Getting Started With AI: Understanding Data Maturity Models
After getting a basic understanding of what data engineering is and its role in the AI process, a good place to continue when getting started with AI is to first understand t...
27/10/2023 | 2 Minute Read
Senior Engineering Manager
After getting a basic understanding of what data engineering is and its role in the AI process, a good place to continue when getting started with AI is to first understand the data maturity model of your organisation.
A data maturity model serves as a comprehensive roadmap that allows organisations to gauge where they currently stand regarding data management and usage. By identifying your existing capabilities, strengths, and weaknesses, this framework offers invaluable insights into your current data landscape. But its utility doesn’t stop at mere evaluation; it extends to strategic planning and optimization as well. With a data maturity model, you can develop a tailored data strategy that aligns with your organisational goals, helping you unlock the full potential of your data assets.
Understanding your level of data maturity is not just about knowing what data you have, but also how effectively you’re using it. Are you simply collecting data, or are you extracting actionable insights from it? Can your data be easily accessed and integrated across departments for more collaborative decision-making? These are the kinds of questions a data maturity model helps you answer.
Understanding your level of data maturity is also a crucial stepping stone for any organisation aiming to implement artificial intelligence (AI) successfully. AI initiatives are fundamentally driven by data—its quality, accessibility, and the insights that can be gleaned from it. Therefore, a data maturity model serves as an essential tool in preparing your organisation for AI adoption.
Understanding Your Data Maturity Levels
To become a data-driven organisation, you need to understand your data maturity levels. As organisations deal with data, they go through different stages, and these stages affect how ready they are for using artificial intelligence (AI). Let’s break down the four levels of this journey:
Level 1 – Informal/Ad-Hoc
Characteristics: Data is collected in silos, with no formal structure or governance. Data usage is reactive rather than proactive
AI Readiness: At this level, AI adoption would be challenging due to fragmented and unstructured data. Basic analytics may be possible but building effective AI models is often unfeasible.
Level 2 – Defined
Characteristics: The organisation starts to develop a data strategy and governance model. Data collection becomes more systematic.
AI Readiness: AI pilot projects could be considered but may face limitations due to data quality and availability.
Level 3 – Managed
Characteristics: Data governance is well-established. Data is integrated across different departments and with various data sources, and analytics capabilities are more sophisticated.
AI Readiness: The organisation is now ready for more advanced AI initiatives, including machine learning and predictive analytics. Data quality is generally high enough to train reliable models.
Level 4 – Optimised
Characteristics: Data is not just managed but utilised effectively for strategic decision-making. The organisation has advanced analytics capabilities and may already be using AI for various applications.
AI Readiness: At this stage, AI can be fully integrated into organisational processes for maximum impact. This includes the utilisation of advanced machine learning algorithms and even the exploration of cutting-edge technologies like neural networks.
Take Our AI Readiness Assessment
Curious about your business’s AI readiness? Discover it for free with our AI Readiness Self Assessment. Download our free assessment by completing your details below:
How did you score?
- Mostly As: You’re at the Ad-Hoc/Informal level. Focus on developing a formal data strategy and governance model to get started on your data and AI journey.
- Mostly Bs: You’re at the Defined level. Consider deploying pilot AI projects but be aware of the limitations you may encounter.
- Mostly Cs: You’re at the Managed level. Your organisation is ready for more advanced AI initiatives, such as machine learning and predictive analytics.
- Mostly Ds: You’re at the Optimised level. AI can be fully leveraged for maximum impact and innovation.
Understanding your data maturity level is a valuable exercise when aiming to understand and adopt AI technologies effectively. At Deimos, regardless of the stage you are in, from Ad-Hoc to Optimised, we guide and support companies across every stage of the maturity level with a focus on helping you get AI ready and getting the best out of your data. You can schedule a free 1:1 data maturity level assessment here to help you understand where you are in your data and AI readiness and get guidance on our team of data experts.