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Our Virtual World

We live in an increasingly virtual world. All functions that can be outsourced have been outsourced or are rapidly moving towards being outsourced. The ‘gig economy’ is well and truly here, and it is making an impact. This virtual world affects how we make decisions, and who is required to make decisions. Factories full of manual workers have disappeared, replaced by thousands of information workers.

However, we must pause, reflect, and ask ourselves some questions. How have the past decade’s technological advancements changed our business paradigm? Moreover, how will it take shape over the next ten years? Also, what is the ideal shape or form for modern business?

And when it comes to business, are we in the products business or the services business? Alternatively, more appropriately, are we in the decisions business? With the global hyper-connected landscape that we reside in, are businesses anything more than the cumulative effect of the decisions they make?

 

Manual Work vs Knowledge Work

In the era of Baby Boomers, “white-collar workers” were those performing managerial, administrative and clerical functions. They were outnumbered by workers engaged in manual tasks in the workplace. This generation defined ‘real work’ as manual labour: something that resulted in a tangible product or service. In the late 1950s, the white-collar workers started outnumbering manual workers. Soon, a new type of worker emerged: one that made a living by leveraging and utilising information. Consultants, bankers and investors multiplied as a product of this shift.

In 1959, Peter Drucker coined the term “knowledge worker”. In his book “Landmarks of Tomorrow”, Drucker suggested: “The most valuable asset of a 21st-century institution, whether business or non-business, will be its knowledge workers and their productivity.” Instead of producing tangible goods or services, these workers analyse, interpret, organise, anticipate, innovate and collaborate. In a nutshell, knowledge workers think for a living.

 

The Decision Factory

How has the rise of the knowledge worker affected business as we know it? At BizCubed, we have a theory. We see contemporary businesses (containing an influx of knowledge workers) as decision factories. Whether they are in the product or the service game, they are all in the decisions game. And be it one producing products or serving professional services, we posit that all businesses are decision factories.

The Decision Factory is a necessary product of our data-driven, virtual world. A modern workplace requires multiple decisions to be made in quick successions, like small parts being bolted together into one final product. Customers are looking for decision-based products: for example, a loan approved, or access given to a SaaS platform. Boards and executives cannot make all the decisions. Even the most junior employee makes decisions that affect customer outcomes. Your Decision Factory comprises all your people and all the decisions they make each day.

 

Is your business a Decision Factory?

Decision factories rely on operational analytics for better and more robust decision making. They use deep insights to plan. I urge business leaders and managers to ask of themselves, “Are our decisions backed by data?” … “Are these decisions made only at the top of our firm, or does the entire organisation make decisions by leveraging tools?” … “Are our decisions based on data from various functional units or are they configured to suit a particular functional line of thinking?”.

If you view your business as a decision factory, decision- making is a significant activity, and the quality of the decisions made is vital. The pace at which you make the decisions also becomes essential. Decision factories are responsible for many facets of decision making and are tasked with correcting poor decision making quickly before more significant problems are created.

 

Cost of poor decision making

History is full of corporate blunders, mostly due to poor decision making. While not every decision can be a good one, the consequences of some poor decisions weigh far more heavily than others – it can result in significant financial losses, and can even be fatal to a company.

Take Kodak and Motorola as examples. Both market leaders in their time. Both ahead of the innovation curve. Both failed to spot tectonic shifts in their industries owing to poor decision making.

For instance, the first hand-held mobile phone was developed by Motorola in 1973. Ten years later in 1983, Motorola released the first commercially available portable mobile phone. Despite creating this device and holding a sustained head-start, Motorola failed to capitalise on the smartphone revolution. This article written in 2010 pointed out that only three years after the iPhone’s release, Apple was already more dependant on mobile phone revenue than Motorola.

Despite holding a patent for digital cameras long before they started getting mass-produced, Kodak was left behind when Sony and Fuji started manufacturing digital cameras on a large scale (and establishing market dominance before Kodak could say, “Smile!”). Kodak filed for bankruptcy in 2012 and has since been desperately trying to claw its way back to success. A devastating outcome, and all because of a lack of good decision making, or failing to spot a poor decision before it wreaked havoc.

 

Does your business have an “Andon cord”?

