Every day, organisations make thousands of decisions that have a direct impact on their overall success.
Intelligent organisations are able to provide the right information to the right people via the right channels, in the right context and at the right time. This makes the organisation better at making decisions (or able to make better decisions) - which in turn helps them improve performance and deliver against their mission more effectively.
This blog focuses on the types of decisions impacting the overall effectiveness of an organisation and the micro-opportunities for data-led continuous improvement.
Decisions, decisions .
Business leaders understand that success can be related to the overall ‘rightness’ of decision making across an organisation. But what are these decisions? Within a business, there tend to be (at least) 4 types:
These are the big business decisions – the ones that are discussed at length by the leadership team. Examples of this sort of decision could include:
Where shall we build our next facility?
Shall we acquire this company?
These strategic decisions tend to be few in number, but high in investment profile and impact, so getting them right is vital.
Day to day.
Beyond the big decisions, an organisation makes thousands of mid-range decisions across every department. These are recognisable decisions made by individuals or a small team that would be considered business as usual. Examples of this sort of decision might include:
What price point should we select?
Which person should we hire?
Often these decisions are taken under some level of governance, but the ultimate decision might be made on a subjective basis. Whilst less visible at a leadership level, the sheer number of decisions in this category can be significant.
I often ask our customers what decisions they make day-to-day – quite often, people will respond with “actually, I don’t make any decisions”. This response is natural when the decisions themselves may not be as clear and recognisable as those discussed above, but in reality our working day is absolutely a set of decisions – we just don’t think about them in this way. Examples of this sort of decision might include:
How should I best prioritise my time?
How should I speak to this customer?
The unconscious decisions are, by far, the most common in any business (and therefore represent, perhaps, the biggest opportunity). There are often processes in place based on good practice but the reality is that we mostly rely on the flair and experience of each employee to make the right call.
Beyond conscious and unconscious decisions, there are scenarios where decisions are made by chance (to a point where we don’t consider them decisions at all) - or are based on availability. Examples of this sort of decision might include:
Who should I route this incoming customer call to?
Which team members should this order fulfilment task be allocated to?
Often the decision mechanisms of these process have been implemented because of high volume. Small incremental changes (in terms of added intelligence) could have a significant impact on outcomes, like building in quality or severity scoring to be more descriptive about an opportunity or challenge, or implementing a skills or experience matrix to align an employee with a task.
Whilst the four types of decision vary greatly in terms of frequency and visibility (i.e. how well a process is recognised as an explicit decision point), each has the potential to generate significant additional value for the business.
Decisions make great data projects.
Whilst organisations understand that data and analytics can add value, finding the right initiatives to invest in can be difficult. There are many reasons that this is non-trivial, but a major factor is the lack of common language between business and data colleagues. A decision point can be an excellent basis for a data initiative for a number of reasons:
It immediately creates a clear end goal that is easy to understand
It creates instant alignment between business users (who understand the impact of a decision) and data practitioners (who can start constructing analytic hypotheses)
It offers a clear baseline against which any success might be measured
It can support a great discussion about the potential value that different routes could generate
It creates a natural framework for testing the benefit of any data-led solution
If decisions and outcomes have been tracked historically, then there will often be rich data sources on which an initiative can be founded. And for this reason, companies are investing heavily in decision sciences, either as an explicit function or as a primary focus for analytic teams.
In this way we will continually improve the overall ‘rightness’ of our decision making and build momentum around the ongoing application of data and analytics to initiatives that are well understood by business and leadership.
The route to identifying high-value data initiatives.
Decisions can provide an excellent mechanism to identify high-value data initiatives, or data domains, creating an instant connection between business and data stakeholders. It can be a fantastic way to engage the organisation around data-led change and in doing so, enable an intelligent business to better deliver on its commercial ambitions.
Can we help you become a more intelligent business? Get in touch – we’d love to hear about your decision-making challenges.
Further reading about data domains: