In the modern era, business intelligence software plays a vital role in business management and development. But is it all about business intelligence tools or something else also supports it? Well, the key word is augmented analytics.
Augmented analytics supports the traditional business intelligence software by various other technologies. Let us read more about its meaning, capabilities and applications.
In this blog we will take a look at the meaning of augmented analytics and its contribution in business intelligence tools. We will also discuss the latest examples of augmented analytics and its features.
What is the meaning of Augmented Analytics?
The term ‘Augmented Analytics’ was coined by Gartner in 2017. Augmented analytics comprises two words, augmented and analytics. Augmented stands for support and analytics stands for analysis of statistics.
So, in simple language, augmented analytics can be defined as the software or technology supporting the study of analytics. In technical language, it is defined as the software that enables the use of technologies such as machine learning and AI to assist the data preparation and modification.
It also guides in insight generation and insight explanation and analyzes how the people explore data analytics. It performs the same function with business intelligence software. At the same time, augmented analytics augments the expert and citizen data scientists.
Augmented analytics ease the process by automating many aspects of data science, deployment, machine learning and artificial intelligence. In other words, augmented analytics is defined as the ‘future of data analytics. It is described as the process where data is extracted from the raw data sources and analyzed in an unbiased manner.
The filtered information is then communicated in report form using natural language processing. Thus, making the process understandable to the humans too. The technology looks for the patterns and signs in the data. It discovers valuable insights without the involvement of data scientists.
The report presented by augmented analytics can be understood even by non-technical people too. The individuals in the organization don’t need to wait for an intermediary tech professional to interpret machine code. Thus, augmented analytics is the easiest means of analytics.
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Goal of an augmented analytics platform
The major goal of an augmented analytics platform is to strengthen the traditional BI applications. Companies like Microsoft are employing augmented analytics to increase their business sales and insights.
The basic purpose of augmented analytics is to ease the working of business intelligence software and ease the customer interaction process for the owners.
Working of the Augmented Analytics
The process of augmented analytics is easier than other analytics such as augmented analytics. It helps in finding data insights and sharing the same. Augmented analytics has the power to transform your business by transforming the three following ways.
Capabilities transformation: The first step of augmented analytics includes the access of analytics platforms.
Market transformation: Later, the augmented analytics study the trends of the market. It understands and evaluates the most important market factors.
Organization transformation: And, at the end, the analytics helps in transforming the organization to exploit the potential of augmented analytics
The report exhibited by augmented analytics shows its capabilities in above mentioned key areas. And, it has always a scope for ratification in terms of organizational and market disruption.
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Which companies use augmented analysis in their BI platforms?
While business intelligence records the insights and activities of customers. Augmented analytics strengthens its walls. Different companies are using augmented analytics to update their traditional platforms.
For example, Answer Rocket, Incites.ai, Data Stories, LTI, Narrative BI, Outlier, Unscramble and Yellowfin BI.
What are the technologies involved in augmented analytics?
An augmented analytics technology is itself a blend of several modern technologies. Augmented analytics comprises a dozen technologies related to different sectors. The major list includes the following emerging, evolving and established techniques:
Augmented data preparation
Augmented data preparation consists of advances in data preparation in all phases. The phases of data preparation include ETL, which stands for extract, transfer and load phase. The process involves the use of algorithms to detect schemas and join. It performs the automation of data transformation by profiling and data enrichment.
NLG stands for Natural Language Generation. The natural language refers to the mainstream in modern BI platforms. The augmented analytics automatically generates the rich description of insights found in data.
NLP stands for Natural Language Processing. This technology makes the complex information of raw data easier to understand and less intriguing. Its function is to read and interpret text or voice data. Thus, making the interface easier for the users to interact and engage.
Automated business monitoring
Automated business monitoring is also called automated analytics. This technology enables the running of analytics in the background. It automates the process of manual data discovery.
At the same time, it supports the immediate surface relevant changes in data. Thus, helping in the time-to-time acceleration of the insights. The augmented analytics mechanically creates the rich explanation of insights found in data and is highly beneficial in the high dimension data or larger set of data.
Machine assisted insights
Machine assisted insights is the form of machine generated visualization, calculation and creation of the analysis. It can be triggered from simple questions. Machine assisted insights involve the deeper analysis and auto generates the analysis, building and calculations of any chart or analysis.
Nowadays one or more of these technologies are included in BI platforms. According to the Forrester Wave report, the modern augmented BI platform plays a critical factor that determines the difference between successful and other industries.
What are the advantages of Augmented Analytics?
Advantages of Augmented Analytics
Augmented analytics is a boon to the Business Intelligence tools. It has improved their existing benefits. Here is the list of key benefits and applications of augmented analytics.
Immediate automated analysis
Augmented analytics offers immediate automated analysis. It reduces the heavy lifting of manually sifting through huge volumes of complex data. This uncertainty usually arises due to lack of skills and time constraints.
The augmented tool automates the faster delivery of insights by studying spike or drop or change. Thus, it ensures that users can act the insights or changes immediately.
Enhanced data literacy
Augmented analytics is full of modern and latest technologies. It includes NLP and NLG. This natural language assistance helps in the explanation of discoveries and automated analysis of end results. It also assists in fostering a data led culture. Thus, benefiting the users and organization for the long-term.
Rapid data preparation
Manual data preparational and traditional approaches are quite time consuming. Augmented analytics collects all the data together from multiple sources faster. As explained above augmented analytics provides algorithms to detect schemas and joins.
The automated transformation and integrations ease the data preparation process. It also eases the data quality and auto generates the recommendations.
The user can automate the profiling, annotation and tagging of the data before the actual data preparation. Augmented analytics provides clean and reliable analysis in a fraction of the time.
Reduced analytical bias
The augmented analytical platform enables the machine to perform analysis. This assists in reducing analytical bias. It helps especially in the cases where analysis is entirely based on assumptions.
It has the capability to minimize the potential bias across a wider depth of data. During the entire process, it counts the factors of statistical significance.
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Data democratization is the most important feature of augmented analytics. Data democratization is the process of operating data access. This is done to relieve data congestion. This frames a sense of data and is often used to speed up the company decision making and uncover opportunities of hidden and unrevealed data.
Augmented analytics is sometimes also called conversational analytics. As it uncovers insights simply by asking questions from the users in simple natural language. Every time after the question is asked, augmented analytics runs algorithms that present relevant charts, graphs and information.
Thus, augmented analytics covers the drawbacks of traditional business analytics systems. It is the soul of modern business intelligence tools. By enforcing various means, it can help in expanding your business further and evolving it in modern terms.