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Challenging Thinking Through Digital Understanding

Do your data-based reports hit all the right notes in the boardroom, or are they sometimes overlooked or misunderstood? In…

Challenging Thinking Through Digital Understanding

9th February 2024

Digital Understanding

Thomas Clements is lead data scientist at management consultancy, Vendigital. 

Do your data-based reports hit all the right notes in the boardroom, or are they sometimes overlooked or misunderstood? In today’s more uncertain world, accurate, real-time data is playing an increasingly critical role in guiding corporate decision making, so a lack of understanding in this area could be holding some businesses back.  

Quality data and data visibility are playing an increasingly important role in driving business performance, but get it wrong and it could restrict growth or even send a commercial enterprise into decline. A strong data ecosystem acts like a beacon or guiding light; helping decision makers to avoid risks and fulfil their strategic potential. 

Knowing what good data looks like is a sensible starting point for raising data understanding within any organisation. Good data should aim to answer the questions that matter most to the business; providing insights that will enable the Board to make the right decisions at the right time, pushing through productivity and other performance-related improvements.  

To gain a full understanding of what questions the business needs to answer to achieve its KPIs, a holistic assessment of existing datasets and data-gathering systems across the organisation is usually required. This will establish how complete the current data picture of the organisation actually is. For example, is it accurate, reliable, and based on real-time information? Is it relevant: are the right data points being collected to support decision-making? Are there any data gaps and are the right processes and procedures in place to protect data integrity?  

For multi-site organisations with a footprint that is spread geographically, data sourced from across the business must be analysed to check for inconsistencies. If plants are operating independently, inconsistent approaches to data capture and reporting could increase the risk of gaps or errors, so careful checks are required. In some cases, Boards may have been caught out by incorrect or incomplete data in the past and the IT or data management team may need to take a step back to rebuild confidence. 

Research conducted by Vendigital last year, based on the views of 200 Board-level executives at UK-based companies, has shown that there is a high degree of data mistrust within many businesses – 79% of respondents said that they ‘don’t always trust their business data’. While most business leaders understand the benefits of data-led decision making, past experience has led them to question what their data is telling them.  

When presenting meaningful data to the board, data scientists can help by developing strong visualisation tools such as dashboards that capture just the right amount of information in a format that is easy to digest. These tools are typically used to display data-based insights and signpost tangible business decisions. Predicting the questions that decision makers within the business are likely to ask will ensure that data scientists are ready to talk through the workings and share the findings of the data assessment where necessary. While some detail may be needed, clear, concise and reliable data reporting is vital and will help to rebuild trust. 

As custodians of quality data within organisations, data scientists can construct data-based models that answer the board’s questions, as well as using data to highlight problems that they might not have been aware of. A growing number of businesses are harnessing the power of AI to increase the accuracy and reliability of data-based insights.  

For example, business AI systems can be trained on historical data such as revenues and order volumes to help Boards identify areas where performance is improving or not. They can also be used to improve demand predictability; potentially uncovering opportunities to make operational efficiencies, for example, by renegotiating demand-based agreements with key suppliers. However, data scientists must ensure these systems are robust and, crucially, that they are trained on sufficient data to deliver accurate and reliable outputs. 

At a time when growth opportunities can arise quickly and those businesses that are able to act there and then often come out on top, up-to-date data is a must. However, there is no room for compromise when it comes to data quality and integrity, and instilling a culture that is rooted in a respect for data can help businesses to make better decisions and thrive as a result. 

Categories: Advice, Articles, Tech

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