What are the best practices for data-driven decision making in a fast-paced business environment?

Define the desired outcome and the leading indicators, and focus on the metrics that matter. Eliminate noise from irrelevant data, and ask your team to explain what’s driving the number (the “why”) rather than just the “what.”

Why?--should determine ----> What? Why is the question which should define Which data is to be monitored? Once the objective of why is clear, identify and define the desired outcomes you wish to achieve from a data-driven decision-making process. This will ensure that all decisions are based on the same objectives (the why). 80 % of businesses are looking at the wrong screens most of the time and focus their energy tweaking things they should not be looking at.

I’ve heard several different opinions on this so here is my take. Data is often validated by Benchmarking so be sure to understand where you are, performance criteria wise when using DDDM. Also, the paradigm that I continue to hear is ‘bad data in, bad data out’. So maybe focus on the quality of your data, the trust of the data before you stump for all out DDDM.

Analyze data with intent! In my view, the best way to make fast, data-driven decisions is by using simple, real-time dashboards - or anything close to real time. They show key trends at a glance, helping businesses react quickly and smartly, just like in fast-paced tech firms.

Optimising data for product and business decisions is crucial. In my experience you can navigate effectively by defining clear objectives with explicit goals for data-driven decision-making. Clearly outline questions and desired outcomes to guide efficient data collection. A key priority is data quality, so ensure you invest in cleaning and preparing data to ensure accuracy. High-quality data prevents misguided conclusions, providing a reliable foundation for decision support. Another key component is building and cultivating a data-driven culture, encouraging all employees to leverage data in shaping choices, making it a cornerstone of strategic decision processes.

To avoid data silos, it’s imperative to connect and integrate data from ALL sources across your business. Think of your supply chain! It’s an interconnected flow for your goods and services to be delivered to your customer. By connecting the systems that connect the supply chain. (ERP + operational tools)YOU will AMPLIFY your efficiency gains because now rather than just calling it your supply chain (which can be linked by human actions + communication) the systems act as your true eco system for your product to flow through, so the business can see the big picture ! Thus leveraging an even better flow, better communication between silos, better efficiency gains, reducing cost from an end to end perspective.

I would suggest understanding the key metrics that influence other measures. In organisations today there are so many metrics being reported that chasing improvements in some will negatively impact others in the longer term. Organisations need a playbook for their business that leads to good practice. Especially if there are multiple shift patterns. Shifts need to be run by the playbook and not by what will make your shift look good at the expense of who follows on. I have on a number of occasions had to explain to SMTs that an individual shifts performance was not down to the supervisor but down to how the shifts were set up. They key with data isn't how much of it you have it's understanding what it is telling you.

One thing I found helpful for informed decision-making is gathering data from diverse sources like internal systems, customer feedback, and industry benchmarks. Integrate this data using tools like data warehouses and APIs to avoid silos and inconsistencies. This consolidation provides a holistic view, preventing duplication and ensuring a comprehensive understanding of your business landscape.

Fully eliminating data silos while continually incorporating new data sources presents an almost Sisyphean task for even the most well funded organizations on the planet. So how should leaders make progress? Start with identifying the most valuable and critical processes in the organization. Maybe it’s new user onboarding, maybe it’s user retention… pick one. You can then task your data analyst teams to work with business leaders on identifying a high impact set of internal AND external data sources and build a use case specific data architecture to measurably improve this critical process. Once you prove success, your org will be ready to make more investments in this space

In addition, my experience also comes to the point that as and when you validate your data, you should also have global traceability mechanisms in place. This ensures that we do RCA (root cause analysis) much more efficiently and correct problems should we find them. One must be aware however of the time you take to do validation, especially when data flow doesn’t stop while product release schedules get shortened. Therefore, risk / severity categorisation of data could become an additional parameter in improving validation timelines.

Engendering a data-driven culture starts with technical training for the internal teams. Typically, companies may have staff who possesses years of the required technical experience and who would have done specific training over a period of years. This creates an internal silo which makes it difficult for the wider team to be inculcated into a new data-culture dynamic. Start with small training for all persons, where feasible, and gradually scale up in complexity. The internal specialists can act as trainers if possible, which would reduce cost.

