AI has been with us for 40+ years and has been used very successfully by business to deliver advantage (the original Amazon bookstores being a leading example). CxO’s are now reacting to the conversations and the impressive AI vendor led demonstrations and are now looking to IT/Digital functions to ‘enable’ these capabilities within their organisations

Yet, many of these AI initiatives will stumble right out of the gate due to one critical issue: poor data quality. If we examine the complex inter-dependencies between data readiness and AI empowerment, it becomes clear that addressing data quality is not just a technical necessity but should be a strategic imperative for business leaders.

The Foundations of Data Quality

Data quality serves as the bedrock (and no, I don’t means the AWS service) for any successful AI project. Without clean, accurate, and comprehensive data, AI systems are likely to produce unreliable results, leading to misguided decisions and wasted investments i.e. “Poor-quality input data leads to suboptimal outcomes.” The journey to business AI empowerment will need to begin with a robust data governance framework that ensures data integrity, consistency, and usability.

Key aspects of data quality include:

  1. Accuracy: Ensuring data correctly represents real-world entities and events.

  2. Completeness: Having all necessary data points to make informed decisions.

  3. Consistency: Standardising data formats and definitions across the organisation.

  4. Timeliness: Maintaining up-to-date data for real-time analytics.

  5. Validity: Adhering to the defined business rules and constraints.

I also continue to have conversation with leaders about their use of data. A nuanced understanding of input and output data is important for these businesses leaders who are aiming to become data-driven and AI-empowered. These concepts play a role in shaping both the backward-looking business intelligence (BI) and the forward-looking analytics and leading indicators, which are essential for strategic planning and real-time decision-making.

The Critical Role of Input and Output Data

Input Data: This refers to the raw data fed into business processes, systems, and models. Input data can include a wide range of metrics such as page views, stock availability, price discounts, customer interactions, and more. Input data serves as the fuel for the organisation, driving operational processes and influencing real-time adjustments. By closely monitoring their input data, organisations can detect trends, identify potential issues early, and make proactive adjustments to improve performance and outcomes.

Output Data: This represents the results generated from business processes, systems, and models. Common examples of output data include revenue, profit, free cash flow, and other financial performance indicators. Output data provides insights into how well the business has performed over a specific period. It is typically used for reporting and retrospective analysis, offering a backward-looking perspective that helps organisations understand past performance and make informed decisions based on historical data.

Backward-Looking BI: Traditional business intelligence (BI) focuses primarily on this output data. It involves analysing historical data to generate reports and dashboards that illustrate past performance. While this is valuable for understanding what has happened, it often lacks the ability to provide actionable insights for future planning. Backward-looking BI is essential for measuring success, identifying areas for improvement, and learning from past experiences. However, it can be limited in its ability to guide proactive decision-making.

Forward-Looking or Leading Indicators: To complement backward-looking BI, organisations must also focus on leading indicators derived from their input data. Leading indicators provide insights into future performance by highlighting potential trends, opportunities, and risks (via ML and analytics). For example, monitoring customer engagement metrics (input data) can help predict future sales trends (output data). Leading indicators enable organisations to be more agile and responsive, allowing them to anticipate changes and adapt strategies accordingly. This forward-looking approach is crucial for staying competitive and driving continuous improvement.

We all like working in trichotomies … The Rule of Three is based on the idea that three elements create a pattern that is easy for us (humans) to recognise and remember, making communication more effective. Let’s, therefore, look to a three span solution to bridge this gap.

Bridging the Gap: Three Core Principles

First, a focus on customer-centric data utilisation is crucial. Understanding and anticipating your customer needs through data should be paramount. Data leaders must work closely with the marketing and product development teams to leverage predictive and prescriptive analytics, turning customer data into actionable insights. Additionally, businesses must incorporate external data sources to stay ahead of market trends and competitive movements. This comprehensive approach allows organisations to adapt swiftly to external changes and disruptions.

Second, integrated governance and architecture play a pivotal role. Effective data governance enhances performance and reduces risk. Building a network of data stewards (both technical and business focused) across the organisation ensures that data is accurate, secure, and utilised effectively. Embracing a data-ecosystem architecture in the cloud allows for greater agility and scalability. By decoupling services and integrating them through APIs, businesses can more easily adapt their data strategies to evolving needs.

Third, metric-driven leadership is essential for continuous improvement. Leading organisations integrate both input and output metrics into their data models, allowing for real-time adjustments and fostering a data-driven culture. Data should serve people within the organisation, empowering employees at all levels to make better decisions. This involves upskilling staff and fostering a culture where data and analytics are integral to every function. Establish a feedback loop, where data and AI insights continually inform and refine business strategy, ensuring that the organisations not only keep up with but also lead in their respective markets.

The Path to AI Empowerment

Achieving AI empowerment for an organisation is a journey that begins with data readiness and can culminate in sustained competitive advantage. Organisations must recognise that while technology is a crucial enabler, it is not the answer, and the real differentiator lies in how their data is managed, governed, and leveraged to drive business transformation (and ultimately value).

By adopting three core principles and addressing the foundational issue of data quality, businesses can unlock the full potential of AI, driving innovation and creating lasting value.

In conclusion, bridging the gap from data readiness to AI empowerment is not a one-time project but a continuous, strategic effort. It requires a shift in mindset, where data is viewed not just as a byproduct of business operations but as a core asset that fuels AI-driven insights and decisions. By investing in data quality and adopting a holistic, metric-driven approach, organizations can pave the way for a future where AI not only supports but transforms their business.

Final thought, the phrase "AI will not replace you, but the person using AI will" is often used to downplay AI's impact on jobs. If we change the the context slightly … "AI will not replace your organisation, but the organisation using AI will" … over to you!

Don't let this AI enabled future pass you by. Partner with AC3 today to redefine what's possible for your business. Together, we can unlock these opportunities from your data.