AI analytics today allow us to break down and analyze all parts of a business. Today, we will quickly talk about using a data lake and data pipelines to help streamline data analytics.

Every internal and external interaction can be scrutinized and perfected to create a well-oiled, efficient machine.

Consumer data, inventory management, market trends, and shipping logistics are just a few complicated processes AI can optimize and streamline.

It can drill down to the smallest detail and show the relationship between hundreds of thousands of different variables interacting with each other.

Current estimates indicate that AI can analyze millions of rows of data nearly instantly, whereas an IT team would take hours or even days to review just thousands of rows.

But even this comes with its problems.

When you are dealing with millions of rows of data, you need to be able to properly send it from the source, store it for later analysis, and make sure the data you are sending is error-free.

This can seem like a daunting task at first, but that is where modern technology comes into play.

Not only can this data be transferred and stored without any hassle, but it can also be verified to ensure there are no errors that could cause issues in analytics.

Data Pipelines

Let’s say you are an e-commerce business and need to send your data for analysis.

The data collected could be from customer purchases, website clicks, email engagement, inventory systems, customer feedback, social media engagement, and more.

In the past, data was sent in batches, which could take hours or even days to process. Furthermore, as the data volume exploded, batch processing could not keep up with the volume.

Data pipelines enable you to send all this data in a constant, raw, and unorganized manner without worrying about sorting, processing, or the volume being sent.

No longer do you have to wait until the workday begins to manually send the data, cutting out the possibility of human error in the process.

You no longer have to limit how much data you send or what format it might be in. Data pipelines offer scalability without the risk of sending too much data at one time.

AI analytics are now smart enough to be able to take this unorganized live data, make sense of it, and give you instant business solutions.
Some problems cannot wait to be solved, and being able to act immediately or having the AI act on your behalf makes a huge difference.

Last but not least, you save yourself an incredible amount of money in the process.

Because data pipelines are cloud-based, you do not need to build infrastructure or hire engineers to maintain them.

Plus, you are only paying for data sent through the pipeline. There is no need to worry about the power and maintenance costs of running a physical server 24/7.

Data Lakes

Once the data passes through the data pipeline, it is stored in a data lake.

These data lakes serve as massive data storage centers that can accommodate any data imaginable.

Before, storage was made for traditional data that could be converted into numbers, such as sales figures. However, with data lakes, companies can send over data such as videos, social media posts, etc.

On top of that, storage was expensive and took processing time to get it back to the user.

Data lakes eliminate all those problems, as AI can analyze and sort any file. Cloud systems enable nearly infinite storage capacity through services like Amazon AWS, Microsoft Azure, or Google Cloud.

As discussed before, data pipelines make transferring data to the data lake instantaneous and streamlined.

The massive storage capacity gives you a significant edge in analysis. The more datasets you add to the lake, the better you can train your AI and machine learning models, resulting in more accurate analysis and solutions.

That is millions of lines of unorganized, wildly varying data read and analyzed in real-time, making it thousands of times more efficient than previous methods.

Ensuring Accurate Data and Giving Your Business AI Solutions

Throughout this entire process in the pipeline and in the lake, AI is working its magic behind the scenes.

As soon as it enters the pipeline, data is scrutinized to check for any errors before it reaches the data lake. Duplicates or missing values can occur, and AI can instantly check for any mistakes.

Furthermore, it runs compliance checks to make sure that the data sent does not break any privacy laws (GDPR, HIPAA, etc.).

This process occurs not only in the pipeline but also in the data lake, and after the data is retrieved, to ensure there are no errors or compliance issues in the data.

The best part is that this is done in the blink of an eye and is fully automated, leaving no room for error.

Once everything is given the green light, your data can be analyzed for immediate business solutions.

Such solutions include addressing warehouse inventory management, analyzing the relationship between marketing emails and revenue, or optimizing shipping logistics.

The possibilities are endless. And with the modern innovations of data pipelines and lakes, it has never been better, now offering scalability, automation, and efficiency like never before.

Click here now and get in contact with ProcureSQL today to let AI jumpstart your business solutions.