Apple’s underlying philosophy is about “better.” It is likely that if Apple’s executives were leading the response to COVID19, they would not be planning for a “Return to Normal” or creating a “New Normal,” they would be focussed on how to make what we have better!

The demands of complex judgment, coupled with changing requirements in a volatile environment, determine that stability is prioritised over change, as risk is already difficult enough to explain and manage. Outstanding leadership in 2020 is to focus on making one thing better; it must make a difference whilst adjusting other concerns for better outcomes. Therefore, what is the one stand out priority that demands attention and focus that delivers “better?”

We remain focused on how to provide data that has provenance and lineage to the Board, the aim to improve decisions and get to better outcomes. Central to the thinking was a linkage between measurement, performance, management and judging if the outcome was better. The insights shared below are independent of company size, structure or leadership.

The prime recommendations are

1. Discover and break the right number of critical links between outcomes and rewards/ incentives.

2. Find and modify reinforcement linkages between outcomes and culture so that all questions can be rewarded.

The focus of this frawework is on the application of #data for #growth through #innovation. The insights are independent of company structure or leadership. The challenge is if you can only make one thing better, as you will read, HR is a strong contender.

Typically a team will come together with an idea, and the Hypothesis Or Thesis determines the data required for the Data Lake. There is a flow from a Data Lake to Knowledge and Insights on the left. The process tests the Thesis the team created through analysis using the available data. The analysis process generates Knowledge and Insight, which closes an agile feedback system as knowledge and Insight refines what data we need in a Data Lake to complement the analysis which tests the team’s Thesis or Hypothesis. Knowledge and Insights give rise to recommendations which leads to Decisions. Decisions over time, become Outcomes, which we measure. Knowing and measuring Outcomes help us refine and define better the requirements we have for the data in our Data Lake, thus creating a second slower iterative improvement system.

Our Data Lake has a relationship or correlation to the Bias and Assumptions we have as a team. There is a closed-loop system between Bias and Assumptions and the Data Lake that further helps inform the Data Lake and reminds us of our Bias and Assumptions. A second influence on Bias and Assumption is the team’s Beliefs and Culture. Open cultures enable teams to question their Bias and Assumptions, whilst other cultures may avoid questions. A culture that encourages questioning will enable open-minded teams to continually check if they have the correct thesis and data in a Data Lake, and undertake the analysis without creating an outcome that was forecast. There is a link between Bias and Assumptions, which helps us in our decision-making process.

In a collaborative, open and free-thinking system, being able to check assumptions will enlighten decisions, leading to better outcomes. In an early-stage company, Reward and Motivation structure are not coupled to Outcomes as there is no revenue or direct causal correlation. The Reward and Motivation is fundamentally the team and individuals driven to do their best, as the team is there to prove their Hypothesis or Thesis, and to grow a company based on their collective thinking. Reward and Motivation structures create Patterns and Alignments. When the team’s perception of Patterns and Alignments are open, the loop is responsive and adaptive to questions and problems. We know our behaviours link Patterns and Alignments with the Knowledge and Insights we search for. We see this linkage in the methods and priorities that are created and focussed on. Priorities will give rise to recommendations which lead to decisions where outcomes help us refine a data Lake to improve continually and hypothesis. An agile loop of continual improvement.

In startups, this system leads to rapid development, creative thinking within an adaptive self-improving process. The improvement is creating a refinement of the hypothesis, the data, the analysis and witnessed as outcomes which create change. In a system driven by data, and the ability to challenge and question everything, we find outcomes and culture focus on refining a hypothesis/thesis and the team strives to refine continually and improve which supports innovation and drives growth. It feels easy as the system enables flow.

In figure 3, starting from the white block positioned bottom middle of the diagram, “Memory and/or Processes”. This block was a “Hypothesis or Thesis” in startups. In the corporate world, trading history has provided experience, teams, revenue and methodology, which includes memory and processes that deliver barriers to entry, IP and often the core value of the company.

A critical link is already established between Memory and/or Processes and, Beliefs and Culture. Often this is visible in recruiting people who already adhere to the Beliefs and Culture of the company (search for organisational fit). An influential culture determines that this is the way we do things, based on Memory and Processes, which usually removes the ability to question Bias and Assumptions (this is how we do it.) We retain the same Bias and Assumptions which taints/ colours or distorts Decisions. Decisions over time, become Outcomes. In mature businesses, everything is measured (what does not get measured cannot be managed).

