Augmented Analytics is one of the latest advancements for business intelligence tools benefits the business. It is a new concept that is said to modernize an organization’s analytical capabilities.
In this article, we delve more into the benefits and the future of Augmented Analytics. It is important that we know the basic concepts and technologies that are its key drivers.
Understanding Augmented Analytics and its Benefits to the Business
According to a report published by Gartner in 2017, “an approach that automates insights using machine learning and natural-language generation mark the next wave of disruption in the data and analytics market.” The article claimed it to be the “future of data analytics.”
Augmented Analytics is a combination of technological components like Automated Insights, Machine Learning, and Natural-Language Generation used to automate and generate actionable insights that benefits the business. It is a process that automatically collects data from raw sources to process and analyze it into information.
Let us understand the components that make Augmented Analytics:
Machine Learning is a concept that leverages Artificial Intelligence algorithms to assess data. Augmented Analytics uses Machine Learning to develop, prepare and transform data.
Augmented Analysis benefits from ML’s abilities to determine patterns, recognize trends and identify relationships between data points. ML helps Augmented Analytics understand the different aspects that benefit a business and the concepts that need to be avoided.
ML works with languages like R and Python to communicate with devices to process data. Natural-Language Generation is what translates the processed data into languages that help humans comprehend.
NLG acts as a mediator between the machines and users. The process of presenting data into actionable information is “data analytics”.
Once the data is processed and translated, it still needs to be analyzed. Automating Insights in Augmented Analytics automates the process of data analysis.
With the help of concepts like ML and NLG, Automating Insights requires no human intervention. It is established that machines evaluate and analyze data much faster.
Hence, while Automating Insights work towards processing, users can utilize that data towards strategies and planning operations.
More than 80% of production time is spent on manual data preparation. Augmented Analytics uses AI, ML, and NLG to automate the process and present outputs within a fraction of the time required.
Here, algorithms replace manual processes. It automatically detects repetition and streamlines data.
The entire process becomes automated and self-reliant. As a result, a process that would take days or weeks can now just be handled within a few hours.
Data Latency refers to the time it takes for a business to retrieve data from relevant sources. Machine Learning in Augmented Analytics reduces the chance of data repetition.
Algorithms help identify schemas and join the data from different data points. Hence reducing data latency and manual interventions.
Analytical Bias arises when results are evaluated from a limited perspective. It creates an issue when there are systematic exclusions made from a narrow point of view.
Machine Learning enables Augmented Analytics to perform repetitive tasks. Although, it does not eliminate data points in the process based on assumptions.
Hence, Augmented Analytics reduces the potential of analytical or sampling bias by thoroughly analyzing the data.
Augmented Analytics reduces the time taken to provide insights. It automatically looks for correlations, segments, and patterns applied while analyzing data.
It evaluates the data with multiple permutations and combinations of ML algorithms. This machine-led ability provides relevant results statistically faster.
Democratization refers to creating accessibility of action to everyone. Augmented Analytics increases the accessibility of using AI and ML to perform data analysis.
It creates more opportunities for data scientists and experts to divert their energies on high-value tasks like decision-making and strategizing.
Companies that have executed Augmented Analytics have reaped multiple benefits. Augmented Analytics uses ML to train and develop tools that benefit the business.
The tools under augmented analytics tend to run on a cloud, which makes them more scalable. Also, these tools evaluate and calculate available data to provide accurate insights into the business.
Data literacy refers to data that is easy to read, analyze and communicate as information. Businesses generate large amounts of data that is critical and sensitive.
It is essential that the data collected is optimized and used as information. Automating Insights in Augmented Analytics processes the data that users can easily comprehend.
Hence, Augmented Analytics benefits from this process and provides data that can contribute towards the strategies of the business.
It provides businesses with the opportunity to get into the depths of data analysis processes. It leads to more optimal usage of available resources.
Augmented Analytics modernizes the use of Business Intelligence Tools. The central aspect of BI tools is to uncover potential opportunities in a business.
