How AI is Shaping Data and Analytics

75% of enterprises will reach the next AI evolution stage within four years. They’re moving from piloting to fully operationalizing artificial intelligence platforms. This shift will increase streaming data and analytics infrastructures by up to 500%.?

Furthermore, AI techniques are proving to be a massive boon during the pandemic. According to Gartner, machine learning (ML) optimization and natural language processing (NLP) provide pivotal insights and predictions about the virus’s spread. Moreover, these expansive technologies are helping predict the value and impact of countermeasures.?

This blog will examine some critical insights about how AI is shaping data and analytics.

?

The Problem with Standard Business Information (BI) Tools?

With standard BI tools, most data analysts?spend 80% of their time performing?the following tasks:

• ? ? ? ??Finding data.
• ? ? ? ??Cleaning up the data.
• ? ? ? ??Modeling the data.?

The primary issue is that these tasks and their time are costly to the business.

Of course, it’s 100% necessary to have those mundane jobs completed. However, those menial tasks shouldn’t chew up so much of the schedule. Analysts only have 20% of their time to work their magic and contribute those critical insights.

?

Streamlining Data Analysis with AI

Bernard Marr is a “Big Data” thought leader, highlighted some enlightening points about AI’s role in the industry.

To paraphrase him, people want to extract the most value from their data. However, they wish to do this without spending up to 4 years developing computer science and statistics expertise. Artificial intelligence and cognitive computing have offered a path toward gaining all the data’s usefulness without the immense learning curve.

The streamlining capabilities of AI are unparalleled. It eliminates inefficiencies, such as teams staring at dashboards, waiting for incidents, and being ready to react in real-time.

Critical thinking skills are a top priority—but AI reduces the menial labor that can bog down a human’s thought process.

What does all this mean?

While many might think this would make data professionals obsolete, it enhances their job security.

Data Analysts will Thrive in the AI era.

Teams of AI ’employees’ will be managed by data analysts who can leverage the algorithms to explore the data. Given the improved process, these individuals will provide keener insights and become even more integral to their organizations’ problem-solving.

As much as humans need AI to dig deeper and more rapidly make cause-and-effect connections, these advanced techs need people to design and maintain them. Beyond that, it’s up to data analysts to ask artificial intelligence tools business-specific questions and concisely communicate results with colleagues.???

Organizations taking advantage of these vast capabilities will open themselves to more opportunities due to bolstered analyst insights.?

The fact is that data analysts and AI make a potent combination.

How Should Businesses Strategize in the AI Era?

Organizations seeking out data analysts need a strategy in mind for who they’ll hire.

Analysts will now spend less time on mundane tasks and more time telling stories based on the information. So, different skill sets will be required to fill the role compared to eras past.

Companies must view their data analysts as significant business partners, which means that they must prioritize abilities such as presentation skills, business savvy, and big-picture vision.

With the power of AI, data analysts will no longer be viewed as strict number crunchers. Now, they’ll be critical difference-makers and primary pillars in the organizational foundation.

?Formulating A Successful Data Analyst Hiring Strategy

AI's data analyst sphere is evolving more rapidly than ever. The last section brought up necessary skills candidates must have to fill the role, but that will remain in a state of flux.

Thus, hiring the ideal candidate requires pinpoint focus and tireless effort. Putting your best foot forward in this regard is challenging on your own. After all, you’re already managing the many moving parts involved in your organization.