Utilize your data promptly

In the rapidly evolving business landscape, data analytics has become an indispensable tool for companies seeking to gain a competitive edge. Several industry leaders, including Per Vase from pharmaceutical engineering company NNE, advocate for the early adoption of data analytics to avoid the pitfall of over-planning.

Companies like Samsung, Nestlé, and Amazon have successfully cultivated a culture of analytical excellence. They have embraced the Kaizen philosophy, focusing on continuous improvement through practices such as Gemba Walks to identify inefficiencies. Amazon, in particular, emphasizes data-driven decision-making, combining relevant data analysis with diverse perspectives, scenario analysis, and structured lessons learned to enhance decision quality and foster an analytical culture.

For new projects, the design of experiments (DoE) is critical for realizing the benefits of data analytics quickly. Julia O'Neill, founder and principal consultant at Direxa Consulting, encourages diving into data analytics to achieve results and motivate continued development. Incorporating data analytics into subject matter knowledge encourages a culture of 'learning by doing' and motivates scientists and engineers to explore new possibilities.

Julia O'Neill, with over 30 years of experience in statistics and chemical engineering, sees data analytics as an incredible consensus builder. Cy Wegman, a consultant and former engineer in the consumer packaged goods industry, shares this sentiment, encouraging scientists and engineers to add data analytics to their toolkit. Within minutes, most scientists and engineers can begin learning and using data analytics, driving analytic excellence across whole organizations.

Data analytics can help businesses make better decisions faster and meet project milestones more predictably. It can also help form a strategy that everyone in a team supports, especially when dealing with long-standing issues. Sharing data analytics with suppliers can improve insight into the supply chain and encourage better collaboration.

Dan Middleton, chief of digital manufacturing at Rolls-Royce turbines unit, finds interaction with data analytics essential for questioning one's own thinking. O'Neill also emphasizes the importance of statistics in reducing the cost of medicines. Learning through data analysis and statistical modeling is a continual cycle, and analysis can provide valuable feedback on data collection and contextualization.

O'Neill suggests introducing Design of Experiments (DoE) as a way to change an organization's approach. By starting small and simple, organizations can save time in the long run. Wegman agrees, suggesting that using data analytics tools specifically designed for scientists and engineers can lead to insights being gained more rapidly.

Many organizations delay creating value from their data due to significant data projects or combining disparate data sources. However, conducting data analytics in parallel with data collection and making it accessible can help organizations create value faster. Demonstrating real results through data analytics can help organizations gain insights more rapidly and work more productively with the same resources.

In conclusion, the adoption of data analytics is no longer a luxury but a necessity for businesses aiming to stay ahead in today's competitive market. By embracing data analytics, organizations can make better decisions, foster a culture of continuous improvement, and drive analytic excellence across their operations.