Data Maturity Model: Source Systems
In too many instances, we’re seeing clients excited about getting started with AI only to realize that they aren’t setup to effectively use the latest in technology. It’s like a kid at the checkout counter, about to buy a Twix bar just to realize he’s 10 cents short. To help marketing and technology teams navigate this new landscape, and more effectively meet the “we must use AI!” mandates set forth by their stakeholder groups, we have developed and honed a robust Data and AI Practice. In actuality, it’s a culmination of our collective skillsets and our passions, as well as our overall view of where the market is going. At the end of the day, we’re just a bunch of tech nerds who get excited about the future technical landscape, and how AI will reshape our world. Our CTO even earned his PhD in AI, and we spend a ton of time (maybe too much time?) picking his brain on how he views the latest advancements, their implications for our clients, and how, or if, they will change the world.
Our AI and data practice is built around our desire to help brands adopt AI correctly, with speed, and in a future-proof and agile manner. In a way that yields measurable business value, quickly.
Aionic helps clients in a number of areas, and at all stages of data maturity. In this new world, getting the foundational elements correct will pay huge dividends for your organization and make it easier to adapt to the latest advancements in record time. While data architecture is not the sexiest of topics – we know, everyone just wants to skip ahead to an Agentic AI world – it is critical to determining the overall success of your AI journey. And it’s the appropriate first step towards achieving your overall business objectives, and in demonstrating how AI can have a significant material impact on your business.
The good news is that it doesn’t take too long to advance from one stage to the next. And it’s important to note that everyone’s journey is different. Each business has a unique set of goals for AI along with different use cases. Whether it’s with a focus on customer experience or internal operations, data is the common thread.
Over the course of the next few weeks, we will walk you through each stage of the data maturity model that we use as the basis for our Data and AI Practice, while also discussing key considerations that can help your business thrive. We had a lot of discussions internally about how many maturity levels to include, with some advocating for a more streamlined 3-tier system. In the end, we agreed that this 6-stage model – while more granular – is more representative of the world that we’re seeing, and more readily allows you and your brand to easily self-evaluate and self-identify. We’re excited to share our thinking with you, and are hopeful that this series – and the resulting artifacts – assist you as your embark on your AI journey.
Maturity Level 1. Source Systems
AI and Machine learning, at their very core, are all about data. From customer data platforms to back-end reporting tools, providing AI and ML tools with a clear view of your data is imperative to the success of your overall initiative.
Most organizations possess a ton of data. The problem is that it is often unorganized, unstructured, and not connected. Identifying and organizing source systems is the first step in establishing a foundation for AI and Machine Learning.
Now, this isn’t something you should feel bad about. In fact, it’s quite the opposite. As we grow and scale, as we obtain new customers, and as we acquire or merge with new businesses, data repositories continue to grow. In this regard, the mountain of data you possess is a sign that you’re crushing it! But all too often, amidst the jubilation of growth, we forget about the organization, structure, and consolidation of data. And we wind up with a complex ecosystem, filled with data that is simply all over the map. A situation that certainly makes it difficult to introduce new technology that benefits from an organized data framework.
If this maturity state resonates with you – you’re not alone! We see a lot of brands who simply need help in establishing a foundation for their data structure. But how do you get past this hurdle and on to the next stage? The goal, at this stage, is to bring order to your data framework, and use advanced tools and mechanism to extract data. Often, this starts with practical steps like conducting data audits to pinpoint your most critical data sources, assessing their quality, and mapping how they interact across your systems. With that clarity, you can prioritize the processes, integrations, and governance mechanisms that will help your data work together.
A great first step is to simply inventory all of the systems where data resides, and use this as a basis for a consolidation path. It sounds obvious but digging through the data to document all the sources, and establishing a clear picture of the full ecosystem, is a critical first step in the maturation process.
With a clear understanding of where data resides, you can then extract data using the right mechanisms for your business. This includes both one time extraction processes, as well as recurring process whereby data is extracted at the right time and in the correct context. Through this process, you can begin to establish a consistent data model and populate it through extraction to create a unified, consistent, and singular view. Once all data sources are identified and organized – and an appropriate extraction model is in place - your business is well along its way to data maturity, and primed for the next critical maturity state: Data Engineering & Sanitization.
Self-Examination
Are you at a Source System level of data maturity? Or are you lightyears ahead? Here are a few self-evaluation questions to see how you stack up with our first data maturity level.
Do you have an inventory of all systems where data lives?
Do you know what data is housed in each system?
Has your team thought about, or operationalized, any type of extraction process?
Do you have a unified data model? Or does your data live disparately in each systems?