How data leaders can cut costs without reducing value
In these straightened times even data & analytics leaders are needing to cut costs. Over recent years many businesses have invested heavily in data science or AI. Now that around the world consumer spending is on a downturn, leaders in those businesses are needing to reduce such budgets.
A few years ago the challenge was for data leaders to show a return on the initial investment. Today’s issue goes beyond that. It requires data leaders to both reduce costs and continue to prove the value they add to their organisation.
How is this possible? Well, fortunately, this situation is not new. All too often we live in a world with short memories. Acting like all such crises are novel challenges. Rather they are today’s version of the challenge that data leaders faced in past recessions. That is, the early 1980s, 1990s and even worst during the financial crisis of 2008-2009 (“the great recession“). A few of us ‘old farts‘ were leading data & analytics teams through at least one of those times. So, in this post let me share the principles I found helped during those times.
Learning from leading data teams through past recessions
Although I wasn’t leading a data team during the early 1980s recession it had an impact on me. Your formative years and childhood experiences before that have a major impact on how you manage money. Seeing my mother & friends’ families live through those years caused me to adopt a prudent mindset. Once I did have budget responsibilities at work, that attitude spilled over to carefully managing those funds too.
During the early 1990s recession I was moving into IT team & project management. Seeing the cost cutting & challenges to investment that happened as a result. Adding to my careful mindset the learning of a repertoire of ways to make a business case or deliver at less cost. This where I started to identify the importance of attention to detail in supplier contracts & time taken to deliver.
But it was the banking crisis driven recession of 2008-2009 where I learned to apply such thinking in a data leadership role. In many ways I was in the worst place for a data leader at such a time. Leading a newly expanded data, analytics & market research (insight) team for a major bank that had needed to rescued by the UK taxpayer. Now government owned, there was a major emphasis on cost reduction & a block to all recruitment.
Principles to help you cut costs as a data leader
How did I cope? Let me share 3 tips that helped me make it through. Each of which is still applicable for data leaders today and which I recommend you consider how you could apply.
Principle 1: Ruthless prioritisation
A common phrase during senior leaders at this time was to stop any ‘hobbies‘. By this was meant activities that leaders or teams were interested in doing but which were not essential to the survival plan for the bank. All too often though, this could mean cutting any activity that was less well understood. Another victim was work not supported by influencing senior leaders to achieve sponsorship.
The lesson I learned was the need to do less but better. In such challenging times get to know your executive committee or directors even better than normal. Learn what they fear, what keeps them up at night. Discover what they are passionate about, what they believe will save the business. This was the time when I built a reputation for delivering relevant & vital capability that could reduce costs. Later was the time to trade on that reputation to secure further investment, until then I focussed on reputation building.
Many data leaders are more introverted by nature and can avoid difficult conversations. The technical teams they lead often take pride in their work & can be emotionally committed to different projects or skills they have worked hard to develop. Here the data leader needs to show tough love. Since they know the commercial priorities of the senior leaders they need to ruthlessly prioritise the team workload. No sacred cows may be spared. Challenge everything. If it is not a priority for where the business is now, then kill that project & refocus your people on what matters most. Plus, share why with your teams. Encourage them that they can make a tangible difference to business survival. But also explain that hobbies have to die. Inspire your team to together prove your worth by delivering what is relevant & useful, on time, to great quality & within budget.
Principle 2: Have difficult conversations without delay
Such times leave no room for cowardice or procrastination. Data leaders need to recognise where a tough conversation is needed and not delay in having that discussion. The first part of that is to spot where you need to stand firm and challenge people, rather than focusing on empathy & relationship building.
During recessions especially, but also throughout the economic cycle, I’ve found there are two common contenders for such chats. First, under performing team members. Second, senior leaders making decisions not justified by the facts.
In the first case, data leaders should take time to assess the evidence. Who is delivering less than you should expect from their role? Are they taking too long to deliver or often blaming others for delays? Who is undermining team morale by regular cynicism or complaining about “them” (the leadership). Are they less capable than expected? Could they lack the skills or intellect to ever master their role?
“Only three things happen naturally in organizations: friction, confusion, and under-performance. Everything else requires leadership.”Peter Drucker
In any such cases, consider the circumstances. But if their performance is unlikely to change with temporary support, address the issue. The sooner a person is challenged & a formal process is started the sooner you will both know if they can improve.
In the second case, data leaders need to prove their metal in dialogue with both peers and more senior leaders. Adopt the mindset of your identity being as part of the leadership team. You are not a technical servant of more commercially focussed roles. Rather you are a vital peer who needs to challenge others based on what you can reveal is data based evidence. Demanding targets to cut costs & the personal pressure this causes will lead to more emotional behaviour from leaders. It is the responsibility of data leaders to challenge decisions that appear to be driven by bias, gut reaction, self preservation or even raw political games.
For all such conversations, I recommend that data leaders checkout my book review of “Radical Candor”. It’s a very helpful resource & Kim Scott’s website also has a range of other resources to help you be more candid in your difficult conversations.
Principle 3: Recycle where possible & integrate your teams
Recycle and reuse used to be the mantra of environmental campaigners decades ago. That mindset can help data leaders during a cost of living crisis when they need to cut costs.
It so happened that I had recently taken responsibility for a market research team alongside my data, analytics & decisioning teams when the 2008 recession hit us. I learned two lessons through that conjunction. First, the value of integrating disparate technical teams into one synchronised department. That enabled higher quality delivery (through multi-skilled collaboration) with less resource. I have shared more on this before when talking about holistic insight.
The second lesson was the power of the secondary research & knowledge management approach of MR teams. Both concepts are less of a focus for data & analytics teams but can be beneficial there too. Put more effort into storing / remembering / accessing past data & analytics delivery. Develop a pragmatic solution to maintaining such a library of knowledge to date that all the team can update & use. Then embed a culture of when working on a new problem thinking first – what do we already know? At best this can identify past work that already provides a good enough solution. At least it should reduce the amount of rework for you team. There are more tips on doing this in my past post on reuse.
Does one of those principles help you act now?
In line with principle 2 above, I encourage you to identify which of those principles could apply for you now. Rather than waiting for a transformative idea or unified theory of everything, seek to act now. Identify one thing you could do differently this week to cut costs and try it. The best learning by data leaders in tough times comes by test and learn. Test a new behaviour and develop a reflective practice to help you become more intentional about sticking with what works.
I’m very aware that cost control and coping with financial challenges is a current challenge for many readers and may be for years to come. For that reason, I have asked our panel of guest bloggers to also share their insights on this challenge. More from them soon.
Other ideas going round my head include:
- where & when to use in-sourcing
- negotiating better value data contracts
- prototyping solutions without IT
- effective partnerships with universities
If you’d like to hear more on any of those topics, or you have a suggestion from your own experience, please get in touch. Whilst we all need to cut costs, let’s still share as a generous community of data & insight leaders. Time to prove our value.
On the money (pun intended) as usual Paul. The 4th point, which might be a subset of point 1 or separate is to show the value of the work commercially. Having work linked to specific outcomes or tangible KPIs is much easier to defend in tough times. If it’s possible to show the work is driving significant uplift in revenue, decrease in costs, etc then the conversations around cost reduction become easier to have.
Thanks Andy & very witty as usual. Really good point about the preventative value of measurable value-add. Well worth the effort beforehand to have such alignment and if possible attribution. As I’ve encouraged before, worth data/analytics leaders taking commercial targets that are beyond their control to make visible the degree to which they have ‘skin in the game’ via such a contribution.