• Written by Azhar Aliyar, Director

A chief operating officer checks the latest sales figures and notices a discrepancy between the numbers from the sales team and the financial department, prompting a search for the source of the error and highlighting the need for reliable data foundations.

Field-level meaning and why column names are not documentation

The foundation of any data-driven system is the meaning of individual fields, which is often taken for granted but can be a major source of errors if not properly defined. Column names in a database are not sufficient documentation, as they may be ambiguous or open to interpretation. For instance, a column named "revenue" may not clearly indicate whether it refers to total revenue, revenue from a specific region, or revenue from a particular product line. Most people get wrong the idea that column names are self-explanatory and that no further documentation is needed.

A thorough understanding of field-level meaning is essential to ensure that data is accurately interpreted and used to inform business decisions. This requires careful documentation of each field, including its definition, data type, and any relevant business rules or constraints. By doing so, organisations can avoid errors and inconsistencies that can arise from misinterpretation of data.

The systems that get left out of scope and the partial answers that follow

When implementing a data-driven system, it is common for certain systems or data sources to be left out of scope, either due to complexity, lack of resources, or perceived lack of importance. However, this can lead to partial answers and incomplete insights, which can be misleading or even dangerous. For example, a system that only considers financial data may not take into account operational constraints or sales performance, leading to decisions that are not optimal for the organisation as a whole.

The consequences of leaving out certain systems or data sources can be significant, and it is essential to carefully consider the scope of the system and ensure that all relevant data sources are included. This may require additional resources or effort, but it is crucial to achieving accurate and reliable insights.

The executive leadership data this depends on: consolidated financials, operational KPIs, sales performance, headcount

Executive leadership relies on a range of data sources to inform their decisions, including consolidated financials, operational KPIs, sales performance, and headcount. These data sources provide a comprehensive view of the organisation's performance and enable leaders to make informed decisions. Consolidated financials provide a complete picture of the organisation's financial situation, while operational KPIs offer insights into the efficiency and effectiveness of business processes. Sales performance data helps leaders understand market trends and customer behaviour, and headcount data informs decisions about staffing and resource allocation.

For instance, a chief financial officer may use consolidated financials to identify areas of cost savings, while a chief operating officer may use operational KPIs to optimise business processes. By considering these different data sources, executive leaders can gain a deeper understanding of the organisation and make more effective decisions.

As described in our previous article, executive leadership plays a critical role in driving business success, and access to reliable and comprehensive data is essential to this role.

What it changes about: cross-functional questions nobody can answer in the room and each function reporting its own version of a figure

The availability of reliable and comprehensive data changes the way organisations approach cross-functional questions and decision-making. No longer must leaders rely on incomplete or inaccurate data, or wait for days or weeks for answers to simple questions. With access to a single, unified view of the organisation's data, leaders can quickly and easily answer questions that previously seemed impossible, such as "what is the impact of a change in sales strategy on our financial performance?" or "how does our operational efficiency compare to industry benchmarks?"

Additionally, the availability of comprehensive data puts an end to the common phenomenon of each function reporting its own version of a figure, which can lead to confusion and mistrust. By providing a single, authoritative source of truth, organisations can ensure that all stakeholders are working from the same set of facts, and that decisions are based on a shared understanding of the organisation's performance.

Measured with: decision latency, actions of the form come back with numbers, definition conflicts resolved

The effectiveness of a data-driven system can be measured in a number of ways, including decision latency, actions of the form "come back with numbers", and definition conflicts resolved. Decision latency refers to the time it takes for leaders to make decisions, which can be significantly reduced with access to reliable and comprehensive data. Actions of the form "come back with numbers" are a common phenomenon in organisations, where leaders ask for data to inform their decisions, and the availability of comprehensive data can reduce the need for these requests. Definition conflicts resolved refers to the ability of the system to resolve disputes over the meaning of data, which can be a major source of errors and inconsistencies.

By measuring these metrics, organisations can gain a clear understanding of the impact of their data-driven system and identify areas for improvement. This can help to ensure that the system is meeting its intended goals and providing the expected benefits.

What this does not do, or when it is the wrong choice

While a data-driven system can provide significant benefits to organisations, it is not a panacea for all business problems. There are certain situations where a data-driven system may not be the best choice, such as when the data is incomplete, inaccurate, or unreliable. In these cases, other approaches may be more effective, such as relying on expert judgment or using alternative data sources. Additionally, a data-driven system requires significant resources and effort to implement and maintain, and may not be feasible for smaller organisations or those with limited budgets.

For organisations that are considering implementing a data-driven system, it is essential to carefully weigh the potential benefits and challenges, and to consider alternative approaches. As described on our contact page, our team is available to provide guidance and support to organisations that are considering implementing a data-driven system.