What is Augmented Analytics and How it Transforms BI

"Business Intelligence" is a legacy technology used to present structured business metrics as dashboards. In today's digital world, the sheer volume of available data and the need for real-time, rapid decisions renders BI obsolete.

To become insight-driven, organizations need to deliver the most actionable intelligence to the right decisonmakers at the right time. Hence, Augmented Analytics was born to fuse the latest in artificial intelligence with data storytelling and automation.

Gartner was the first to coin the term “Augmented Analytics” in 2017. The research firm defined it as an approach to automate data management and processing to prepare data, derive insights, and deliver results. Forrester describes these solutions as “Augmented BI Platforms” that enable last-mile intelligence for business pros and democratize business insights.

The components of augmented analytics include:

• Machine Learning and Artificial Intelligence
• Natural Language Generation
• Automation
• Data Visualization and Exploration

In short, augmented analytics and BI empower executives and business users to transform raw data into actionable insights that drive business outcomes.

Gartner says, “Predefined manual dashboards will be displaced by automated, conversational, and dynamically generated insights that are delivered to users as personalized data stories.

Tech Target's breakdown of how augmented analytics capabilities go beyond traditional BI and self-service BI to deliver AI-assisted insights to users.

Benefits of Augmented Analytics

Augmented analytics will shift advanced analytical power to the information consumer — the augmented consumer — giving them capabilities previously only available to analysts and citizen data scientists. The major advantages include:

Unlocking Data Potential

When using traditional Business Intelligence tools, users require hypotheses or questions to begin looking for insights. However, with augmented data analytics, algorithms carry out all the grunt work and offer contextual suggestions that help derive more granular insights that the human eye would have otherwise missed. When combined with AI-based data visualization platforms, users can discover correlations, relationships, and outliers that “go beyond” simple questions.

Fostering Trust

Machine learning technologies learn more about users at each point of interaction and use that information to improve future insights. This combination of big data and augmented analytics allows the system to personalize recommendations based on factors such as their role in the organization, intent, business context, and skill set. Over time, these actionable recommendations become more accurate and relevant, which will improve users’ trust in business intelligence and data.

Improving Data Literacy

In our world that will only become more data-centric, businesses will continue to harness data to improve decisions and outcomes. Regardless of a user’s role or responsibilities, the ability to understand, communicate, and take action based on data analytics is invaluable. Data literacy and self-service analytics result in better and faster decisions at all levels of the organization.

5 Ways Augmented Analytics Transform Business Intelligence

Augmented analytics was touted as the future of business intelligence and analytics – and now that future is here. Let’s take a look at how augmented BI helps organizations elevate their data consumption beyond traditional BI:

1. The integration of technologies like AI and ML with data analytics creates an ecosystem that can extract the most value from data, gain new insights, and share them throughout the organization.
2. Augmented analytics lay the foundation for data literacy, which democratizes data. With augmented analytics tools in place, users do not need to rely on technical resources to draw data insights and actionable recommendations.
3. Interactive, visual-based analytics combined with automated insight discovery and exploration enables business users to receive easily consumable insights in a way everyone can understand.
4. Leaders can receive relevant insights delivered to them and real-time updates in response to data changes, which can significantly expedite the decision-making process.
5. Augmented analytics can reduce time-consuming report creation and optimize decisions and actions. Machine learning algorithms combined with the explanation of actionable findings reduce the risk of missing important insights in the data, in comparison to manual exploration.

12 Features of an Excellent Augmented Analytics Product

While looking for the top augmented analytics product, use this feature checklist to make sure you’re getting the best ROI for your budget:

1. Augmented data preparation to profile, model, catalog, group, classify, and enrich data.
2. Cloud analytics to support building, deployment, and management of analytics in the cloud, based on data stored both in the cloud and on-premises.
3. Data source connectivity to enable users to connect to, query, and ingest data, without sacrificing performance.
4. Automated insights that automatically generate findings for end users and deliver anomaly alerts based on changes in data.
5. Data visualization capabilities that are highly interactive and dynamic, and encourage consumption of data.
6. Data storytelling that combines interactive data visualization with narrative techniques in order to package and deliver insights in a compelling, easily understood form for presentation to decision makers.
7. Data Catalog that automatically generates and curates a searchable catalog of analytic content, thus making it easier for analytic consumers to know what content is available.
8. Natural language query (NLQ) that enables users to ask questions and query data in plain language either typed into a search box or spoken.
9. Natural language generation (NLG) that automatically creates simple and understandable descriptions of answers, data, and insights.
10. Reporting abilities to synthesize data and create and distribute reports to users on a scheduled basis.
11. Security capabilities that enable data governance, administering of users, and auditing of platform access and authentication.
12. Manageability to track usage of the platform and manage how information is shared.

Source: Gartner 2021

Augmented Analytics for Everyone

If you’re looking to become insight-driven with augmented analytics, then MachEye, powered by LLM analytics, has you covered! Elevate your organization's data literacy and create better decision intelligence with an analytics copilot. By combining intelligent search, actionable recommendations, and interactive stories in one cloud-native platform, MachEye transforms the future of analytics. Get started with a free trial today!