How can you use data to make informed decisions as a leader in Business Strategy?

Data-driven decision making is imperative for any business leader in any organization. Strategic decisions should always be driven by data insights, and coupled with intuition. Having said that, it is equally important to not get carried away by looking at data insights at a surface level. For e.g. a specific region might be growing much faster than other regions consistently at a surface level. One can't make strategic decisions like investing more into that region, without knowing the underlying reasons for the outlier growth! Doing a deep-dive analysis of growth in that region and comparing with other regions might give a holistic picture and the factors driving the growth. Unit economics might throw a completely different picture!

Defining our objectives ahead gives us a solid foundation when making strategic decisions. Starting with clear Key Performance Indicators (KPIs) helps us measure our progress. Additionally, conducting a thorough analysis of the market conditions ensures we consider external factors. Importantly, fostering cross-functional collaboration allows us to tap into insights from different teams, providing a well-rounded perspective for more informed and effective strategic decision.

Die wichtigste Basis sind die Business-Ziele. Es muss klar sein, was man erreichen m?chte. Als zweiten Schritt würde ich die Erfolgsfaktoren und Herausforderungen für die Erreichung dieser Ziele ableiten. Wenn hier ein klares Bild besteht, kann man sich gezielt Gedanken machen, an welchen Stellen bessere Informationen, Vorhersage etc. dabei helfen k?nnten, Erfolgsfaktoren voranzutreiben oder Risiken und Herausforderungen zu mitigieren. Und erst dann sollte man prüfen, wie Data, Analytics & AI unterstützen k?nnen, hierfür konkrete L?sungen zu finden. Wenn man dadurch klare Use Cases ermittelt hat, ist noch zu prüfen, wie umsetzbar die Use Cases sind, ob de ben?tigten Daten vorliegen usw.

Utilizing data is crucial for informed decision-making. A prime example is Netflix, which used customer data to pivot from DVD rentals to streaming, significantly increasing subscribers and revenue. Data-driven organizations, as noted by Harvard Business Review, are 5% more productive and 6% more profitable. "Competing on Analytics" by Thomas H. Davenport is a valuable resource in this realm. McKinsey’s website offers in-depth articles on data-driven strategies. Tools like Tableau and Google Analytics are key for data analysis and visualization. Additionally, watching TED Talks by data experts can provide further insights. This data-centric approach allows for a robust business strategy.

As a leader, you need to familiarize yourself with the platform you will be using and how data is delivered. Analysis and deep understanding of the presented information are essential to making profitable decisions.

Internal data: Utilize sales figures, financial records, customer feedback, and operational metrics. External data: Consider market research reports, industry trends, competitive analysis, and economic indicators. Qualitative vs. quantitative data: Choose the type of data that best answers your questions. Structured data like sales figures might require quantitative analysis (e.g., correlations, regressions), while unstructured data like customer reviews might benefit from qualitative analysis (e.g., text mining, sentiment analysis).

I often find that much of the information available is not reliable and tends to be biased. In some cases, third-party sources are more trustworthy, while in other instances, in-house information is better. I generally remain skeptical of data until I can verify it myself. I recommend adopting a similar approach for discerning the accuracy of information.

Too often, companies that are "Data-Driven" are making their decisions based on data that is inaccurate or downright wrong. System architecture, connectivity and system processes can improve or destroy data. Before choosing your systems and sources make sure that they are accurate and delivering credible data.

Utilize statistical analysis and data visualization tools to extract meaningful insights. Identify trends, patterns, and correlations that can inform strategic decisions. Visualizations can help communicate complex information more effectively.

Utilize data analysis tools: Choose software suited to your data type and analysis needs. Popular options include spreadsheets, BI tools, and data science platforms. Clean and prepare your data: Ensure data accuracy and consistency before analysis. Apply relevant analytical techniques: Explore correlations, regressions, clustering, or other techniques to extract insights. Visualize your data: Use charts, graphs, and dashboards to communicate findings effectively.

my observation is that incorporating AI, when properly trained with domain-specific knowledge, greatly enhances our ability to understand and identify patterns. This deeper understanding simplifies complex data, making it easier to prepare for future challenges and decisions in the business context.

Think of data interpretation as solving a mystery. Use Sherlock Holmes' deductive reasoning—eliminate biases like Watson, scrutinize details, and acknowledge uncertainties. Validation is the process of checking alibis—ensuring data accuracy and relevance. Peer reviews act as the jury, providing a collective verdict on the trustworthiness of your findings.

As I have been told by many a researcher, data tells whatever story you want it to. This is why having your data/RevOps team separate from a function like sales will allow for a more unbiased interpretation. This will allow the team to focus on solving the problems not pointing fingers at who is at fault.

Avoid overreliance on data: Consider contextual factors and potential biases influencing your results. Conduct sensitivity analysis: Test the robustness of your findings under different assumptions and scenarios. Gather feedback from stakeholders: Seek diverse perspectives to validate your interpretations and identify potential blind spots.

In data interpretation, it's crucial to avoid biases like confirmation bias, where we favor data that matches our beliefs, and overconfidence, especially in confusing correlation with causation. Validating data means checking its accuracy, consistency, and relevance, while acknowledging its limitations. Employ methods like peer review and testing for a thorough analysis. Remember, "Don't bullshit" is key - it's about integrity and methodological rigor in making ethically and scientifically sound decisions.

Develop actionable strategies: Translate your insights into concrete plans and initiatives. Clearly communicate your data-driven rationale: Ensure stakeholders understand the reasoning behind your decisions and build confidence. Monitor and adapt: Track progress, measure outcomes, and be prepared to adjust your strategies based on new data and feedback.

Without proper communication plans data is useless. The best businesses have ongoing consistent communications around their most important KPI's and metrics. This allows for monitoring and adjustment as new things are uncovered. Speed is more important then ever and waiting for quarterly reviews often puts teams behind.

Before an organization gets to this step (or before they even start with this approach) they need to make sure their data means the same thing to everyone. If the greater team is misaligned then any analysis will be wasted effort. Socializing the definitions of data either through a wiki or a data catalog tool will improve alignment to the data before decisions are made.

Data-driven is an exciting facet; most of the time we make colossal mistakes with data-driven decision-making by just focusing on numbers; there are always two parts to data-driven business decision-making: 1)operating data collected for time till now 2)objective data for strategic decision (it is not historical alone) I have always used GHOST framework of my own to do diligent business decisions. G - Goal/s strategic for the decision to be done H - Hypothesis, without hypothesis any decision can be good or bad - tradeoffs are not known O - Objectivity of the decision S - Strategic focus to look at the data, not just historical but the narrative that provides the lenses on data for future T - Target & track parts of data that help measure

How can you make decisions without data? The challenge is choosing the right data - historic data is good for the past but how do you use data that can help you make choices in the future....