Data Analysis and the role of Data/Business Analysts in the AI era?

Let's define Data Analysis and how Data or Business Analysts are different from Data Scientists. This is important for us to stay away from generalizing a very large field of science.

Data analysis is the process of inspecting, cleansing, transforming, and modelling data to discover useful information, draw conclusions, and support decision-making

Now a Data Analyst/ Business Analyst in essence works with data, manipulates it, transforms it and tries to generate usable logical insights or interpretations. These are to an extent simple to mid-level complexity. 

But here is where most of the data analysts and Business Analysts' role ends. Data Scientists operate at the next level, they combine Data Analysis, Software, ML and large statistical analysis tools to build complex data models.

What role does AI have to play in this data Analysis Universe?

The ability to process large amounts of data and generate quality insights, sometimes even better than Humans is what puts AI applications on the top pedestal. AI tools today rely on all the statistical models, most popular data analytical methods, graphical insight tools and many such resources to train itself and apply these learnings to any given data set to generate the best possible results.

But does it replace the Data Analysts?

The short answer is ‘NO’. However, if you do a deep dive, you can observe that, most of the mundane data analysis that a data analyst does/ Business analyst does is something that the AI can easily replicate at a much faster pace and at a fraction of the cost. 

The advancements in AI have also put us in a position to enable Non-tech, business, Operations or simply a layman to give commands in everyday conversational language to generate insights.

Does it end there? Definitely No. 

• Faster & Better Insights
• Better pattern recognition over large data sets
• Non-Tech users can interact and get insights
• On-the-fly analysis
• Responds to conversation language or voice inputs

What it can’t replace is the Human ingenuity that senior data/business analysts can bring to the table. This is something the AI tool will rely on these senior resources to help it perform better.

Also, it's not all hunky dory with AI Data Analyst. Like humans, AI tool also operates with a success rate, i.e. not all the answers generated by these bots can be 100% correct all the time. There will be times when the AI can get things wrong.

So how does one leverage AI in Data Analytics?

AI with Data teams working together is the way forward. This combination is destined to produce the best comes an organization can look for. Better, faster and Cheaper are the three adjectives that I can use here. It’s better because end users/ people with no data analytics expertise can get their insights on the fly. It's faster because of the computing power and 24/7 availability. It’s cheaper because on App can replace most of your Junior and Mid-Level Data and business Analysts.

Are there any plug-and-play AI Data Analysts in the market?

Talktodata.ai is precisely addressing this need gap. The proprietary IP of Talktodata.ai allows you an option to choose an industry-specific AI Data Analyst who can understand your Industry Terms and respond to you in your business language. 

The enterprise and Personal Solutions are offered to suit most SMB, Individual, and Large scale enterprise requirements. Given the SaaS nature of the tool, 

It's as easy as plugging in, asking your questions, and gaining valuable insights.

The tool is SOC2 compliance standards ensuring the highest data security. 

You can try the product by clicking here. 

You can reach out to the team by emailing them here or by raising a demo request.

For more info about Talktodata.ai, please use this link.