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Plan, Do, Check, Act: A data-driven cycle

Photo Daan van Beek
Author: Daan van Beek
Business Partner
Table of Contents

Connect data with continuous improvement and PDCA and your organization will blossom all over again. People will enjoy their work more because they will regain control. Datacracy is a promising and already proven way of working that every organization should embrace immediately. However, many companies still have a long way to go before they have a data-driven culture. Structure, processes, and people’s behavior will all have to change. A data-driven PDCA cycle also starts with the people. Data-driven working signals a movement and the thinking can be mastered by anyone.

The 4 elements of the data-driven PDCA

The NPRS case study refers to a ten-year urban development program in the Netherlands, and it shows us what can be achieved by linking data to working with improvement cycles. We’re also linking some other important concepts to the term PDCA. These other terms (Reflect, Inspire, Mobilize, and Evaluate) help with data-driven learning and make the learning loop suitable for the 21st century. And so, we arrive at the data-driven PDCA cycle (see Figure 1):

The datacratic PDCAFigure 1: The data-driven PDCA cycle

  • Passion: every plan starts with personal passion. With someone who wants to do something differently, who has a dream, who feels the urgency to really improve something.
  • Data: The Do phase alone is not enough. This phase is expanded by Doing based on data and reliable, irrefutable facts, and monitoring the impact of attitude and behavior over a long period of time.
  • Conscious control: The check phase isn’t just about monitoring what happens in the Check phase. The term conscious control comprises two parts. Reflecting on the data and monitoring it ensures that awareness grows, and people understand what’s really going on. Control means not just becoming aware but also getting more grip on those actions and improvement efforts.
  • Autonomy: The result of running PDCA cycles is that the professional develops more and more autonomy. They no longer have to dance to the manager’s tune, but can independently determine what works and where course correction is necessary based on data.

An interplay

There’s an interplay hidden between Plan and Passion. The plan is born from passion. At the same time, a good plan inflames professional passion. The Do phase uses data, and the data leads to new content in the Do phase. The Check creates more Conscious control and its development ensures that the Check is more integral, with more depth and attention to detail. The Act phase creates autonomy.

The result of running PDCA cycles is that professionals no longer have to dance to the manager’s tune

The data-driven PDCA cycle leads to an alternative based on facts (data), which ensures that the professional can determine the next step with more autonomy. More autonomy leads to people taking more ownership over the Act phase, so that they can execute it based on their own ideas – for the greater good, of course.

NPRS: lessons learned

Some valuable lessons can be gleaned from the case study of the National Program Rotterdam South that coincides with the eight terms from the data-driven PDCA cycle.

  • Passion: Passion starts with putting people to work in a sustainable way. All the systems and interventions have to be judged by this passion. Decide what’s truly necessary and ensure purposeful interventions that make a real difference. NPRS was set up to work for an entire generation. The program can’t consist of short-term goals or a political ideas without deep-rooted passion.
  • Plan: Use KPIs to focus on the signals that really matter.
  • Data: Realize that political statements about the facts don’t always reflect said facts. Data leads to better decision-making because it makes you look at the whole, and you can make quicker decisions based on the latest available data.
  • Do: If you set a target based on a superficial analysis or an opinion, you’ll get a superficial result.
  • Conscious control: Dashboards ensure that you can zoom in on your area and demographic. You can see exactly what’s really going on. It creates awareness and increases control.
  • Check: Perform a data-driven root cause analysis and determine which problems get in the way of your goals (in NPRS’ case: helping people get jobs).
  • Autonomy: Through dashboards, you can check which interventions work for which demographic, and which ones don’t work (well). That’s how you remain in control of your functioning and your improvement actions.
  • Act: When adjusting plans or actions, don’t look from a general perspective, look at multiple levels as well as holistically.

Impact on learning and improving

When consistently developing the data-driven PDCA cycle, the terms Passion, Data, Conscious control, and Autonomy will keep growing as more PDCA cycles are completed. Plan, Do, Check, Act keep rotations while Passion, Data, Conscious control, and Autonomy increase. This implies that the PDCA cycle isn’t just a systemic form of learning, and that it definitely doesn’t stop there.

The core of the data-driven PDCA cycle is the following, coinciding with the technical learning:

  • Developing humanity (passion).
  • Maintaining objectivity (data).
  • Becoming aware of the relevant factors we can control (conscious control).
  • Growing in self-reliance (autonomy).

Looking for more depth

Before I zoom in on the P for passion and plan, the D for data and do, the C for check and conscious control, and the A for actualize and autonomy, first it’s time to look at some subjects that add further depth, so that we can assign the proper meaning and activities to the data-driven PDCA cycle. Take the leap from data to improvement by:

  • thinking in terms of processes and dependencies
  • applying different levels of learning
  • using data for decision-making
  • working with powerful Key Performance Indicators

In another blog post, we’ll cover how these developments from data to improvement ensure that your organization is becoming more intelligent and data-driven, and what the value of data is in the context of storytelling.

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