Businesses of every size and in every industry benefit daily through the use of augmented analytics.
Before the advent of this advanced tool, companies used generalized data analytics to measure their performance and improve efficiency. Through its added capabilities for automation and enhanced accuracy, however, augmented analytics software allows business owners to collect and interpret data in the most effective way possible.
Augmented analytics software combines the precision of machine learning and automation to provide business analysts with valuable data and accurate interpretations. This information can then be applied in the company. This process also entails natural language processing, which simplifies the interactions between workers and technology.
Looking at the history of augmented analytics, it was first defined in 2017 by Gartner, the technological consulting/research firm. As a concept, it’s still very much in its infancy. Yet it’s undeniably highly advanced software with the potential to develop even further and assist in so many ways.
Natural language processing is one component of augmented analytics. The entire software relies heavily on machine learning. But its value also comes from the use of language in a way that computers can understand and interpret. Augmented analytics is not simply an automated data collection technology. It’s a highly specialized tool that allows businesses to see and understand patterns that may not have been obvious without it.
Augmented analytics can get broken down into three components:
the process of using automation and machine learning to improve data quality and prepare it for interpretation by analysts. This element of augmented analytics software supports data accuracy and cataloging.
a means of allowing stakeholders and data scientists to handle data with the help of machine learning. This type of data discovery cuts out a large chunk of work by automatically finding and illustrating patterns. Data analysts, scientists, analytics translators, and others can then interpret and use these patterns.
an element of augmented analytics that automates advanced analytic modeling and facilitates informed decision-making and predictions. People do not need to be specialists or data analysts in order to interpret and use data gathered and collated with augmented data science.
The world of analytics can be disorienting. The sheer amount of data often seem overwhelming to those who aren’t specialist data scientists.
Put simply, augmented analytics bypasses the need for people to break down this data themselves, eliminating their confusion.
Human capital refers to personal attributes brought by employees to a business; and, more specifically, their value in terms of productivity and efficiency. This concept includes a wide variety of elements, including:
The idea of human capital has been around for centuries. But the application of augmented analysis to it is a relatively recent development. The qualities considered human capital can get developed through learning. And although it is near impossible to quantify the monetary value of an individual employee, time and experience have shown that investment in people through education eventually pays for itself. The same way that purchasing new equipment ends up saving money in the long run.
For startups, it can be hard to tread the fine line between over-expenditure and investing in human capital. Long-term, it is clear that well-educated, experienced and well-adjusted employees are invaluable. However, getting them on board can prove a challenge.
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Human capital management is all about boosting productivity by focusing on workers themselves. The term human resource management is sometimes used interchangeably with this. But it’s not entirely accurate. By comparison, HR management concentrates instead on developing business processes to support employees.
Managing human capital is a complex and demanding task, as it requires companies to consider a massive range of factors. Every new recruit adds an additional and unique element to the mix, whether the business operation is a startup or has been around for decades. The ongoing process of improving human capital is multifaceted.
One way that companies use augmented analytics to improve their human capital is to collect and collate data automatically that gives an accurate timeline of an employee’s “life cycle” in the business and their individual development.
There are many more ways that augmented analytics can support human capital.
An employee’s contribution to a company begins when they’re recruited. This process can be extremely time-consuming when done manually as opposed to the use of AI. Augmented analytics can expedite the hiring of new employees by recording various bits of data and automatically sorting applicants. The sorting can be according to qualifications and experience, or any other pre-programmed criteria and preferences.
It’s possible to do this without the assistance of augmented analytics, of course. But using the software to extract all relevant data frees up existing employees and facilitates speedier and more efficient decision-making. Having a clear and comprehensive understanding of new employees makes it possible to chart the course ahead in the most cost-effective and productive way.
One of the great things about human capital is that it includes qualitative elements as well as quantitative (personal qualities of an employee, for example, as well as their calculated financial value in the business). Someone with great leadership qualities is easily identified and nurtured when augmented analytics software automatically collects data and presents insights to managers.
How exactly augmented analytics software gets utilized in a company depends on its size and industry. A startup definitely does require thorough and continued data analysis but it’s likely to have significantly fewer employees than an established business.
Augmented analytics software can provide interpretations of data that any stakeholder can understand and use—not only specialist data analysts, coders, or scientists. This makes it easier for companies to decipher data and use it without having to “decode” it.
Machine learning has become the norm in many companies these days. By now, it’s advanced enough to act human (in some ways at least) and, consequently, to interpret and represent/visualize data in a more nuanced way.
Augmented analytics don’t only deal with numbers; natural language processing means that software can get fed information translated from a “natural language” (English or Spanish, for example).
Once the AI understands this data, it is processed thoroughly to extract all possible patterns, insights, predictions, and suggestions, before getting converted back into the original natural language. This is an extremely helpful tool to use in human capital management as it takes personal qualities into account as well as quantifiable data.
There are many elements of human capital that are inexact. Qualities such as emotional intelligence cannot be quantified. But through the use of augmented analytics, companies are better equipped when it comes to addressing potential issues within the workforce. Once again, the implementation of augmented analytics depends on the size and type of business in question.
One example of augmented analytics in practice is using the software to address the issue of employee churn or attrition. A business that loses employees regularly, whether through voluntary or involuntary reasons, inevitably faces deterioration in its human capital. The factors behind a high employee churn rate are almost never clear-cut. But augmented analytics technology has the ability to translate those into actionable data.
Augmented analytics can provide managers and business owners with detailed breakdowns of information about employee attrition: the ages of those who leave, gender and assigned department, for example. This can help to identify patterns and shape the way that a company approaches human capital.
The ability of augmented analytics to understand and compute more nuanced questions saves time and effort. Without this software, many businesses have to rely on the work of data scientists, which takes much longer than the automated process.
The process of ETL (extract, transform and load) requires a massive amount of labor and time when done manually. This ends up draining resources that could get directed towards using insights to improve business practices and manage human capital.
Looking at the needs of startups, it is evident that augmented analytics can drive businesses towards progress through in-depth insights. The more patterns identified, the more entrepreneurs can plan for the future. In turn, this makes success far more likely. Just as thriving businesses depend on talented employees—great human capital—skilled workers get attracted to prosperous establishments. In contrast, if employees spend a lot of time on ETL, their productivity in other sectors of the business automatically decreases.
It’s becoming more evident as time goes on that managing and supporting human capital is an integral part of every business’s success. Through augmented analytics, cultivating valuable employees can become an easier, streamlined, and more accurate process.