Augmented Analytics

What Is Augmented Analytics?

According to Gartner, Augmented analytics is the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation, and insight explanation to augment how people explore and analyse data in analytics and BI platforms. It also augments data scientists by automating many aspects of data science, machine learning, and AI model development, management, and deployment.

How augmented analytics fit into the modern data stack

As the modern data stack evolves, Augmented Analytics levels up insights generation by incorporating AI and Machine Learning in traditional BI. As more and more data is collected and transformed throughout the data pipeline, augmented analytics enables data consumers to make sense of this data and turn it into insights. It augments the largely manual and bias-prone process of preparing and analysing data, and sharing findings. It also fills the technical skill gaps in the analyst population and helps them spend the bulk of their time interpreting insights and deriving recommendations and actions. In essence, analysts can now control a lot more of the data value chain and increase their impact on organisations

Augmented Analytics solutions follow the two core principles of the modern data stack:

• End-user accessibility and scalability: augmented analytics solutions are cloud-based and require little technical configuration by the user.
• Technical barrier: augmented analytics solutions automate part of the data exploration process, lowering the technical barriers. These solutions are built with analysts and business users in mind.>

Why Augmented Analytics?

On an organizational level, Augmented Analytics addresses a number of problems currently faced across organisations:

Problem 1: It takes days to find answers to business questions, which is often too late to take action

Solution 1: Increasing speed to insight

• The time it takes to get insights is aligned with the pace of the business - shortens from days to minutes
• IT and data professionals are freed up to focus on strategic matters and special projects.

Problem 2: Analysts do not know where to look in the data

Solution 2: Providing a comprehensive picture

• Augmented analytics solutions can scale traditional analytical workflows by automating part of the data exploration process, testing every factor and combination of factors in the dataset. This enables in-depth analysis that wouldn’t be otherwise possible given the large amounts of the data collected.
• Users don't have to worry about not knowing where to look in the data or rely on limited domain knowledge to generate insights

Problem 3: Insights are not contextualised or easily consumable

Solution 3: Prioritising actionable insights that move the needle

• Numerous tools (e.g Kausa) rank results which allow users to prioritize insights that are relevant and actionable.

Problem 4: High dependency on data teams to conduct analyses

Solution 4: Improving data-democratization

• Individuals with little to no data expertise are empowered to become more data-driven without depending on data professionals.
• Data democratisation directly increases speed to insight, as it gets rid of slow internal bureaucracy and data congestion

What entails Augmented Analytics?

It’s one thing to ask “What are sales for Category A?”, which most tools in the current data stack can easily answer. Questions like “Why are sales declining for Category A?” are much more complex, requiring more processing power and machine learning capabilities, which are at the forefront of modern advancements in data and analytics. And that is where Augmented Analytics for businesses steps in.

When it comes to Gartner’s definition, there are three key components of Augmented Analytics that businesses should understand:

1. Machine Learning

Machine Learning (ML) programs are capable of adapting to different uses without being explicitly programmed to do so. In practice, this means machines process a ton of data until they get really good at completing tasks. ML can apply statistical models to business data and identify trends that directly impact the bottom line.

2. Natural Language Generation

Natural-language generation (NLG) refers to the process that translates a machine’s findings into words and phrases that humans can understand. NLG is not just about communicating data trends effectively, but also about transforming intangible algorithms into something human, so business users can internalize and apply the insights they’re receiving

3. Automating Insights

Data-driven insights determine business strategy. The combination of machine learning and NLG allows businesses to automate the labor-intensive process of analyzing data and communicating important findings to business people.

Use cases

Amongst the many use cases of Augmented Analytics, they are most commonly found across the following industries:

Examples of use cases by industry and function

• Gaming: Augmented analytics enables publishing studios to leverage their high volume of fast-changing data to increase growth and monetization
• User Acquisition: Drill down into every segment and channel to improve CLV and identify new sources of growth
• Player Loyalty: Quickly test and diagnose which factors have the biggest impact on player LTV and engagement across your portfolio
• Retention: Diagnose across platforms, user demographics, programs and geographies reasons behind changing churn rates
• SaaS and online platforms: Augmented analytics solutions enable these companies to tap into all the marketing, product and user data collected
• ○ GTM funnel: Optimise conversions across your funnel by looking comprehensively into every touchpoint, action, program, and activity that lead to sales
• Engagement: Comprehensively analyze what features, outreach, and actions drive the most engagement and increase DAU
• Retention: Identify at-risk accounts comprehensively and before it is too late to prevent churn and increase retention
• E-commerce: Augmented analytics solutions enable e-commerce players to leverage all their marketing, customer and transactional data
• Customer acquisition: Get a comprehensive view of factors impacting CAC and conversion rates across the entire marketing and sales funnel
• Revenue tactics: Get comprehensive and fast insights into what's driving average order value (AOV), purchase frequency, and customer lifetime value (CLV)
• Customer cohorts: Identify more granular customer cohorts leveraging all marketing, customer, and transactional data