Enabling Citizen Data Workers with Augmented Analytics

Author: Jakim Hartford

You may have heard the term Augmented Analytics. It sounds like another buzzword created by marketing teams to generate hype for a new product, right? Well, this “buzzword” has staying power and can provide tremendous value to organizations, big and small.

What is Augmented Analytics?

To boil it down to its simplest form, Augmented Analytics is a set of technologies that help people and organizations get value from data, faster and smarter. The term Augmented Analytics was first coined by Gartner back in 2017. At the time, it was used primarily to describe automation techniques in cumbersome data discovery and analysis tasks, that when combined with a quality dataset, will provide new insights and value for your business.

Augmented analytics is a class of analytics powered by artificial intelligence (AI) and machine learning (ML) that expands a human’s ability to interact with data at a contextual level. Augmented analytics consists of tools and software that bring analytical capabilities to more people, whether recommendations, insights, or guidance on a query.  Tableau

Since then, further advancements in Artificial Intelligence have boosted the power of Augmented Analytics and provided additional, user-friendly ways to interact with data and extract value from data – without having to be an expert programmer or full-fledged data scientist.

Are Augmented Analytics for you?

Augmented Analytics helps bring all users closer to the data conversation. For users or executives without deep technical knowledge, it allows for getting value from their data quickly. They can more easily find relevant data, ask better questions, and quickly uncover insights in the context of their business.

In short, augmented analytics speeds up data preparation work and enables more thorough, efficient, and intelligent analysis of data.

How can you benefit?

Core components of Augmented Analytics, Machine Learning (ML), and Natural Language Processing (NLP) technologies provide domain experts the ability to interact more naturally with their data. ML and NLP give access to advanced techniques for less tech-savvy users, and enable the following for citizen data workers:

• Agility: Increasing speed to insight
• Accuracy: Providing a more complete picture
• Efficiency: Automating operational tasks
• Confidence: Powerful analysis with context

What key features do Augmented Analytics tools provide?

1. Statistical techniques and data preparation: ML based algorithms with the ability to learn from data without relying on rules-based programming.
2. Natural Language Generation (NLG): A component of NLP which translates machine findings into words and phrases humans can understand, providing context and making data feel and sound more human.
3. Recommendations and automation insights: Data-driven insights that help determine your business strategy.

Combining these three components offers a deep dive into hidden patterns and provides a cohesive picture into what your raw data and insights can provide your users as well as how your team should inform the business of what to do next.

What technologies are available to start using now?

There are a variety of analytic technologies that offer Augmented Analytics features. In this article, we will be focusing on two leading visualization technologies that are prevalent across many enterprises: Tableau and Power BI.

[Read More: How can you decrease time to insight?]

Tableau

Tableau offers four features to get started with Augmented Analytics:

1. Ask Data
2. Explain Data
3. Ask Data for Salesforce
4. Einstein Discovery for Reports

The first two products, Ask Data and Explain Data, are available in Tableau’s server, desktop, and online products while the second two, Ask Data for Salesforce and Einstein Discovery for reports, can be leveraged within Salesforce.com.

1 – Ask Data

Ask Data allows people to use natural language to interact with data through a fast, powerful interface. It’s as simple as typing a question with guided search suggestions to get instant answers. Results come in the form of rich data visualizations that enable business users to get the insights they want from their data.

2 – Explain Data

Explain Data automatically provides AI-driven explanations for the value of a data point with a single click. Based on advanced statistical models, explanations are integrated in existing workflows, saving users time, and unearthing new understandings about their data that they may not have found otherwise. The interactive feature offers analysts and business users alike a jumping-off point to fuel deeper data exploration.

3 – Ask Data for Salesforce

Ask Data for Salesforce enables Salesforce users to ask any question using natural language and semantic search and receive answers in the form of insights, instantly generated reports, and recommended dashboards, tailored to the context of their business.

Because Einstein learns the structure and context of language in your organization directly from CRM, you spend less time coming up with the right question, and more time acting on intelligent insights.

4 – Einstein Discovery for Reports

Einstein Discovery for Reports automatically gives you AI-powered insights directly within your Salesforce Reports, helping you quickly understand what happened and why, so you can take action intelligently.

Einstein scans your report data quickly and thoroughly using machine learning and comprehensive statistical analysis. Then, with just a few clicks, users can dive into the associated Einstein Discovery story for deeper analysis.

[How did RevGen help our manufacturing client transition from a legacy data system to Tableau and AWS? Read the Success Story.]

Power BI

Power BI has offered Augmented Analytics for several years and the feature set continues to grow. Four core Augmented Analytics features of Power BI include:

1. Quick Insights
2. Q & A
3. Arria
4. Key Influencers, Decomposition Tree, & Smart Narratives

1 – Quick Insights

The Insights feature helps users easily explore and find insights such as anomalies or other trends in their data as they interact with and consume their reports. It notifies a user if there are interesting insights in the reports and provides explanations. It works straight out of the box on any report so the consumers can automatically start getting insights from their reports without any setup.

2 – Q & A

Sometimes the fastest way to get an answer from your data is to ask a question using natural language, such as “what were total sales last year.” You can use Q&A to explore your data using intuitive, natural language capabilities and receive answers in the form of charts and graphs.

However, Q&A is different from a search engine – Q&A only provides results about the data in Power BI datasets.

3 – Arria

With Arria NLG’s add-in for Power BI, you can narrate key facts and insights you want to highlight for your readers using natural language generation. Arria’s add-in offers three levels of customization:

1. Out-of-the-box insights: This option lets you tell the add-in what it needs to know to automatically generate insights from your dataset with a few clicks of configuration.
2. Extendable insights: In this option, you can quickly customize the out-of-the-box narratives by revising narrative scripts in NLG Studio.
3. Programmable narratives: This option gives you the most flexibility and enables you to create your own custom narratives for specific use cases, with the full programmability of NLG Studio.

4 – Key Influencers, Decomposition Tree, and Smart Narratives

• Key Influencers

The aim of this visual is to help users understand what factors influence a particular continuous or categorical variable of interest, known as the dependent variable. For example, HR may want to gain an understanding of what factors cause an employee to leave, Sales and Marketing may want to understand the main factors that lead to a sale, or Finance may want to understand what causes higher income and expenses.

• Decomposition Tree

The aim of this visual is to provide the user with the ability to perform ad hoc exploratory data analysis, and root cause analysis within a single visual. Root cause analysis is a method for identifying and understanding the main cause of problems or events. The decomposition tree supports this by automatically aggregating data by various dimensions and offering the ability for the user to drill into further details to understand what is driving each of the values.

• Smart Narratives

The aim of the smart narratives visual is to automatically summarize a report or visual within a report. The smart narratives visual identifies the key insights within the data, such as trends and patterns, then auto-generates text that describes what the data shows, providing a narration of your data.

Hopefully, this article has helped to provide clarity around the concept of Augmented Analytics, how it can add value to your organization, and what makes it real with features from leading technology platforms Tableau and Power BI. At RevGen, we feel that empowering citizen data workers broadens the reach of value realization from data and are excited to see more organizations adopt these kinds of tools.

While we have outlined the benefits of Augmented Analytics, keep in mind that the quality of data is always going to be key to leveraging its full capabilities. Fortunately, there have been many advancements on this front as well.

Interested in adding Augmented Analytics to your data toolbox but unsure where to start? Contact one of our technology experts to schedule a quick chat about your organizations’ needs and goals.

Jakim Hartford is a Business Intelligence professional and Architect at RevGen with over ten years of experience with Tableau and Power BI.