How will AI change data management? Let's embrace the era of AI-driven automation!

We are entering a new era of big data, where data sets have become so large that humans simply cannot analyze them effectively in a reasonable amount of time. So much data bodes well for the future of business intelligence. But, as always, the value of data depends on the insights extracted from it.

The second wave of big data almost coincides with the rise of generative artificial intelligence. This exciting new technology has transformative potential in almost every industry worldwide. When AI is unleashed into these unfathomable huge data sets, it can complete complex analysis in just a few seconds and identify patterns that would take weeks or even months for a human observer to complete.

AI will also have a huge impact on the way we interact with computers. This will make software solutions more personalized and user-friendly. We will see a gradual shift towards AI solutions playing a more supervisory role: we will direct what needs to be done, and AI-based solutions will do more for us. We have already seen artificial intelligence have a huge impact on new software development, and even existing software solutions are being redesigned to use artificial intelligence to provide users with a better user experience. I believe that AI will relieve us a lot of the burden in terms of automated solutions.

AI is already helping businesses of all sizes extract more value from data, automate repetitive tasks, and streamline existing data pipeline solutions. The AI revolution represents 1 shocking technological change and an opportunity for data-driven enterprises to increase productivity and efficiency. To succeed in the new world of AI-driven data management does require some planning. However, if properly planned, the benefits cannot be ignored.

Infrastructure

It's an exciting time, and everyone is trying to do something with AI. But from an implementation perspective, for any business to embark on its own AI journey, it must ensure that it has a robust data infrastructure. You need the right storage capacity, the right computing power, and the right data tools.

Without these basic components, data quality suffers. This, in turn, can limit the ability of AI modules to extract meaningful insights from enterprise data sets. We have already seen the quality of the AI Large Language Model (LLM) and how it is trained. A clear trend is that their success or failure often depends on the quality of the data." The old programming adage "garbage in, garbage out" applies here as well. Therefore, for AI to succeed, it must be provided with high-quality data. This requires the right data sets and tools.

With the advent of artificial intelligence, things are changing very fast. Many organizations are experimenting with different approaches to unstructured data. Unstructured data is more difficult to work with than neat rows and columns. With AI, actionable insights can be extracted even from large amounts of unstructured data. Process is very important and infrastructure is very important. Previously, we always converted unstructured data into structured data first. Now we want to do both.

Automation

Automated data management platforms help companies transform data into a viable state in less time than ever before. This frees up resources for key tasks such as strategic thinking, customer partnerships, and understanding what actually drives your search, the story you're trying to tell, or the problem you're trying to solve. Artificial intelligence and automation can create capabilities where they are really needed, rather than mining through rows of unstructured data.

From the perspective of solution architecture, we recommend that enterprises ensure the efficiency of their processes, thereby avoiding spending time on trivial tasks. Spending time on these tasks is a waste of time. We believe that everything that can be automated should be automated, and human capital should only be invested in tasks that cannot be automated. We 've been seeing examples of low-code/no-code solutions for some time that help users of our products quickly build solutions and improve their data pipelines. But with the development of artificial intelligence, we have seen another huge transformation. We have seen AI capable of taking on repetitive tasks that take a lot of time, but the gains in productivity and value are not obvious.

Let's say you spend hours organizing a solution, extracting certain types of data from documents and entering them into a database. This is a simple pipeline. If such a system is to be established, it may take several days or even a week. Now, it can be done in a few minutes. This is the benefit of artificial intelligence. AI has made existing solutions leaner, and users can now spend their time where they should be. In the past, repetitive work like checking every comment, rule, or result took up a lot of time. With artificial intelligence, we are able to minimize this.

Culture

A key element of a successful automated data strategy is gaining support from members at all levels of the organization. In recent years, with the emphasis on data literacy in enterprises, we have seen this take shape. Today, issues such as data governance, data security, and how to handle data in an organization's pipeline have become mandatory subjects from the CEO down to the average employee.

But at the same time, companies also need to be thoughtful when doing artificial intelligence. Including whether to carry out artificial intelligence. Otherwise, they might just be chasing shiny objects with no specific target. Companies must ensure that these technologies meet their business objectives: increasing revenue, reducing cancellations, opening new markets, etc.

The key is to have a practical project or proof-of-concept to embed AI and automation technologies in their respective silos before rolling them out to the entire organization. Identify key benefits, determine if they are appropriate, then involve key stakeholders in the POC, and then roll it out as appropriate.