What is augmented analytics?
Artificial intelligence (AI) and machine learning (ML)-based augmented analytics are a subset of analytics that increase a human’s capacity for contextual data interaction. The term “augmented analytics” refers to the tools and software that enable more individuals to access analytical capabilities, such as suggestions, insights, or query advice.
“Augmented analytics” is the use of enabling technologies like machine learning and AI to help with data preparation, insight creation, and insight explanation to supplement how humans explore and analyze data in analytics and BI systems, according to global research and consultancy firm Gartner.
The part machine learning plays in enhancing analytics
Machine learning, a field of computer science that uses data to extract algorithms and learning models, is used in many of the augmented analytics features. Machine learning makes analysis easier by often reducing or eliminating laborious work so that people may get insights and use data to make decisions more rapidly. For a more in-depth investigation, this entails preparing, evaluating, filtering, and cleaning the data.
The outcomes of complex algorithms are frequently exposed as suggestions by machine learning capabilities within BI tools. In addition, some enhanced analytics systems employ machine learning (ML) to gradually understand the semantics of the business, the organization, as well as user preferences. This enables the analysis to be more effective and tailored in the context of the business.
Augmented analytics vs. automation
It’s vital to recognise the distinction between automating processes, as many technologies do, and automating the decision-making that analytics informs. Automation is a typical component of augmented analytics systems. While augmentation offers a framework for underlying technology to help users to uncover insights they would not perceive or discover otherwise, automating data-driven decision-making eliminates the need for human competence.
Who is augmented analytics for?
Because these technologies enable business users and executives to swiftly extract value from their data without requiring in-depth technical knowledge or data experience, augmented analytics is incredibly valuable to them. With the aid of augmented analytics, business users and executives may access pertinent data, formulate the best queries, and swiftly obtain insights in the context of their company.
While augmented analytics primarily benefits people without deep analytical experience, it also speeds up data preparation and more detailed analysis activities for analysts and skilled users.
What are the benefits of augmented analytics?
The use of augmented analytics can improve the speed, effectiveness, and accuracy of analyst work. By lowering technological hurdles to analysis, including opening up more sophisticated approaches to those with less developed data skills and expertise, machine learning and natural language technologies also aid in bringing domain experts—people engaged in business—closer to their data.
Agility: Increasing speed to insight
By narrowing the search field, presenting pertinent data to the appropriate person at the appropriate time, and recommending successful avenues for investigation, AI-powered augmentation can speed up the hunt for insights. Systems may offer wiser defaults, promote activities, and tweak and customize them over time based on how people respond by comprehensively recording user behaviors. People may concentrate on more important duties and spend less time searching through data for insights when they can have their data inquiries answered more quickly.
Accuracy: Providing a more complete picture
Machines are exceptionally good at repeating jobs and computations since they don’t sleep. Because augmented analytics uses AI and ML, it can effectively examine every object, allowing the user to base their judgments on a full study and be as informed as possible. Humans are able to avoid confirmation bias in their findings thanks to this kind of comprehensive vision.
Efficiency: Automating operational tasks
Artificial intelligence and machine learning have come a long way in fields where algorithms are driven by repeated, highly specialized tasks. (Consider websites that offer further items or information that “you may also be interested in…” or even fraud detection tools.) When working with data—whether for data preparation, data discovery, doing statistical analysis, and more—augmented analytics enables job automation that saves workers time and energy.
Confidence: powerful analysis in context.
Because they are frequently simple to use, augmented technologies make working with data more approachable and insights more accessible to larger audiences. With the help of augmented technologies, you can model and surface facts in context, confirming your gut feelings and giving you confidence in the accuracy of your judgments. Business users can utilize their knowledge of their sector or field to assess how to use the results provided by augmented analytics, even if they may not fully comprehend analytical procedures. Other augmented technologies are connected with other tools and software and implemented into business workflows, enabling individuals to swiftly investigate their particular topic without interfering with their research. In some circumstances, no extra data preparation is required.
Augmented Analytics Features
Automatic data identification
Some contemporary BI platforms employ AI to automatically identify specific data characteristics, such as whether a field contains personal or geographic information (such as a postal code or phone number or email addresses).
The system can also read data tables in formats like PDFs and text documents, automatically stripping them of any specific formatting and converting them for efficient analysis.
Statistical techniques
Additionally, augmented analytics tools may automatically choose the optimum grouping, forecasting, and other statistical techniques based on which provides the greatest degree of assurance. Some systems run models automatically to uncover and provide users with data insights they would not have otherwise noticed. The “why” behind a data point, such as the causes of an outlier or an unexpected value in a data collection, can be explained using these strategies. These functions may be accessed by an end user with only a few clicks, without the need for complex computations or programming knowledge.
Smart data prep.
Powerful algorithms operate in the background to prepare data for consumers more quickly while minimizing human cleaning. In order to save time, augmented analytics systems can index and aggregate related words according to their pronunciation or common characters. This eliminates the need for human field and value updates.
In some cases, the system may also suggest cleaning procedures, such as eliminating null values or dividing fields into distinct columns.
Recommendations
Making AI-driven recommendations to users is a standout feature of many augmented analytics systems. Data prepping, discovery, analysis, and sharing are all covered by recommendations. For instance, depending on the rows and columns of data the user puts into view, the system may suggest data sources to combine, cleaning processes to take during preparation, or useful chart kinds to utilise.
Similar to how many organisations provide “you may also enjoy,” people also get recommendations to explore analytical material based on their roles, teams, and browsing habits, which may speed up user onboarding and help them locate the data assets that are most pertinent to them.
Natural language interactions
A natural language query is an enhanced analytics feature that enables a user to type a question in everyday language rather than a data query language or code to query the data. The system offers a guided experience by converting the text into a query and offering suggestions to complete any gaps in order to try to comprehend the user’s questions’ purpose and context. Insights may now be gained from data by many individuals without their needing to comprehend the underlying data model.
Textual descriptions of insights from the data are produced using natural language generation, which may also explain data visualizations. People can better comprehend the stories in their data by having these explanations in plain English rather than by having to have a thorough understanding of navigating and analyzing visuals.
