TOP FIVE INHERENT CHALLENGES WITH AUGMENTED ANALYTICS

“In the long term, artificial intelligence and automation are going to be taking over so much of what gives humans a feeling of purpose.” _Matt Bellamy?

The fear that goes along with the arrival of every new technology or new idea and leaving out the old ways has existed throughout our history, in an age where artificial intelligence and machine learning have taken their seat to bring out the advanced practices of working with data. To solve our big business problems, we need to consider our traditional methods to incorporate them with the new and challenging ways of interacting more effectively with machines.

?Harnessing the strength of machine learning, artificial intelligence, and natural language processing has the power to transform our modern businesses.?A 2020 report by Fortune Business Insights forecasts that the AI industry market size is expected to grow to be worth $266.92 billion by 2027, and its annual growth rate will compound to 33.2% from 2022 to 2027. ?With the help of AI-enabled augmented analytics, organizations across departments can analyze the data on their own without the requirement of having prior data skills or expertise.?

CHALLENGES WITH AUGMENTED ANALYTICS

With the coming of any new technology, the resistance and turbulence associated with the change are well understood. Having misconceptions and lacking clarity about the new technology can cause more trouble than good. It is vital to discuss and understand the challenges that arrive with such a powerful analytical tool for such reasons. We will have a look at five exclusive challenges with augmented analytics that modern businesses face:

1.?????Augmented analytics: expectation vs. reality

People often have unreasonably high expectations of what these AI-driven advanced technologies can achieve and offer to their business. Therefore, at times businesses make big investments without completely understanding how the technology works, which often leads to sunk costs.?

Despite having all the data, machines cannot understand a person’s intent within a limited context. A person with domain expertise has the ability to grasp the bigger picture; in contrast, machines take time to learn people’s preferences and choices by monitoring user behavior and through consistent user feedback.?

2.?????Data literacy and analytics proficiency

Using advanced technology like augmented analytics is beneficial only when it gets acted on the right data; wrong data will lead to incorrect recommendations, which will result in a waste of resources. For this reason, data literacy and analytics proficiency are essential across departments to understand the language of data and relate it to their business.??

3.?????Ethical use of AI

Since the algorithms and models within a highly advanced AI-driven technology get complex, it is crucial to keep them under our comprehensible reach. The concept of having a transparent and explainable AI is essential so that people will understand the logic and operations that get applied in them that work to bring out the causation. Ultimately, it helps build people’s convictions and ensures that the organization uses unbiased models for making informed decisions.?

4.?????Certain misconceptions of AI and ML

Many have a widely held misconception against AI and machine learning that its primary focus is on technology and that regular people can’t interact with it and benefit from it. Another misconception is that machines are taking people’s jobs, making the adoption of AI-driven augmented analytics solutions harder, which in fact, offers practical benefits to people whose expertise lies in working with data. If people don’t understand and trust in the value of what augmented analytics really is, they won’t invest in it.?

5.?????Data governance, management, and curation

To use AI-enabled augmented analytics to its fullest capacity, one must understand that the quality and reliability of the data input used to train the system is highly important to get the most reliable prescriptive recommendations from automated analytics. The enterprises that have not well-invested in sound data governance management and have struggled to deploy their BI solutions will stand little chance of successfully embracing AI.?

CONCLUSION

Even though augmented analytics is customized for every user to perform complicated tasks quickly and easily by clicking a button. However, there will always be a need for a data expert who possesses a fair amount of knowledge to judge data and its solutions. The quality and reliability of data entirely depend upon the quality of the input data; for that reason having sound data governance and data management is critical for businesses to embrace AI-powered augmented analytics.