How do you leverage data and evidence to validate and justify your business case assumptions and claims?

Identify key assumptions and claims: Begin by clearly identifying the assumptions and claims made in your business case. These can be related to market trends, customer behavior, financial projections, or any other aspect relevant to your business.

If your company has an analytics group, this can be an opportunity to collaborate on sourcing and evaluating data. Data scientists have tools to evaluate the quality of the data or designing surveys if you need primary data. After collecting the data, be sure to understand what its limitations. It's easy to link up data sets that look related but can be misleading or confusing when analyzing the insights.

Make sure you don't just measure the things that are easy to measure and readily available. Look for the gaps and common assumptions, and work with subject matter experts to understand the less tangible impact measures.

One thing I've found helpful in identifying and determining data to use for business case is to explore all available data. You shouldn't discard a data source just because they presently look messy- all it needs is cleasing and behold, you start getting some insightful informations that will guide your thoughts and lead to significat milestone in your task. Data cleansing, data profiling, data validation etc are some exercises need to be performed on a 'rough' data to bring out the magic you need to succeed in your work.

Be cautious about inadvertent selective bias. If you are looking for data to support a perceived problem, you will probably find it. Look for data that contradicts your problem statement, too, so that you have a balanced picture.

Leverage scenario planning capabilities to evaluate your options. And be sure to account for potential changes in the market or business strategy to avoid committing to a plan that becomes obsolete due to factors outside your control.

A cost benefit analysis is important, but also consider how contingency and risk can be factored into that. Consider impacts on people which may bring unanticipated future costs as well as the obvious spend vs income assessment.

Integrating predictive analytics and AI can enhance data-driven risk management. Predictive analytics helps forecast risks for early mitigation. AI refines this with machine learning, evolving predictions based on past data. It's also invaluable in handling 'Big Data', swiftly processing vast information to identify risks. By merging these technologies, we transition from simply reacting to risks to preemptively managing them.

Many organizations have several layers of approvals for large projects. An executive will want a different level of detail or focus than the departmental leader. Be prepared to tell the story differently each time and anticipate the questions or concerns your audience will raise. Keep the pitch short and too the point. Focus on the outcomes, not the detail on how you got there. And if there are multiple options, have your recommendation ready along with an explanation on why its the best choice.

I highly recommend using data to communicate and influence your stakeholders when delivering your business case. Here are my top 4 analytics strategies: 1. Descriptive Analytics: This involves analyzing historical data to identify patterns and trends. Techniques such as data aggregation and mining are used here. 2. Predictive Analytics: Predictive analytics models trends and behaviors to predict future outcomes. Techniques include regression models, forecasting, statistical analysis, and machine learning. 3. Prescriptive Analytics:. Techniques used are optimization models, simulations, and decision-tree analysis. 4. Data Visualization: Visualization tools like Tableau or Power BI are excellent for interpreting complex data sets.

Present findings and communicate effectively: Compile your findings in a clear and concise manner, highlighting the data and evidence that support your assumptions and claims. Use visualizations, such as charts or graphs, to enhance understanding. Communicate your findings effectively to stakeholders, ensuring they grasp the rationale behind your business case.

In my experience it is important that the data used is about the user actions that lead to the business success. What I mean by this is that business cases are laden with assumptions and many of the assumptions are what we THINK users & customers will do if we build something. It's about user behavior change. For example: "Customers using our online self service option will lessen the cost of serving them in person.", this is a business case assumption. So, track this data, track the user behavior. This often gets lost when focussing on data to validate business case assumptions.