A collection of DataIQ members gathered to delve into whether their own visions for data are just better business as usual results, or if there is more to a data vision that that. The members started off looking at the idea of whether they were seeking to improve existing processes to optimise what’s already there, or if their vision aiming for something more transformative.
What is BAU?
The first member to delve into the topic at length was from an energy firm and they immediately focused in on the idea that the answer to business as usual (BAU) depends on the framing of the question and the context.
“I was thinking about how it was framed – is it just a better BAU?” said the member. “And this led me to think, is it a better BAU based on what I know BAU is today? Absolutely not. I hope for a day that data is so intertwined with the fabric of the DNA of the organisation that it becomes BAU in the future.”
Members examined what they consider BAU in their current operations and concluded that BAU is arguably a poor metric of measurement. BAU varies between organisations and within the teams of that organisation. As one participant stated, “the only constant is change” which was widely agreed upon.
With the development of new tools and technologies for data teams to harness, the idea of BAU, in some regards, does not truly exist. Yes, there is BAU when it comes to things such as identifying efficiencies and cost-saving initiatives, improving data literacy and bolstering data culture, but the data vision itself is always evolving and cannot truly become BAU.
Multiple members of the roundtable agreed with this and demonstrated the reality of this where the old core principals of their business have shifted over recent years. The most prominent examples would be the evolution of digital tools for everything from stock management to financial services, or energy providers shifting from fossil fuels to multiple renewable energy sources.
At the crux of it, the BAU is still providing financial services or energy needs, but the daily operations and future aspirations are far removed from the original BAU concepts that once existed.
The member continued to explain that “where I started [on my data journey] was an organisation that knew that they needed and wanted to be data driven, but really had no idea how to achieve it.”
This was then further demonstrated with a story from when the member was first hired: “One of the decision makers in a meeting said to me, ‘you have been here a couple months, do you think we are data driven now?’ I responded, ‘no, but I think we are very data interested’.
“Of course, they wanted to know why, and I posed a simple question to them. Our CFO was at the meeting, and I said, ‘our CFO manages all the cash in the business – and cash is an important corporate asset. The CFO and their team know everywhere that that cash exists: they know the bank accounts that exist, they know the currencies that it exists in and they reconcile daily to the penny. If data is such an important asset – and we are supposedly data driven – where is our data?’ And they all just went quiet.”
This highlights the importance of decision makers understanding the definition of specific data terminology as well as appreciating the journey that needs to take place to become truly data driven. This cannot take place overnight, but it can be achieved with a clear and concise data vision that is then disseminated across the organisation. Eventually, with time and training – notably data literacy and the development of a data culture – the vision for data to become a data-driven business can, in theory, become BAU, but the data vision itself will then change to achieve more.
Implementing the vision
A data vision needs to be set in motion and this can be done in different ways depending on the size, legacy and ambitions of the organisation. One roundtable participant described how they set their vision as everyone in the organisation to truly understand what being data driven means. To achieve this, they designed and ran a programme with interactive sessions – which was even more impressive as it was to be delivered to 6,000+ colleagues across 28 countries.
With six months of planning to craft an enablement programme, the data team then planned to have one day where everyone would take part in the programme together. They began at 4am UK time and connected with colleagues in multiple time zones across the globe to deliver the 90-minute programme one after the other. “One of our objectives was to do something that have never been done before, which was to have all of our colleagues having completed the course on the same day,” said the member.
This target was chosen as dragging out the programme over a period of months would lose its impact and the metrics of measuring who has completed the course would be skewed. “As we all did it on the same day, we could definitively state that everyone has received training that demonstrates what data driven means, achieving our objective.”
This task took many months to complete, and dates had to be moved to consider different national holidays, religious holidays and weekends. Furthermore, there were language considerations with the programme needing to be translated into different national languages to make it as accessible as possible for colleagues around the world.
A second member described their own experience with trying to implement the data vision which also included extensive training. They were unable to do a synchronised international training day, but they were able to achieve success by getting the C-suite executives to complete the data training and then have them explain to different teams why it is so important for all colleagues to complete it.
The participants agreed that there is no one answer for achieving this success, but by learning from the experiences of other DataIQ members they were able to assess what would be most likely to succeed for their own organisation.
The roundtable participants agreed that foundational elements are essential for implementing the vision and cannot be glossed over in the pursuit for data excellence. It is imperative that a data leader is sure of the infrastructures and data needs of the organisation. Without these in place, it is difficult and limiting to introduce a data vision as the quality of the data and therefore the analytics will be subpar, and the true potential of the data-led decisions will be impacted.
As one participant highlighted, there is also the need to work closely with stakeholders “for them to understand and interpret the insights in the way you are trying to deliver it.” This was agreed, but it was highlighted that it is a long-term strategy that will inevitably evolve and change in nature, which in turn confirms the notion that a data vision is not just BAU.
Another participant described their experiences with stakeholders and implementing change and they noted that there are some stakeholders willing to make the necessary sacrifices to achieve the data vision, but there is often a short-term tension that impedes the rollout of a data vision: notably workloads, other internal or external business pressures and a lack of understanding of the end goal. “Sometimes you have to go slower to go faster,” the member concluded when discussing getting stakeholders on side.
To conclude the session, one member remarked that BAU is more of a cultural aspect. The participants all agreed that they want people to see data as part of their day-to-day roles and responsibilities, but because there will always be transformational activities this becomes a delicate balance. There needs to be a certain amount of time doing a process in a particular way for it to become engrained as BAU, but this is not possible if the ways in which people are being asked to work change regularly.
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