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We are living through a tornado of change in organizations. When things change, our old practices often don’t work as well – or, we’re worried that they won’t work as well. So, we change them – often on pretty shaky evidence. But most of the time when we do this, we are looking at a symptom – not at the root cause. Like a Sisyphean game of whack-a-mole, that root cause keeps throwing up problems in new places.
When intuition based on the past is unreliable as a guide for moving forward, organizations can shift to using an analytics approach as a way to reduce uncertainty.
Personally, I love math and, frankly, all things data. I find it fun (puzzles!) and comforting (there’s a right answer, and I can find it!). But, I get that a lot of people don’t share my delight. Here’s the thing: analytics isn’t about complicated math and intimidating formulas full of Greek letters. It’s about a mindset – analytics asks, “How can we know about this?” An analytical mindset is about building ways to test your beliefs and assumptions.
Getting to the Right Questions
People tend to focus a lot on the output of an analysis. But here’s the secret – the inputs – the question you are pursuing, and the information you are using to get at that question – matter a lot more. The most elaborate math in the world won’t matter if you’re poking at an irrelevant question. Expertise in analytical processes combined with insight into the organization – its priorities, its quirks and all the little details that make it unique – are essential to building out meaningful, relevant questions.
External experts can often help with the analytical frameworks and the mathematical models – but only internal experts can bring the deep acumen into the particulars of any unique organization. Cookie cutter solutions don’t have a great track record – at best they make you just like everyone else, and at worst they waste time, effort and funds, while breaking things that were working.
Great analytical work starts with clarity around priorities and constraints – exactly the kinds of things that deep internal experts like HRBPs bring to an organizational change question. Figuring out what really matters, and where the richest opportunities lie is essential for any other part of a change process to be worthwhile. The most sophisticated algorithm in the world won’t improve your business if it’s built to solve a non-problem.
Right now, excitement around the new capabilities that AI creates means that there are a lot of people running around with a bag of hammers, desperately looking for nails. Some are building houses, some are just banging on the ground… and more than a few are chucking things through windows. Organizational success depends on the ability to tell the difference and devote energy to things that are useful!
Be a Scientist, Not a Lawyer
One common, critical mistake is approaching questions like a lawyer – looking for evidence that you are right. This feels gratifying, and often is what stakeholders ask for – either directly or indirectly. However, especially when the stakes are high, it’s important to approach difficult questions by thinking like a scientist. Instead of trying to prove that you are right, look for evidence that you might be wrong – that is, really, truly test your assumptions. Ask, what other answer could explain this? By doing that, you can avoid, well, being wrong. You can find the risks and errors in thinking much earlier and build strategies to mitigate or avoid them.
Driving Organizational Change with Data
You can help your organization make better decisions in these turbulent times by doing four key things:
1. Find good problems and opportunities. Things with high volume, high variability, high expense, and high impact are a good place to start. Quick wins can be helpful to reduce anxiety around a change, but be sure you’re targeting substantive changes as well. Often stakeholders or clients will come with a preconceived notion around the right answer – frequently copying what someone else is doing. The most valuable organizational partners will take that as the start of a conversation, rather than an order. Remember to anchor your questions around driving business value, and root your evaluation in your organization’s specific characteristics.
2. Interrogate the problem. Depending on the problems and opportunities, this could be quantitative, qualitative or a mix of both. Be sure to look for evidence and counter-evidence. Consider individual, team, and system-level information and constraints. Including multiple viewpoints will slow down data collection, but improve answers. Leverage external expertise – including from AI models – as well as internal acumen to surface and test hypotheses about root causes and expected benefits from specific changes.
3. Prioritize your efforts. The RICE framework, common in IT teams, is a good one for assessing Reach, Impact, Confidence and Effort. Effort is a critical one – often people consider only the direct effort of implementation, and not all the other factors that go into getting to full adoption. Remember to capture the risks of doing nothing also.
4. Activate change early and often. Too many people still think that a launch email is the end of a change process. Activating to full adoption generally involves bringing change champions into a design process, running pilot studies and genuinely adjusting on the basis of results, intentionally designing sunsetting of current processes as new ones ramp to full performance and actively adapting as problems emerge.
At each step, the ability to approach the work analytically and rigorously will support the most effective, sustainable outcomes.