Founder: Turnaround Talk
We are all familiar with the proverb a leopard never changes its spots. Human behaviour is constant and predictable, especially when a company is profitable. Can big Data lend a helping hand?
However, even the most profitable businesses have been disrupted or face disruption. This disruption will continue as companies try to compete with significant challenges such as digitalisation and the Global Supply Chain Crisis.
At the SARIPA Northern Conference, National Chair Eric Levenstein pointed out feedback from a recent Insol meeting that business turnaround and corporate restructuring will continue in 2023 while many African companies struggle to adjust to tough economic headwinds. However, I recently read an article on the McKinsey website pointing out that corporate transformations are only possible if people change their behaviours. But leaders are often at a loss to understand how investing in capability building can enable the necessary behaviour change.
This is where Big Data, one of the biggest disrupters companies must deal with, can offer significant benefits.
One step at a time
The McKinsey article points out that the first task is to set a long-term data strategy, leaders can create end statements that define the kinds of business questions they are seeking to address and, therefore, the kind of data they need to collect. Second, to better understand when behavioural changes could be meaningful and in what context, leaders can use comparative analytics to look at important statistics over time. Finally, leaders should ensure that any employees who are working with data are also trained to understand the key terms and principles of behavioural and organizational science.
Focusing on all three areas can help organizations and their leaders unlock next-level insights and analytics. For instance, this three-pronged approach helped the aerospace organization increase the number of capability-building champions in the company by almost 40%. These are employees who, after previously reporting little or no change in their daily behaviours, later reported material changes in their behaviours, as well as a strong desire to continue improving.
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Use key hypotheses
The McKinsey article points out that many organizations build extensive dashboards to track key performance indicators but often do not examine whether the data they collect are actually informing their decisions or just creating noise. Before deciding which data to gather, leaders should identify a list of questions they want the data to answer—what we call end statements—and then work backward to isolate the information they need to answer those questions. This list can be the backbone of an organization’s behavioural-insights plan.
End statements should not confirm an existing bias; rather, they should be designed to ensure that the insights gathered are relevant and actionable. The aerospace organization, for instance, began with the following end statement: we need to understand what is preventing our employees from holding regular feedback meetings. Was it a matter of employees’ or leaders’ lack of capacity or interpersonal skills—or something else entirely? With this end statement in mind, the company asked employees whether they felt they needed more time to hold feedback meetings, more information on how to conduct effective feedback meetings, or rewards and recognition for having feedback meetings. With this information in hand, leaders were better able to make informed decisions about which obstacles they needed to remove. In this case, that meant establishing capability-building programs that would help employees understand how to conduct feedback meetings more effectively.
Compare key statistics over time
The McKinsey article points out that leaders need to remember that data are often contradictory. For a clearer picture, it can be helpful to gather multiple sets of data and compare information over time. That way, leaders may be both better able to recognize trends and less likely to attribute those trends to a broader narrative that may be based in bias. Indeed, comparative analytics can help leaders reconcile the complexity in their data, paint a more nuanced picture of performance, and help build a change narrative based on clear evidence.
One not-for-profit organization, for instance, took a comparative approach to understand the effectiveness of its leadership trainings. Initial survey data plus anecdotal evidence about the trainings suggested lots of engagement in and satisfaction with the workshops. The mechanics of the program itself were working fine—but an important question remained unanswered: What impact was it actually having on the ground?
The article adds that, to find out, the company collected data on individual leaders’ and employees’ behaviours both before and after leaders had completed their leadership trainings. Among the leadership behaviours analysed were those related to coaching, adaptability, decision making, sponsorship, and motivation. The company gathered data on these and other behaviours from the trainees themselves, as well as their peers, managers, and direct reports. The data revealed improvements in all leadership behaviours, in some cases by up to 8%. Even more significant was a marked increase in the number of employees rated as top performers in specific categories. Prior to the leadership trainings, 3.5% of employees were rated as top performers in self-awareness; after the training, the percentage swelled to nearly 40%. Similarly, 35% of employees were rated as top performers in adaptability before the leadership training. After, 67% achieved a top-performer rating.
The numbers confirmed that the leadership program was having an impact; but the data also revealed opportunities to scale up the program and create more opportunities for professional development at all levels. This insight, as well as increased employee capability scores and personal-growth anecdotes, allowed leaders to build support for the program throughout the organization and develop plans for broader rollout.
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Upskill employees to work with data
The McKinsey article points out that, for organizations to generate the most accurate interpretations of data, everyone in the company who is working with the data should share a common language and a grounding in the essentials of data, analytics, and human behaviour.
Leaders, data scientists, frontline workers, back-office functional leaders—all can be trained on concepts relating to behavioural and organizational science. For instance, with appropriate upskilling, data scientists in a company could use their understanding of human behaviour to inform their analytical approaches. A basic foundation in common cognitive biases, for instance, could help them begin to recognize—and battle—biases in their own interpretations of data.
The article adds that, at the aerospace organization, leaders, data scientists, and learning experts used their new understanding of common terms like behavioural intervention, archetype, and role modelling as a shared anchor for their behavioural-insights plan and related discussions. Empowered team members were better able to interpret and discuss key data findings and were more confident about the important decisions they were being asked to make.
Goal alignment
From my engagements with business rescue professionals, it is becoming clear that companies are struggling to move beyond their financial distress. Finding the golden thread that will save them from business rescue – or even liquidation – is becoming impossible.
Goal setting and goal alignment are challenging in a disruptive environment. However, this is where value can be found. This must be based on data analytics and motivating employees across the company’s value chain to buy into the process.
The McKinsey article states that using behavioural insights to transform an organisation can be challenging. This is because there are many variables and data sets for leaders to capture and monitor and just as many possible interpretations of those data. But in our experience working with companies trying to get the most from their analytics programs, it’s worth making an effort. The leaders who start now to generate behavioural insights and incorporate end statements, comparative analyses, and upskilling in behavioural and organisational sciences into their analytics programs can improve the odds of successful transformations. More importantly, they can build analytical capabilities to get the most from their data now and far into the future.