The Andon Cord is a Toyota innovation for car manufacture. Pull the Andon Cord, and the entire production line halts. Even the most junior worker in the factory may pull this cord. But what happens if nobody notices the poor decision? The Andon cord can only be pulled, and the process can only be stopped, if the people who are part of it have the ability to recognise poor decision making. In addition to being able to spot a poor decision, they must also have the tools and the ability to fix it, and most importantly the organisational culture and support to do this in real time. Effective decision making relies on increasing the number of people that can participate in the decision-making process. This does not mean hiring more people: this means ensuring everyone who should be making decisions is enabled to make the best possible decisions.

 

Knowledge Workers in your decision-making process

Manual workers are responsible for executing the actions required after knowledge workers make decisions. Therefore decision-making processes need knowledge workers. Although both types of workers have a role to play in the modern business, knowledge workers come with a unique set of challenges. The work done by manual workers can be observed and optimised. It is relatively simple to measure productivity and to put cost-saving measures in place.

Knowledge workers have data as their raw material – their production processes are called meetings, and their finished goods are decisions. None of this as tangible as say, a mobile phone or a digital camera manufactured by a team of manual workers. Knowledge workers are expensive, and it is difficult to measure their productivity, which is only one of the challenges faced by modern businesses. And yet, they have no option but to hire more and more knowledge workers. In pursuit of growth and efficiency, 21st-century companies spend increasing amounts on information technology, automation and branding projects – none of which would be possible without knowledge workers.

 

Let’s pause for a moment…

Please note that these investments are considered to be projects, not routine daily tasks. Knowledge work is mainly conducted in this way. This means there are massive fluctuations in decision-making activity. There are busy spells and quiet spells for knowledge workers. This means management teams in modern businesses most likely have more knowledge workers than they need, and they probably know it. The question is, where does this excess lie, and how to deal with it.

 

Tackling productivity issues in decision factories

An unfortunate consequence of periods of relative inactivity (during which knowledge workers are paid to check their emails) are binge-and-purge cycles. When a new project is initiated, a new role is created. Once the project is over, the person in the role is no longer productive. Larger corporates take an approach of hiring and firing through project cycles.

A different approach would be to tackle each project fully realising exactly what it is: a project – and treating it as such. The trick is to ensure growth by organising around projects instead of jobs. An upsurge in the availability of data these days provides the necessary tools for a professional services team hired for the task at hand with the ability to get the job done professionally and effectively. Management teams should learn from industry giants.

Take the Royal Bank of Scotland as an example. They needed to establish a reliable data source to make data-backed decisions about finances and resources. The project involved decommissioning 35 legacy systems and automating processes contained within hundreds of spreadsheets. The project was taken care of by a professional team that achieved phenomenal success – they not only delivered on their business case, but also achieved ROI within 12 months, improved transparency, and reduced costs in each business unit. And nobody got fired once the project was over. As individual team members completed their tasks, they moved on to other projects, leaving nobody to check their email and get paid for it – or succumb to a binge-and-purge cycle.  

 

Knowledge is power

It is important to note that success stories like this are not possible without knowledge transfer – yet another challenge faced by decision factories, and something a project-based approach cannot do without. Procter & Gamble launched a flagship project with the goal of codifying its knowledge as early as the 1990s. The project involved gathering data and converting it into algorithms with the aim of streamlining functions and processes while also helping future employees or contractors to make better decisions without relying on too much hands-on training. Traditionally a manager would depend on experience to know what data is necessary and relevant to decision making. With projects like this, companies of all sizes can gain valuable insights into how data can best be utilised to improve efficiency, and ultimately boost their bottom line.

Not every part of a business can become project-based. There will most likely always be a need for full-time jobs and manual workers. And of course, not every bit of data can be converted into an algorithm geared to improve productivity and slash costs. What is possible is for companies to start investing in the tools required for the most effective decision making: knowledge worker resources, knowledge transfer and operational analytics. Making data-backed decisions is the only way to gain useful insights into ensuring improved productivity and efficiency.

 

How would you rate the quality of decision making in your business?

Is it excellent? Good? Average? Poor?  

How many people in your organisation make decisions? Is it the right of the select few in leadership and management positions? Alternatively, is every team member empowered to make decisions? Is your organisation a decision factory?

At BizCubed, we help organisations make better decisions. Contact us. Allow us to demonstrate how we can take you on the journey of becoming a decision factory that uses relevant data and operational analytics to ensure effective decision making.