In recent years, the business world has seen data become very prominent. Scarce is the company that has not undergone some degree of digital transformation, and this has brought data to the core of its optimal performance. This focus on data has meant the use of streamlined methods of collecting and processing data, as well as the need to and the practice of analyzing it.
And from new-age startups high on technology to well-established traditional businesses, data analytics is everywhere! The field of data science and analytics is on an upward trend, which is not surprising given the advantage it offers to firms in a scenario of an unpredictable business environment and a very competitive marketplace. It has thus caught the attention of senior management across domains, including HR leaders.
What is augmented analytics?
The analytics landscape is forever changing, and a major driver of this dynamism in the future could well be the rise of augmented analytics. This refers to augmenting or enhancing the effectiveness of data analytics, data sharing and business intelligence by machine learning (ML) and natural language processing (NLP). The wider use of data analytics will soon lead to more innovative implementation cases as well as skills in making business models more efficient. And the digital architecture of a number of firms is evolving slowly but surely to incorporate augmented analytics in the day-to-day work of HR professionals.
How widely used is augmented analytics?
Quite. A recent report from Gartner supports this contention, stating that: “By 2020, augmented analytics will be a dominant driver of new purchases of analytics and business intelligence.” The global market for augmented analytics is expected to touch $22.4 billion by 2025. Also, according to the report, the highest CAGR in this market is likely to come from the Asia-Pacific region during the period 2019-2025.
How important is analytics for talent management?
The primary advantage of data analytics is the provision of valuable insights that help to predict and harmonize decision-making. This makes it very useful for all verticals within a firm. However, with the war for talent raging on and the troubles HR professionals face in recruiting the right people, the employee management function too has started to make use of data analytics, or HR analytics. Companies need to make their people decisions robust, and in order to achieve such results, an increasing number of firms are making use of data analytics.
How relevant is it for businesses?
Artificial intelligence (AI) is seeing acceptance by an increasing number of enterprises eager to adopt it into their operations, within which HR is a key domain. The correct or most suitable method of utilization and implementation remains a worry for HR leaders. Augmented analytics helps by taking a solution-oriented approach, looking at a HR challenge that can be measured, and using data to solve it. The payoff is tangible results from an investment in AI. In fact, the future is likely to see AI working alongside traditional HR analytics, with the result that:
Can augmented analytics speed up the work of human resources?
The coming years could see augmented analytics becoming vital for HR professionals, especially with its potential to merge traditional data analytics with:
It helps HR leaders avoid the need for in-depth knowledge of analytics as a means of getting insights and frees up time for impactful decision-making.
What are the advantages of augmented analytics?
The biggest advantage is that a broader group of stakeholders can now make use of advanced analytics. Be it IT, payroll, the C-suite, or third-party employment consultants, almost any team can improve its processes through augmented HR analytics. Augmented analytics has a clear advantage over manual systems, significantly quicker than data scientists in collecting and cleansing data, thus leading to quicker generation of insights.
Looking ahead, there will continue to be a need to make the right talent decisions, and different technologies and methods could be used together for more efficient operations and useful insights. This could be the strongest use case yet for augmented analytics!
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