What do you do if your business decisions are not data-driven?

Assessing your current data situation is indeed the foundation for becoming more data-driven in your approach. Evaluating factors such as the quality, quantity, and accessibility of your data is crucial in understanding where you stand and what steps to take next. By asking key questions like the sources, reliability, relevance, frequency of collection, storage, accessibility, and security measures around your data, you gain insights into its strengths and weaknesses.

The emphasis on assessing both the quality and quantity of data is pivotal. Quality refers to the accuracy, completeness, and reliability of data, while quantity ensures that there's enough data to support robust analyses. It might also be helpful to discuss specific metrics or criteria for evaluating data quality, such as consistency, validity, and timeliness.

I think it’s important to analyze why your decision is not data driven? Unavailability of data? Bad quality data? No patterns in data that would lead to certain decisions? Culture in the organization? Experience in the business? Most of these needs to be resolved so that moving forward that’s not an issue but if everything looks good and decision was taken based on experience it’s fine and you don’t have to worry too much about it, though backing it up with data will add more colors to it.

Without data-driven decision-making, assessing your current data situation becomes challenging, potentially leading to inefficient operations and missed opportunities. Lack of data mining means missing out on valuable insights buried within your data, hindering strategic planning and innovation. It can result in reliance on intuition rather than evidence-based strategies, increasing the risk of errors and suboptimal outcomes. Addressing this gap requires prioritizing data collection, analysis, and implementation of data mining techniques to empower informed decision-making and drive business growth.

Pour devenir plus axé sur les données, évaluez votre situation actuelle : qualité, quantité et accessibilité des données. Identifiez les sources, évaluez la fiabilité, la pertinence, la fréquence de collecte, le stockage, l'accessibilité, l'analyse et la sécurité des données pour planifier des améliorations.

Asking the right question to business can help define objective and success metrics. The metrics can primary or secondary 1. Primary Success Metrics: These are the main measures directly linked to the primary objectives of the business Example: Revenue generated by selling product Or services How to measure: Calculating Revenue each month, Month-over-Month % change in Revenue. 2: Secondary Metrics: These metrics provide additional context and insights that contribute to a more comprehensive evaluation of performance. Example: Net Promoter Score(NPS), Brand Loyalty, Customer Satisfaction Score(CSAT). How to measure: Conducting survey and getting customer feedback on a scheduled basis. Comparing monthly feedback to observe the trend

Know your business objectives this can help you address data and what you really want to achieve and to measure the success of your business and also know what factors influencing your objectives and cost efficiency.

I believe business objectives and answers you are trying to get is first step in the process. Define them clearly and see if you can get answers from data that would help you making decisions.

After outlining your business objectives, the next crucial step is to formulate clear and relevant questions that will guide your data analysis. These questions should address specific aspects of your goals, such as identifying your most loyal customers or determining the factors influencing cost efficiency. By asking targeted questions, you can focus your analysis and derive actionable insights that directly contribute to achieving your business objectives.

If a data driven decision is not the best decision, I will opt for a non data driven decision instead. That said, however, not having used (or the intention to use) a data driven approach to a certain task, does not mean that data-driven decision making, that comes from the right intuition and/or interpretation, on the adoption of data mining and/or engineering techniques, shouldn't be employed at all, just that they should/might be employed to a lesser extent.

Important step to choose right tools and technology. If tools are available at your disposal choose what makes more sense. If the learning curve of the tool is steep chose what you know the best and you can express yourself in. If not learn and deliver.

When business decisions lack data-driven insights, exploratory data analysis tools like Tableau or Power BI can help uncover patterns. Techniques such as clustering or association rule mining can reveal hidden relationships within the data. Implementing predictive modeling using tools like Python's scikit-learn can anticipate future trends. Decision trees provide a clear visualization of decision-making processes for non-data-driven scenarios. Lastly, employing sentiment analysis tools can gauge customer opinions and preferences from unstructured data sources.

Choisissez les techniques et outils adéquats pour explorer les données, comme la classification ou la régression. Sélectionnez des applications adaptées à la taille et à la complexité des données, telles que Python

La troisième étape de l'exploration de données consiste à choisir les bonnes techniques et outils adaptés à vos objectifs. Ces méthodes incluent la classification, le clustering, la régression, etc. Les outils comme Excel, Python, Tableau sont utiles.

Identify key business problems where data-driven decisions could improve outcomes. Collect relevant data from various sources within your business operations. Utilize data mining techniques to analyze patterns, trends, and insights within the collected data. Translate findings into actionable insights that inform and guide decision-making processes. Implement strategies to integrate data-driven approaches into your business culture for sustained improvement.

If theres not enough or reliable data to support a decision one way is to implement an A/B test that will validate hypothesis and also collect valuable information , this test can be applied on any business decision that implies go to market . If we want to drill down then we can add segments to the a/b test and make the process.

The success metric and monitoring KPI should be defined right in the beginning while objectives is being defined so that it is not biased based on agreement across different groups. It can be altered later but it can leave a very useful trail of where things went wrong and where it worked as expected.

Start by defining clear objectives for your data-driven approach. Gather relevant data from various sources within your business. Utilize data mining techniques to extract valuable insights. Implement these insights into your decision-making process. Continuously monitor and evaluate the outcomes to refine your strategies further.

Understanding the gaps and limitations in your data landscape allows you to outline a roadmap for necessary improvements, whether it's enhancing data collection methods, implementing better storage and management practices, or strengthening security measures. This assessment sets the stage for leveraging data more effectively to drive informed decision-making and achieve better outcomes.

Gather data and identify key metrics which are most important to the business. Derive your decisions based on data and insights from analysis. Ensure everyone in the team understands the importance of data-driven decision-making and knows how to use data effectively. Continuously monitor to improve the results.