Why should I use AI when working with Data?

We have more data than ever.

First, we must acknowledge that we live in an era of data explosion.

The amount of data generated annually has grown year-over-year since 2010.
It is estimated that 90% of the world's data was generated in the last two years alone.

Within 13 years, this figure has increased by an estimated 60x from just 2 zettabytes in 2010.

The 120 zettabytes generated in 2023 are expected to increase by over 150% in 2025, hitting 181 zettabytes. 

Mind-boggling numbers. But not for computers...

(Read more about the rapid data growth by Fabio Duarte at https://explodingtopics.com/blog/data-generated-per-day).

Why is data important?

Data = Knowledge

Good data equips you with objective evidence, while anecdotal evidence, assumptions, or personal observation might lead to wasted time and money due to decisions made based on incorrect or incomplete information.

"Data allows organizations to measure the effectiveness of a given strategy: When strategies are put into place to overcome a challenge, collecting data will allow you to determine how well your solution is performing and whether or not your approach needs to be tweaked or changed over the long-term."  (Check out these 12 reasons why data is important by the Council on Quality and Leadership)

According to a survey of more than 1,000 senior executives conducted by PwC, highly data-driven organizations are three times more likely to report significant improvements in decision-making compared to those who rely less on data.

Data is important...how can AI help?

Working with large data sets can be very time consuming and often overwhelming.

AI thrives on large data.
Due to several key capabilities, it is well-suited for handling repetitive and complex tasks. It can process vast amounts of data quickly and accurately.

AI algorithms can identify patterns and trends in data that might not be immediately apparent to humans, enabling more informed decision-making.

AI can utilize advanced analytics techniques, such as predictive modeling and deep learning, to make complex decisions based on multiple variables and data sources.

Generally speaking, AI can support three important business needs:

1. Automating business processes
2. Gaining insight through data analysis, and
3. Engaging with customers and employees.

If you are working with larger data sets, it is worth discovering how AI can empower you to be more efficient, create useful automation, and gain more insights from the data you already have.

How does Querri fit it?

At Querri, we have used the #AI and Large Language Models (#LLMs) abilities to help with automation and data analysis.

It all starts with your data.

You upload one or multiple related data sets into Querri and start on the journey of putting your data to work.

You can start by cleaning “dirty data”—the typos, duplicates, missing values, misspellings, and inconsistencies dreaded by data analysts, engineers, and scientists.

It also allows you to combine multiple related data sets into one project and analyze the information in context.

Using simple prompts, Querri starts working for you.

For example, I combined related car sales data from two years into one data set.

Then I asked Querri to clean up the dates (they had confusing extra numbers).

Which it did:

Then I asked Querri to "Please remove all lines with the status canceled," which it did.

Once you have cleaned and organized your data, you can analyze it.

In my data example, I wanted to know which orders took the longest to get shipped, so I asked, "Please sort by which order numbers took the longest to ship based on their order date and shipped date."

Querri did that and automatically added a new column summarizing the "shipping duration." 

Depending on your data set, Querri can do complex data analysis and visualize the results in various ways.

Once you are satisfied with your answers, you can create repeatable pipelines to automate your data process.

All this is done with a No-Code interface (Querri writes the code for you in the background). Using natural language only, every Operations Manager with access to the company's data sets can start using the data and getting answers.

Sign up for a free trial to start putting your data to work.