Effective decision-making relies heavily on data. It identifies issues, uncovers new opportunities, and helps us understand performance changes, making it easier to achieve desired goals and benchmarks. In this discussion, we will explore the significant architectural shift in business intelligence, delve into augmented analytics, and examine the current use cases and benefits of this technology.

With the increasing complexity of data, traditional business intelligence platforms and self-serve BI tools have struggled to provide valuable insights. Businesses often face a dilemma: either limit their analysis to a few variables, compromising the quality of insights, or invest significant time in data preparation, analysis, and model-building to obtain detailed information.

However, businesses cannot afford this compromise. To fully capitalize on new opportunities, the time to gain insights must be quick, and decisions must be based on comprehensive data analysis.

What is augmented analytics?

First introduced by Gartner, the term “augmented analytics” refers to the use of machine learning (ML), artificial intelligence (AI), and various natural language processing (NLP) technologies, such as natural language generation and natural language query, to enhance and expedite data analytics.

As the latest advancement in business intelligence technology, augmented analytics improves upon traditional data analysis models, making insights more accessible to all business users, even those without extensive technical expertise. With augmented analytics, data analysts and citizen data scientists can uncover deeper, more detailed insights in minutes, a task that might take a professional data scientist much longer with conventional BI tools.

The three generations of BI

Business intelligence can be categorized into three generations of technology, each distinguished by its level of accessibility, speed, dataset size, and quality of insights provided.

Traditional Business Intelligence

Traditional business intelligence is the earliest form of BI and is known for being the least user-friendly. It relied on legacy systems requiring IT professionals to aggregate data and structure OLAP cubes for analysis. The main outputs were reports and static dashboards, with IT handling all aspects—from manually retrieving and cleaning data to building models, mining data, and generating detailed reports needed by various departments and teams.

The major drawback was the time it took to deliver critical insights, often weeks, as IT had to design models and reports. Additionally, traditional BI systems only offered retrospective views on queries, lacking the ability to provide predictive insights. Consequently, decision-makers often had to rely on intuition when addressing new opportunities and significant problems.

Self-Service Business Intelligence

Self-service business intelligence solutions significantly enhance traditional data analytics models. These solutions empower data analysts and other business users to generate their own reports and data visualizations using the models created by IT. Available to everyone in the organization on demand, self-service analytics enables users to create ad hoc reports, pose follow-up questions, and delve deeper into insights for more specific information.

However, self-service BI still struggles with the “last-mile” problem due to its visualization interface for data exploration, which relies on traditional drag-and-drop measures and dimensions that can be complicated for business users. As data volume and complexity have increased, even expert data analysts find it challenging to manually analyze every combination of data points using self-service BI tools to uncover significant insights.

Augmented Analytics

As the latest evolution in business intelligence, augmented analytics enhances the self-service analytics model in several unique ways. By leveraging artificial intelligence, it automates data preparation by sourcing data from various databases and integrated tools. Once the data is in the platform, users can create ad hoc reports using natural language queries on a conversational user interface.

Augmented analytics not only simplifies backend data analysis but also presents insights and visualizations through natural language generation (NLG), making data more accessible and valuable to the average user. The software can dynamically analyze data to explain the “why” behind the reported information, not just the what, who, and when. Over time, the algorithm gains a better understanding of user intent, enabling it to provide more targeted and nuanced responses to complex queries.

These capabilities allow data analysts and citizen data scientists to design custom visualizations and generate insights more quickly and easily than ever before.

How does augmented analytics work?

While augmented analytics shares a similar workflow with other forms of BI, it enhances the four major stages of data analysis using machine learning (ML), natural language generation (NLG), and artificial intelligence (AI). Here’s how it works:

Data Preparation

Data preparation encompasses all the steps needed to ready data for query and analysis, including collection, cleaning, connection, and validation of datasets. This stage typically requires the expertise of developers and data scientists.

Augmented analytics tools, however, automate the data preparation process and simplify integration with various data sources, such as data warehouses like Amazon Redshift, cloud platforms like Salesforce, web services like Amazon S3, and analytics platforms like Google Analytics.

Once the data (and metadata) is added to the pipeline, tasks like data cleaning and dataset unification are automatically handled. This automation allows data scientists, data engineers, and developers to concentrate on creating new analyses and gaining deeper insights.

Insight Discovery

Insight discovery is the phase in the data analytics process where algorithms analyze data using a predefined model to answer questions like quarterly revenue or customer acquisition rates. Traditionally, data scientists manually develop these models, which can result in less specific insights.

With augmented analytics, insight discovery becomes easier and more comprehensive. Users can make queries using natural language and voice inputs instead of relying on specific keywords. Machine learning algorithms then sift through all the data, regardless of volume, to uncover detailed, targeted insights that directly address the user’s questions.

Insights Sharing

Insights sharing involves delivering data insights to end-users through reports or visualizations. Traditionally, this process requires IT teams to manually generate data reports and create charts and graphs for business users to interpret.

Augmented analytics significantly reduces the time and effort needed for insights sharing. Using natural language generation, augmented analytics platforms provide real-time insights that can be accessed via an online dashboard. These insights include both the direct answer to the natural language query and the rationale behind it.

