By Jonathan Whiteside, Director & Principal Technology Consultant at Dept Agency
Systems thinking is not a new concept. The holistic idea of ‘the whole being more than the sum total of its parts’ goes back to Ancient Greece. In current day business operations, systems thinking is a holistic approach that focuses on the way that a system’s constituent parts interrelate, and how they work overtime and within the context of larger systems.
Apply this approach to your current tech stack and you’ll see the relevance. No department is an island; how does each area of the business interact and integrate? And, indeed, how do the systems each department uses to interact and integrate? It’s these combinations between different functions that make all the difference. Interactions cause emergence; new, more efficient actions that make the process streamlined and even more effective.
Industry is in the middle of a technological step change. This is clear at a department level, as Industry 4.0 shepherds in more automation on the assembly line and digital tracking throughout the supply chain. But it’s also clear if we zoom out and look at the business as a whole. Innovations around digital connectivity, such as data tracking, cloud software and AI, have created new opportunities for interaction between previously siloed departments.
“Digital transformation is the enabler, said Jonathan Whiteside, Director & Principal Technology Consultant at Dept Agency. “Increased connectivity and communication rely on digital services that can share, analyse and apply information gathered from each area of your business. The impact is felt when the workforce gets access to more efficient software and business systems, when the gathered insights are applied, and when newly emerging processes open up.”
These turning points are exciting periods; advancements branch out and open the door to a series of new opportunities. With systems thinking applied to digital transformation, businesses can start to move more towards NoOps, increased specialisation, predictive data modelling and even more agile supply chains. This process begins with planning. Putting together a digital roadmap codifies the short, medium and long term aims of the business, establishing the immediate goals that must be met, and the smaller projects to test and scale as a backdrop to the priority focus.
Applying systems thinking to your digital roadmap
Building a digital roadmap begins with a set of goals, the key performance indicators of the digital transformation that need to be met. For digital transformation projects that focus on one aspect of the business, such as modernising the eCommerce platform or digitising the hiring process, this is a simple task. One team bases the needs around their KPIs as a department. When considering the impact of systems thinking, more departments need to be involved from the outset.
Bring together key stakeholders from each department to put together the list of needs, distinguishing between the ones that affect the entire company and those that specifically relate to one department. Think broad, rather than overly specific aims. As an example, improving data collection could be a need for the entire business, while lowering shipping costs relates to the needs of the logistics team.
With a list of business-wide and department-specific needs that systems thinking digital transformation must meet, start to discuss how the department needs intersect. This matters for two reasons, the first being the interconnected nature of systems thinking improvements. The second is that this sets the tone for the solution; right from the outset, the company is focused on discussing connected solutions, joint thinking. It’s important to begin on the same page and ensuring company-wide buy-in is likely to lead to better implementation and adoption.
With a view of what the company and each department want to achieve, it is time to establish the solutions that the company aims to implement over the upcoming months and years. Working with a digital transformation partner at this stage can open the business’s eyes to what can be achieved, and the range of options that lie on the table.
The purpose of the roadmap is to show that digital transformation is a journey, rather than a race. Take the crawl, walk, run approach; always think about the potential cross-business impact a particular solution could have. There are two major questions to ask yourself at each step: how can this solution flex and how can this solution scale?
In the short term, the company should be looking to put in place a series of digital proof points that each solve a key business aim. Over time, this initial idea scales up and, as more and more test cases are run, the company steadily adds a new weapon to the digital arsenal.
In a systems thinking focused digital transformation, the important turning point begins when the initial solutions have been scaled. The business now has a secure set of digital tools and can begin aligning each system. It is almost like beginning the process again, but this time with converging departments.
The digital roadmap isn’t set in stone. Like a real-life journey, there could be traffic that makes a different route quicker, or you come across a new road that didn’t exist when your map was created. The business will have collected data on each of the digital solutions put in place; learn from the insights and adapt the approach. It’s particularly important to put in place a setup that shares this knowledge, allowing both department decision-makers to learn from cross-business actions, and automated programmes to adapt based on the data.
Data collection & interpretation
As previously mentioned, being able to collect and analyse data is the difference between understanding what is working in both the digital transformation and business at large and flying blind in decision making. This is a challenge for each department, let alone a company looking to take a systems thinking approach and integrate collected data across the business.
The first step is understanding what can be measured and how the business can measure this. Some are simple, like tracking profit margins and cost of materials, though other areas will require the use of new technology to improve measurement. One example can be found by looking at the supply chain; accurately tracking orders can be a challenge, leading many companies to consider the implementation of blockchain or RFID technology.
Quantitative data lends itself well to collecting, pooling and comparison at scale, but advances in natural language processing, a form of machine learning, means that companies now have the means to process this information without requiring hours and hours of extra analysis time. This is particularly important when surveying the staff that are at the coalface of the digital transformation. This should be considered by any company that wants to go beyond the rigidity of a 1-10 scale survey for a more in-depth understanding of how their staff feel about the digital transformation and what they can learn.
Having put in place a system for measurement, businesses must ensure that they can safely store the data and that the appropriate information is accessible for key stakeholders. Establishing a data hub is often provided by CMS or CRM systems, each providing data protection in line with the rules and regulations of the territory. Many of these systems also offer ‘levels of access’, allowing the business to regulate which members of staff can access certain data.
Data collection systems are empowered by the growth of cloud software. The systems thinking approach is possible as data is stored in one central area – a cloud server that provides the same information to everyone accessing it.
Once the interconnected data collection system is in place, the business can begin tracking the systematic consequences of actions that have been taken. Through analysis, the business can study correlations, often finding unexpected effects of new solutions. Certain organisations will have a data analysis team already in place. For those that don’t, insight tools are available, often through the CMS or CRM system used to collect the data. Examples like Salesforce’s Einstein tool use AI to parse through data and find insights based on the aims the user selects.
Even with the analytics A-team in place, humans take time to put together data sets. Depending on the size of the project, analytics can take weeks or months, making the use of a machine learning data analysis tool a major boost to project speed. When taking the systems thinking approach, the business will likely be swimming in seemingly disparate data, making fast and simple modelling highly important to ensuring the digital roadmap is on the right course.