What is Data-Driven Strategy?

Data-Driven Strategy uses data and insights to plan and achieve business goals. It contrasts with strategy based on intuition, past experiences or theory. Data-driven strategy relies on data analysis. Data analysis gives an evidence base to enable effective decision-making. This isn’t to say traditional methods should be ignored but providing evidence to support intuition can substantiate the decision-making process. A stronger evidence base makes it more likely that strategy implementation will succeed.

An element of data-driven decision-making is demand modelling. Demand modelling helps you understand customer preferences, estimate market demand, and make well-informed decisions. Depending on the context, demand modelling through data analysis uses various techniques, such as:

• Time series analysis
• Collation, coding and sorting
• Regression models
• Machine learning

A structured, demand modelling process improves the model’s reliability. Increased reliability allows for ongoing improvement leading to more effective business decisions.

What to consider when using data to support strategy?

Ask Appropriate Questions

The starting point of an effective demand model is asking the right questions. What insights do we want from the data? What data do we have now relative to what we need? How can we use this data to calculate demand? Questions do not need to be complicated. It can be as simple as ‘Does our current supply match the current demand?’ These questions guide the data collection and analysis process.

Gather Relevant Data

The questions you ask determine the data you collect. We recently completed a feasibility study on building a new product. We gathered relevant data relating to current usage vs. estimated future usage for said product relative to the target market. Issues arise at this stage due to unsatisfactory data collection methods. It is important that you get the right data from the right places.

Analyse Findings

Data is often collected in non-standard formats. A data standard, even an in-house one, can help. Once data is collected, process and format it for analysis. Remove irrelevant data. Organise remaining data for accurate modelling. For demand modelling, categorising the data by customer type can provide valuable segmentation insights.

Reliability

If the data is incomplete, missing, or unreliable it will lack accuracy. This isn’t to say missing data is fatal, truth be told, it can be informative. Re-engaging with customer groups or the originator of the data source to gather more information can often give you reasons as to why this is the case. You can then improve your data and how you present it. The more reliable the data is, or the better understanding you have of it, the more effective your demand model will be.

Present the Data

When presenting the data, create a clear link between the data and your plans. Ensure consistency when presenting comparison charts. Inconsistent data makes it harder to make sense of facts. If the data looks wrong, or too good to be true, then it may be worth a closer look. Check the data sources, context, and techniques used to create the charts and models. Label everything clearly.

Help with your data-driven strategy

Hague Consulting can help with market research, desk-based research and other forms of data collection.  We can carry out simple or more complex data analysis. Data can provide evidence to your decision-making process. These decisions could include cases. Our first steps are working with the data you currently have and seeking ways of improving it. See this case study to see how we helped a client create a sector wide data standard which is currently in use to help them in making strategic decisions and resource allocation.

Salman Motara, Analyst, Hague Consulting Ltd. © Hague Consulting Ltd 2023.