Is your data mature enough for ArtificiaI Intelligence?

Here are some crucial tips to consider before jumping on the AI bandwagon.

Associate Consultant, Lulu Chen, details the steps to consider before diving in.

Yeah, everyone likes fancy Artificial Intelligence and cool Machine Learning, but…

Artificial Intelligence (AI) and Machine Learning (ML) have become buzzwords in the business world, with many organisations eager to explore the potential benefits of these technologies. However, while Artificial Intelligence and Machine Learning can offer exciting new opportunities, it’s important to remember that they are only as good as the data they rely on. In other words, “garbage in, garbage out.”

At 2PM, we believe getting the basics right is important when creating a strong foundation for excellence. In this article, we will explore what crucial steps needed to be taken before undertaking AI or ML initiatives.

When starting a data journey, many people are eager to dive right into building advanced machine learning models like Linear Regression, Random Forest, PCA, Decision Tree, and KNN. However, it’s essential to take a step back, focus on your current data maturity, and build a robust data strategy and governance framework before taking the Artificial Intelligence leap!

So, how do we do that? Let’s start by asking some important questions…

Where are we? – Examine data maturity

Data maturity refers to the level of sophistication in how an organisation manages its data. This includes data collection and storage, data analysis and decision-making. Asking this question helps ensure your organisation has high-quality, reliable data to feed into reports or ML models.

If your organisation lacks sufficient data quality and governance, it will likely run into problems with inconsistency and incomplete or incorrect data. This can negatively impact the accuracy and effectiveness of any models or reports.

Examining data maturity helps organisations identify gaps or areas where they need to improve their data practices.

What do we want to achieve? – Establish a data strategy

Developing a data strategy is essential to align business objectives and data practices. A well-thought-out data strategy helps businesses identify what data is needed to achieve their goals. It will also help you map out the data practices for effectively collecting, storing, analysing, and using that data. Thus, the organisation can leverage data insights to gain a competitive advantage and make better decisions.

How do we get there? – Design and implement data governance

Data governance is a combination of small pieces of data regulation just like traffic is a combination of traffic lights, road regulations and rules of vehicle drivers.

Data governance involves defining policies and procedures for collecting, storing, and using data. Establishing a clear framework for data governance is crucial to ensure your data is reliable, accurate, and secure. This includes determining who has access to what data, how it will be used, and how it will be protected.

Implement a solution to suit your needs and get it right!

To address the need for effective data governance, organisations must first evaluate their current data maturity level and determine their business goals. This will allow them to implement a customised solution that meets their needs. It’s like a doctor diagnosing a patient’s illness and prescribing the most effective treatment.

Defining the scope of the data governance implementation and establishing a comprehensive framework, including roles, responsibilities, departments, data processes, policies, and compliance, is crucial.

Another critical step is developing data policies that comply with industry standards and regulations. As the solution is implemented, it’s essential to monitor the adoption among business units and gather feedback to ensure continuous improvement. This feedback should inform ongoing refinements to policies, processes, and standards to ensure they remain aligned with the organisation’s goals and effectiveness in managing data.

Iterative change and improvements

One of the great aspects of working with data is that it’s a constantly evolving journey. An organisations’ goals change over time as new challenges arise, and businesses should always improve their data analysis and reporting capabilities to stay ahead.

Businesses can refine their data process, skills, and tools by adopting iterative change and improvement. This allows them to stay agile and responsive to changing markets while steadily improving the quality of data analysis and reporting.

What next?

It is important to understand that before starting AI and ML initiatives, organisations should assess their current position in the data journey, their data and business strategies and their data practices.

If you are still trying to figure out where to begin, the 2PM Business Intelligence (BI) team can help! Our team uses a systematic methodology to objectively determine your organisation’s current level of data maturity, assist in establishing a solid foundation, and help align your objectives with your data practices. Our team will then provide ongoing support to improve your data processes through iterative improvements to increase the success rate of your solution adoption.

Need help figuring out where to begin? Get in touch!