With increasing data complexity, businesses with large volumes of data are struggling to identify patterns and make decisions quickly. Modern analytics tools are less complex and more user-friendly, enabling greater access and self-service. However, many data processes are manual and prone to personal bias. Relying on business users and data scientists could result in them exploring their own biased hypotheses, missing important findings and drawing their own incorrect or incomplete conclusions. This could negatively impact decisions, actions, and outcomes.
Augmented analytics is the next step in the evolution of analytics, according to Gartner. Augmented analytics enables business users and data scientists to use machine learning (ML) to automatically find and visualize relevant findings without having to build models or write algorithms. Data scientists can use augmented analytics to analyze data without any bias or preconceived notions of relationships among variables in data. It reduces the need for specialized skills to create and manage an advanced analytics model. It opens up data science and ML content creation to citizen data scientists and developers who must embed ML/AI into applications. Highly skilled data scientists have more time to focus on creative tasks and on building the most relevant models.
How Augmented Analytics Transforms Each Part of the Analytics Workflow
Augmented analytics capabilities accelerate the time-to-insight for business users. Automated insights embedded in enterprise applications and conversational analytics could reach beyond data scientists and assist operational users in business transformation. It augments their analysis by using ML algorithms to automate three main parts of the data and analytics workflow.
1. Preparing the Data
Preparing data for analysis is the most time-consuming task facing data and analytics users. Most modern analytics and BI platforms offer basic data preparation capabilities for joining, data manipulation, and transformation. This leaves much of the data preparation work to the business users, data scientists, or IT staff. It also creates business risk due to a lack of governance, as organizations give more users the ability to build analytic content.
Augmented data preparation uses algorithms to find relationships in data. It also profiles and recommends the best approaches for preparing data and enables users to combine more data sources from both trusted and external sources. It is faster than using traditional data integration approaches. It supports the deployment of advanced analytics to distributed users, potentially using different tools.
2. Finding Patterns In the Data and Building Models
Data discovery methods used in modern analytics and BI platforms enable business users to visually identify relationships and patterns in data, using various interactive techniques. With augmented analytics, instead of a user manually testing the data, algorithms for detecting patterns and relationships are automatically applied to the data. Only statistically significant and relevant results are presented to the user, in the form of smart visualizations. Applying these algorithms minimizes the risk of missing important insights in the data.
AutoML enables modern data science platforms to streamline model management processes. At a high level, AutoML is about using machine learning techniques to automate the process of applying machine learning. Augmented analytics includes AutoML but extends automation beyond the feature engineering and model selection to model deployment, model management, and model operationalization, including model tuning and life cycle management.
3. Sharing and Operationalizing Findings From the Data
Modern analytics and BI platforms have many advanced data visualization and collaboration capabilities that enable social sharing. However, visualizations often obscure truly significant insights and many users are lack the ability to interpret them.
Augmented analytics platforms with Natural-Language Generation (NLG) have the ability to mine large volumes of data, identify patterns and share that information in a way that is easy for humans to understand. NLG enables the platforms to highlight key insights, trends, correlations, and predictions in the data that are not readily apparent from the visualization alone. Together with visualization, this informs the user about what is most important for them to act on in the data.
