Master Your Supply Chain:

How a Structured Analytical Technique Can Bring Benefits Across Industries and Transport Modes.

There are several benefits to adopting a structured analytical methodology or technique across an enterprise's data holdings:

1. Improved efficiency: A structured analytical methodology allows analysts to work more organised and efficiently, reducing the time and effort needed to extract insights from data.
2. Enhanced quality: A structured analytical methodology can ensure that the analysis is thorough and accurate, resulting in higher-quality insights.
3. Greater consistency: By standardising the analytical process, an enterprise can ensure that all analysts follow the same process and produce consistent results.
4. Better communication: A structured analytical methodology helps clarify the steps taken in the analysis, making it easier for others to understand and replicate the results.
5. Increased transparency: A structured analytical methodology can make it easier to document and communicate the analysis results, increasing transparency and accountability.
6. Enhanced credibility: By following a structured analytical methodology, an enterprise can demonstrate that it follows best practices and produces high-quality insights.

Adopting a structured analytical methodology can help an enterprise extract more value from its data holdings and make more informed decisions.

The first principle of intelligence I learned was centralised control, so another must-have for me is an opinionated data model with a fixed structure designed for a specific purpose. In contrast, a dynamic data model is more flexible and can arguably be adapted more quickly to a broader range of uses. However, it would be best if you had a high degree of intelligence know-how and analytical expertise across the enterprise, with policies, procedures and rigorous adherence monitoring in place to guarantee you don't end up with data holdings that require a team of technical experts to serve the rest of the organisation. In my experience, when this occurs, responsiveness, objectivity, systematic exploitation, continuous review, accessibility and timelessness are all degraded - all of which are principles of intelligence.

Some benefits of an opinionated data model include the following:

1. Simplicity: An opinionated data model is often simpler to understand and use because it has a clear and defined structure.
2. Improved performance: Because the structure of an opinionated data model is fixed, it can be optimised for specific queries or use cases, leading to improved performance.
3. Greater reliability: An opinionated data model is less prone to errors or inconsistencies because it has a fixed structure that is less prone to change.

However, a dynamic data model has its own set of benefits, including:

1. Ease of use: A dynamic data model is often easier to work with because it can be modified on the fly without requiring extensive changes to the underlying structure. This point is valid for an individual user's ease of use or a small team. However, suppose you're trying to get a large team or multiple teams on the same page and compound your organisation's data's value. In that case, the ease of use argument quickly diminishes.
2. Flexibility: A dynamic data model can be easily adapted to changing needs or requirements, making it more versatile. This can be good for putting out fires. However, I wouldn't recommend regular changes being everyday business-as-usual occurrences.

A structured analytical methodology such as bolster's smaq can help bring the best of both worlds by allowing an enterprise to use an opinionated data model as a foundation while retaining the flexibility to adapt to changing needs. For example, an enterprise could adopt a structured analytical methodology that includes defined steps for data preparation, analysis, and visualisation but allows for the use of different tools or techniques depending on the specific needs of the analysis. This helps ensure the analysis is reliable and efficient while allowing flexibility and adaptability.

Whatever industry you work in, especially those with a growing responsibility for their own supply chain or striving toward a holistic value-driven, data-driven ESG lens, we believe that getting data out of your systems and sensors in a way that makes sense and can be used by more of your organisation, more of the time is how you will continue to succeed in the future.