We are entering a new era of Big Data wherein data sets have become so vast that humans simply cannot effectively analyze it in a reasonable amount of time. The availability of so much data portends many great things for the future of business intelligence. But as has always been the case, data is only as valuable as the insights that can be extracted from it.
Almost as if on cue, this second wave of Big Data has coincided with the rise of generative AI. This new and exciting technology has transformative potential across nearly every industry on the planet. When turned loose on these unfathomably large sets of data, AI can, in mere seconds, perform complex analyses and identify patterns it would take human observers weeks or even months to complete.
AI is also going to make a huge impact with the way we interact with computers. This will result in software solutions becoming more personalized and user-friendly. We will be seeing a gradual shift in the direction of a more supervisory role to AI-based solutions: We will be directing what needs to be done and AI based solutions will be doing more of the work for us. We’re already seeing AI making a huge impact on new software development, and even existing software solutions being reimagined to give users a better user experience using AI. I believe AI is going to take a lot of the burden off our shoulders in terms of the automated solutions it enables.
AI is already assisting businesses of all sizes extract more value from their data, automate repetitive tasks, and streamline existing data pipeline solutions. The AI revolution represents a seismic technological shift, and an opportunity to enhance both productivity and efficiency for data-driven businesses. Setting yourself up for success in this new AI-driven world of data management does require some planning. But when done right, the benefits are too great to ignore.
These are exciting times, where everyone is trying to do something with AI. But from an implementation perspective, any business setting out to embark on an AI journey of their own must be sure they have a strong data infrastructure in place. You’ll need the right storage capacity, the right computing power, and the right data tools.
Without these fundamental components, the quality of your data will suffer. This, in turn, will limit your AI module’s abilities to extract meaningful insights from your organization’s data sets. We’ve already seen the quality of AI’s large language models (LLMs) and how they’re trained. There’s a clear trend that their success or failure usually depends on the quality of data. The old programming adage “garbage in, garbage out” can be applied here. So, you need to deliver quality data to your AI in order for it to be successful. That comes from having the right data sets and tools.
With the emergence of AI, things are changing very rapidly. Many organizations are experimenting with different ways to handle their unstructured data. Unstructured data is more difficult to handle compared to neat rows and columns. With AI, actionable insights can be extracted even from large amounts of unstructured data. The processes are very important, and infrastructure is very important. Previously we used to always start by converting unstructured data to structured data. Now we’re looking to do both.
Automated data management platforms are helping businesses get their data into a workable state in a much quicker timeframe than ever before. This frees up resources for mission-critical tasks like strategic thinking, client partnerships, and understanding the factors that are actually driving what you’re looking for, the story you’re trying to tell, or the problem you’re trying to solve. AI and automation create capacity where it’s really needed, instead of digging through rows of unstructured data.
From a solutions architecture perspective, we recommend businesses ensure their processes are efficient so they’re not spending time on mundane tasks. If you’re spending time on those tasks, you’re wasting time. We believe you should automate whatever can be automated, and that human capital should only be devoted to tasks that cannot be automated. We’ve seen examples of low-code/no-code solutions for some time now, which help users of our products quickly build solutions and improve their data pipelines. But with AI, we’re seeing another dramatic shift. We’ve seen it be able to take on repetitive tasks, the tasks where you spend a lot of time but the gain in terms of productivity and value just aren’t there.
Let’s say you spend several hours putting together a solution to extract certain types of data from a document and going into a database. This is a simple pipeline. To build that would take a few days, maybe a week. Now that can be done within a few minutes. That’s the kind of gain you can see with AI. AI has made existing solutions even more streamlined, and users are now spending time where they should be spending it. Repetitive tasks like checking every comment, rule, or result used to take up a lot of time. With AI, we’re able to minimize that.
A key component of undertaking a successful automated data strategy is achieving buy-in from members at all levels of the organization. We’ve seen this take shape as companies have placed a significant emphasis on data literacy in recent years. Today, things like data governance, data security, and how that data is handled across organizations’ pipelines has become mandatory knowledge from the C-suite down to rank-and-file employees.
At the same time, however, organizations need to be deliberate with their AI undertakings. Including whether they pursue it at all. Otherwise, they risk merely chasing shiny objects with no particular objective in mind. Companies must ensure these technologies are in line with their business goals: increasing revenue, decreasing cancellations, exploring new markets, etc.
It’s key to have a tangible project or proof-of-concept to embed AI and automation technologies in silos before expanding them across the organization. Identify your key gains, determine if it’s the right fit, then have key stakeholders involved in POCs, then expand in due course.
Astera is a leading provider of end-to-end data management platform that puts the power of data-driven decision making into the hands of every user. Astera’s suite of products addresses data extraction, integration, warehousing, and API management needs of a modern enterprise. With a focus on usability, Astera’s products have a short learning curve and are designed to save time and reduce costs.