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Marked as Technology Trend No.1 by Gartner, augmented data analytics draws scientists’ and businesspersons’ attention like a magnet due to the huge opportunities opening on augmented analytics market at present. 

Gartner that coined the term back in 2017 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...

Globally, the BI and analytics market was valued at $1.6 billion in 2018 and is projected to grow at a CAGR of over 39% to cross $11 billion by 2024

Key market players are investing into research of this advanced technology for the launch of new products as implementing this innovation greatly helps in maintaining a competitive marketing status. 

Therefore, meeting the demands of the data analytics trends, in 2016, Salesforce acquired BeyondCore to enhance its augmented data analytics platform by extending smart data discovery.

Generally speaking, augmented analytics as the future of data and analytics, demonstrates the wide functionality and impeccable delivery when it comes to the range of augmented data discovery while accelerating the speed of analysis and providing insights for taking optimal business decisions.   

Defining Augmented Analytics  

Augmented analytics implies utilizing statistical and linguistic technologies to improve data management performance and deliver business intelligence solutions for taking strategically justified decisions under the conditions of extreme data economy.

With the capability of transforming big data into smaller and more usable datasets, augmented analytics turns out to be the best method for implementing optimal data sharing and data analysis. 

An augmented analytics engine unlike the basic data analytical tools not only generates data charts and reports but highlights the information relevant for searching and identifying your particular business issue solution.

Hence, augmented analytics software builds and utilizes knowledge bases of business information to spot business trends by using machine learning algorithms and eliminating the process of slicing data upon inputting a query. 

With augmented analytics tools, user simply asks a readable question and the machine interprets and categorizes data applying natural language processing to present the findings promptly. This mechanism drives analysis to ‘conversational’ model when numbers will play a secondary role compared to the verbal input. Augmenting the data capabilities means that businesses can perform their operations much more effectively. Analysis through data augmentation guarantees achievement of high performance by feature engineering that goes through all sorts of variables without having to spend lots of time on crunching and delivers the interpreted and cleansed data swiftly.         

Why is Augmented Analytics Important?

Augmented analytics is perfect in terms of performing the assistive role to humans while enhancing humans’ interpretation capabilities and providing the deeper and more insightful look into data for searching its ‘business sense’ and defining its significance regardless of structural complexity.       

Data analytics software with data analytics characteristics makes use of machine learning so that it would be possible to implement and provide on a large scale the ‘human-like’ interaction with the data. 

In general, the analysis process starts with collecting data from public and authorized private sources like web or private databases. After data is available it needs to be prepared and organized for extracting the relevant insights. It is followed by transferring them to end-user alongside with sharing thoughts on action plan pertaining to results of analysis. 

Augmented Analytics Challenges 

The potential of augmented analytics has not been fully discovered yet, though, at this stage, it is clear that its implementation in business can considerably increase revenue if users know how to incorporate the results of data analysis into the business context of the specific industry. 

The main challenges for applying augmented analytics properly pertain to: 

  • making insights actionable
  • connecting them to vocalized business issues
  • encompassing the augmented data analytics in business priorities.

Apparently, this technology is not yet mature enough to be labeled as commercially viable and fully accessible, which means that demand for augmented analytics platforms overlaps the existing supply. 

Apart from tech giants (Microsoft, IBM, DataRobot, SAP) that actively implement augmented data analytics, a lot of start-ups emerge on the market indicating the immense industry interest to developing cutting-edge augmented analytics platforms that would provide the unbiased and accurate actionable insights into data.    

Augmented Analytics Use Cases

The idea of machine giving you comprehensive analysis of data charts and a clear textual reply seems equally appealing either to small businesses or big enterprises. 

In particular, augmented analytics market delivers solutions to customers in telecom, healthcare, government, retail, logistics, to name a few. 

Telecom has the biggest market share as the end-user in the modern augmented analytics market, as it activates the instantly insightful analysis of large amounts of data for optimizing the process of decision-making in this business sector. 

Nowadays,  augmented analytics examples can be found in everyday business practice of many enterprises due to improvements that augmented analytics brings to practically any business platform usability.

Augmented analytics can be used to automate the process of data extract-transform-load so that parties involved in interacting with the data could allocate more time on diving deep into deriving insights from data and catering recommendations in terms of taking mindful business decisions. 

Practically speaking, getting access to augmented data analytics means that, for instance, you can ask the augmented analytics tool to analyze the sales perspective for your service within a certain marketing campaign and, furthermore, to suggest what changes you could implement to achieve better sales results. 

Besides, there are lots of cases when advanced analytics is the most adequate method to leverage business operations. In particular, it delivers the business intelligence solutions for: 

  • developing fraud detection processes and operations monitoring
  • controlling service quality to prevent losing customers in various market niches
  • planning the strategy according to business demands 
  • understanding the mechanism of brand and customer loyalty within the specific business 
  • optimizing marketing operations 
  • avoiding high turnover and retaining employees.   

Augmented Analytics and Data Science 

The expertise of data scientists might cost the business really much to the point of being cost-prohibitive, especially to smaller businesses that might need augmented analytics service.

The McKinsey Global Institute stated that data scientists’ shortage in the U.S. would translate into vacancies for 250000 data scientists by 2024. 

But even under removing this limiting obstacle off the job market, data scientists would still need considerable time to meet all the business requirements due to lack of expertise within specific business sectors.

Data analytics that has practical business application, thus, requires the business experts for providing assistance to data scientists. Overall, looking deeper into augmented data analytics as  the domain of data scientists’ work, we can confidently assume that the lion's share of scientists’ work time is devoted to collecting and managing data while about 20% of data scientists’ remaining time is allocated to finding and extracting valuable insights.  

The main goal of augmented analytics is to flip this proportion in favor of generating insights and automate the process of collecting and managing data. This way, data scientists can avoid time allocation disproportion by saving data scientists’ tons of time on data collection, preparation and management.

Another ultimate goal of augmented analytics is to replace data scientists’ teams with AI that would deliver the required solutions ranging from collecting data to highlighting optimal decisions within a fraction of time.

However, data scientists should not fear professional ‘extinction’ in the near future. Instead, the tech advancements of automation should be regarded as the foundation for the new level in business productivity and organizing workflows more efficiently.

In the long run, augmented data analytics will not replace data science professionals but facilitate their work significantly with automating engineering-centered tasks implying data collection, data pipelining, and design creation of algorithm.

Concluding Thought 

Augmented analytics is designed to provide analysis by generating business insights automatically, which means decrease or total elimination of supervision within work process, and, thus, direct utilization of data insights by marketers and business owners for catering to their business needs hassle-free. 

The next expected generation of augmented data analytics tools could potentially bring huge changes in terms of boosting business performance, empowering it with a new digital strategy roadmap and instant unlocking the benefits of data through diverse and insightful reporting.

Innovative deep-learning functionalities of augmented analytics software demonstrate a variety of applications across all sectors of economy. Augmented data analytics provides insightful overview into strategic development for customer service, logistics, communications and healthcare, and promises to transform the digital landscape by streamlining business operations and increasing access to useful data.


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