Data-driven improvement cycles lay groundwork for success
Dr. Deming’s PDCA cycle continues to be the foundation for achieving success using management information and data analytics. You also need to organize a learning system and facilitate it with data.
The four steps towards better performance
- Set goals and targets based on defined KPIs, regularly evaluate and adjust them (plan, act).
- Consistently use data and information for analysis and action (plan, do).
- Use data and information to improve and innovate (do).
- Review and discuss the results/numbers, give feedback about positive and negative performances (check).
Research has shown that the four crucial PDCA steps are essential for improving business performance (PDCA cycle lifts up your business results). These four factors can be seen as the engine of a fast and agile sports car. A third of all better business results can be attributed to these four factors, according to the research. A data-driven PDCA cycle can make the difference between failure and success on every level and discipline of the organization.
Data alone is not enough
Data alone doesn’t won’t do the trick, however. And only completing the PDCA cycle is usually not fast or accurate enough. Take the bicycle business. There’s a reason why agile companies like the International Bike Group are gaining ground in more and more places, taking the market share that previously belonged to more traditional companies. What sets them apart? A smart and advanced form of learning and improving using data analytics. Read all about the top 7 data analytics trends of 2020.
There’s no single perfect business model to strive for – you have to keep adjusting, changing, and optimizing
The crux is combining the two aspects of data and improvement cycles. Implementing data-driven improvement cycles doesn’t only help the business stay in motion, but also to develop improvements and iterations on products and services more precisely and faster than before. Embedding PDCA in a cycle means the concept is never finished, but you get a little bit better every time.
No such thing as a perfect business model
Even the International Bike Group keeps changing, even though they’re very successful right now. You can’t get stuck in an existing business model. There’s no single perfect business model to strive for. You have to keep adjusting, changing, and optimizing. Not in the least because people, their needs, expectations, and environments are also changing. The big pitfall for many companies is that their business model stagnates as soon as they become successful. The process of continuously adjusting and improving stops. And then it becomes harder and harder to change the lumbering machine. Like strategies, business models have limited life cycles.
Preparing the data analysis (plan), working with data (do), comparing data (check), and adjusting based on the data (act) are also continuous processes. In other words, there’s an interplay between data and completing the improvement cycle, which partly automates a continuous learning and improvement process.
Plan – Learning from a PDCA model doesn’t happen separately from the business, despite what some training courses and workshops may tell you (“you’ll always learn something’). No, learning takes place against the background of the organization’s higher goals, strategy, and the SMART Key Performance Indicators.
Do and Check – Data doesn’t just give you hints about golden combinations, but data also provides real-time feedback regarding the behavior of people, teams, departments, and organizations. That’s how you make the link between data and improvement. What works and what doesn’t?
Act – Based on data-driven simulations and datacratic scenario planning, you can see what works, what doesn’t work, and what will work when. That creates new improvement plans based on facts and scenarios, and the data-driven cycle has been completed. Every cycle ends with an improvement, for example a course correction that influences the next version, a next product, or one more or fewer action in the process.
Conclusion: the flywheel effect
Data-driven working and improvement cycles ensure that learning leads to revenue for the organization. People learn what they have to do to achieve better performance. Nothing is as frustrating as making decisions that work out badly, or changes that don’t work out. Completing the cycle again and again creates the motions that lead to processes running better and better, and people doing what’s really necessary. The more steps in the PDCA cycle you take based on data, the more the processes are improved, and the improvement cycle is accelerated in turn. The flywheel effect is set into motion.