Behavioural inertia is powerful. Changing mindsets and behaviours in the organizational context is hard work that requires much leadership effort and time. How then do organizations - and the leaders who steer them - adapt to changing technology, processes and competition while changing employee mindsets and behaviours? Organizations from across the spectrum are constantly looking for ways to adapt to the ever-changing business landscape while keeping employees and team members engaged and on-board.

In this article, lets explore one of many key tactics that isn’t just endemic to managing change, but an important leadership trait in general. The tactic is feedback. Real-time, in the moment, contextual feedback.

Let’s evaluate this using a ‘Situation, Complication, Resolution’ paradigm.

Situation:

An large organization was implementing a new sales analytics tool to a large...

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In 1951, Dr. Solomon Asch conducted the famous Asch Conformity Study to measure how individuals respond to the pressure to conform. Here’s what happened during the experiment.

A number of individuals were organized into small groups and presented with two images of lines with various lengths as shown below:

Each participant was asked to then identify which line was the longest. It sounded simple enough but there was a twist. Within each group, there was only one true participant (à la Truman Show). All other participants were actually actors who were pre-instructed to provided the wrong answer.

More often than not, the one ‘true’ participant usually always provided the wrong answer, even when they knew it was wrong. The ‘true’ participants generally chose to respond incorrectly, ignoring their own judgement. The study in very simple terms illustrated how...

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Across industry today, the success and health of projects are typically measured against the binary response to the following questions: Was the project completed on time? Was the solution delivered under budget? Generally speaking, project success is rarely measured against the extent to which underlying strategic objectives were actually met at completion.

Critical questions such as - did our bottom line improve after we implemented the ERP system? Are we realizing benefits from the (insanely) expensive technology stack we implemented to support knowledge exchange? Are our portfolio of projects aligned with our strategy? - are not being posed or asked only after project completion (a potentially expensive and wasteful option).

So, how do we better monitor the congruence between projects, their outcomes and organizational strategy? In this article, we will first...

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In the 1970s, Daniel Kahneman and two collaborators wrote a book titled, Judgement under Uncertainty: Heuristics and Biases. The book profiles a number of fascinating studies in the domain of decision making and judgement. One fascinating study within the book takes a closer look at the effects of choice on the illusion of control we have as individuals.

I’d like to draw a parallel between some core assumptions and known truths in change management and the study by Kahneman.

Before I draw the connection, lets state two commonly known truths about change management. The first is the notion of perceived control being a key factor in how individuals respond to change. In other words, the more control people have on the outcomes of a change (or the notion thereof), the more comfortable they will be in aligning and perhaps even supporting the change.

The second truth is...

According to the book ‘Data Science for Business’ by Provost and Fawcett (2013), in a competitive environment where companies are constantly looking to gain a marginal competitive edge over competitors, using big data is associated with additional productivity (and maybe profit?)

It was interesting to read that when confounding factors are controlled, using big data technology contributes to additional productivity in growth for firms (pg8). The paper by Tambe indicates that one standard deviation higher utilization of big data can contribute to 1% to 3% greater productivity compared to the average firm. In other words, a firm that uses data-driven decision making on a regular basis may indeed obtain that elusive competitive advantage. The same study also found that the opposite holds true where one less standard deviation in terms of big data utilization gives rise to 1% to a 3%...

Big data is a term that has become quite ubiquitous; so ubiquitous that there isn’t an industry where the word isn’t being used to describe everything from making a chart in excel using 100 rows of data to cleaning up and presenting data graphically.

Based on a new understanding of what these terms mean, I would like to clarify the what the differences are.

1. Big Data refers to large amounts of data, data that is not meant to fit into your typical excel sheet. We are talking millions of rows of data that can be typically managed through a file system such as the Hadoop Distributed File System (HDFS).

2. Data engineering and (pre-)processing are the first steps in working through a data analytics project. In other words, first comes the data engineering and pre-processing, then comes the cleaning of the data, removing duplicates and redundancies and such.

3. Data science is a discipline in...