Your AI Sucks: Part 2

Where to Start? – Data Quality for Trusted AI

In Part 1, we explored how AI offers tremendous potential for businesses, but its success depends heavily on the quality of the data driving it. Without reliable data, AI can lead to inaccurate outcomes and undermine trust. In this section, we’ll discuss how to establish a robust data quality strategy that ensures your AI initiatives are both effective and trustworthy.

You can’t just ‘do’ Data Quality. To ensure the data you’re focusing on is accurate, consistent, and reliable, you need to take a structured approach.

1. Define Objectives: Start by defining the business value-aligned goals for data quality improvement. Identify the data owners, stakeholders, and the specific business processes that will be improved. Establish clear data quality rules and standards based on their needs and your organization’s KPIs (Key Performance Indicators).
2. Data Profiling: Assess the current state of your data. This involves analysing the data to understand its structure, completeness, uniqueness, and accuracy.
3. Data Quality Assessment: Evaluate the data against the rules and standards that you agreed on with your stakeholders. This step helps identify and quantify any discrepancies or issues in the data—lots of organizations that I speak to say ‘our data is crap,’ but without putting some numbers behind this statement, how do you know that you’ve improved?
4. Data Cleansing: Address the issues identified during the assessment. This may involve correcting errors, filling in missing values, removing duplicates, and standardizing data formats. There is an opportunity to remediate these issues at the source, but this can be challenging if politics are at play or you’re relying on an external data provider.
5. Data Monitoring and Control: Implement ongoing monitoring to ensure data quality is maintained over time. This includes setting up automated checks and alerts for any deviations from the established standards.
6. Continuous Improvement: Regularly review and update data quality processes to adapt to new requirements and challenges. There’s a big people element here too. The people who are capturing the data have to understand the importance of accurate data entry and its impact further down the line.

Steps 5 and 6 are absolutely critical. Data Quality isn’t a ‘once and done’ exercise—it must be regularly monitored and maintained, or all your hard work will be undone.

Data Quality isn’t just about AI adoptions. It’s a key component of enabling an organization to modernize their business, applications, and legacy data estates and transform digitally.

Time to grow up – what is your AI implementation and adoption strategy? 

If you’re going on a journey – plane, train or automobile – you need to know where you’re headed and where you’re starting from.  The same is true of an organisation on a journey of AI adoption which also requires organisations to embrace data transformation and modernisation.   

At Xpedition, we work hand in hand with your teams to understand where you need to get to – what are your key strategic goals and outcomes?  What tangible value are you looking to unlock?  How can you build a long-term roadmap whilst also taking advantage of quick-win opportunities? 

We help you look at your maturity – how is your organisation currently managing data? What tools and solutions do you have in place?  What are your critical processes and how are your people leveraging these investments? 

This allows us to build a roadmap.  How can you start to understand what your journey looks like and what are the steps you need to take to get there.  The key here is aligning your journey to those strategic goals and outcomes to ensure that your data transformation and modernisation aligns with the AI initiatives that are going to enable your organisation’s vision, values, and goals.   

Where do we start?  

Xpedition are offering a zero-risk, zero-cost Innovation Discovery Workshop, specifically designed to help you explore the AI opportunities that your business can leverage with the Microsoft Intelligent Data Platform, Copilot & Fabric.    

Our Inner Circle Microsoft partnership gives our specialists the ability to stand up tailored demonstrations, with operational AI services, in a matter of hours – helping you and your team explore the opportunity and visualise specific use-cases quickly.   

For organisations looking to make significant changes to their Data and Technology strategy, Xpedition have been working with Microsoft to deliver digital transformation scoping and planning programmes for financial services businesses for 25+ years.  

For established organisations looking to explore iterative adoption projects, Xpedition’s Power Platform offers give you the ability to access highly skilled platform specialists at a more predictable and more affordable project size. Whether you’re looking to explore a specific capability or add specific skills to your existing team – we can help.​ 

Register below to book your workshop and start working with our experts.