Creating a Culture of Data-Driven Demand Forecasting

Last week, I posted about data strategies’ tendency to focus on the data itself, overlooking the (data-driven) decisioning process itself. All it not lost. First, it is appropriate that the majority of the focus remains on the supply of high-quality #data relative to the perceived demand for it through the lenses of specific use cases. But there is an opportunity to complement this by addressing the decisioning process itself. 7 initiatives you can consider: 1) Create a structured decision-making framework that integrates data into the strategic decision-making process. This is a reusable framework that can be used to explain in a variety of scenarios how decisions can be made. Intuition is not immediately a bad thing, but the framework raises awareness about its limitations, and the role of data to overcome them. 2) Equip leaders with the skills to interpret and use data effectively in strategic contexts. This can include offering training programs focusing on data literacy, decision-making biases, hypothesis development, and data #analytics techniques tailored for strategic planning. A light version could be an on-demand training. 3) Improve your #MI systems and dashboards to provide real-time, relevant, and easily interpretable data for strategic decision-makers. If data is to play a supporting role to intuition in a number of important scenarios, then at least that data should be available and reliable. 4) Encourage a #dataculture, including in the top executive tier. This is the most important and all-encompassing recommendation, but at the same time the least tactical and tangible. Promote the use of data in strategic discussions, celebrate data-driven successes, and create forums for sharing best practices. 5) Integrate #datascientists within strategic planning teams. Explore options to assign them to work directly with executives on strategic initiatives, providing data analysis, modeling, and interpretation services as part of the decision-making process. 6) Make decisioning a formal pillar of your #datastrategy alongside common existing ones like data architecture, data quality, and metadata management. Develop initiatives and goals focused on improving decision-making processes, including training, tools, and metrics. 7) Conduct strategic data reviews to evaluate how effectively data was used. Avoid being overly critical of the decision-makers; the goal is to refine the process, not question the decisions themselves. Consider what data could have been sought at the time to validate or challenge the decision. Both data and intuition have roles to play in strategic decision-making. No leap in data or #AI will change that. The goal is to balance the two, which requires investment in the decision-making process to complement the existing focus on the data itself. Full POV ➡️ https://lnkd.in/e3F-R6V7

Leaders, here’s a reality check! A data-driven future isn’t just about systems and strategies—it’s about people. Your success depends on: → Connecting people to your vision → Empowering them with the tools and skills to succeed → Leading with a focus on collaboration and inclusivity Data may drive decisions, but it’s the people that unlock its full potential. As you scale your organization, don’t overlook the human connections that turn data into meaningful impact. When your people grow, your organization thrives.     Want to harness the full potential of data? Want to drive smarter decisions and stronger organizations?   Start by building an inclusive data infrastructure where everyone can:   • Access data • Act on data • Align with data   Here's how:   1. Engage Individuals Show the value of data in decision-making.   2. Educate Teams Teach them how to leverage data to meet their goals.   3. Enable Infrastructure Connect systems, drive governance, foster literacy.   4. Promote Transparency Ensure data is open and accessible.   5. Encourage Collaboration Create a culture where data is shared and used collectively.   6. Support Continuous Learning Offer training and resources to build data skills.   7. Lead by Example Use data-driven insights in your leadership.   With these steps, you can transform your organization. Or enhance the data culture you already have.   It's not just good for your people. It's good for your community, too.   Data matters. Make it count.   P.S. Want to chat about keynotes? DM me “KEYNOTE”

As a data professional one thing you may be tasked with is helping drive a culture that embraces data. Culture and the people in an organization can hinder data work if they aren't wanting to utilize data, lack understanding around data, or are fearful. How can we help to excite and ignite the data work in an organization? One key thing you can do is to help generate curiosity in your organization and allow that to ignite innovation and use of data. Remember, the majority of the employees in an organization aren't data professionals by trade or title. Helping employees to embrace data can be difficult but you have the opportunity to help them succeed with data. Curiosity is a catalyst to data and AI work. How can we help to drive more curiosity in your organization? First, we need to foster a culture where we question things. As a data professional, help people to develop a pattern and habit of asking questions. What else can be done to help foster curiosity? ✅ Teach that experimentation is welcome. Allow people to share ideas and experiment on these ideas. ✅ Teach that you don't fail but you learn. As Nelson Mandela said: I never lose. I either win or learn. ✅ Free up people's time to do deep work. Allow or encourage people to block off 30 minutes a day to explore and learn. Teach that this time should be free from email, Teams or Slack, and text. Create an environment where critical thinking is encouraged. ✅ Provide access to resources. If we want non-data people to get excited about data we should provide resources for learning. ✅ Celebrate discovery and innovation. Celebrate questions, ideas, wins. ✅ Ask open-ended questions and encourage people to go and find answers. Overall, curiosity can ignite data work so allow it to do so. As a data professional lead the way and don't allow your own thoughts or ideas to block others. Foster the right culture. Stay nerdy, my friends #dataliteracy #AIliteracy #datastrategy #AIstrategy #data #AI #curiosity

Yesterday at the Gartner C-Level Communities event in Washington DC someone asked me for signs that indicate that a company has a strong data culture. Here's my take: 1. Decisions Start with Data, Not Opinions Leaders and teams consistently ask, “What does the data tell us?” before making decisions. Gut feel is supplemented—not replaced—by evidence, and this is demonstrated from the ELT down. 2. Data is Accessible and Trusted Employees across functions can easily find, understand, and use data. This is backed by: Clear data ownership and governance Documented definitions and quality metrics Data curiousity is encouraged A culture of accountability around data accuracy 3. Data Literacy is Woven into Every Role Everyone—from finance to marketing to operations—knows how to interpret the data relevant to their function. Data training isn’t limited to analysts; it’s democratized and encouraged company-wide. Even as a career data nerd, I have data literacy issues. Let me give you an example - I grew up in Ireland where it rains A LOT, yet for years, I didn’t understand what a “50% chance of rain” meant. I thought it meant the forecaster was hedging her bets, but it turns out it means there’s a 100% chance it’ll rain on 50% of the forecast area. That’s the thing about data, having access to it isn’t enough—understanding it is what matters. 4. There’s a Shared Language Around Data The organization leverages a shared glossary to uses consistent definitions, KPIs, and metrics. People don’t spend half their meetings debating which numbers are “right”—they debate what actions to take based on them. 5. Data Champions Exist at Every Level You don’t just have a data team—you have advocates throughout the org. These are individuals who evangelize data best practices, help peers level up, and keep the momentum going between major initiatives. What would you add to the list? #DataCulture #DataLiteracy #Actian