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Top Augmented Analytics Trends to Watch out for in 2022

As we step into 2022, two things are very clear: Covid will continue to disrupt business models and analytics, and artificial intelligence (AI) will continue to transform how we live and work.  Enterprise leaders are now shifting focus from just ‘surviving’ the uncertainty to augmenting their decisions with robust, self-service analytics.  

By automating the generation of insights, augmented analytics products help decision-makers navigate the data onslaught and get to results faster. Every year, with the introduction of new capabilities, augmented analytics continues to cater to a diverse set of industries and business users.  Here’s a look at some of the augmented analytics trends that will shape and disrupt business in 2022. 

Augmented Analytics = Advanced Analytics

“By 2025, a scarcity of data scientists will no longer hinder the adoption of data science and machine learning in organizations.(Source Gartner Research)  

The most noteworthy change in the past few years has been the addition of augmented analytics capabilities to traditional BI products. Vendors continue to make progress with natural language processing (NLP) to enable business users to query their data and a few have also added anomaly detection capabilities. 

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But, for augmented analytics to truly enable decision-makers to make a decision, there is an ever-growing need for more advanced analytics capabilities. Traditionally, a team of data analysts required weeks of experimentation and exploration to find a robust predictive analytics model that showed value. With augmented analytics, the capability to support deep-dive analysis and complex modeling has enabled decision-makers to independently explore the data to get actionable insights.  This has become especially critical in remote working set-ups where decision-makers are becoming more self-reliant.

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Advanced analytics capabilities will become the mainstay for augmented analytics products with: 

  • Embedded AI and Data Science Machine Learning (DSML) modeling capabilities to enable predictive and prescriptive analytics 
  • Live querying and self-service model runs to build, train and deploy data models with minimal expert interventions 
  • Explain the ability to ensure users have confidence in data quality, model robustness and insights interpretation

Data Storytelling at Scale

“No one ever made a decision because of a number. They need a story.” – Daniel Kahneman 

Typically, data stories have been the forte of analysts who are keen observers of trends and anomalies critical for business. But with the rise of augmented analytics capabilities, enterprises can now extend data storytelling at scale, uncovering hidden insights and enabling machines to tell stories to humans. 

But with most augmented BI tools, the need of the hour will be to create an engaging data story that compels action. Most of the ABI products provide extensive dashboarding capabilities that enable analysts to put a data story for end-users but for augmented analytics to become mainstream it is imperative that the products are enabling end-users to extract and explore relevant data stories. 

What this means for augmented analytics products is that they must enable:

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  • Context-specific insights with rich business summaries and visualizations
  • AI-driven intelligence layer to nudge business users to identify the inherent connections in their data
  • Explanations of analysis in a language that is easily understood for business users to trust the machine-generated insights
  • Seamless collaboration and presentation capabilities to enable decision-makers to use the insights presentation platform (potentially replacing traditional presentation media)

Enter Personalization 

“By 2023, overall analytics adoption will increase from 35% to 50%, driven by vertical- and domain-specific augmented analytics solutions.” (Source  Gartner Research

Digital personalization has completely changed the way we see and consume information online. With every interaction being personalized to our tastes and preferences, a massive trend in augmented analytics will be ‘personalized decision insights’ at scale. 

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Some vendors are already taking formative steps in building personalized journeys for users based on their behavior, from showing what they access most frequently to sending automated notifications for business-critical KPIs. Over the course of the next year, we will also see augmented analytics products roll out distinct domain and persona-based personalization features such as:

  • Prebuilt domain-based data connectors and ontology libraries
  • Domain-specific analyses and KPI’s pre-built into the product for quick setup
  • Autogenerated domain- and industry-specific insights and data stories

Pervasive Insights- Anytime, Anywhere

‘By 2023, 60% of organizations will compose components from three or more analytics solutions to build business applications infused with analytics that connect insights to actions (Source- Gartner Research ) 

In order to create a data culture where data and analytics is core to how decisions are made, decision-makers should be able to seamlessly query and understand what is happening and why – not just stats and figures. It is critical that business applications have built-in augmented analytics capabilities with automated delivery of actionable recommendations that are infused in their day-to-day workflows via application SDK’s to seamlessly embed these capabilities in any business application. 

As insights become pervasive across business applications, we could potentially see decision-makers interact with the augmented analytics interfaces via their workplace collaboration apps (like Teams and Slack) using their mobile devices. Also, with voice assistance gaining popularity, users will slowly transition to asking and getting insights (similar to asking Alexa for weather updates). This could potentially mean augmenting Natural Language Generation (NLG) technology to bring in a conversational flavor that is tailor-made for the business. 

Building a Culture of Curiosity

“By 2024, 80% of organizations will use iterative, experimental methodologies such as design thinking, lean startup and agile to support business and product design”(Source-  Gartner Research

An open data-driven culture and intuitive user experience is essential for the success of augmented analytics products since the end consumer is always a business user. Given that most business users don’t have backgrounds in coding or technology, they may not have the requisite training to interpret data, which is why they have traditionally been dependent on analysts for all their advanced analytics needs. This means that the entire design philosophy must focus on the business user so those insights are not lost in the unending data deluge.

Even the most thoughtfully designed augmented analytics products could face headwinds in terms of usage and adoption in the coming years. Product design teams will have to employ gamification techniques and conduct deep behavioral research whilst designing the end-user experience. A clear strategy to drive behavioral change will have to be designed to enhance data literacy that empowers them to solve business problems.

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