which makes algorithms uninclined to the biases.

Augmented analytics, therefore, empowers business users to perform on the insights they receive, allowing data scientists to concentrate on much more complicated queries.

How Can Augmented Analytics Add Significance to Business Intelligence?

Augmented analytics approach to business intelligence has countless advantages and, with the right self-serve analytics and data discovery tool, the average enterprise can expand these benefits and add even more value. We are listing some of the benefits to augmented analytics approach to business intelligence. Or in other words, here are just a variety of ways that self-serve augmented analytics can add value to business intelligence.

Sets Focus

Augmented Analytics solution helps analysts, data scientists, and inner IT staff to center on crucial projects as well as offer support to strategic problems which results in releasing them from centering on the business community’s day-to-day analytical requirements.

Empowers Business Users

As augmented analytics provide critical information itself, it enables business users to put a focus on their goals. Also, it provides objective metrics and allows data sharing which advance business interests.

Easy to Use Interface

In an easy-to-use interface, it offers complex, advanced techniques and tools to collect data from disparate data sources and enable confident, precise and accurate decisions to be made.

Simplified Toolkit

With augmented analytics simplified tools you can improve user adoption, data sharing, data popularity advancement, the integration of social BI within the organization and data and metrics literacy.

Enhanced ROI and TCO

Augmented Analytics delivers immediate, objective results and enhances ROI and TCO.

Apt Business Forecast

Another business benefit of this approach is perfect business forecasting and predictions. Augmented analytics offers metrics so that one can be sure that the right decisions are made and that the business takes suitable action with regard to products and services, pricing, competition, and other key business factors.

Augmented Analytics Tools and BI

In order to automate the operations of data, augmented analytics uses the power of machine learning which helps find insights as well as share the insights for business users, operational staff and data scientists.

Augmented data discovery, augmented data science and machine learning, and augmented data preparation are counted among the key capabilities of augmented analytics tools. Augmented data preparation has its focus on automating data intake into analytics systems in a system that involves data modelling, metadata adding, data profiling, data quality assurance, and catalogue storage. With augmented data discovery, relevant data is provided to users via automating, visualizing and narrating relevant findings. The skill-gap needed for model building to test new hypotheses or write algorithms can also be attained with the help of machine learning. Startups and big vendors have the potential to disrupt data integration, leading BI and analytics, data science and embedded data analytics vendors.

A more precise take on Augmented Analytics Tools

In their interaction with top BI vendors, an analytics manager must consider supporting the following five augmented analytics capabilities:

  • It has been predicted by Gartner that by 2020, natural-language generation and artificial intelligence will be a standard feature of 90% of modern BI platforms. So, users must be able to use the power of text-based-voice enabled technology in order to interact with data in a conversational mode.
  • BI tools with augmented analytics functionality should be able to use the power of language to narrate performance results in an interactive way leaving behind complexities.
  • The system should be easily able to forecast; what is the best visual for specific data, how to enrich data for deeper analysis and understanding, and how to clean and plan data for business use. This also comprises the use of algorithms to train predictive models for all types of cases of use, such as churn, attrition or behavior.
  • Augmented analytics tools should be able to drill more profoundly into what drives particular performance. These tools should be able to bring in their own insights to the process to help their hypotheses by seeking out results. The tools need to identify the behavior of outliers differently from the expected outcomes. Furthermore, describing the data, identifying key drivers and explaining what segments affect the results are essential for these algorithms.
  • These tools must have the ability to predict and predict a trend; recognize cluster groups and outliers by clicking a button like values. Additionally, this process involves the use of algorithms to train predictive models for all types of use cases based on churn, attrition or user behavior.
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