This allows decision-makers to consider all relevant factors before making decisions and facilitates the effective sharing of knowledge across the organization, leading to better overall outcomes.

What are the benefits of augmented analytics?

Real-Time Insights

Unlike traditional BI and self-service analytics software that require significant time for processing, augmented analytics solutions can provide insights within minutes. Users simply ask a question and press enter. The AI rapidly analyzes billions of rows of raw data in milliseconds using ML models, generating detailed, natural language insights and visualizations to clearly convey the information. Since these insights are delivered in real time, business users at all levels can quickly act on time-sensitive opportunities.

Deeper Analysis

Extracting valuable insights from detailed data has always been challenging. Augmented analytics allows you to examine data at a granular level by automatically querying billions of data points and numerous variables within seconds. This capability provides more accurate insights, helps understand the underlying reasons behind results, and improves predictions for future outcomes.

Democratization of Data

Previous BI technologies relied heavily on IT teams for data processing and analysis, but augmented analytics software allows data scientists and analysts to focus on more critical tasks. With augmented analytics, anyone in the organization, regardless of their data or coding skills, can access detailed insights in real time and create custom visualizations to better understand the data. This increased accessibility is expected to boost organizational adoption of BI from 30% to 50%.

Breaking Down Data Silos

In traditional and self-service BI systems, custom models are needed for answering specific queries, but these models often overlook some of the available data. For instance, a marketing team might analyze data using a particular KPI and pull information from their cloud-based CRM and analytics tools. However, without integrating data from sales and customer support, they could miss key opportunities to enhance their campaigns. Similarly, the insights generated might remain isolated within the marketing team, creating an organizational silo.

Augmented analytics transforms this approach by creating a unified pool of data and insights accessible across the organization. When a natural language query is performed, data from all integrated platforms, databases, and big data storage is analyzed to provide a comprehensive answer. ML models are continuously updated with each query to reflect new data connections, making this information available for use across the organization. Additionally, insights and visualizations can be shared through dashboards, facilitating effective cross-departmental collaboration on multi-departmental projects.

Better Business Decisions

Waiting weeks for critical insights can result in missed opportunities, while rushing the process may lead to overlooking important details in the data. Augmented analytics addresses both issues by analyzing billions of records within seconds. It also provides additional context by explaining the “why” behind each insight, offering business users the detailed knowledge required to make informed decisions.

Augmented Analytics Use Cases

In the Pharmaceutical Industry: Enhancing Go-to-Market Strategies

Pharmaceutical and biotech companies require extensive data, including prescriber, patient, and market share information, to develop effective market access strategies and gain commercial insights. Augmented analytics significantly reduces the time spent on data analysis by using AI-driven techniques to automate the process. Organizations can quickly delve into the reasons behind performance fluctuations and access natural language insights, enabling data analysts and business teams to make crucial decisions without the need for extensive coding.

For Financial Lenders: Evaluating Credit Risk

In assessing applications for mortgages, car loans, or small-business loans, financial lenders need to evaluate customer risk to avoid poor investments and defaults. Augmented analytics enables a detailed analysis of all relevant data points, allowing for a rapid determination of customer attributes and behaviors associated with higher risk.

Machine learning algorithms automatically evaluate every data combination to assess risk and pinpoint key factors such as age, credit score, and savings, removing the need for manual data analysis. This helps lenders make more informed decisions about loan approvals and amounts while accelerating the approval process for eligible customers.

Consumer Goods Companies: Enhancing Customer Satisfaction

For consumer goods businesses and retailers, data is crucial in guiding decisions related to marketing, sales, and customer service. Augmented analytics provides real-time insights into product sales, customer churn, satisfaction levels, and cart abandonment rates.

Rather than presenting insights through cumbersome Excel spreadsheets or data-intensive reports, augmented analytics uses natural language generation to deliver key metrics through visual charts and explanatory text. This approach allows brand managers and sales teams to quickly access the information they need. Additionally, augmented analytics helps identify anomalies and understand the reasons behind specific outcomes and customer behaviors, enabling companies to engage customers more effectively and enhance their satisfaction.

Augmented Analytics with Technoforte

Technoforte offers business intelligence and analytics services that can help your business:

• Handle billions of records and terabytes of data
• Integrate with diverse data sources, like Snowflake and Redshift, as well as data warehouses and data lakes
• Conduct smart data processing and use the computing power of cloud-based data warehouses when needed, without moving the data
• Prepare and transform data at scale to get it ready for analysis
• Create ML models and find segments to improve business outcomes
• Use machine learning and artificial intelligence to pinpoint patterns, detect anomalies, highlight key drivers, and determine reasons for trend changes in large amounts of data
• Use natural language query and natural language generation to deliver data insights
• Present insights with a combination of visuals and natural language to business teams
• Scale elastically to deal with billions of records and dozens of data sources

Transform the way your business deals with data. Speak with our experts today!

Technoforte is an IT Services company with over three decades of experience in the industry. Read more about our Managed IT services and IT Staff Augmentation services.