The fastest way to implement a data driven culture is for the senior leadership team to model this behavior. This then cascades down to the rest of the organization as an effective method to pursue organizational goals and obtain resources. Note that it’s not enough to merely SAY you’re data driven, it’s critical to make decisions backed by data and then ensure your rationale is as clear to as many people as possible. If you do this consistently and reward teams that bring data backed initiatives with resources, this will create the right incentive structure for all teams to invest in developing the skills and capabilities necessary for a sustainable data driven culture.

Fostering a data-driven culture and mindset within an organization involves instilling the importance of data, analytics, and evidence-based decision-making throughout the entire workforce. Few ways to do this is via leadership commitment, education and training, clearly communicating the value of data and rewarding data driven behavior. Building a data-driven culture is an ongoing process that requires commitment, education, and a willingness to adapt. By consistently reinforcing the importance of data and integrating it into everyday practices, organizations can cultivate a mindset where data is a fundamental driver of decision-making.

Fostering a data-driven culture is more than a process—it's a mindset. Cultivate data literacy, curiosity, and collaboration within your team and stakeholders. I adopt an open-minded, critical, and agile approach. A data-driven culture transforms data into a strategic asset, providing a competitive edge in decision-making.

While data provides valuable insights and measurable outcomes, it can also confine thinking within existing parameters, potentially stifling innovation. Creativity, on the other hand, encourages exploration beyond traditional boundaries, fostering unique solutions. To effectively integrate these two, one should view data as a compass, not a map. It should guide, not dictate, the creative process. Use data to inform and refine creative ideas, ensuring they are grounded in reality, but avoid letting it suppress imaginative thinking. Encourage a culture where creative risk-taking is informed by data insights, but not overpowered by them. This synergy creates an environment where innovative ideas are both imaginative and strategically sound.

In fact intuition and creativity spur you into seeking data validation, so you start scrutinising and shaping investigations accordingly. Einstein first had the insight and then he went on to seek tools to formally validate his hypothesis.

Effective decision-making is crucial in today's dynamic business environment. From strategic decisions shaping the company's future to operational choices impacting daily workflows, different types of decision-making play a pivotal role. Whether it's intuitive decisions based on experience and gut feeling, data-driven decisions leveraging analytics, or collaborative decisions involving diverse perspectives, each approach brings its unique value to the table. Understanding and mastering these various decision-making methods can empower professionals to navigate complexities and drive success in their respective roles.

One thing I want to add from my experience: Always look for the outlier in the data A careful analysis of the outlier can intimate us about the probable Black swan event.

Remember that data isn’t valuable on its own. It’s value comes from the benefit that people or organizations obtain by leveraging data. This value is increased tremendously by how quickly you can take a data point and act on it. Unfortunately it takes time to clean, prepare and certify data driven insights. So to succeed in this field you need to carefully balance the risk of imperfect data with the potential value from speedy decision making. In my experience this can only be calibrated by experience and making mistakes. Good luck!

For smart decision-making using data, start by setting clear goals that are specific, measurable, and aligned with your overall strategy. Find a balance between business instincts and data-driven approaches, and consider frameworks like 1-3-5-10x vision to stay on track. Collect data from various sources to get a complete picture of your business without creating data silos. Tools like data warehouses and APIs can help with this. When analyzing data, pick the right tools based on your needs and evaluate them with a 3-5x framework. Test your assumptions through experiments and A/B testing, and keep your decision-making process flexible. Create a data-driven culture by encouraging curiosity and collaboration within your organization.

One thing I have found helpful is encouraging my team members to develop data literacy skills so that everyone can confidently interpret and utilize data. This involves offering training sessions and providing resources to enhance their proficiency in working with data. Approaching data-driven decision-making as an ongoing and iterative process within our team and embracing the opportunity to learn from both successful outcomes and setbacks, using this knowledge to consistently enhance and refine our decision-making processes.

In a fast-paced business environment, it is sometimes better to make decisions quickly based on intuition and gut feelings rather than waiting for all the data to be available. For example, if you are a startup competing in a fast-growing market, you may need to make decisions quickly in order to stay ahead of the competition. In these cases, it may be better to make a decision based on intuition, even if you are not 100% sure that it is the right decision. This does not mean that you should ignore data altogether. Data can still be a valuable resource for making decisions, even if you don't have all the time in the world to analyse it. The key is to use data and intuition together to make the best possible decisions for your business.