digital marketing

Posted On : 27th May, 2019 by Pranjal Kaushiley

As businesses become inundated with data, augmented analytics becomes crucial for presenting only what’s important for users across the business in their context to act upon at that moment. It drives less biased decisions and more impartial contextual awareness — transforming how users interact with data, make decisions and act on insights. 

The Requirement for Better Analysis 

The power of analysis has been transforming organizations and industries. Subjective decisions such as employee recruitment, marketing, product branding, etc. are increasingly data-driven, and objective decisions such as logistics planning or customer risk analysis are being made using more sophisticated analysis on more complex data than was previously available. 

With more and more organizations transforming digitally, expansion in the use of data science and Machine Language/Artificial Intelligence for data analysis is inevitable. Companies can leverage it to create new differentiated analytic applications and embed Machine Language/Artificial Intelligence into existing applications. 

The scarcity of expert data science skills, however, has become a significant barrier. By automating much time consuming and bias-prone tasks, augmented analytics expands those capabilities with more widely available skill sets — the business analyst and the application developer (a new breed of citizen data scientists). In addition to expert data scientists, augmented analytics allows these roles to generate insights and create augmented-analytics-assisted models to embed in applications. 

What is Augmented Analytics? 

To define Augmented Analytics, it is basically a form of analysis that automates data insight using machine learning and natural language generation/processing. Furthermore, it uses machine learning (ML) and natural language processing (NLP) to enhance data analytics, data sharing and business intelligence. The concept of augmented intelligence, an overarching concept to augmented analytics, was introduced by the research firm Gartner, in their 2017 edition of the “Hype Cycle for Emerging Technologies.” 

Future of Augmented Analytics 

The Gartner report published on July 27, 2017, entitled, ‘Augmented Analytics Is the Future of Data and Analytics’ predicted that ‘by 2020, organizations that offer users access to a curated catalogue of internal and external data will derive twice as much business value from analytics investments as those that do not.’

Key Findings  

  • Analysis will become increasingly embedded in processes, devices and applications; it will, therefore, be both more pervasive and less visible — flipping the burden of promoting its use from the user to the machine.  
  • Augmented analytics will enable easier interactions with analytic tools by pushing more of the complex analytics workload away from the user and over to the machine.  
  • The shortage of data scientists will gradually cease to be a constraint as a variety of automation and people-centric innovations reduce the need for high-end specialists.  

Another report by Gartner identified Augmented Analytics among the Top 10 Strategic Tech Trends for 2019. 

According to a more recent report published by Allied Market Research, titled, “Augmented Analytics Market by Component, Deployment, Organization Size, Business Function, and Industry Vertical: Global Opportunity Analysis and Industry Forecast, 2018 – 2025,” the global augmented analytics market was valued at $4,094 million in 2017 and is projected to reach $29,856 million by 2025, growing at a CAGR of 28.4% from 2018 to 2025. 

Influence of Augmented Analytics on Businesses  

Relevancy is a key criterion for analytics. Users should not have to look for information — the system needs to recognize that a piece of information is relevant to the user and deliver the insights preemptively.  

By leveraging sophisticated analytics techniques and algorithms in an automated environment, these solutions allow for advanced use, manipulation and presentation of data and simplify the analytical process for the average user, so that users are presented with clear results. As platforms and solutions have matured, augmented analytics has evolved into a self-serve environment, so that business users can leverage these sophisticated tools to get answers to questions, identify problems and opportunities and share data with other users. 

The augmented analytics approach provides tools for better clarity and insight into and, as this approach evolves, it continues to change and disrupt the more traditional forms of analysis.  

Augmented analytics can reduce time-consuming exploration and the identification of false or less relevant insights. It applies a range of algorithms and ensemble learning to data in parallel, generating the most important insights and explaining actionable findings to users. This reduces the risk of missing important insights in the data in comparison to manual exploration. It also optimizes resulting decisions and actions.  

As stated above augmented analytics automates the data insights by utilizing machine learning and natural language generation for automating data preparation, data discovery, and insight sharing. It embeds artificial intelligence in business intelligence tools to make the analytics work easier for citizen data scientists and other business users. It helps expert data scientists in focusing on specialized problems, provide most relevant actionable insights to decision makers, and reduce the time spent on exploring data.  

Benefits of Augmented Analytics 

It is quite clear that augmented analytics can benefit businesses in a myriad of ways. Augmented analytics does the heavy lifting for you. By analyzing exhaustive data combinations, augmented analytics can pinpoint which factors are truly influencing your output. Augmented analytics allows business users to get answers to their questions directly, in a matter of seconds. No longer will they have to wait for data analysts to fill the gap. When your data analysts aren’t bogged down with questions, they have more time to focus on deeper research that machines can’t yet support, providing more bang for your buck. Ultimately, augmented analytics simplify the data analysis process so you can gain important insights into your data that can inform your business strategies.  

Moreover, they amplify efficiency. Without a long, drawn-out process to get business questions answered, your team can work with more up-to-date and relevant data. Quick, agile analytics are increasingly driving revenue. They can play a key role in edging out the competition and, ultimately, propelling your business forward. 

These benefits are the foundation of a solid business strategy that addresses the ever-changing needs of consumers and shifts in the market. 

 Source 

Gartner & Allied Market Research