Hence, Augmented Analytics, with its in-depth processing of data, could also contribute towards hidden business opportunities.
Augmented analytics is a new wave of business intelligence. There are fewer examples of augmented analytics and a few use cases across industries:
The healthcare industry has always been notorious for using the latest available technologies. The industry does benefit from augmented analytics.
The healthcare industry generates massive amounts of data with research, clinical trials, treatments, and patient records. Augmented analytics helps by increasing real-time efficiency by providing accurate insights.
In a healthcare institution, there can be patients that require various sets of medical attention. There may be patients for emergency care, scheduled operations, vaccination, physical therapies, etc.
Now, this data also needs to be processed according to age, gender, and medical history. The process could take a long time for an official authority.
Hence, augmented analytics leverages its benefits and components to quickly perform results. The outcome can be used to provide alternate treatments, simplify insurance processes or just understand the result of a patient reacting to a specific drug or medication.
The Human Resource department deals with employee data daily. For an organization with a strength of 100 or more employees, keeping track of the data could be a tedious task.
HR’s job is not only limited to maintaining the data of current employees but also to look after new joiners, their retention, and attrition processes. Augmented analytics allows HR to feed the information into software that evaluates the data.
It also helps keep track of the performance of an employee and their potential in the company. This helps maintain the data and analyze any voluntary attrition to prevent or accept.
Augmented analytics is not limited to provide aid to maintain employee data. It also provides insights into the employee’s lifecycle and what could potentially motivate them to perform well.
Whether it is offering opportunities to earn incentives, increase employee engagement, or improving work culture. The information can be collected by deep-diving into the data and gaining perspective for developing strategies.
When it comes to Finance, Banking or Insurance, there is no place for an error. A financial firm may be dealing with the investments of multiple individuals or organizations.
Converting data into actionable information for financial gains is one of the significant aspects of Finance. Maintaining and analyzing data plays a major role in the industry.
Whether it is through investment in companies or keeping track of customers and clients, data plays a key role.
Augmented analytics enables businesses to make decisions based on assessments and predictive insights.
Oracle analytics is a significant example of augmented analytics in Fusion ERP. It uses augmented analytics to assess structured and unstructured data into actionable insights.
The solution is built on Oracle Autonomous Data Warehouse. It uses augmented financial analytics and manages financial functions.
Augmented analytics is still a developing technology. As a result, there are challenges that may affect its credibility in the market.
Here are the major challenges it faces in the market:
Commercialization of Augmented Analytics:
Tech giants like Oracle, IBM, Microsoft, Google, etc. have implemented augmented analytics. But the technology is not fully matured yet and would take some more time to present solutions.
Although, it still shows a lot of potential if only businesses comprehend the value this technology offers.
Governance and Compliance
When it comes to data, security is a very pivotal part. Augmented analytics provides in-depth knowledge of the data it processes.
Although, it needs a way to govern and secure its automated processes. It needs to assure businesses that their data is in good hands.
Data has become one of the most valuable assets for businesses. Organizations have invested well in maintaining and securing the data.
Companies already generate revenue using the data. Imagine having that data assessed and consistently providing insights with fewer errors.
The assessment of data is quick and provides an in-depth analysis that can be easily comprehended. This reduces the dependency on data scientists.
Augmented analytics is an advanced technology. It requires tools like machine learning and AI to integrate its benefits into business completely.
According to Access Research Report of Augmented Analytics Market, “The augmented analytics technology has helped in delivering more detailed and relevant insights from the copious amount of data stored within an organization or generated during everyday operations and procedures.”
The global augmented analytics market was valued at $1.6 billion in 2018. This was only a year after the term was introduced.
The forecast then showed it to reach over $11 billion by 2018 with a CAGR of over 39%. This showcases the hidden potential in the technology.
Augmented analytics incorporates advanced technologies like Machine Learning, Natural-Language Generation, and Automating Insights. It is also the next wave in business intelligence.