We measure the success of our Outcomes. Successful Outcomes re-enforces that our Beliefs and Culture are right so we should not change. Unsuccessful outcomes are seen as an error in the model, data or analysis and that we need more of the right data. The Outcome tends to favour or aligns to Rewards and Motivations as incentives and bonuses are linked directly to the outcome. With Outcomes measures and aligned to Rewards and Motivations, we have a confirmational bias towards certain Patterns and Alignments, that favours individual success. This restricts the options and learning from outcomes and will direct our priorities, meaning we will search for certain Knowledge and Insights in our data set, especially ones that we can recommend as a decision, that creates an outcome which aligns to our personal performance metrics, established by the memory in the system.

Central in the diagram in Figure 3 is Delta in Risk. This is an important point. When an organisation is finance-led, risk is modelled, defined and understood. Financial data and controls ensure that processes are designed to control risk at the agreed level. Data creates many dynamic changes within an organisation. Critically data introduces clarity and understanding of risks that could not be seen through the pure lens of finance. Ignoring cyber attacks and loss of data as a systemic risk, the data risk of interest here is the identification of new insights about products, services, teams, partners, processes that data brings to the attention of employees and Directors — a delta in risk. Many of the closed-loop reinforcement and confirmational biased feedback loops can be identified, but are structured to ignore this new risk. Where risk is identified, it creates instability in memory, bias, assumptions and decision making. Still, because of the strength of existing processes, the tension may not lead to change but rather a higher level of frustration, denial and protectionism.

Existing processes have memory and efficiency of flow that can improve incrementally what already exists. The same memory is designed to resist flow-oriented towards a change of the process itself.

In a complicated closed-loop system driven by data; where outcomes drive rewards and culture removes the ability to challenge and question, our memory and processes continually re-enforce the same patterns. We find we are unable to adapt to the new vulnerabilities and risks that data adds, which is destabilising. We unpack that our processes are not set to support disruptive innovation or iteration at scale. Data can define a set of possibilities and constraints located in the past. Data has and is biased, and those biases are different and unique. Data has no imagination or creativity, and it will keep doing the same thing. Data cannot create a solution on its own.

By example, from Mike Smith, here are three measurements that create continual frustration for innovation teams.

1. “As a legal representative for this company, it’s my job to make sure we don’t sign any contracts that put us in a perilous position, and to make sure that we’re abiding by all local laws and regulations”. I am a Fellow of my legal institution and am professionally bound by their code of conduct, and I am measured by how few lawsuits we are involved in, both with other companies and with other institutions. I am incentivised to avoid that risky innovative contract.

2. “As the lead for HR in this company, and being responsible for employee retention and well-being, it’s my responsibility to make sure we recruit carefully following appropriate laws (no discrimination etc), and ideally minimize employee dismissals as they are disruptive and represent risk to the company. “ I am measured on the avoidance of claims, and really don’t want to recruit that brilliant and outspoken evangelist who has 1,000,000 followers on BLM.

3. “As CFO, I need to make sure our books balance and are fiscally responsible. When it comes to the product team needing money for research and development, we need to balance investment with income, and ensure that the solvency of the company isn’t in question.” I am a member of a professional body and my reputation is my next job. It will count against me if as the CFO I agree to a large loan to accomplish some critical work that others view as essential for company growth and it goes wrong. My bonus is based on avoiding risky financial transactions.

Often the system we have created for stability, measurement and risk avoidance which rewards key individuals for doing their job well, but this can stand counter to innovation.

People in the Loop

Whilst the model recognises the processes that surround our peers, teams, people and staff, it does not wholly recognise the agency of individuals. Our company may have ethics but our divisions, functions and eco-systems all have their own unique moral codes, which changes. Our company often thinks about culture rather than seeing the 100 cultures by clusters of our stakeholders and wider dependent communities, including extended family. What is driving an individual at this moment and how they make a choice which will have implications is not seen by the systems, as our systems are crafted for efficiency and stability. Humans are both in the loop, but more often are the loop. Humans are the workforces and feedback loops with their creativity, reluctance and behaviour creating colour in a very greyscale process-driven organisation. This simple model would be a 100-page book, just by adding people’s behaviour and attitude to innovation at each point.